Determinants of computer vision system’s technology acceptance to improve incoming cargo receiving at Eastern European and Central Asian transportation companies’ warehouses. Mixed methods pilot study

Askar Aituov, Ramesh Kini

Abstract


Transportation companies' warehouses are an integral component of the global supply chain. However, SMBs have limited technology awareness to assess the impact of digitization on certain processes. In particular, the incoming cargo receiving process at transportation companies worldwide has a substantial fraction of manual labor.  In this study, we focus on the cargo dimensioning process of LTL and retail companies’ warehouses in Poland, Estonia, Belarus Republic, and Kazakhstan and identify whether computer vision dimensioning system usage has a positive effect on warehouse performance and its adoption determinants. Combining data from 20 expert interviews, literature review, and quantitative process mining experiments with computer vision dimensioning system performing daily dimensions within 6 months, we conclude that system reliability might be an additional acceptance determinant, which has an influence on Perceived Usefulness. Next, based on the process mining experiments we conclude that the computer vision system is capable to increase information flow in control conditions forty times and four times in the experiment condition. Finally, we find that increase in dimensioning speed as a result of IT system implementation could not be used to assess the impact on the material flow at LTL transportation company but could be a valuable source of data for the capacity monitoring process.


Full Text:

PDF

References


D. Ivanov, “Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case,” Transp. Res. Part E Logist. Transp. Rev., vol. 136, no. March, p. 101922, 2020, doi: 10.1016/j.tre.2020.101922.

UNCTAD, “COVID-19 and maritime transport: Impact and responses,” Rep. No. UNCTAD/DTL/TLB/INF/2020/1, p. 77, 2020, [Online]. Available: https://unctad.org/en/PublicationsLibrary/dtltlbinf2020d1_en.pdf.

D. Ivanov and A. Dolgui, “Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak,” Int. J. Prod. Res., vol. 58, no. 10, pp. 2904–2915, 2020, doi: 10.1080/00207543.2020.1750727.

R. Accorsi, G. Baruffaldi, R. Manzini, and A. Tufano, “On the design of cooperative vendors ’ networks in retail food supply chains : a logistics-driven approach,” Int. J. Logist. Res. Appl., no. 27 Jul 2017, pp. 1–18, 2017, doi: 10.1080/13675567.2017.1354978.

G. Baruffaldi, R. Accorsi, and R. Manzini, “Warehouse management system customization and information availability in 3pl companies: A decision-support tool,” Ind. Manag. Data Syst., vol. 119, no. 2, pp. 251–273, 2019, doi: 10.1108/IMDS-01-2018-0033.

J. E. Hobbs, “A transaction cost approach to supply chain management,” Supply Chain Manag., vol. 1, no. 2, pp. 15–27, 1996, doi: 10.1108/13598549610155260.

DHL, “Logistic trends radar,” 2020. [Online]. Available: https://www.dhl.com/global-en/home/insights-and-innovation/insights/logistics-trend-radar.html.

I. Lee and Y. J. Shin, “Machine learning for enterprises: Applications, algorithm selection, and challenges,” Bus. Horiz., vol. 63, no. 2, pp. 157–170, 2020, doi: 10.1016/j.bushor.2019.10.005.

T. Masood and P. Sonntag, “Industry 4.0: Adoption challenges and benefits for SMEs,” Comput. Ind., vol. 121, p. 103261, 2020, doi: 10.1016/j.compind.2020.103261.

M. W. Chiasson and E. Davidson, “Taking Industry Seriously Information Systems Research1,” vol. 29, no. 4, pp. 591–605, 2015.

McKinsey, “How do you measure success in digital? Five metrics for CEOs,” Mckinsey Digital, 2021. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/how-do-you-measure-success-in-digital-five-metrics-for-ceos (accessed Feb. 02, 2021).

J. Mlimbila and U. O. L. Mbamba, “The role of information systems usage in enhancing port logistics performance: evidence from the Dar Es Salaam port, Tanzania,” J. Shipp. Trade, vol. 3, no. 1, 2018, doi: 10.1186/s41072-018-0036-z.

P. Karhade and J. Q. Dong, “Information Technology Investment and Commercialized Innovation Performance : Dynamic Adjustment Costs and Curvilinear Impacts,” MIS Q., no. February, 2020.

ABPMP, Guide to the Business Process Management Common Body of Knowledge (BPM CBOK) version 4.0. 2019.

V. Kickham, “For warehouse robotics, the dock is the final frontier,” DC Velocity, Boston, 2020.

P. Baker and M. Canessa, “Warehouse design: A structured approach,” Eur. J. Oper. Res., vol. 193, no. 2, pp. 425–436, 2009, doi: 10.1016/j.ejor.2007.11.045.

C. Chase, What Is Demand-Driven Forecasting?, Second Edi. SAS Institute, Inc., John Wiley & Sons, Inc., 2013.

SCOR, “Supply Chain Operations Reference Model - version 12.0,” Cypress, no. San Jose, pp. 559–567, 2017, doi: 10.15358/9783800639960_559.

C. B. Kreitzberg, B. Shneiderman, E. Gerber, E. Rosenzweig, and E. F. Churchill, “Careers in HCI and UX: The digital transformation from craft to strategy,” Conf. Hum. Factors Comput. Syst. - Proc., pp. 1–6, 2019, doi: 10.1145/3290607.3311746.

J. Stecken, M. Ebel, M. Bartelt, J. Poeppelbuss, and B. Kuhlenkötter, “Digital shadow platform as an innovative business model,” Procedia CIRP, vol. 83, pp. 204–209, 2019, doi: 10.1016/j.procir.2019.02.130.

E. Bendoly, N. Craig, and N. DeHoratius, “Consistency and Recovery in Retail Supply Chains,” J. Bus. Logist., vol. 39, no. 1, pp. 26–37, 2018, doi: 10.1111/jbl.12174.

T. Nguyen, L. ZHOU, V. Spiegler, P. Ieromonachou, and Y. Lin, “Big data analytics in supply chain management: A state-of-the-art literature review,” Comput. Oper. Res., vol. 98, pp. 254–264, 2018, doi: 10.1016/j.cor.2017.07.004.

L. Saarinen, L. Loikkanen, K. Tanskanen, and R. Kaipia, “Agile planning : Avoiding disaster in the grocery supply chain during the COVID-19 crisis,” no. July, 2020, doi: 10.13140/RG.2.2.21508.55686.

R. Klein and A. Rai, “Interfirm strategic information flows in logistics supply chain relationships,” MIS Q. Manag. Inf. Syst., vol. 33, no. 4, pp. 735–762, 2009, doi: 10.2307/20650325.

Verizon, “Fuel Tax Reporting Software,” Verizon, 2020. https://www.verizonconnect.com/au/solutions/fuel-tax-reporting/.

M. Keil, P. E. Cule, K. Lyytinen, and R. C. Schmidt, “A framework for identifying software project risks,” Commun. ACM, vol. 41, no. 11, pp. 76–83, 1998, doi: 10.1145/287831.287843.

M. A. McCarthy, L. M. Herger, S. M. Khan, and B. M. Belgodere, “Composable DevOps: Automated Ontology Based DevOps Maturity Analysis,” Proc. - 2015 IEEE Int. Conf. Serv. Comput. SCC 2015, pp. 600–607, 2015, doi: 10.1109/SCC.2015.87.

L. Fink, J. Shao, Y. Lichtenstein, and S. Haefliger, “The ownership of digital infrastructure: Exploring the deployment of software libraries in a digital innovation cluster,” J. Inf. Technol., 2020, doi: 10.1177/0268396220936705.

T. K. Landauer, The Trouble with Computers Usefulness, Usability, and Productivity, June 1996. A Bradford Book, 1995.

V. Venkatesh, “Determinants of perceived ease of use : integrating control , intrinsic motivation , acceptance model,” Inf. Syst. Res., vol. 11, no. 4, pp. 342–365, 2000, doi: http://dx.doi.org/10.1287/ isre.11.4.342.11872.

D. Ivanov, A. Dolgui, A. Das, and B. Sokolov, “Handbook of Ripple Effects in the Supply Chain,” vol. 276, no. January, pp. 309–332, 2019, doi: 10.1007/978-3-030-14302-2.

C. Narayanaswami, R. Nooyi, S. G. Raghavan, and R. Viswanathan, “Blockchain Anchored Supply Chain Automation,” IBM J. Res. Dev., vol. 63, no. 2/3, p. 1, 2019.

A. Rai, R. Patnayakuni, and N. Seth, “Firm Performance Impacts of Digitally Enabled Supply Chain Integration Capabilities,” Manag. MIS Q., vol. 30, no. 2, pp. 226–246, 2006.

Peerless Research Group, “Labor management strategies in the warehouse.,” Report from August 2014, pp. 2–5, 2014.

A. Atif, D. Richards, and D. Richards, “A Technology Acceptance Model For Unit Guide Information Systems,” Proc. - Pacific Asia Conf. Inf. Syst. PACIS 2012, no. July 2012, 2016.

N. Fathema, D. Shannon, and M. Ross, “Expanding The Technology Acceptance Model ( TAM ) to Examine Faculty Use of Learning Management Systems ( LMSs ) In Higher Education Institutions,” vol. 11, no. 2, pp. 210–232, 2015.

V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User Acceptance of Information Technology: Toward a Unified View,” MIS Q. Manag. Inf. Syst., vol. 27, no. 3, pp. 425–478, 2003.

V. Venkatesh and H. Bala, “Technology Acceptance Model 3 and a Research Agenda on Interventions,” vol. 39, no. 2, pp. 273–315, 2008.

D. Gefen, E. Karahanna, and W. D. Straub, “Trust and TAM in Online Shopping: An Integrated Model,” MIS Q., vol. 27, no. 1, pp. 51–90, 2003.

T. Mayor, “Traditional Financial Methods For Calculating IT Value: Economic Value Added, TCO, Total Economic Impact, Rapid Economic Justification,” CIO Journal, 2002. https://www.cio.com/article/2440691/traditional-financial-methods-for-calculating-it-value--economic-value-added--tco--t.html (accessed Feb. 03, 2021).

A. Apfel and M. Smith, “TVO Methodology: Valuing IT Investments via the Gartner Business Performance Framework,” Gartner, 2003. https://www.gartner.com/en/documents/387459 (accessed Feb. 03, 2021).

R. Kahli and V. Grover, “Business value of IT: An essay on expanding research directions to keep up with the times,” J. Assoc. Inf. Syst., vol. 9, no. 1, pp. 23–39, 2008, doi: 10.17705/1jais.00147.

A. Rai, P. A. Pavlou, G. Im, and S. Du, “Interfirm IT capability profiles and communications for cocreating relational value : Evidence from the logistics industry,” MIS Q. Manag. Inf. Syst., vol. 36, no. 1, pp. 233–262, 2012, doi: 10.2307/41410416.

K. Ruan, “Digital Assets as Economic Goods,” Digit. Asset Valuat. Cyber Risk Manag., pp. 1–28, 2019, doi: 10.1016/b978-0-12-812158-0.00001-6.

A. A. Mashli Aina, W. Hu, and A.-N. Noofal Ahmed Mohsen Mohammed, “Use of Management Information Systems Impact on Decision Support Capabilities: A Conceptual Model,” J. Int. Bus. Res. Mark., vol. 1, no. 4, pp. 27–31, 2016, doi: 10.18775/jibrm.1849-8558.2015.14.3004.

B. G. Jamehshooran, A. M. Shaharoun, and H. N. Haron, “Assessing supply chain performance through applying the SCOR model,” Int. J. Supply Chain Manag., vol. 4, no. 1, pp. 1–11, 2015.

A. Kim, J. Obregon, and J. Y. Jung, “PRANAS: A process analytics system based on process warehouse and cube for supply chain management,” Appl. Sci., vol. 10, no. 10, 2020, doi: 10.3390/app10103521.

V. Kasi, “Systemic assessment of SCOR for modeling supply chains,” Proc. Annu. Hawaii Int. Conf. Syst. Sci., vol. 00, no. C, p. 87, 2005, doi: 10.1109/hicss.2005.574.

J. Bartholdi and S. Hankman, “Warehouse and distribution science,” Supply Chain Logist. Inst., no. Release 0.96, pp. 1–323, 2016.

A. Ramaa, K. Subramanya, and T. Rangaswamy, “Impact of Warehouse Management System in a Supply Chain,” Int. J. Comput. Appl., vol. 54, no. 1, pp. 14–20, 2012.

D. L. Morgan, “From themes to hypotheses: Following up with quantitative methods,” Qual. Health Res., vol. 25, no. 6, pp. 789–793, 2015, doi: 10.1177/1049732315580110.

R. Singleton and B. C. Straits, Approaches to social research, Sixth edit. New York: Oxford University Press, 2018.

P. Twining, R. S. Heller, M. Nussbaum, and C. C. Tsai, “Some guidance on conducting and reporting qualitative studies,” Comput. Educ., vol. 106, pp. A1–A9, 2017, doi: 10.1016/j.compedu.2016.12.002.

E. H. Bradley, L. A. Curry, and K. J. Devers, “Qualitative Data Analysis for Health Services Research : Developing Taxonomy , Themes , and Theory,” pp. 1758–1772, 2007, doi: 10.1111/j.1475-6773.2006.00684.x.

W. van der Aalst, Process Mining: Data Science in Action, 2nd ed. Springer Publishing Company, Incorporated, 2016.

ISO/IEC 25010, “ISO/IEC JTC 1/SC 7 Software and systems engineering,” Edition : 1, 2011. https://www.iso.org/standard/35733.html (accessed Jan. 21, 2021).

K. Christoffersen and D. Woods, “How Complex Human-Machine Systems Fail,” no. January 2002, pp. 34-1-34–16, 2003, doi: 10.1201/9780203507926.sec3.

C. Mhamdi, S. F. Alhashmi, and S. A. Salloum, “Implementing Artificial Intelligence in the United Arab Emirates Healthcare Sector: An Extended Technology Acceptance Model,” Int. J. Inf. Technol. Lang. Stud., vol. 3, no. 3, pp. 27–42, 2019, [Online]. Available: http://journals.sfu.ca/ijitls.

M. Solano-Lorente, E. Martinez-Caro, and J. G. Cegarra- Navarro, “Designing a framework to develop eLoyalty for online healthcare services,” Electron. J. Knowl. Manag., vol. 11, no. 1, pp. 107–115, 2013, [Online]. Available: http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=psyc10&NEWS=N&AN=2013-27023-011.

S. Verma, S. S. Bhattacharyya, and S. Kumar, “An extension of the technology acceptance model in the big data analytics system implementation environment,” Inf. Process. Manag., vol. 54, no. 5, pp. 791–806, 2018, doi: 10.1016/j.ipm.2018.01.004.

C.-G. Samia, K. Halil İbrahim, and K. Utku, “Toward Fault Tolerant Management of Big Data Supply Chains : Case of Toward Fault-Tolerant Management of Big Data Supply Chains : Case of Butterfly Effect,” no. April, 2018.

R. Handfield and E. L. Nichols., Supply chain redesign: Transforming supply chains into integrated value systems., Ft Press. 2002.

R. Lodmark, “Putting theory into practice: Capacity management,” Core insights, Warwick Business School, 2021. https://www.wbs.ac.uk/news/putting-theory-into-practice-capacity-management (accessed Feb. 17, 2021).

G. Schryen, “Revisiting IS business value research: What we already know, what we still need to know, and how we can get there,” Eur. J. Inf. Syst., vol. 22, no. 2, pp. 139–169, 2013, doi: 10.1057/ejis.2012.45.

Grover and Kohli, “Cocreating IT Value: New Capabilities and Metrics for Multifirm Environments,” MIS Q., vol. 36, no. 1, p. 225, 2012, doi: 10.2307/41410415.

J. Wirtz, Balancing Capacity and Demand in Service Operations, vol. 7. 2017.


Refbacks

  • There are currently no refbacks.


Abava  Absolutech Convergent 2020

ISSN: 2307-8162