warehouse research papers

A literature review of smart warehouse operations management

• Review Article
• Open access
• Published: 12 January 2022
• Volume 9 , pages 31–55, ( 2022 )

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• Lu Zhen 1 &
• Haolin Li 1  

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E-commerce, new retail, and other changes have highlighted the requirement of high efficiency and accuracy in the logistics service. As an important section in logistics and supply chain management, warehouses need to respond positively to the increasing requirement. The “smart warehouse” system, which is equipped with emerging warehousing technologies, is increasingly attracting the attention of industry and technology giants as an efficient solution for the future of warehouse development. This study provides a holistic view of operations management problems within the context of smart warehouses. We provide a framework to review smart warehouse operations management based on the characteristics of smart warehouses, including the perspectives of information interconnection, equipment automation, process integration, and environmental sustainability. A comprehensive review of relevant literature is then carried out based on the framework with four perspectives. This study could provide future research directions on smart warehouses for academia and industry practitioners.

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Aldarondo F J, Bozer Y A (2020). Expected distances and alternative design configurations for automated guided vehicle-based order picking systems. International Journal of Production Research, in press, doi: https://doi.org/10.1080/00207543.2020.1856438

Amato F, Basile F, Carbone C, Chiacchio P (2005). An approach to control automated warehouse systems. Control Engineering Practice, 13(10): 1223–1241

Article   Google Scholar  

Ang M, Lim Y F (2019). How to optimize storage classes in a unit-load warehouse. European Journal of Operational Research, 278(1): 186–201

Article   MathSciNet   MATH   Google Scholar  

Azadeh K, de Koster R B M, Roy D (2019a). Robotized and automated warehouse systems: Review and recent developments. Transportation Science, 53(4): 917–945

Azadeh K, Roy D, de Koster R B M (2019b). Design, modeling, and analysis of vertical robotic storage and retrieval systems. Transportation Science, 53(5): 1213–1234

Bartolini M, Bottani E, Grosse E H (2019). Green warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production, 226: 242–258

Basso F, Epstein L D, Pezoa R, Varas M (2019). An optimization approach and a heuristic procedure to schedule battery charging processes for stackers of palletized cargo. Computers & Industrial Engineering, 133: 9–18

Ben-Daya M, Hassini E, Bahroun Z (2017). Internet of Things and supply chain management: A literature review. International Journal of Production Research, 57(15–16): 4719–1742

Google Scholar  

Bottani E, Vignali G (2019). Augmented reality technology in the manufacturing industry: A review of the last decade. IISE Transactions, 51(3): 284–310

Boysen N, Briskorn D, Emde S (2017). Parts-to-picker based order processing in a rack-moving mobile robots environment. European Journal of Operational Research, 262(2): 550–562

Boysen N, de Koster R B M, Weidinger F (2019). Warehousing in the e-commerce era: A survey. European Journal of Operational Research, 277(2): 396–411

Boysen N, Stephan K (2016). A survey on single crane scheduling in automated storage/retrieval systems. European Journal of Operational Research, 254(3): 691–704

Boywitz D, Boysen N (2018). Robust storage assignment in stack- and queue-based storage systems. Computers & Operations Research, 100: 189–200

Boywitz D, Schwerdfeger S, Boysen N (2019). Sequencing of picking orders to facilitate the replenishment of A-Frame systems. IISE Transactions, 51(4): 368–381

Bozer Y A, Aldarondo F J (2018). A simulation-based comparison of two goods-to-person order picking systems in an online retail setting. International Journal of Production Research, 56(11): 3838–3858

Cainiao (2018). The new pattern of logistics in China. Available at: taobao.com/markets/cnwww/cn-news-detail?spm=a21da.144546.0.0.77103045qpjGh5&id=90

Chen H L, Xue G L, Wang Z B (2017). Efficient and reliable missing tag identification for large-scale RFID systems with unknown tags. IEEE Internet of Things Journal, 4(3): 736–748

Chen W Y, Gong Y M, de Koster R B M (2020). Performance estimation of a passing-crane automated storage and retrieval system. International Journal of Production Research, in press, doi: https://doi.org/10.1080/00207543.2020.1854886

Chen Z X, Li X P, Gupta J N D (2015). A bi-directional flow-rack automated storage and retrieval system for unit-load warehouses. International Journal of Production Research, 53(14): 4176–4188

Chen Z X, Li X P, Gupta J N D (2016). Sequencing the storages and retrievals for flow-rack automated storage and retrieval systems with duration-of-stay storage policy. International Journal of Production Research, 54(4): 984–998

Cheng Z M, Fu X, Wang J, Xu X H (2021). Research on robot charging strategy based on the scheduling algorithm of minimum encounter time. Journal of the Operational Research Society, 72(1): 237–245

China Daily (2017). How Shanghai’s Yangshan port can run without humans. Available at: english.pudong.gov.cn/2017-12/12/c_118557.htm

Choy K L, Ho G T S, Lee C K H (2017). A RFID-based storage assignment system for enhancing the efficiency of order picking. Journal of Intelligent Manufacturing, 28(1): 111–129

Custodio L, Machado R (2019). Flexible automated warehouse: A literature review and an innovative framework. International Journal of Advanced Manufacturing Technology, 106(1–2): 533–558

Dadhich P, Genovese A, Kumar N, Acquaye A (2015). Developing sustainable supply chains in the UK construction industry: A case study. International Journal of Production Economics, 164: 271–284

de Koster R B M, Le-Duc T, Roodbergen K J (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182(2): 481–501

Article   MATH   Google Scholar  

Derhami S, Smith J S, Gue K R (2019). Space-efficient layouts for block stacking warehouses. IISE Transactions, 51(9): 957–971

Digani V, Hsieh M A, Sabattini L, Secchi C (2019). Coordination of multiple AGVs: A quadratic optimization method. Autonomous Robots, 43(3): 539–555

Digani V, Sabattini L, Secchi C, Fantuzzi C (2015). Ensemble coordination approach in multi-AGV systems applied to industrial warehouses. IEEE Transactions on Automation Science and Engineering, 12(3): 922–934

Dou J J, Chen C L, Yang P (2015). Genetic scheduling and reinforcement learning in multirobot systems for intelligent warehouses. Mathematical Problems in Engineering, 2015: 597956

Draganjac I, Miklic D, Kovacic Z, Vasiljevic G, Bogdan S (2016). Decentralized control of multi-AGV systems in autonomous warehousing applications. IEEE Transactions on Automation Science and Engineering, 13(4): 1433–1447

Durach C F, Kembro J, Wieland A (2017). A new paradigm for systematic literature reviews in supply chain management. Journal of Supply Chain Management, 53(4): 67–85

Emde S, Polten L, Gendreau M (2020). Logic-based benders decomposition for scheduling a batching machine. Computers & Operations Research, 113: 104777

Ene S, Kucukoglu I, Aksoy A, Ozturk N (2016). A genetic algorithm for minimizing energy consumption in warehouses. Energy, 114: 973–980

Epp M, Wiedemann S, Furmans K (2017). A discrete-time queueing network approach to performance evaluation of autonomous vehicle storage and retrieval systems. International Journal of Production Research, 55(4): 960–978

Fager P, Sgarbossa F, Calzavara M (2021). Cost modelling of onboard cobot-supported item sorting in a picking system. International Journal of Production Research, 59(11): 3269–3284

Fottner J, Clauer D, Hormes F, Freitag M, Beinke T, Overmeyer L, Gottwald S N, Elbert R, Sarnow T, Schmidt T, Reith K B, Zadek H, Thomas F (2021). Autonomous systems in intralogistics — state of the art and future research challenges. Logistics Research, 14(1): 2

Foumani M, Moeini A, Haythorpe M, Smith-Miles K (2018). A cross-entropy method for optimising robotic automated storage and retrieval systems. International Journal of Production Research, 56(19): 6450–6472

Fragapane G, de Koster R B M, Sgarbossa F, Strandhagen J O (2021). Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda. European Journal of Operational Research, 294(2): 405–426

Gagliardi J P, Renaud J, Ruiz A (2012). Models for automated storage and retrieval systems: A literature review. International Journal of Production Research, 50(24): 7110–7125

Gagliardi J P, Renaud J, Ruiz A (2015). Sequencing approaches for multiple-aisle automated storage and retrieval systems. International Journal of Production Research, 53(19): 5873–5883

Gareis M, Hehn M, Stief P, Korner G, Birkenhauer C, Trabert J, Mehner T, Vossiek M, Carlowitz C (2021). Novel UHF-RFID listener hardware architecture and system concept for a mobile robot based MIMO SAR RFID localization. IEEE Access, 9: 497–510

Gharehgozli A H, Xu C, Zhang W D (2021). High multiplicity asymmetric traveling salesman problem with feedback vertex set and its application to storage/retrieval system. European Journal of Operational Research, 289(2): 495–507

Gharehgozli A H, Zaerpour N (2020). Robot scheduling for pod retrieval in a robotic mobile fulfillment system. Transportation Research Part E: Logistics and Transportation Review, 142: 102087

Gharehgozli A H, Yu Y G, Zhang X D, de Koster R B M (2017). Polynomial time algorithms to minimize total travel time in a two-depot automated storage/retrieval system. Transportation Science, 51(1): 19–33

Ghelichi Z, Kilaru S (2021). Analytical models for collaborative autonomous mobile robot solutions in fulfillment centers. Applied Mathematical Modelling, 91: 438–457

Giusti I, Cepolina E M, Cangialosi E, Aquaro D, Caroti G, Piemonte A (2019). Mitigation of human error consequences in general cargo handler logistics: Impact of RFID implementation. Computers & Industrial Engineering, 137: 106038

Glock C H, Grosse E H, Abedinnia H, Emde S (2019). An integrated model to improve ergonomic and economic performance in order picking by rotating pallets. European Journal of Operational Research, 273(2): 516–534

Glock C H, Grosse E H, Neumann W P, Feldman A (2021). Assistive devices for manual materials handling in warehouses: A systematic literature review. International Journal of Production Research, 59(11): 3446–3469

Gong Y M, Jin M Z, Yuan Z (2021). Robotic mobile fulfilment systems considering customer classes. International Journal of Production Research, 59(16): 5032–5049

Grosse E H, Glock C H, Neumann W P (2017). Human factors in order picking: A content analysis of the literature. International Journal of Production Research, 55(5): 1260–1276

Gružauskas V, Baskutis S, Navickas V (2018). Minimizing the trade-off between sustainability and cost effective performance by using autonomous vehicles. Journal of Cleaner Production, 184: 709–717

Gu J X, Goetschalckx M, McGinnis L F (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1): 1–21

Gu J X, Goetschalckx M, McGinnis L F (2010). Research on warehouse design and performance evaluation: A comprehensive review. European Journal of Operational Research, 203(3): 539–549

Guo X L, Yu Y G, de Koster R B M (2016). Impact of required storage space on storage policy performance in a unit-load warehouse. International Journal of Production Research, 54(8): 2405–2418

Ha Y, Chae J (2019). A decision model to determine the number of shuttles in a tier-to-tier SBS/RS. International Journal of Production Research, 57(4): 963–984

Habibi Tostani H, Haleh H, Hadji Molana S M, Sobhani F M (2020). A Bi-Level Bi-Objective optimization model for the integrated storage classes and dual shuttle cranes scheduling in AS/RS with energy consumption, workload balance and time windows. Journal of Cleaner Production, 257: 120409

Hahn-Woernle P, Gunthner W A (2018). Power-load management reduces energy-dependent costs of multi-aisle mini-load automated storage and retrieval systems. International Journal of Production Research, 56(3): 1269–1285

Han S D, Yu J J (2020). DDM: Fast near-optimal multi-robot path planning using diversified-path and optimal sub-problem solution database heuristics. IEEE Robotics and Automation Letters, 5(2): 1350–1357

Hao J J, Yu Y G, Zhang L L (2015). Optimal design of a 3D compact storage system with the I/O port at the lower mid-point of the storage rack. International Journal of Production Research, 53(17): 5153–5173

Hassan M, Ali M, Aktas E, Alkayid K (2015). Factors affecting selection decision of auto-identification technology in warehouse management: An international Delphi study. Production Planning and Control, 26(12): 1025–1049

He Z J, Aggarwal V, Nof S Y (2018). Differentiated service policy in smart warehouse automation. International Journal of Production Research, 56(22): 6956–6970

Heshmati S, Toffolo T A M, Vancroonenburg W, Vanden Berghe G (2019). Crane-operated warehouses: Integrating location assignment and crane scheduling. Computers & Industrial Engineering, 129: 274–295

Jaghbeer Y, Hanson R, Johansson M I (2020). Automated order picking systems and the links between design and performance: A systematic literature review. International Journal of Production Research, 58(15): 4489–4505

Jiang M, Leung K H, Lyu Z Y, Huang G Q (2020). Picking-replenishment synchronization for robotic forward-reserve warehouses. Transportation Research Part E: Logistics and Transportation Review, 144: 102138

Jiang Z Z, Wan M Z, Pei Z, Qin X W (2021). Spatial and temporal optimization for smart warehouses with fast turnover. Computers & Operations Research, 125: 105091

Kabir Q S, Suzuki Y (2018). Increasing manufacturing flexibility through battery management of automated guided vehicles. Computers & Industrial Engineering, 117: 225–236

Keung K L, Lee C K M, Ji P, Ng K K H (2020). Cloud-based cyber-physical robotic mobile fulfillment systems: A case study of collision avoidance. IEEE Access, 8: 89318–89336

Kress D, Boysen N, Pesch E (2017). Which items should be stored together? A basic partition problem to assign storage space in group-based storage systems. IISE Transactions, 49(1): 13–30

Kumawat G L, Roy D (2021). A new solution approach for multi-stage semi-open queuing networks: An application in shuttle-based compact storage systems. Computers & Operations Research, 125: 105086

Lam H Y, Choy K L, Ho G T S, Cheng S W Y, Lee C K M (2015). A knowledge-based logistics operations planning system for mitigating risk in warehouse order fulfillment. International Journal of Production Economics, 170: 763–779

Lamballais Tessensohn T, Roy D, de Koster R B M (2017). Estimating performance in a robotic mobile fulfillment system. European Journal of Operational Research, 256(3): 976–990

Lamballais Tessensohn T, Roy D, de Koster R B M (2020). Inventory allocation in robotic mobile fulfillment systems. IISE Transactions, 52(1): 1–17

Lee C K M, Lin B B, Ng K K H, Lv Y Q, Tai W C (2019). Smart robotic mobile fulfillment system with dynamic conflict-free strategies considering cyber-physical integration. Advanced Engineering Informatics, 42: 100998

Lee C K M, Lv Y Q, Ng K K H, Ho W, Choy K L (2018). Design and application of Internet of Things-based warehouse management system for smart logistics. International Journal of Production Research, 56(8): 2753–2768

Lee C W, Wong W P, Ignatius J, Rahman A, Tseng M L (2020). Winner determination problem in multiple automated guided vehicle considering cost and flexibility. Computers & Industrial Engineering, 142: 106337

Lee H F, Schaefer S K (1996). Retrieval sequencing for unit-load automated storage and retrieval systems with multiple openings. International Journal of Production Research, 34(10): 2943–2962

Lee H Y, Murray C C (2019). Robotics in order picking: Evaluating warehouse layouts for pick, place, and transport vehicle routing systems. International Journal of Production Research, 57(18): 5821–5841

Lenoble N, Hammami R, Frein Y (2021). Fixed and rolling batching for order picking from multiple carousels. Production Planning and Control, 32(8): 652–669

Lerher T (2016). Travel time model for double-deep shuttle-based storage and retrieval systems. International Journal of Production Research, 54(9): 2519–2540

Lerher T (2018). Aisle changing shuttle carriers in autonomous vehicle storage and retrieval systems. International Journal of Production Research, 56(11): 3859–3879

Lerher T, Ficko M, Palcic I (2021). Throughput performance analysis of Automated Vehicle Storage and Retrieval Systems with multiple-tier shuttle vehicles. Applied Mathematical Modelling, 91: 1004–1022

Li X W, Hua G W, Huang A Q, Sheu J B, Cheng T C E, Huang F Q (2020). Storage assignment policy with awareness of energy consumption in the KIVA mobile fulfilment system. Transportation Research Part E: Logistics and Transportation Review, 144: 102158

Liu J M, Liao H T, White Jr J A (2021). Stochastic analysis of an automated storage and retrieval system with multiple in-the-aisle pick positions. Naval Research Logistics, 68(4): 454–470

Liu T, Gong Y M, de Koster R B M (2018). Travel time models for split-platform automated storage and retrieval systems. International Journal of Production Economics, 197: 197–214

Lu S P, Xu C, Zhong R Y, Wang L H (2018). A passive RFID tag-based locating and navigating approach for automated guided vehicle. Computers & Industrial Engineering, 125: 628–636

Mahroof K (2019). A human-centric perspective exploring the readiness towards smart warehousing: The case of a large retail distribution warehouse. International Journal of Information Management, 45: 176–190

Małopolski W (2018). A sustainable and conflict-free operation of AGVs in a square topology. Computers & Industrial Engineering, 126: 472–481

Man X Y, Zheng F F, Chu F, Liu M, Xu Y F (2021). Bi-objective optimization for a two-depot automated storage/retrieval system. Annals of Operations Research, 296(1–2): 243–262

Article   MathSciNet   Google Scholar  

Manavalan E, Jayakrishna K (2019). A review of Internet of Things (IoT) embedded sustainable supply chain for Industry 4.0 requirements. Computers & Industrial Engineering, 127: 925–953

Manzini R, Accorsi R, Baruffaldi G, Cennerazzo T, Gamberi M (2016). Travel time models for deep-lane unit-load autonomous vehicle storage and retrieval system (AVS/RS). International Journal of Production Research, 54(14): 4286–4304

Manzini R, Accorsi R, Gamberi M, Penazzi S (2015). Modeling class-based storage assignment over life cycle picking patterns. International Journal of Production Economics, 170: 790–800

McFarlane D, Giannikas V, Lu W R (2016). Intelligent logistics: Involving the customer. Computers in Industry, 81: 105–115

Meneghetti A, Monti L (2015). Greening the food supply chain: An optimisation model for sustainable design of refrigerated automated warehouses. International Journal of Production Research, 53(21): 6567–6587

Mirzaei M, de Koster R B M, Zaerpour N (2017). Modelling load retrievals in puzzle-based storage systems. International Journal of Production Research, 55(21): 6423–6435

Mo L F, Li C Y (2019). Passive UHF-RFID localization based on the similarity measurement of virtual reference tags. IEEE Transactions on Instrumentation and Measurement, 68(8): 2926–2933

Nicolas L, Yannick F, Ramzi H (2018). Order batching in an automated warehouse with several vertical lift modules: Optimization and experiments with real data. European Journal of Operational Research, 267(3): 958–976

Pan C H, Wang C H (1996). A framework for the dual command cycle travel time model in automated warehousing systems. International Journal of Production Research, 34(8): 2099–2117

Pan J C H, Shih P H, Wu M H, Lin J H (2015). A storage assignment heuristic method based on genetic algorithm for a pick-and-pass warehousing system. Computers & Industrial Engineering, 81: 1–13

Qiu X, Luo H, Xu G Y, Zhong R Y, Huang G Q (2015). Physical assets and service sharing for IoT-enabled Supply Hub in Industrial Park (SHIP). International Journal of Production Economics, 159: 4–15

Ramtin F, Pazour J A (2015). Product allocation problem for an AS/RS with multiple in-the-aisle pick positions. IIE Transactions, 47(12): 1379–1396

Reaidy P J, Gunasekaran A, Spalanzani A (2015). Bottom-up approach based on Internet of Things for order fulfillment in a collaborative warehousing environment. International Journal of Production Economics, 159: 29–40

Roodbergen K J, Vis I F A (2009). A survey of literature on automated storage and retrieval systems. European Journal of Operational Research, 194(2): 343–362

Roozbeh Nia A R, Haleh H, Saghaei A (2017). Dual command cycle dynamic sequencing method to consider GHG efficiency in unit-load multiple-rack automated storage and retrieval systems. Computers & Industrial Engineering, 111: 89–108

Rouwenhorst B, Reuter B, Stockrahm V, van Houtum G J, Mantel R J, Zijm W H M (2000). Warehouse design and control: Framework and literature review. European Journal of Operational Research, 122(3): 515–533

Roy D, Krishnamurthy A, Heragu S, Malmborg C (2015a). Queuing models to analyze dwell-point and cross-aisle location in autonomous vehicle-based warehouse systems. European Journal of Operational Research, 242(1): 72–87

Roy D, Krishnamurthy A, Heragu S, Malmborg C (2015b). Stochastic models for unit-load operations in warehouse systems with autonomous vehicles. Annals of Operations Research, 231(1): 129–155

Roy D, Nigam S, de Koster R B M, Adan I, Resing J (2019). Robot-storage zone assignment strategies in mobile fulfillment systems. Transportation Research Part E: Logistics and Transportation Review, 122: 119–142

Saidi-Mehrabad M, Dehnavi-Arani S, Evazabadian F, Mahmoodian V (2015). An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Computers & Industrial Engineering, 86: 2–13

Salah B, Janeh O, Noche B, Bruckmann T, Darmoul S (2017). Design and simulation based validation of the control architecture of a stacker crane based on an innovative wire-driven robot. Robotics and Computer-integrated Manufacturing, 44: 117–128

Sartoretti G, Kerr J, Shi Y F, Wagner G, Kumar T K S, Koenig S, Choset H (2019). PRIMAL: Pathfinding via reinforcement and imitation multi-agent learning. IEEE Robotics and Automation Letters, 4(3): 2378–2385

Shahzad M, Liu A X (2015). Fast and accurate estimation of RFID tags. IEEE/ACM Transactions on Networking, 23(1): 241–254

Shi Y Y, Arthanari T, Liu X J, Yang B (2019). Sustainable transportation management: Integrated modeling and support. Journal of Cleaner Production, 212: 1381–1395

State Post Bureau of PRC (2020). China’s annual express delivery volume exceeded 70 billion items for the first time. Available at: spb.gov.cn/xw/dtxx_15079/202011/t20201117_3513569.html

Tao F, Zuo Y, Xu L D, Lv L, Zhang L (2014). Internet of Things and BOM-based life cycle assessment of energy-saving and emission-reduction of products. IEEE Transactions on Industrial Informatics, 10(2): 1252–1261

Tappia E, Marchet G, Melacini M, Perotti S (2015). Incorporating the environmental dimension in the assessment of automated warehouses. Production Planning and Control, 26(10): 824–838

Tappia E, Roy D, de Koster R B M, Melacini M (2017). Modeling, analysis, and design insights for shuttle-based compact storage systems. Transportation Science, 51(1): 269–295

Tappia E, Roy D, Melacini M, de Koster R B M (2019). Integrated storage-order picking systems: Technology, performance models, and design insights. European Journal of Operational Research, 274(3): 947–965

Technical.ly (2019). Amazon fulfillment center brings robotics to Sparrows Point. Available at: technical.ly/baltimore/2019/03/22/amazon-fulfillment-center-brings-robotics-to-sparrows-point-artificial-intelligence

Thanos E, Wauters T, Vanden Berghe G (2021). Dispatch and conflict-free routing of capacitated vehicles with storage stack allocation. Journal of the Operational Research Society, 72(8): 1780–1793

Tutam M, White J A (2019a). Multi-dock unit-load warehouse designs with a cross-aisle. Transportation Research Part E: Logistics and Transportation Review, 129: 247–262

Tutam M, White J A (2019b). A multi-dock, unit-load warehouse design. IISE Transactions, 51(3): 232–247

van Gils T, Ramaekers K, Braekers K, Depaire B, Caris A (2018a). Increasing order picking efficiency by integrating storage, batching, zone picking, and routing policy decisions. International Journal of Production Economics, 197: 243–261

van Gils T, Ramaekers K, Caris A, de Koster R B M (2018b). Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review. European Journal of Operational Research, 267(1): 1–15

Wang K, Yang Y, Li R (2020a). Travel time models for the rack-moving mobile robot system. International Journal of Production Research, 58(14): 4367–4385

Wang W, Wu Y H, Zheng J, Chi C (2020b). A comprehensive framework for the design of modular robotic mobile fulfillment systems. IEEE Access, 8: 13259–13269

Wang Y Y, Liu Z W, Huang K, Mou S D, Zhang R X (2020c). Model and solution approaches for retrieval operations in a multi-tier shuttle warehouse system. Computers & Industrial Engineering, 141: 106283

Wang Y Y, Mou S D, Wu Y H (2015). Task scheduling for multi-tier shuttle warehousing systems. International Journal of Production Research, 53(19): 5884–5895

Wauters T, Villa F, Christiaens J, Alvarez-Valdes R, Vanden Berghe G (2016). A decomposition approach to dual shuttle automated storage and retrieval systems. Computers & Industrial Engineering, 101: 325–337

Weidinger F, Boysen N, Briskorn D (2018). Storage assignment with rack-moving mobile robots in KIVA warehouses. Transportation Science, 52(6): 1479–1495

Wen J M, He L, Zhu F M (2018). Swarm robotics control and communications: Imminent challenges for next generation smart logistics. IEEE Communications Magazine, 56(7): 102–107

Winkelhaus S, Grosse E H (2020). Logistics 4.0: A systematic review towards a new logistics system. International Journal of Production Research, 58(1): 18–43

Xie L, Thieme N, Krenzler R, Li H Y (2021). Introducing split orders and optimizing operational policies in robotic mobile fulfillment systems. European Journal of Operational Research, 288(1): 80–97

Xu X, Zhao X, Zou B, Gong Y, Wang H (2020). Travel time models for a three-dimensional compact AS/RS considering different I/O point policies. International Journal of Production Research, 58(18): 5432–5455

Xu X H, Gong Y M, Fan X X, Shen G W, Zou B P (2018). Travel-time model of dual-command cycles in a 3D compact AS/RS with lower mid-point I/O dwell point policy. International Journal of Production Research, 56(4): 1620–1641

Xu X H, Shen G W, Yu Y G, Huang W (2015). Travel time analysis for the double-deep dual-shuttle AS/RS. International Journal of Production Research, 53:3

Xu X H, Zou B P, Shen G W, Gong Y M (2016). Travel-time models and fill-grade factor analysis for double-deep multi-aisle AS/RSs. International Journal of Production Research, 54(14): 4126–4144

Yalcin A, Koberstein A, Schocke K O (2019). An optimal and a heuristic algorithm for the single-item retrieval problem in puzzle-based storage systems with multiple escorts. International Journal of Production Research, 57(1): 143–165

Yang H, Kumara S, Bukkapatnam S T S, Tsung F (2019). The Internet of Things for smart manufacturing: A review. IISE Transactions, 51(11): 1190–1216

Yang P, Miao L X, Xue Z J, Qin L (2015a). An integrated optimization of location assignment and storage/retrieval scheduling in multishuttle automated storage/retrieval systems. Journal of Intelligent Manufacturing, 26(6): 1145–1159

Yang P, Miao L X, Xue Z J, Qin L (2015b). Optimal storage rack design for a multi-deep compact AS/RS considering the acceleration/deceleration of the storage and retrieval machine. International Journal of Production Research, 53(3): 929–943

Yang P, Miao L X, Xue Z J, Ye B (2015c). Variable neighborhood search heuristic for storage location assignment and storage/retrieval scheduling under shared storage in multi-shuttle automated storage/retrieval systems. Transportation Research Part E: Logistics and Transportation Review, 79: 164–177

Yetkin Ekren B (2017). Graph-based solution for performance evaluation of shuttle-based storage and retrieval system. International Journal of Production Research, 55(21): 6516–6526

Yetkin Ekren B (2021). A multi-objective optimisation study for the design of an AVS/RS warehouse. International Journal of Production Research, 59(4): 1107–1126

Yetkin Ekren B, Akpunar A (2021). An open queuing network-based tool for performance estimations in a shuttle-based storage and retrieval system. Applied Mathematical Modelling, 89: 1678–1695

Yetkin Ekren B, Akpunar A, Sari Z, Lerher T (2018). A tool for time, variance and energy related performance estimations in a shuttle-based storage and retrieval system. Applied Mathematical Modelling, 63: 109–127

Yoshitake H, Kamoshida R, Nagashima Y (2019). New automated guided vehicle system using real-time holonic scheduling for warehouse picking. IEEE Robotics and Automation Letters, 4(2): 1045–1052

Yu H, Yu Y (2019). Optimising two dwell point policies for AS/RSs with input and output point at opposite ends of the aisle. International Journal of Production Research, 57(21): 6615–6633

Yu M F, de Koster R B M (2009). The impact of order batching and picking area zoning on order picking system performance. European Journal of Operational Research, 198(2): 480–490

Yu Y, de Koster R B M, Guo X (2015). Class-based storage with a finite number of items: Using more classes is not always better. Production and Operations Management, 24(8): 1235–1247

Yu Y G, Han X Y, Hu G P (2016). Optimal production for manufacturers considering consumer environmental awareness and green subsidies. International Journal of Production Economics, 182: 397–408

Yuan R, Graves S C, Cezik T (2019). Velocity-based storage assignment in semi-automated storage systems. Production and Operations Management, 28(2): 354–373

Yuan Z, Gong Y M (2017). Bot-in-time delivery for robotic mobile fulfillment systems. IEEE Transactions on Engineering Management, 64(1): 83–93

Zaerpour N, Yu Y G, de Koster R B M (2017a). Small is beautiful: A framework for evaluating and optimizing live-cube compact storage systems. Transportation Science, 51(1): 34–51

Zaerpour N, Yu Y G, de Koster R B M (2015). Storing fresh produce for fast retrieval in an automated compact cross-dock system. Production and Operations Management, 24(8): 1266–1284

Zaerpour N, Yu Y G, de Koster R B M (2017b). Optimal two-class-based storage in a live-cube compact storage system. IISE Transactions, 49(7): 653–668

Zaerpour N, Yu Y G, de Koster R B M (2017c). Response time analysis of a live-cube compact storage system with two storage classes. IISE Transactions, 49(5): 461–480

Zhang F, Shang W W, Zhang B, Cong S (2020). Design optimization of redundantly actuated cable-driven parallel robots for automated warehouse system. IEEE Access, 8: 56867–56879

Zhang Z, Guo Q, Chen J, Yuan P J (2018). Collision-free route planning for multiple AGVS in an automated warehouse based on collision classification. IEEE Access, 6: 26022–26035

Zhao X F, Zhang R X, Zhang N, Wang Y Y, Jin M Z, Mou S D (2020a). Analysis of the shuttle-based storage and retrieval system. IEEE Access, 8: 146154–146165

Zhao Y L, Liu X P, Wang G, Wu S B, Han S (2020b). Dynamic resource reservation based collision and deadlock prevention for multi-AGVs. IEEE Access, 8: 82120–82130

Zhong R Y, Huang G Q, Lan S L, Dai Q Y, Chen X, Zhang T (2015). A big data approach for logistics trajectory discovery from RFID-enabled production data. International Journal of Production Economics, 165: 260–272

Zhou W, Piramuthu S, Chu F, Chu C B (2017). RFID-enabled flexible warehousing. Decision Support Systems, 98: 99–112

Zou B P, de Koster R B M, Xu X H (2018a). Operating policies in robotic compact storage and retrieval systems. Transportation Science, 52(4): 788–811

Zou B P, Gong Y M, Xu X H, Yuan Z (2017). Assignment rules in robotic mobile fulfilment systems for online retailers. International Journal of Production Research, 55(20): 6175–6192

Zou B P, Xu X H, Gong Y M, de Koster R B M (2018b). Evaluating battery charging and swapping strategies in a robotic mobile fulfillment system. European Journal of Operational Research, 267(2): 733–753

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Zhen, L., Li, H. A literature review of smart warehouse operations management. Front. Eng. Manag. 9 , 31–55 (2022). https://doi.org/10.1007/s42524-021-0178-9

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Logistics 4.0 in warehousing: a conceptual framework of influencing factors, benefits and barriers

The International Journal of Logistics Management

ISSN : 0957-4093

Article publication date: 7 October 2022

Issue publication date: 19 December 2022

In the last decade, the Industry 4.0 paradigm had started to rapidly expand to the logistics domain. However, Logistics 4.0 is still in an early adoption stage: some areas such as warehousing are still exploring its applicability, and the technological implementation of this paradigm can become fuzzy. This paper addresses this gap by examining the relationship among influencing factors, barriers, and benefits of Logistics 4.0 technologies in warehousing contexts.

Design/methodology/approach

Starting from a Systematic Literature Review (SLR) approach with 56 examined documents published in scientific journals or conference proceedings, a conceptual framework for Logistics 4.0 in warehousing is proposed. The framework encompasses multiple aspects related to the potential adopter’s decision-making process.

Influencing factors toward adoption, achievable benefits, and possible hurdles or criticalities have been extensively analyzed and structured into a consistent picture. Company’s digital awareness and readiness result in a major influencing factor, whereas barriers and criticalities are mostly technological, safety and security, and economic in nature. Warehousing process optimization is the key benefit identified.

Originality/value

This paper addresses a major gap since most of the research has focused on specific facets, or adopted the technology providers’ perspective, whereas little has been explored in warehousing from the adopters’ view. The main novelty and value lie in providing both academics and practitioners with a thorough view of multiple facets to be considered when approaching Logistics 4.0 in logistics facilities.

• Logistics 4.0
• Warehousing
• Technology adopters

Perotti, S. , Bastidas Santacruz, R.F. , Bremer, P. and Beer, J.E. (2022), "Logistics 4.0 in warehousing: a conceptual framework of influencing factors, benefits and barriers", The International Journal of Logistics Management , Vol. 33 No. 5, pp. 193-220. https://doi.org/10.1108/IJLM-02-2022-0068

Emerald Publishing Limited

Copyright © 2022, Sara Perotti, Roman Felipe Bastidas Santacruz, Peik Bremer and Jakob Emanuel Beer

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and no commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Logistics is an ever-growing business that has gained increasing importance at a global level. Logistics market size was €5.6 trillion in 2018 and is projected to have a 4.6% compound annual growth rate (CAGR) until 2023 ( Transport Intelligence, 2019 ). In Europe, logistics market size was €0.9 trillion in 2019 with a 2.4% CAGR forecasted for the 2018–2023 timespan ( Transport Intelligence, 2019 ) and about 10.3 million citizens employed in 2018, thus making this industry highly relevant for the global economy ( Eurostat, 2018 ). Within the logistics market, in-house warehousing and Third-Party Logistics (3PL) represent key activities with 30% of the total market value, and 38% in Europe ( Transport Intelligence, 2019 ). Among logistics processes, warehousing is one of the most critical cost components ( Rodrigue, 2020 ; Perotti et al. , 2022 ), accounting for about 20% of logistics costs ( Dhooma and Baker, 2012 ). Logistics facilities have been challenged by a substantial evolution over time ( Baglio et al. , 2019 ), as they have transformed from simple repositories for inventory into multi-functional logistics hubs ( Baker, 2004 ; Onstein et al. , 2019 ). This brought along challenges with higher requirements in terms of efficiency and service level fulfillment ( Kembro et al. , 2018 ).

In the past decade, also the manufacturing sector has started experiencing substantial changes, driven by factors such as sustainability concerns ( Ghobakhloo, 2020 ). These changes have taken the manufacturing industry to experience a new transformation, for which Kagermann et al. (2011) have coined the term “Industry 4.0”, claiming to describe the fourth industrial revolution. In Industry 4.0, centralized control systems give way to decentralized decision-making. The aim of improving performances, and in some cases, the increase in complexity of business environments and more demanding requirements, are reshaping logistics and warehousing processes ( Dev et al. , 2021 ). To cope with this scenario, digitalization and the transition toward the Logistics 4.0 paradigm have become powerful means to compete in the market and help companies address the fragile trade-off between improved service levels and reasonable operating costs. Based on embedded sensors integrated with other technologies, objects such as machines, products, or orders, autonomously control themselves and are fully vertically integrated into the company’s information systems ( Kagermann et al. , 2011 ).

Since the term was coined in 2011, Industry 4.0 has become a dominant topic ( Phuyal et al. , 2020 ; Tang and Veelenturf, 2019 ). This is reflected by the growing number of publications, including an increasing number of logistics-related contributions since 2015 ( Grzybowska and Awasthi, 2020 ). In this context, the exploration of Industry 4.0 technologies such as Autonomous Mobile Robots ( Fragapane et al. , 2021 ), Machine Learning, Artificial Intelligence (AI), and the Internet of Things (IoT) has also increased ( Culot et al. , 2020 ; Phuyal et al. , 2020 ; Salamone et al. , 2018 ). These technologies modify how the manufacturing industry operates, leading to a higher complexity of the manufacturing processes ( Culot et al. , 2020 ). In this context, some papers center their attention on the investigation of drivers and barriers to Industry 4.0 technologies adoption by considering different industrial perspectives. For instance, Tortorella et al. (2021) and Frederico et al. (2021) investigate the effect of Industry 4.0 technologies on supply chain resilience, showing a positive relationship between disruptive technology adoption and supply chain performance. Chauhan et al. (2021) focusing on companies in an emerging economy, propose to further explore this topic by investigating barriers as well as effects on companies’ performance. Also, Raj et al. (2020) study the barriers to Industry 4.0 adoption, considering both developed and developing countries. They suggest analyzing enabling factors for Industry 4.0. Lastly, Horváth and Szabó (2019) explore the barriers and driving forces of Industry 4.0 adoption from a general industry perspective while Stentoft et al. (2020) investigate the same topic from an SME perspective.

Logistics, directly affecting company’s productivity and service level as well as customer satisfaction, must also be able to adapt to the characteristics of the new Industry 4.0 manufacturing environment. Hence, it is questionable whether the current logistics systems and structures will be able to handle the increased complexity generated by Industry 4.0, more specifically without increasing costs or decreasing quality ( Wang et al. , 2020 ; Winkelhaus and Grosse, 2020 ). Companies need to align their logistics performance and development with the new requirements to support the vital link between manufacturers and customers that depends on logistics and warehousing operations ( Winkelhaus and Grosse, 2020 ), resulting in the concept of “Logistics 4.0”. Logistics 4.0 is still a fuzzy term ( Bag et al. , 2020 ), and it is unclear which concepts it comprises ( Oleśków-Szłapka and Stachowiak, 2019 ). For instance, a recent definition of Logistics 4.0 by Winkelhaus and Grosse (2020) , refers to “the logistical system that enables the sustainable satisfaction of individualized customer demands without an increase in costs and supports this development in industry and trade using digital technologies”. Such definition, on the one hand, relates Logistics 4.0 to specific market factors (sustainability, individualized demand), while on the other hand is vague in the “digital technologies” required to implement them.

Warehouses play a key role in the Logistics 4.0 transition ( Valchkov and Valchkova, 2018 ). Kumar et al. (2021) highlight relevant gaps related to Logistics 4.0 in warehouses and, more specifically, the need for frameworks to identify and address the challenges of its technological adoption. Indeed, most of the extant research mainly addresses two streams: either general benefits related to Logistics 4.0 adoption or the description of innovative technologies and solutions.

The first stream analyses possible benefits related to Logistics 4.0 in the warehousing context ( Domański, 2019 ; Douaioui et al. , 2018 ; Issaoui et al. , 2021 ) and how operations could profit from Logistics 4.0 ( Feng and Ye, 2021 ). For instance, Loureiro et al. (2020) concentrate on how Logistics 4.0 solutions help improve transaction costs and business coordination. Other researchers focus on the implications of Industry 4.0 for the logistics sector, emphasizing concepts such as digitalization and automation ( Bag et al. , 2020 ; Barreto et al. , 2017 ; Schmidtke et al. , 2018 ). Finally, Winkelhaus and Grosse (2020) investigate the possible benefits and challenges of Logistics 4.0 and provide a framework combining external triggers, underlying technological innovations, and impacts on human interactions and logistic tasks. Looking at the second stream, Cano et al. (2021) identify technologies framed into the Industry 4.0 concept that can be implemented also in logistics. Golpîra et al. (2021) investigate the areas of application, current development stage, and gaps of IoT in Logistics 4.0 transformation. Other authors discuss IoT applications in logistics from the perspectives of both, advantages and challenges that limit their adoption ( Ding et al. , 2021 ; Song et al. , 2021 ; Tran-Dang et al. , 2020 ). Chung (2021) focuses on the applications which various Industry 4.0 technologies could have in logistics processes. Intralogistics is explored by Fottner et al. (2021) who investigate the level of automation in intralogistics and the technologies that can enable it. Winkelhaus et al. (2021) analyze the socio-technological effects of Industry 4.0 on order picking systems.

Although the academic literature has started exploring how companies are approaching Logistics 4.0 adoption, a comprehensive conceptual framework addressing the adoption process of Logistics 4.0 in warehousing is missing. The aim of this paper is to offer a comprehensive conceptualization of Logistics 4.0 adoption in warehousing by embracing the adopters’ perspective and addressing the main influencing factors, achievable benefits as well as potential criticalities and barriers. This paper intends to address this research gap with a Systematic Literature Review (SLR) approach to provide robustness to the proposed conceptual framework. SLRs have been proved valuable as the initial step of defining a framework ( Oleśków-Szłapka and Stachowiak, 2019 ; Winkelhaus and Grosse, 2020 ; Zoubek and Simon, 2021 ). Starting from the available literature on this topic, we categorize the relevant elements into a conceptual framework that can be used as a guideline by academics and practitioners.

The novelty and value of this paper lie in providing both academics and practitioners with a thorough view of the different facets to be considered when approaching the adoption of Logistics 4.0 solutions in logistics facilities. Specifically, influencing factors towards adoption, achievable benefits, and possible hurdles or criticalities will be extensively analyzed and structured into a consistent picture.

The remainder of the paper is structured as follows. The next section motivates and describes the SLR methodology adopted to ground the conceptual framework. Then, we present and discuss the results of our analysis. Finally, we draw conclusions and suggest future research directions.

Methodology

Systematic literature review (slr) approach.

As Logistics 4.0 in warehousing is a cutting-edge topic, an SLR approach is ideal to gather the most relevant information ( Tranfield et al. , 2003 ). The final goal of the SLR is to perform a critical analysis of research papers on Logistics 4.0 in warehousing to better comprehend the existing trends and research gaps ( Carter and Rogers, 2008 ). Hence, the five-step methodology suggested by Denyer and Tranfield (2009) was adopted and hereinafter described.

Question formulation

1 Context: The specification of individuals, relationships, institutional settings, or wider systems that are studied. Higher service levels requested by the market and the increasing logistics complexity require companies to develop new solutions for their logistics activities and, more specifically, for their warehouses.

2 Intervention: The events, actions, and activities that are studied. In this paper, the intervention is the application of Logistics 4.0 technologies.

3 Mechanisms: The mechanisms that explain the relationship between interventions, outcomes, and the circumstances under which these mechanisms are active. This should help companies find the most suitable solutions that leverage the benefits of Logistics 4.0 while mitigating risks and controlling costs.

4 Outcome: The effects of intervention, both intended and unintended ones. The aims associated with Logistics 4.0 in warehouses include, on the one hand, cost and time reduction for decision-making and for operations while maintaining service levels; on the other hand, providing higher service levels (e.g. by better utilizing the data emanating from ubiquitous sensors, higher quality of decision-making) while maintaining or optimizing costs ( Winkelhaus and Grosse, 2020 ). The combination of these two objectives and their trade-offs is a constant challenge for managers and decision-makers.

What are the main factors influencing a company’s level of readiness for the adoption of Logistics 4.0 in their warehouses?

What are the benefits that companies could achieve by implementing Logistics 4.0 solutions in their warehouses?

What are the main barriers and criticalities faced by companies when implementing Logistics 4.0 solutions in their warehouses?

The focus is set on influencing factors, benefits, and barriers with the purpose of specifically investigating the adoption process of Logistics 4.0 in warehouses, in line with previous logistics literature dealing with adoption processes (e.g. Li et al. , 2020 ; Perotti et al. , 2015 ).

Locating documents

1 Group A comprehends keywords referring to Logistics 4.0, i.e.: “smart logistic*” OR “logistic* 4.0” OR “autonomous logistic*” OR “warehous* 4.0” OR “smart warehous*”.

2 Group B encompasses the specific aspects under investigation, i.e.: “adopt*” OR “demand*” OR “benefit*” OR “advantage*” OR “opportunit*” OR “barrier*” OR “criticalit*” OR “challeng*” OR “maturity” OR “readiness” OR “impact*” OR “factor*” OR “driver*”.

Paper selection and evaluation

328 documents were initially retrieved from Scopus and 201 from Web of Science, including duplicates. Merging and removing duplicates delivered 363 documents dated between October 2003 and April 2021. At this stage (Phase 3), a rigorous selection process, structured into screening, eligibility, and qualification, was applied using the inclusion and exclusion criteria reported in Table 1 .

In the screening stage, phase, criteria 1 to 4 ( Table 1 ) were considered to limit the results to those publications central to the purposes of this study. More specifically, criterion 1 evaluates the date of publication, due to the fact that the term Industry 4.0 has been first coined and used by Kagermann et al. (2011) . Criterion 2 considers the attribution of the research, while criterion 3 ensures the quality of the papers, as scientific journals have a more rigorous review process than other document types ( Colicchia et al. , 2018 ) and conference proceedings cover emerging trends and challenges. Criterion 4 evaluates the language of publication. English is the language of choice as it is the most adopted and formally approved language for publications in the field of supply chain management ( Colicchia et al. , 2018 ). The screening phase delivered 274 papers out of 363 for the long list of papers.

In the eligibility stage , criteria 5 and 6 were applied. Both criteria are directly related to the main topics of the research questions. In this phase, the abstract, introduction, and conclusions of the papers were analyzed. This led to the exclusion of 185 papers, with 89 papers remaining in the sample.

Finally, in the qualification stage, all 89 papers were entirely read by two reviewers and carefully examined. As a result of this process, 33 papers have been excluded, because they were not specifically centered on the topics of interest. This led to a shortlist of 56 papers for critical in-depth analysis.

Review results

1 Descriptive characteristics, i.e. general details such as article title, year of release, source title, and first author’s country.

2 Methodology adopted, namely literature reviews, conceptual works, analytical papers, empirical contributions (case studies/interviews and surveys), action research (implementation of a technology), and simulations. If a paper presented multiple methodologies, the prevailing one was considered for classification.

3 Research question addressed, by identifying the topics addressed i.e. (1) influencing factors regarding the company’s level of readiness for the adoption of Logistics 4.0 technologies assigned to RQ1 , (2) benefits of the implementation of Logistics 4.0 solutions assigned to RQ2 , and (3) barriers and criticalities that companies face when searching to implement Logistics 4.0 solutions assigned to RQ3 . The results led to the development of a conceptual framework integrating three main dimensions associated with Logistics 4.0 adoption, namely motivations to adoption, benefits achieved, and barriers that emerged.

The following sections illustrate the descriptive analysis of the papers and describe the proposed conceptual framework as a result of the SLR study.

Descriptive analysis

Figure 2 shows the number of publications over time and by source. Initially, researchers gave priority to the development of Industry 4.0 concepts rather than Logistics 4.0. However, the number of publications per year related to Logistics 4.0 has steadily increased over time, and recently accelerated the pace, with 73% of the shortlisted papers published after 2018. The peak is in 2019, while 2020 recorded a small drop, possibly because of the COVID-19 pandemic. It is interesting to notice that the number of papers published in the first quarter of 2021 is almost the same as the sum of the two previous years, highlighting the growing interest of academics in Logistics 4.0 in warehouses.

Looking at the sources of the documents, a balance was found between papers published in scientific journals (34 papers, 48.6% of the sample) and conference proceedings (36 papers, 51.4%). The journals chiefly belong to the engineering and production management area, while a few are centered in other disciplines, such as policy management. As expected, most of the earliest papers were published in conference proceedings, indicating their ability to catch emerging trends.

Focusing on the first author’s affiliation country, most contributions (30) were Europe-based, followed by Asia (17), indicating strong interest from these regions.

Figure 3 illustrates the main research methodology used. Most of the early papers belong to the theoretical and conceptual domain whereas more recently the number of empirical contributions has increased substantially. Action research only started to appear in the last years. This shows that Logistics 4.0 in warehousing is attracting rising attention and it is likely going to become a well-developed research topic. Following a similar methodology as some documents found in the literature ( Golpîra et al. , 2021 ; Kumar et al. , 2021 ; Winkelhaus et al. , 2021 ), in our study, all the research methodologies (theoretical, conceptual, and empirical or action research) are considered relevant. Since the results of some methodologies can complement others, this helps to get a clearer idea of current Logistics 4.0 adoption as well as of future trends.

Finally, as far as the research question(s) being addressed, topics connected to RQ2 (35 related papers) and RQ3 (25 results) are prevailing, thus indicating that benefits from adoption as well as related barriers and criticalities have already started to be analyzed. Conversely, it seems that so far very little has been explored regarding the influencing factors on the company level for the readiness for adopting Logistics 4.0 in their warehouses.

Conceptual framework of logistics 4.0 adoption in warehousing

1 Influencing factors, referring to the elements that might influence the company’s decision to adopt Logistics 4.0 solutions in their warehouses. Companies are chiefly affected by their warehouse management and operation, their digital awareness and readiness, their employees’ educational level, and governmental support and policies.

2 Benefits, indicating the advantages that Logistics 4.0 solutions applied in warehouses might offer. In terms of operations, these benefits are process optimization, transaction cost reduction, flexibility increase, traceability and visibility enhancement, human error reduction, human resource management and safety enhancement, and sustainability improvement. Additionally, from the customer perspective, the main benefits are increased customer loyalty and satisfaction.

3 Barriers and criticalities, dealing with all the challenges that companies might face when embracing Logistics 4.0 in warehousing. Several types of hurdles can be identified: strategic (e.g. no standardized implementations exist), economic (e.g. high implementation costs), technological (e.g. obsolete infrastructures), cultural (e.g. companies are not ready for advanced technologies), and safety and security related (e.g. risk of cyber-attacks).

In the framework, the elements that compose each of the three dimensions are organized by their relative importance in the examined literature i.e. the frequency with which each aspect was a relevant point of discussion. This gives a clear view of the most and least relevant factors from the academic perspective. Additionally, the framework shows how each of the influencing factors is related to specific barriers and criticalities, giving an insight into how these two dimensions are interrelated and affected by one another. Finally, the benefits that Logistics 4.0 adopters could obtain are shown and organized from most to least investigated in the literature, which is relevant as Logistics 4.0 adopters can relate the specific requirements in their warehouses with the benefits identified by academics.

Our approach is in line with typical technology acceptance models (TAMs). In its basic form ( Figure 4 ) it is similar to the original TAM developed by Davis et al. (1989) : The influencing factors resemble the external variables while benefits correspond to perceived usefulness, and barriers and criticalities indicate the obstacles to the ease of use. We did not follow TAM2 ( Venkatesh and Davis, 2000 ), as we consider its main extensions compared to the original TAM, namely a more differentiated approach to external factors like social influence and cognitive processes, not relevant for our study. For the same reason, we have not used the unified theory of acceptance and use of technology (UTAUT) model suggested by Venkatesh et al. (2003) , as we think that factors like gender and age do not affect Logistics 4.0 adoption or moderate key influencing factors substantially. Our approach is in accordance with general concerns that the more elaborated models suggest additional moderators without explaining the reasons behind the proposed interaction effects ( Bagozzi, 2008 ). Following Bagozzi (2008) , we believe that the parsimony of the framework, its simple set-up, is strength rather than weakness and fits well into the managerial decision-making context.

Table 2 reports a detailed analysis of the framework elements and related references. In the subsequent paragraphs, each element is carefully described, as well as its related factors.

Influencing factors ( RQ1 )

Warehouse management and operation, the company’s digital awareness and readiness, employees’ educational level, and governmental support and policies have emerged as the main influencing aspects, thus addressing RQ1 .

First, the warehouse management and operations currently in place represent a major influencing factor. From this viewpoint, companies need to carefully consider their as-is configuration first – e.g. financial as well as operational factors, product characteristics as well as supply chain structure – together with the related performance and criticalities before deciding whether and how to embrace the digital transition that Logistics 4.0 implies ( Boonsothonsatit et al. , 2020 ). For example, Zoubek et al. (2021) propose a methodology to address the rationalization of a warehouse system by offering a range of 4.0 scenarios with different digital solutions that can be evaluated and selected based on the specific warehouse setting and requirements.

The second key influencing factor refers to the company’s digital awareness and readiness ( Zouari et al. , 2020 ). The lack of technological culture is one of the biggest hurdles the logistics industry is facing, and the company’s maturity and attitude toward the digital landscape affect the implementation of Logistics 4.0 in warehouses. As companies are not always fully aware of the digital options and how such solutions might impact their business, their perception might be biased and, consequently, implementation of Logistics 4.0 technologies in warehouses might be perceived as risky ( Barczak et al. , 2019 ). Some researchers have started analyzing the company’s technological maturity level, e.g. by means of frameworks such as the one proposed by Mahroof (2019) with technology, organization, and environment as the main pillars or five levels ( Stachowiak et al. , 2019 ) ranging from “ignoring” (i.e. full unawareness of Logistics 4.0) to “integrated” (i.e. companies that have effectively implemented fully integrated Logistics 4.0 solutions). Also, more general characteristics such as automation level or capability to manage data are included ( Zoubek and Simon, 2021 ). Finally, Modrak et al. (2019) propose a self-assessment model for smart logistics maturity, in which one of the five clusters is entirely focused on warehouses.

As far as employees’ educational level is concerned, Logistics 4.0 requires at its base a certain level of digital education. The development of human skills is one of the main requirements to maintain competitiveness ( Krishnan and Wahab, 2019 ; Wrobel-Lachowska et al. , 2018 ), and employees must be educated in a way that permits them to stay in line with cutting-edge trends. When approaching the 4.0 paradigm, training in technological knowledge and software/hardware usage is required ( Woschank and Pacher, 2020a ) and a combination of scientific, industry-specific, and firm-related capabilities should be promoted ( Wrobel-Lachowska et al. , 2018 ). Some scholars have investigated the learning process and suggested specific methods in the context of logistics engineering education, seeking to guarantee comprehensive training, characterized by both a theoretical and practical approach ( Nazir et al. , 2019 ; Woschank and Pacher, 2020b ). Anecdotal evidence from a large number of planning and consulting projects in the warehousing industry conducted by the authors indicates that, traditionally, warehouses have not been considered work environments that require any significant level of technological education on the operational level, suggesting that a high employee’s educational level, if present, would likely rather be qualified as an influencing factor (e.g. higher technology awareness and understanding of the benefits potentially achievable) than a barrier to implementation.

Finally, policies used by different countries to promote the transition to the 4.0 paradigm and their governments’ intervention can significantly affect the implementation of Logistics 4.0 in warehouses. For instance, actions such as (1) cost reductions in the import of external technology or (2) the promotion of international exchange of knowledge can support the local development of technologies and competence ( Krishnan and Wahab, 2019 ). Moreover, the government could financially support companies through incentives and strategic programs. Also, the collaboration among companies, academia, and the public sector might be fundamental for accelerated Logistics 4.0 implementation by increasing the adopters’ readiness level ( Stachowiak et al. , 2019 ).

Benefits ( RQ2 )

The main advantages emerging from Logistics 4.0 implementation refer to warehousing process optimization, transaction costs reduction, flexibility increase, traceability and visibility enhancement, human error reduction, human resource management, safety enhancement, sustainability improvement, and increased customer loyalty and satisfaction.

The possibility to improve process performance through the implementation of Logistics 4.0 technologies in warehouses is a widely addressed topic, especially from a conceptual perspective ( Barreto et al. , 2017 ; Correa et al. , 2020 ; Issaoui et al. , 2021 ; Kuczyńska-Chałada et al. , 2018 ; Nantee and Sureeyatanapas, 2021 ; Oleśków-Szłapka and Stachowiak, 2019 ; Song et al. , 2021 ; Wen et al. , 2018 ; Winkelhaus and Grosse, 2020 ; Woschank and Zsifkovits, 2021 ).

For instance, Wang (2016) suggests potential cost savings and a reduction in inventory costs. Some other scholars offer empirical studies to corroborate their views ( Affia and Aamer, 2021 ; Domański, 2019 ; Gialos and Zeimpekis, 2020 ; Hamdy et al. , 2018 ; Kekana et al. , 2020 ; Krishnan and Wahab, 2019 ; Lee et al. , 2018 ; Plakas et al. , 2020 ; Zhang et al. , 2021 ). However, it is necessary to critically assess the benefits directly associated with the technologies mentioned in the Logistics 4.0 literature to clearly point out whether and how they add something new to the technologies already adopted in warehouses, i.e. it is necessary to carve out what Logistics 4.0 adds to standard automation in warehouses.

One of the key factors that must be addressed in order to optimize logistics and warehousing processes is increasing their efficiency ( Domański, 2019 ; Krishnan and Wahab, 2019 ; Zhang et al. , 2021 ). For instance, this can be obtained with the implementation of technologies such as IoT-based solutions which offer real-time data visibility ( Hofmann et al. , 2019 ; Lee et al. , 2018 ), Augmented Reality, and Smart Glasses which improve operations performance ( Plakas et al. , 2020 ), or AI tools to automate the recognition of objects and, through Machine Learning, to infer insights valuable for decision-making ( Wen et al. , 2018 ).

Transaction cost reduction has been also highlighted as a benefit of Logistics 4.0 implementation. Transaction costs are defined as “the consumption of economic resources resulting from adapting, structuring, and monitoring the interactions between the different agents, ensuring compliance with contracts” ( Loureiro et al. , 2020 ). According to these authors, the implementation of Logistics 4.0 solutions can reduce transaction costs in warehousing by providing timely information supporting the decision-making process and improving the relationship with other stakeholders. One example is the implementation of smart sensors to locate items inside the warehouse. Transmitting the information to other partners of the supply chain, optimizing resources assignment, and reducing the costs associated with the process have emerged as the foremost achievements.

The implementation of Logistics 4.0 in warehouses might increase flexibility and/or responsiveness ( Barreto et al. , 2017 ; Karunarathna et al. , 2019 ; Oleśków-Szłapka and Stachowiak, 2019 ; Song et al. , 2021 ). Several authors suggest equipping existing automation technology such as automated guided vehicles (AGVs) with smart features to increase flexibility. For instance, Mehami et al. (2018) combine AGVs with RFID technology to allow RFID-tagged items to determine the path of the AGV at runtime. The implementation of robots in the warehousing context has been a topic of discussion for its possibilities to increase efficiency and reduce repetitive tasks for humans ( Raji et al. , 2021 ). To this end, Lourenco et al. (2017) prototyped an autonomous mobile robot that can handle transportation from manufacturing supermarkets to assembly stations while avoiding obstacles, as it is intended to operate in a dynamic environment together with other autonomous robots and human operators. The approach of adding autonomous features to existing technologies is also in line with the maturity model proposed by Zoubek and Simon (2021) related to Logistics 4.0 in internal processes.

However, although many scholars support the view that Logistics 4.0 might offer ample opportunities for flexibility increase, this is not endorsed by the entire academic community ( Nantee and Sureeyatanapas, 2021 ). For instance, Cimini et al. (2021) found that the introduction of Logistics 4.0 in the picking process did not prove to be the best option in terms of flexibility, thus preferring humans to robots.

A major benefit refers to traceability and visibility enhancement, intended as the availability of data, the visibility of logistics objects and actors, and the transparency of processes within the value chain. Thanks to the implementation of Logistics 4.0, information flows can be synchronized with product flows ( Barreto et al. , 2017 ; Douaioui et al. , 2018 ; Oleśków-Szłapka and Stachowiak, 2019 ; Wang, 2016 ). For instance, as the IoT enables device connectivity, the visibility of logistics activities and sharing capabilities in warehouses can be considerably improved ( Winkelhaus and Grosse, 2020 ; Nantee and Sureeyatanapas, 2021 ).

To guarantee the visibility and traceability of logistics objects, it is necessary to be able to precisely localize them inside and outside warehouses. Liu et al. (2018) discuss the state-of-the-art technologies available to perform this task. The most common technologies are GPS, Bluetooth, and RFID. For several years, RFID has been considered to have a possible positive effect on visibility and efficiency in warehousing ( Vijayaraman and Osyk, 2006 ). Nevertheless, the specific drawbacks of each technology must be considered. While GPS has high accuracy for outdoor localization, it cannot be used indoors. RFID help localize objects indoors with a high degree of accuracy, while it requires an extensive infrastructure that can have limitations in large-scale outdoor applications. In addition, in some cases, the calculation of its ROI can be fuzzy ( Vijayaraman and Osyk, 2006 ). Therefore, each warehouse case must be assessed based on its specific needs. From a more practical perspective, Affia and Aamer (2021) propose a roadmap to design and apply an IoT-based smart warehouse infrastructure allowing data recording, tracking, reporting, and immediate distribution to all authorized stakeholders. Despite the increase in visibility and traceability, it is noteworthy to say that these shared data could represent a challenge for digital security.

The reduction in error rates and associated risks are two of the main benefits related to the implementation of Logistics 4.0 in warehouses. Numerous studies have tackled this issue, either theoretically ( Karunarathna et al. , 2019 ; Nantee and Sureeyatanapas, 2021 ; Oleśków-Szłapka and Stachowiak, 2019 ; Plakas et al. , 2020 ; Wang, 2016 ; Zoubek et al. , 2021 ; Zoubek and Simon, 2021 ) or empirically ( Lee et al. , 2018 ). For instance, the implementation of cyber-physical system (CPS) which combines virtual and physical worlds through smart objects can reduce errors during the process ( Zoubek et al. , 2021 ). In this context, AR picking, and RFID solutions could mitigate the risk of human error ( Karunarathna et al. , 2019 ; Nantee and Sureeyatanapas, 2021 ; Plakas et al. , 2020 ; Winkelhaus and Grosse, 2020 ).

Another key benefit refers to human resource management and safety enhancement. Employees are expected to work in a safe environment, allowing them to perform their tasks and improve their skills while feeling safe and aligned with the company’s mission. Logistics 4.0 technologies can help minimize stressful and repetitive human tasks and reduce the risk of injuries, fatigue, and mental stress. For instance, Nantee and Sureeyatanapas (2021) highlighted that employees perceived increased ease in their daily operations and the development enhancement of their analytical and computing skills. A general improvement in operational efficiency in the warehouse has been also highlighted ( Cimini et al. , 2019 , 2021 ; Halawa et al. , 2020 ).

Sustainability improvements have also been identified ( Calza et al. , 2020 ), e.g. poor energy management ( Buntak et al. , 2019 ). The reduction of costs generated by inefficiencies would make available additional resources for environmental and social improvements. Some studies suggest that Logistics 4.0 technologies in long-term and high-scale operations have the potential to bring sustainable advantages in terms of increased efficiency and reduced waste and emissions ( Krishnan and Wahab, 2019 ; Nantee and Sureeyatanapas, 2021 ).

Additional advantages are increased customer satisfaction and the possibility of improved customer loyalty, thus reducing the churn rate ( Kekana et al. , 2020 ). In this sense, four dimensions have appeared highly significant: (1) reliability of the delivery, (2) process visibility, (3) empathy for the customer, and (4) tangibility of the company. Logistics 4.0 can leverage these domains to build a long-term relationship between a company and its customers. From this perspective, Kekana et al. (2020) assessed the relationship between the warehousing style of an organization and both customer satisfaction and loyalty. It was found that IoT and RFID were the main levers enhancing logistics performance in the warehouse. In other cases, it was pointed out that Logistics 4.0-automated warehouses can increase customer satisfaction by improving shipping and information accuracy, product customization, and reducing lead time ( Nantee and Sureeyatanapas, 2021 ). These results are also supported by other sources which highlight that improved visibility, achieved by means of technologies such as IoT, blockchain, and cloud platforms, is another key dimension that leads to higher customer satisfaction ( Markov and Vitliemov, 2020 ).

Barriers and criticalities ( RQ3 )

Different types of hurdles have been identified for Logistics 4.0 adoption in warehouses i.e. strategic, economic, technological, cultural, and safety- and security-related obstacles.

The first obstacle to Logistics 4.0 implementation involves strategic considerations. Implementation of 4.0 technologies in warehouses cannot be standardized but needs to be tailored to the specific case ( Jung and Kim, 2015 ). The design of a Logistics 4.0 warehouse needs to be adapted to the specific company’s operating environment ( Affia and Aamer, 2021 ), while the company’s targets and priorities must be carefully taken into account ( Wen  et al. , 2018 ).

Looking at the economic perspective, the costs associated with the investment for warehousing 4.0 represent another barrier. These costs, of course, depend on the technologies being implemented. When a complete warehouse re-design is required, the investment tends to be high ( Cyplik et al. , 2019 ; Markov and Vitliemov, 2020 ; Oleśków-Szłapka and Stachowiak, 2019 ; Zoubek et al. , 2021 ), thus preventing companies from easily embracing the Logistics 4.0 paradigm. In some cases, a step-by-step implementation strategy is preferred ( Phuyal et al. , 2020 ; Schmidtke et al. , 2018 ). The investment costs to be considered include numerous factors, such as equipment, deployment, and training costs ( Tran-Dang et al. , 2020 ). To cope with these factors, a detailed cost and Return on Investment analysis should be performed by companies before deciding on implementation of Logistics 4.0 technologies ( Verma et al. , 2020 ). Companies are sometimes reluctant since they find it difficult to quantify the beneficial effect of Logistics 4.0 implementation in advance. This involves not only direct but also indirect effects that are hardly measurable ( Poenicke et al. , 2019 ).

Technological barriers exist, too ( Cyplik et al. , 2019 ; Verma et al. , 2020 ; Zoubek et al. , 2021 ). They include the lack of reliable infrastructures or difficulties of integration with the legacy systems running within the warehouse. For instance, the use of cutting-edge engineering applications such as multi-robot collaboration requires companies to develop algorithms that must be supported by robust middleware systems and programming models ( Liu et al. , 2018 ). In general, as Logistics 4.0 is still in its infancy, immature technologies together with unstandardized function modules are also identified as key barriers to Logistics 4.0 adoption ( Feng and Ye, 2021 ). Overall, suitable digital infrastructure has been identified as a basic requirement for implementing Logistics 4.0 applications ( Schmidtke et al. , 2018 ).

Furthermore, cultural hurdles have been highlighted. Logistics 4.0 implementation requires the integration of a broad range of technologies, and companies require additional knowledge and skills that can be achieved through investments and training ( Correa et al. , 2020 ). However, many companies tend to act as routine-blinded adopters as their digital maturity level is still low, and also resistance to change might be another hurdle to adoption ( Correa et al. , 2020 ).

Also, the lack of specific skills to operate the components of a Logistics 4.0 warehouse is considered an obstacle ( Affia and Aamer, 2021 ; Zoubek et al. , 2021 ). Since collaboration with smart equipment and technologies will be increasingly common in future warehouses, the education of specialized employees will become a key requirement ( Schmidtke et al. , 2018 ; Verma et al. , 2020 ). Such a shift in terms of technical skills must be accompanied by a change of mentality in the companies themselves ( Mahroof, 2019 ).

Finally, safety and security issues represent another important barrier. Making logistics and warehousing systems secure is vital for technology adopters. This involves several concerns related to cyber-attacks ( Hamdy et al. , 2018 ; Jamai et al. , 2020 ; Markov and Vitliemov, 2020 ). The higher the number of devices connected to the IoT network, the higher the possibility of security and privacy issues ( Song et al. , 2021 ). As an example, privacy violations related to tracking the locations of certain items could compromise a company’s competitive advantages ( Ding et al. , 2021 ). For this reason, companies must consider security and privacy urgent requirements ( Verma et al. , 2020 ; Zhu et al. , 2020 ). In this context, blockchain-based systems are often proposed. However, blockchains are not able to avoid and defuse cyber-attacks ( Liu et al. , 2018 ) but are centered on ensuring that information cannot be modified ex-post ( Tan and Ngan, 2020 ). Besides, additional physical safety challenges have been raised for automated devices, such as robots, drones, or AGVs, that can cause harm for operators ( Trab et al. , 2017 ).

Discussion and conclusions

Warehouses are crucial components of logistics networks, and their strategic role has been increasingly recognized by both researchers and practitioners. Logistics 4.0 in warehousing involves the introduction of Industry 4.0 technologies and practices within warehouses with the intention to enhance operations and service levels. In recent years, this field has gained growing interest among academics and a rising number of studies emphasize the relevance of this topic in the logistics domain.

Looking at RQ1 (What are the main factors influencing a company’s level of readiness for the adoption of Logistics 4.0 in their warehouses?), four main clusters of factors have been identified, namely warehouse setting and management, company’s digital awareness and readiness, employees’ educational level, and governmental support and policies. Specifically, warehouse setting and requirements (e.g. goods flows to be managed, products to be stored, service level, expected lead times) as well as the company’s digital awareness ( Zouari et al. , 2020 ) are critical elements impacting Logistics 4.0 adoption in warehousing.

As for RQ2 (What are the benefits that companies could achieve by implementing Logistics 4.0 solutions in their warehouses?), the literature reviewed mentions a variety of possible benefits that Logistics 4.0 technologies in warehousing can bring about. However, the lack of empirically validated data does not allow one to state with certainty which (or even if ) benefits can be achieved in practice. In some cases, benefits claimed by suppliers of technology associated with Logistics 4.0 for warehouses were uncritically repeated (e.g. Mahroof, 2019 ). In other cases, it is impossible to tell apart whether proclaimed improvements can be attributed to the introduction of technology or simply to the review and reorganization of warehouse processes that typically accompany the introduction of technology. This challenge is further exacerbated by the finding that the technologies associated with the label Logistics 4.0 are highly inconsistent among the authors of the literature reviewed. Indeed, some authors point out that technologies that have existed in warehouses for decades, preceding the concept of Industry 4.0 and Logistics 4.0, e.g. Automated Storage and Retrieval Systems ( Domański, 2019 ) RFID, and AGV, are placed under the 4.0 umbrella.

With respect to RQ3 (What are the main barriers and criticalities faced by companies when implementing Logistics 4.0 solutions in their warehouses?), strategic, economic, technological, cultural, and safety and security-related barriers and criticalities have been identified. Particularly, the coverage of economic aspects, arguably the most important decision-making criterion for technology adoption, has been weak. Generally speaking, economics suggest technology adoption when the capital invested will lead to overall cost savings within a defined period of time. Since tangible benefits from the adoption of Logistics 4.0 technology in warehouse applications were found to be only vaguely defined, and with little reliable quantitative underpinning, it is not surprising that the discussion of economic barriers has remained equally vague. Also, the organizational structure has received little attention in the context of economic considerations, though it can be speculated that (for example in the case of third-party logistics providers) the interplay between independently managed warehouses (as profit centers) and headquarters (which include marketing and sales functions) would influence the adoption of Logistics 4.0 technologies.

Both academic and practical implications can be identified. From an academic perspective, this paper, by means of an SLR approach, offers a conceptual framework for Logistics 4.0 adoption in warehousing from the technology adopter’s perspective. It provides a clear outlook on the motivations, benefits, and challenges the implementation of Logistics 4.0 in warehousing could entail. From a practical viewpoint, the framework intends to ease the understanding of the technological possibilities that Logistics 4.0 could bring, with the final objective to better understand the specific technology adoption process. It also highlights the importance of analyzing the individual requirements for each specific company and application. The overall aim is to promote knowledge on the topic of Logistics 4.0 in the warehousing domain, stimulating a higher awareness of the topic, and fostering the adoption process of such applications. More practically, it helps organizations understand the breadth of technologies associated with Logistics 4.0, as well as both, challenges and benefits that can reasonably be expected, albeit predominantly qualitatively rather than quantitatively.

A more sober implication for academia results from the finding that the use of the term Logistics 4.0 in the warehousing concepts with its synonyms (e.g. “smart”) and related concepts (e.g. “IoT”) in the literature reviewed seems sometimes ambiguous, ranging from pure automation to decades-old identification technology to picking support devices (e.g. pick-by-voice) to more recent digital technologies such as artificial intelligence. Considering the breadth of its use, it can be questioned whether the term Logistics 4.0 is useful at all. Since academics should strive for conceptual and terminological clarity, the ambiguity of the term and its related concepts is creating serious concerns for use outside of corporate marketing departments. Should researchers decide to continue using the term, it is strongly recommended to focus efforts on some of the research lines pointed out in the section “Research gaps and suggested future research directions”.

Lastly, the study’s limitations must be acknowledged. In particular, the main limitation lies in the potential omission of relevant contributions from the review as the process of selection considered only journal and conference papers. Although the keyword structure was designed through several trials to ensure the most effective and feasible research space, it cannot be excluded that other papers dealing with this subject exist under different labels. Several papers discussed the same terms with a different understanding or definition of them. Further research is, therefore, recommended to encourage a higher degree of standardization. Moreover, it can be assumed that the more generic term “Industry 4.0” is sometimes used when Logistics 4.0 would apply as a more specific label. Nevertheless, because of the methodology adopted, it is believed that this analysis provides an adequate representation of the state of the art of literature related to influencing factors, benefits, and barriers dealing with Logistics 4.0 in warehousing. The study should be further supplemented with empirical research, including challenging the proposed framework.

Research gaps and suggested future research directions

Develop strong conceptualization and taxonomies clarifying 4.0 technologies for warehousing.

Foster empirical research in the field of Logistics 4.0 adoption in warehousing.

Improve the examination of the relationship between Logistics 4.0 application and specific warehousing activities.

Promote further investigation on the role of governmental support in influencing Logistics 4.0 investments at logistics sites.

Encourage further cost-benefit trade-off analyses of Logistics 4.0 in warehouses.

Develop quantitative assessment research of the sustainability implications of Logistics 4.0 in warehousing.

As a final remark, quantitative assessment of sustainability-related impacts of Logistics 4.0 in warehousing has emerged as a promising research arena. According to the SLR, one contribution has been specifically found that assesses the impact of 4.0 in warehousing through the lenses of the Triple Bottom Line (TBL) framework ( Nantee and Sureeyatanapas, 2021 ). However, in their assessment, only a qualitative approach centered on a single case was included, leaving ample room for further contributions in this field; additional quantitative-based studies, models, or simulations are recommended.

Methodological framework of the study

Examined publications over time

Publications by methodology over time

Conceptual framework for logistics 4.0 adoption in warehousing: influencing factors, benefits, and barriers

Inclusion and exclusion criteria

Detailed analysis of framework elements and related references

Documents resulting from the SLR

Note(s): * The term “empirical” refers to case studies, interviews, and surveys, while the term “action research” refers to the implementation of a Logistics 4.0 technology

Affia , I. and Aamer , A. ( 2021 ), “ An internet of things-based smart warehouse infrastructure: design and application ”, Journal of Science and Technology Policy Management , Vol.  13 No.  1 , pp.  90 - 109 .

Agalianos , K. , Ponis , S.T. , Aretoulaki , E. , Plakas , G. and Efthymiou , O. ( 2020 ), “ Discrete event simulation and digital twins: review and challenges for logistics ”, Procedia Manufacturing , Vol.  51 , pp.  1636 - 1641 .

Bag , S. , Gupta , S. and Luo , Z. ( 2020 ), “ Examining the role of logistics 4.0 enabled dynamic capabilities on firm performance ”, International Journal of Logistics Management , Vol.  31 No.  3 , pp.  607 - 628 .

Baglio , M. , Perotti , S. , Dallari , F. and Garagiola , E.R. ( 2019 ), “ Benchmarking logistics facilities: a rating model to assess building quality and functionality ”, Benchmarking: An International Journal , Vol.  27 No.  3 , pp.  1239 - 1260 .

Bagozzi , R.P. ( 2008 ), “ The legacy of the technology acceptance model and a proposal for a paradigm shift ”, Journal of the Association for Information Systems , Vol.  8 No.  4 , 3 .

Baker , P. ( 2004 ), “ Aligning distribution centre operations to supply chain strategy ”, The International Journal of Logistics Management , Vol.  15 No.  1 , pp.  111 - 123 .

Barczak , A. , Dembińska , I. and Marzantowicz , Ł. ( 2019 ), “ Analysis of the risk impact of implementing digital innovations for logistics management ”, Processes , Vol.  7 No.  11 , 815 .

Barreto , L. , Amaral , A. and Pereira , T. ( 2017 ), “ Industry 4.0 implications in logistics: an overview ”, Procedia Manufacturing , Vol.  13 , pp.  1245 - 1252 .

Boonsothonsatit , G. , Hankla , N. and Choowitsakunlert , S. ( 2020 ), “ Strategic design for warehouse 4.0 readiness in Thailand ”, Proceedings of the 2020 2nd International Conference on Management Science and Industrial Engineering , Osaka , 7-9 April 2020 , pp.  214 - 218 .

Buntak , K. , Kovačić , M. and Mutavdžija , M. ( 2019 ), “ Internet of things and smart warehouses as the future of logistics ”, Tehnički Glasnik , Vol.  13 No.  3 , pp.  248 - 253 .

Calza , F. , Parmentola , A. and Tutore , I. ( 2020 ), “ Big data and natural environment. how does different data support different green strategies? ”, Sustainable Futures , Vol.  2 , 100029 .

Cano , J.A. , Salazar , F. , Gómez-Montoya , R.A. and Cortés , P. ( 2021 ), “ Disruptive and conventional technologies for the support of logistics processes: a literature review ”, International Journal of Technology , Vol.  12 No.  3 , pp.  448 - 460 .

Carter , C.R. and Rogers , D.S. ( 2008 ), “ A framework of sustainable supply chain management: moving toward new theory ”, International Journal of Physical Distribution and Logistics Management , Vol.  38 No.  5 , pp.  360 - 387 .

Chauhan , C. , Singh , A. and Luthra , S. ( 2021 ), “ Barriers to industry 4.0 adoption and its performance implications: an empirical investigation of emerging economy ”, Journal of Cleaner Production , Vol.  285 , 124809 .

Cheng , I.L. , Cheng , C.H. and Liu , D.G. ( 2019 ), “ A learnable unmanned smart logistics prototype system design and implementation ”, Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 , Hsinchu , 18-20 March , pp.  221 - 224 .

Chung , S.H. ( 2021 ), “ Applications of smart technologies in logistics and transport: a review ”, Transportation Research Part E: Logistics and Transportation Review , Vol.  153 , 102455 .

Cimini , C. , Lagorio , A. , Pirola , F. and Pinto , R. ( 2019 ), “ Exploring human factors in Logistics 4.0: empirical evidence from a case study ”, IFAC-PapersOnLine , Vol.  52 No.  13 , pp.  2183 - 2188 .

Cimini , C. , Lagorio , A. , Pirola , F. and Pinto , R. ( 2021 ), “ How human factors affect operators’ task evolution in Logistics 4.0 ”, Human Factors and Ergonomics in Manufacturing , Vol.  31 No.  1 , pp.  98 - 117 .

Colicchia , C. , Creazza , A. , Noè , C. and Strozzi , F. ( 2018 ), “ Information sharing in supply chains: a review of risks and opportunities using the systematic literature network analysis (SLNA) ”, Supply Chain Management: An International Journal , Vol.  24 No.  1 , pp.  5 - 21 .

Correa , J.S. , Sampaio , M. , de Casto Barros , R. and de Castro Hilsdorf , W. ( 2020 ), “ IoT and BDA in the Brazilian future logistics 4.0 scenario ”, Production , Vol.  30 , pp.  1 - 14 .

Culot , G. , Nassimbeni , G. , Orzes , G. and Sartor , M. ( 2020 ), “ Behind the definition of Industry 4.0: analysis and open questions ”, International Journal of Production Economics , Vol.  226 , August , 107617 .

Cyplik , P. , Oleskow-Szlapka , J. , Tobola , A. and Adamczak , M. ( 2019 ), “ Building a model for assessing the maturity of polish enterprises in terms of logistics 4.0 assumptions ”, Proceedings of The 19th International Scientific Conference Business Logistics in Modern Management , Osijek , 10-11 October , pp.  105 - 122 .

Davis , F.D. , Bagozzi , R.P. and Warshaw , P.R. ( 1989 ), “ User acceptance of computer technology: a comparison of two theoretical models ”, Management Science , Vol.  35 No.  8 , pp.  982 - 1003 .

Denyer , D. and Tranfield , D. ( 2009 ), “ Producing a systematic review ”, The Sage Handbook of Organizational Research Methods , Sage Publications , London , pp.  671 - 689 .

Dev , N.K. , Shankar , R. , Zacharia , Z.G. and Swami , S. ( 2021 ), “ Supply chain resilience for managing the ripple effect in Industry 4.0 for green product diffusion ”, International Journal of Physical Distribution and Logistics Management , Vol.  51 No.  8 , pp.  897 - 930 .

Dhooma , J. and Baker , P. ( 2012 ), “ An exploratory framework for energy conservation in existing warehouses ”, International Journal of Logistics Research and Applications , Vol.  15 No.  1 , pp.  37 - 51 .

Ding , Y. , Jin , M. , Li , S. and Feng , D. ( 2021 ), “ Smart logistics based on the internet of things technology: an overview ”, International Journal of Logistics Research and Applications , Vol.  24 No.  4 , pp.  323 - 345 .

Domański , R. ( 2019 ), “ Logistics 4.0 in warehousing - current state and trends ”, Proceedings of The 19th International Scientific Conference Business Logistics in Modern Management , Osijek , 10-11 October , pp.  387 - 398 .

Douaioui , K. , Fri , M. , Mabroukki , C. and Semma , E.A. ( 2018 ), “ The interaction between industry 4.0 and smart logistics: concepts and perspectives ”, 2018 International Colloquium on Logistics and Supply Chain Management, LOGISTIQUA 2018 , Tangier , 26-27 April , pp.  128 - 132 .

Eurostat ( 2018 ), “ Annual enterprise statistics by size class for special aggregates of activities (NACE Rev. 2) ”, available at: https://ec.europa.eu/eurostat/databrowser/view/SBS_SC_SCA_R2__custom_905984/bookmark/table?lang=en&bookmarkId=2b85609f-993e-48a4-9930-b595f5ef96e2 ( accessed 6 December 2021 ).

Feng , B. and Ye , Q. ( 2021 ), “ Operations management of smart logistics: a literature review and future research ”, Frontiers of Engineering Management , Vol.  8 No.  3 , pp.  344 - 355 .

Fottner , J. , Clauer , D. , Hormes , F. , Freitag , M. , Beinke , T. , Overmeyer , L. , Gottwald , S.N. , Elebert , R. , Sarnow , T. , Schmidt , T. , Reith , K.-B. , Zadek , H. and Thomas , F. ( 2021 ), “ Autonomous systems in intralogistics - state of the art and future research challenges ”, Logistics Research , Vol.  14 No.  2 , pp.  1 - 41 .

Fragapane , G. , de Koster , R. , Sgarbossa , F. and Strandhagen , J.O. ( 2021 ), “ Planning and control of autonomous mobile robots for intralogistics: literature review and research agenda ”, European Journal of Operational Research , Vol.  294 No.  2 , pp.  405 - 426 .

Frederico , G.F. , Kumar , V. , Garza-Reyes , J.A. , Kumar , A. and Agrawal , R. ( 2021 ), “ Impact of I4.0 technologies and their interoperability on performance: future pathways for supply chain resilience post-COVID-19 ”, International Journal of Logistics Management , ahead of print , doi: 10.1108/IJLM-03-2021-0181/FULL/PDF .

Ghobakhloo , M. ( 2020 ), “ Industry 4.0, digitization, and opportunities for sustainability ”, Journal of Cleaner Production , Vol.  252 , 119869 .

Gialos , A. and Zeimpekis , V. ( 2020 ), “ Testing vision picking technology in warehouse operations: evidence from laboratory experiments ”, International Journal of Industrial Engineering and Management , Vol.  11 No.  1 , pp.  19 - 30 .

Golpîra , H. , Khan , S.A.R. and Safaeipour , S. ( 2021 ), “ A review of logistics Internet-of-Things: current trends and scope for future research ”, Journal of Industrial Information Integration , Vol.  22 , 100194 .

Granillo , R. , Simón , I. , González , I.J. and Zuno , J. ( 2020 ), “ Traceability in industry 4.0: a case study in the metal-mechanical sector ”, Acta Logistica , Vol.  7 No.  2 , pp.  95 - 101 .

Grzybowska , K. and Awasthi , A. ( 2020 ), “ Literature review on sustainable logistics and sustainable production for industry 4.0 ”, in Grzybowska , K. , Awasthi , A. and Sawhney , R. (Eds), Sustainable Logistics and Production in Industry 4.0 , Springer , Cham , pp.  1 - 18 .

Halawa , F. , Dauod , H. , Lee , I.G. , Li , Y. , Yoon , S.W. and Chung , S.H. ( 2020 ), “ Introduction of a real time location system to enhance the warehouse safety and operational efficiency ”, International Journal of Production Economics , Vol.  224 , June 2019 , 107541 .

Hamdy , W. , Mostafa , N. and Elawady , H. ( 2018 ), “ Towards a smart warehouse management system ”, Proceedings of the International Conference on Industrial Engineering and Operations Management , Washington DC , 27-29 September , pp.  2555 - 2563 .

Hofmann , E. , Sternberg , H. , Chen , H. , Pflaum , A. and Prockl , G. ( 2019 ), “ Supply chain management and Industry 4.0: conducting research in the digital age ”, International Journal of Physical Distribution and Logistics Management , Vol.  49 No.  10 , pp.  945 - 955 .

Horváth , D. and Szabó , R.Z. ( 2019 ), “ Driving forces and barriers of Industry 4.0: do multinational and small and medium-sized companies have equal opportunities? ”, Technological Forecasting and Social Change , Vol.  146 , pp.  119 - 132 .

Issaoui , Y. , Khiat , A. , Bahnasse , A. and Ouajji , H. ( 2021 ), “ Toward smart logistics: engineering insights and emerging trends ”, Archives of Computational Methods in Engineering , Vol.  28 No.  4 , pp.  3183 - 3210 .

Jamai , I. , Ben Azzouz , L. and Saidane , L.A. ( 2020 ), “ Security issues in industry 4.0 ”, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 , Limassol , 15-19 June , pp.  481 - 488 .

Jung , J.U. and Kim , H.S. ( 2015 ), “ Big data governance for smart logistics: a value-added perspective ”, Lecture Notes in Computer Science , Springer , Cham , Vol.  9247 , pp.  95 - 103 .

Kagermann , H. , Lukas , W.-D. and Wahlster , W. ( 2011 ), “ Industrie 4.0: mit dem Internet der Dinge auf dem Weg zur 4. industriellen Revolution ”, VDI Nachrichten , Vol.  1 , April , p. 2 .

Karunarathna , N. , Wickramarachchi , R. and Vidanagamachchi , K. ( 2019 ), “ A study of the implications of logistics 4.0 in future warehousing: a Sri Lankan perspective ”, Proceedings of the International Conference on Industrial Engineering and Operations Management , Bangkok , 5-7 March , pp.  1024 - 1035 .

Kawa , A. ( 2015 ), “ SMART logistics chain ”, Proceedings of the ACIIDS Asian Conference on Intelligent Information and Database Systems , Kaohsiung , 19-21 March , pp.  432 - 438 .

Kekana , P. , Bakama , E.M. , Mukwakungu , S.C. and Sukdeo , N. ( 2020 ), “ The impact of smart-warehousing on a local foodservice equipment-company’s external customers ”, Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management , Singapore , 14-17 December , pp.  771 - 775 .

Kembro , J.H. , Norrman , A. and Eriksson , E. ( 2018 ), “ Adapting warehouse operations and design to omni-channel logistics: a literature review and research agenda ”, International Journal of Physical Distribution and Logistics Management , Vol.  48 No.  9 , pp.  890 - 912 .

Krishnan , E.R.K. and Wahab , S.N. ( 2019 ), “ A qualitative case study on the adoption of smart warehouse approaches in Malaysia ”, 2019 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control (ICBTE 2019) , Hefei , 1-3 November , 01039 .

Kuczyńska-Chałada , M. , Furman , J. and Poloczek , R. ( 2018 ), “ The challenges for logistics in the aspect of industry 4.0 ”, Multidisciplinary Aspects of Production Engineering , Vol.  1 No.  1 , pp.  553 - 559 .

Kumar , S. , Narkhede , B.E. and Jain , K. ( 2021 ), “ Revisiting the warehouse research through an evolutionary lens: a review from 1990 to 2019 ”, International Journal of Production Research , Vol.  59 No.  11 , pp.  3470 - 3492 .

Lee , C.K.M. , Lv , Y. , Ng , K.K.H. , Ho , W. and Choy , K.L. ( 2018 ), “ Design and application of internet of things-based warehouse management system for smart logistics ”, International Journal of Production Research , Vol.  56 No.  8 , pp.  2753 - 2768 .

Li , A. , Chen , Y. and Wang , D. ( 2020 ), “ An empirical study of the factors influencing the willingness to implement green coal logistics in China ”, Journal of Cleaner Production , Vol.  245 , 118932 .

Liu , X. , Cao , J. , Yang , Y. and Jiang , S. ( 2018 ), “ CPS-based smart warehouse for industry 4.0: a survey of the underlying technologies ”, Computers , Vol.  7 No.  1 , 13 .

Loureiro , R. , Silva , S. , Pacheco , A. and Simões , J. ( 2020 ), “ Logistics 4.0: the cooperative strategy and reducing costs ”, The International Journal of Business and Management , Vol.  8 No.  7 , pp.  80 - 87 .

Lourenco , A. , Marques , F. , Mendonca , R. , Pinto , E. and Barata , J. ( 2017 ), “ On the design of the ROBO-PARTNER Intra-factory logistics autonomous robot ”, Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 , Budapest , 9-12 October , pp.  2647 - 2652 .

Mahroof , K. ( 2019 ), “ A human-centric perspective exploring the readiness towards smart warehousing: the case of a large retail distribution warehouse ”, International Journal of Information Management , Vol.  45 , April , pp.  176 - 190 .

Markov , K. and Vitliemov , P. ( 2020 ), “ Logistics 4.0 and supply chain 4.0 in the automotive industry ”, Proceedings of the 9th International Scientific Conference TechSys 2020 - Engineering, Technologies and Systems , Plovdiv , 14-16 May , 012047 .

Mehami , J. , Nawi , M. and Zhong , R.Y. ( 2018 ), “ Smart automated guided vehicles for manufacturing in the context of Industry 4.0 ”, Procedia Manufacturing , Vol.  26 , pp.  1077 - 1086 .

Melacini , M. , Perotti , S. , Rasini , M. and Tappia , E. ( 2018 ), “ E-fulfilment and distribution in omni-channel retailing: a systematic literature review ”, International Journal of Physical Distribution and Logistics Management , Vol.  48 No.  4 , pp.  391 - 414 .

Modrak , V. , Soltysova , Z. and Poklemba , R. ( 2019 ), “ Mapping requirements and roadmap definition for introducing I 4.0 in SME environment ”, Advances in Manufacturing Engineering and Materials Proceedings of the International Conference on Manufacturing Engineering and Materials (ICMEM 2018) , Nový Smokovec , 18–22 June 2018 , pp.  183 - 193 .

Nantee , N. and Sureeyatanapas , P. ( 2021 ), “ The impact of Logistics 4.0 on corporate sustainability: a performance assessment of automated warehouse operations ”, Benchmarking , Vol.  28 No.  10 , pp.  2865 - 2895 .

Nazir , S. , Teperi , A.-M. and Polak-Sopińska , A. ( 2019 ), “ Challenges for logistics education in industry 4.0 ”, Proceedings of the AHFE 2018 International Conference on Human Factors in Training, Education, and Learning Sciences , Orlando , 21-25 July , pp.  329 - 336 .

Oleśków-Szłapka , J. and Stachowiak , A. ( 2019 ), “ The framework of logistics 4.0 maturity model ”, Proceedings of the International Conference on Intelligent Systems in Production Engineering and Maintenance , Wroclaw , 17-18 September , pp.  771 - 781 .

Onstein , A.T.C. , Ektesaby , M. , Rezaei , J. , Tavasszy , L.A. and van Damme , D.A. ( 2019 ), “ Importance of factors driving firms’ decisions on spatial distribution structures ”, International Journal of Logistics Research and Applications , Vol.  23 No.  1 , pp.  24 - 43 .

Perotti , S. , Micheli , G.J.L. and Cagno , E. ( 2015 ), “ Motivations and barriers to the adoption of green supply chain practices among 3PLs ”, International Journal of Logistics Systems and Management , Vol.  20 No.  2 , pp.  179 - 198 .

Perotti , S. , Prataviera , L.B. and Melacini , M. ( 2022 ), “ Assessing the environmental impact of logistics sites through CO 2 eq footprint computation ”, Business Strategy and the Environment , Vol.  31 No.  4 , pp.  1679 - 1694 .

Phuyal , S. , Bista , D. and Bista , R. ( 2020 ), “ Challenges, opportunities and future directions of smart manufacturing: a state of art review ”, Sustainable Futures , Vol.  2 , 100023 .

Plakas , G. , Aretoulaki , E. , Ponis , S.T. , Agalianos , K. and Maroutas , T.N. ( 2020 ), “ A proposed technology solution for enhancing order picking in warehouses and distribution centers based on a gamified augmented reality application ”, Proceedings of the 14th IADIS International Conference Interfaces and Human Computer Interaction , 23-25 July , pp.  217 - 221 .

Poenicke , O. , Groneberg , M. and Richter , K. ( 2019 ), “ Planning method for IoT applications in logistics ”, Smart SysTech 2019 - European Conference on Smart Objects, Systems and Technologies , Munich , 4-5 June , pp.  115 - 120 .

Raj , A. , Dwivedi , G. , Sharma , A. , Lopes de Sousa Jabbour , A.B. and Rajak , S. ( 2020 ), “ Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective ”, International Journal of Production Economics , Vol.  224 , 107546 .

Raji , I.O. , Shevtshenko , E. , Rossi , T. and Strozzi , F. ( 2021 ), “ Industry 4.0 technologies as enablers of lean and agile supply chain strategies: an exploratory investigation ”, International Journal of Logistics Management , Vol.  32 No.  4 , pp.  1150 - 1189 .

Rodrigue , J.P. ( 2020 ), The Geography of Transport Systems , 5th ed. , Routledge , London .

Salamone , F. , Belussi , L. , Currò , C. , Danza , L. , Ghellere , M. , Guazzi , G. , Lenzi , B. , Megale , V. and Meroni , I. ( 2018 ), “ Application of IoT and machine learning techniques for the assessment of thermal comfort perception ”, Energy Procedia , Vol.  148 , pp.  798 - 805 .

Schmidtke , N. , Sc , M. , Thater , L. , Sc , B. , Meixner , S. and Sc , B. ( 2018 ), “ Technical potentials and challenges within internal logistics 4.0 ”, Proceedings of the 4th International Conference on Logistics Operations Management (GOL) , Le Havre , 10-12 April , pp.  1 - 10 .

Song , Y. , Yu , F.R. , Zhou , L. , Yang , X. and He , Z. ( 2021 ), “ Applications of the internet of things (IoT) in smart logistics: a comprehensive survey ”, IEEE Internet of Things Journal , Vol.  8 No.  6 , pp.  4250 - 4274 .

Stachowiak , A. , Oleśków-Szłapka , J. and Cyplik , P. ( 2019 ), “ Triple helix model: the role of cooperation between academy, economy and public sector for increasing readiness for logistics 4.0 ”, Proceedings of ICEBI 2019 3rd International Conference on E-Business and Internet , Prague , 9-11 November , pp.  23 - 28 .

Stentoft , J. , Adsbøll Wickstrøm , K. , Philipsen , K. and Haug , A. ( 2020 ), “ Drivers and barriers for Industry 4.0 readiness and practice: empirical evidence from small and medium-sized manufacturers ”, Production Planning and Control: The Management of Operations , Vol.  32 No.  10 , pp.  811 - 828 .

Strandhagen , J.O. , Vallandingham , L.R. , Fragapane , G. , Strandhagen , J.W. , Stangeland , A.B.H. and Sharma , N. ( 2017 ), “ Logistics 4.0 and emerging sustainable business models ”, Advances in Manufacturing , Vol.  5 No.  4 , pp.  359 - 369 .

Tan , A. and Ngan , P.T. ( 2020 ), “ A proposed framework model for dairy supply chain traceability ”, Sustainable Futures , Vol.  2 , 100034 .

Tang , C.S. and Veelenturf , L.P. ( 2019 ), “ The strategic role of logistics in the industry 4.0 era ”, Transportation Research Part E: Logistics and Transportation Review , Vol.  129 , pp.  1 - 11 .

Tortorella , G. , Fogliatto , F.S. , Gao , S. and Chan , T.K. ( 2021 ), “ Contributions of Industry 4.0 to supply chain resilience ”, International Journal of Logistics Management , Vol.  33 No.  2 , pp.  547 - 566 .

Trab , S. , Bajic , E. , Zouinkhi , A. , Thomas , A. , Abdelkrim , M.N. , Chekir , H. and Ltaief , R.H. ( 2017 ), “ A communicating object’s approach for smart logistics and safety issues in warehouses ”, Concurrent Engineering Research and Applications , Vol.  25 No.  1 , pp.  53 - 67 .

Tran-Dang , H. , Krommenacker , N. , Charpentier , P. and Kim , D.S. ( 2020 ), “ The internet of things for logistics: perspectives, application review, and challenges ”, IETE Technical Review , Vol.  19 No.  1 , pp.  93 - 121 .

Tranfield , D. , Denyer , D. and Smart , P. ( 2003 ), “ Towards a methodology for developing evidence-informed management knowledge by means of systematic review ”, British Journal of Management , Vol.  14 No.  3 , pp.  207 - 222 .

Transport Intelligence ( 2019 ), “ Total logistics 2019 ”, available at: https://www.ti-insight.com/wp-content/uploads/2019/02/Total-Logistics-2019.pdf ( accessed 26 August 2021 ).

Valchkov , L. and Valchkova , N. ( 2018 ), “ Methodology for efficiency improvement in warehouses: a case study from the winter sports equipment industry ”, Proceedings in Manufacturing Systems , Vol.  13 No.  3 , pp.  95 - 102 .

Venkatesh , V. and Davis , F.D. ( 2000 ), “ A theoretical extension of the technology acceptance model: four longitudinal field studies ”, Management Science , Vol.  46 No.  2 , pp.  186 - 204 .

Venkatesh , V. , Morris , M.G. , Davis , G.B. and Davis , F.D. ( 2003 ), “ User acceptance of information technology: toward a unified view ”, MIS Quarterly: Management Information Systems , Vol.  27 No.  3 , pp.  425 - 478 .

Verma , P. , Dixit , V. and Kushwaha , J. ( 2020 ), “ Risk and resilience analysis for industry 4.0 in achieving the goals of smart logistics: an overview ”, Proceedings of the International Conference on Industrial Engineering and Operations Management , Dubai , 10-12 March , pp.  151 - 157 .

Vijayaraman , B.S. and Osyk , B.A. ( 2006 ), “ An empirical study of RFID implementation in the warehousing industry ”, The International Journal of Logistics Management , Vol.  17 No.  1 , pp.  6 - 20 .

Wang , K. ( 2016 ), “ Logistics 4.0 solution - new challenges and opportunities ”, IWAMA 2016: 6th International Workshop of Advanced Manufacturing and Automation , Manchester , 10-11 November , pp.  68 - 74 .

Wang , M. , Asian , S. , Wood , L.C. and Wang , B. ( 2020 ), “ Logistics innovation capability and its impacts on the supply chain risks in the Industry 4.0 era ”, Modern Supply Chain Research and Applications , Vol.  2 No.  2 , pp.  83 - 98 .

Wen , J. , He , L. and Zhu , F. ( 2018 ), “ Swarm robotics control and communications: imminent challenges for next generation smart logistics ”, IEEE Communications Magazine , Vol.  56 No.  7 , pp.  102 - 107 .

Winkelhaus , S. and Grosse , E.H. ( 2020 ), “ Logistics 4.0: a systematic review towards a new logistics system ”, International Journal of Production Research , Vol.  58 No.  1 , pp.  18 - 43 .

Winkelhaus , S. , Grosse , E.H. and Morana , S. ( 2021 ), “ Towards a conceptualisation of order picking 4.0 ”, Computers and Industrial Engineering , Vol.  159 , 107511 .

Woschank , M. and Pacher , C. ( 2020a ), “ Program planning in the context of industrial logistics engineering education ”, Procedia Manufacturing , Vol.  51 , pp.  1819 - 1824 .

Woschank , M. and Pacher , C. ( 2020b ), “ A holistic didactical approach for industrial logistics engineering education in the LOGILAB at the Montanuniversitaet Leoben ”, Procedia Manufacturing , Vol.  51 , pp.  1814 - 1818 .

Woschank , M. and Zsifkovits , H. ( 2021 ), “ Smart logistics conceptualization and empirical evidence ”, Chiang Mai University Journal of Natural Sciences , Vol.  20 No.  2 , pp.  1 - 9 .

Wrobel-Lachowska , M. , Wisniewski , Z. , Polak-Sopinska , A. and Lachowski , R. ( 2018 ), “ Ict in logistics as a challenge for mature workers. Knowledge management role in information society ”, Advances in Intelligent Systems and Computing , Vol.  605 , pp.  171 - 178 .

Zhang , D. , Pee , L.G. and Cui , L. ( 2021 ), “ Artificial intelligence in E-commerce fulfillment: a case study of resource orchestration at Alibaba’s Smart Warehouse ”, International Journal of Information Management , Vol.  57 , April , 102304 .

Zhu , X. , Li , Y. and Lei , Y. ( 2020 ), “ A forwarding secrecy based lightweight authentication scheme for intelligent logistics ”, Proceedings of 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2020 , Dalian , 25-27 August , pp.  356 - 360 .

Zouari , D. , Ruel , S. and Viale , L. ( 2020 ), “ Does digitalising the supply chain contribute to its resilience? ”, International Journal of Physical Distribution and Logistics Management , Vol.  51 No.  2 , pp.  149 - 180 .

Zoubek , M. and Simon , M. ( 2021 ), “ A framework for a logistics 4.0 maturity model with a specification for internal logistics ”, MM Science Journal , March , pp.  4264 - 4274 .

Zoubek , M. , Poór , P. , Broum , T. and Šimon , M. ( 2021 ), “ Methodology proposal for storage rationalization by implementing principles of industry 4.0. In a technology-driven warehouse ”, Transactions of Famena , Vol.  44 No.  4 , pp.  75 - 98 .

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I will be staying in Adler during the Olympics but going frequently to Rosa Khutor for the alpine skiing. Any idea what is the best public transport to get there? Is the train to Krasnaya Polyana the best solution? How long will it take? Best thanks your advice!

You can find lots of information about the spectator transportation system here:

http://www.sochi2014.com/en/games/spectator/transport/

Will any of the ski resorts near Sochi be open in January or February? I'll be working at the Olympics and would love to get some skiing in. Thanks

This topic has been closed to new posts due to inactivity.

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• 10 Things To Do In...

10 Things to Do in Sochi If You Love Nature

Host to 2014 Winter Olympics , Sochi is now mostly known for the snowy slopes of Krasnaya Polyana and Rosa Khutor Alpine resort. However, the “Russian Riviera” is much more than a glorified ski-resort. With its picturesque waterfalls and pristine lakes, alpine meadows and spruce-fir forests, snow-capped mountains and dreamy river valleys, Sochi is an ultimate nature lover’s dream.

Aul tkhagapsh.

Founded in the middle of the 19th century, this village only consists of two streets and two lanes. Circled by a picturesque chestnut forest, Aul Tkhagapsh is surrounded by many visually-arresting natural landmarks – a mysterious rock formation called “the canyon of a hundred crying eyes”, beautiful waterfalls with organically formed stone basins and the Tiger cave, which is called so because of the whimsical clay dripstones. Despite its tiny size, the village itself has a lot to offer. You can see the only wooden mosque on the coast, learn about the customs and traditions of the Adyghe people, try on traditional clothes and taste authentic food and local wines.

Aul Tkhagapsh, Krasnodar Krai, Russia

If you love picturesque ancient ruins put the Loo Temple on your must-see list. Drowning in the lush greenery of the Sochi National Park, Loo Temple is the remains of a 10th-century Byzantine temple, that’s been ruined and reconstructed multiple times. The temple was used as a place of worship and a fortification over the years.

Loo Temple, Bolshoy Sochi, Krasnodar Krai,Russia

Aibga Ridge

This spectacular mountain ridge stretches for 23 kilometers and has the Rosa Khutor Alpine Resort nestled at its feet. The ridge comprises of 10 peaks, with the four tallest being the best known: Aigba peak I (2391 m), peak II (2450,5 m), peak III (2462,7 m) and Black Pyramid (2375,3 m). Save a day or two to explore the ridge, full of rapid rivers, alpine meadows and waterfalls.

Aibga Ridge, Krasnodar Krai, Russia

Achepsinskie Waterfalls

To admire the spectacular views that Achepsinskie Waterfalls offer, you’ll have to endure a pretty tiring trekking route through the Achishkho Mountain to the Achipse River. But those striking panoramas are totally worth the sweat and while the trekking may be tough going, it has a very decent infrastructure.

Achipse River, Krasnodar Krai, Russia

Khmelevskie Lakes

Located almost 2000 meters above sea level, Khmelevskie Lakes is an alpine lake system, named after the Russian botanist Vikenty Khmelevsky. Spread around emerald-green alpine meadows and surrounded by lush green forests, there are four rather sizable overgrown lakes and a few smaller ones.

Khmelevskie Lake, Krasnodar Krai, Russia

Lake Kardyvach

Arguably the most popular tourist spot near Sochi, Lake Kardyvach is simply breathtaking. Situated 44 kilometers from the Krasnaya Polyana resort at the altitude of 1838 meters, the lake stays frozen for seven to eight months a year and even in summer the water temperature is never hotter than 12℃. The water in the lake changes its color depending on the time of year: in spring it turns green and in autumn it becomes dark blue, and no matter what season, it’s unbelievably clear. Lake Kardyvach, Krasnodar Krai, Russia

Akhshtyrskaya Cave

A unique monument of prehistoric architecture, Akhshtyrskaya Cave is set on the right side of Akhshtyrskaya Gorge, about 120m above the Mzymta River and 185m above sea level. The cave begins with a 20m corridor and then gets divided into two halls, 10m and 8m wide. The cave has been heavily explored by archaeologists, who discovered traces of Neanderthal culture dating back to 40,000 BC.

Akhshtyrskaya Cave, Bolshoy Sochi, Krasnodar Krai,Russia

Shakhe River

Sochi’s second most significant river, Shakhe begins high in the mountains and flows down to the Black Sea . 59 kilometers long, the river has some amazing natural attractions in its valley: Dzhegosh Gorge, 33 waterfalls, stone lake basins, ancient oak trees, rare plant life and so much more.

Shakhe River, Krasnodar Krai,Russia

Agura Waterfalls and Orlinyye Rocks

This is one of the most exciting hiking routes in the area. Taking you through spruce fir forest, to three cascading waterfalls and the sheer cliffs of the Orlinyye Rocks with head-spinning views. Agura Waterfalls, Bolshoy Sochi, Krasnodar Krai,Russia

Words can’t do justice to the virgin beauty of the Khuko Lake and scientists are still puzzling over the absence of any life in it. Set between Adygea and Krasnodar Krai, the lake offers incredible views of the mountains Fisht, Oshten and Pshekha-Su.

Khaki Lake, Krasnodar Krai,Russia

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List of Wood suppliers in Krasnodar Krai

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