Order picking and loading-dock arrival punctuality performance indicators for supply chain management: a case study

• Autorzy: Marzialia Micaela , Rossit Daniel Alejandro , Toncovicha Adrián

Identyfikatory

DOI

10.2478/emj-2022-0003

• Języki publikacji: EN

Abstrakty

EN

Supply chain activity control is an essential part of Supply Chain Management (SCM), ensuring compliance with customer requirements. This paper presents a case study into the control of SCM activities. The study analysed two areas involving two different SC links associated with order picking, and outsourced truck freights, respectively. The studied company had problems with these links. An approach based on developing a KPI (Key Performance Indicator) was proposed to address the issues. Consequently, different affected processes were analysed and characterised, considering the relevant data for defining a KPI. Then, strategies and methods were devised for data collection and processing regarding the system’s current state. Finally, tools were designed to facilitate the interpretation of the system’s current state and thus, pave the way for the decision-making process on corrective measures.

Słowa kluczowe

EN

supply chain management; key performance indicators; control; logistics; operations management

PL

zarządzanie łańcuchem dostaw; kluczowe wskaźniki wydajności; kontrola; logistyka; zarządzanie operacjami

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Engineering Management in Production and Services
• Rocznik: 2022
• Tom: Vol. 14, no. 1
• Strony: 26--34
• Opis fizyczny: Bibliogr. 34 poz., tab., wykr.

Twórcy

autor

Marzialia Micaela

• Engineering Department, Universidad Nacional del Sur (UNS), Argentina

autor

Rossit Daniel Alejandro

• Engineering Department, Universidad Nacional del Sur (UNS), Argentina INMABB, CONICET-UNS, Argentina

• https://orcid.org/0000-0002-2381-4352

autor

Toncovicha Adrián

• Engineering Department, Universidad Nacional del Sur (UNS), Argentina

• https://orcid.org/0000-0002-1841-8726

Bibliografia

• Bieńkowska, A. (2020). Controlling Effectiveness Model-empirical research results regarding the influence of controlling on organisational performance. Engineering Management in Production and Services, 12(3), 28-42.
• Broz, D. R., Rossit, D. A., Rossit, D. G., & Cavallin, A. (2018). The Argentinian forest sector: opportunities and challenges in supply chain management. Uncertain Supply Chain Management, 6, 375-392.
• Bukowski, L. (2019). Logistics decision-making based on the maturity assessment of imperfect knowledge. Engineering Management in Production and Services, 11(4), 65-79.
• Carter, C., & Rogers, D. (2008). A framework of sustainable supply chain management: moving toward new theory, International Journal of Physical Distribution & Logistics Management, 38(5), 360-387.
• Chae, B. K. (2009). Developing key performance indicators for supply chain: an industry perspective. Supply Chain Management: An International Journal, 14(6), 422-428.
• Christopher, M. (2011). Logistics and supply chain management: creating value-adding networks. 4th Edition. Dorchester, Financial Times: Prentice Hall
• Colledani, M., & Tolio, T. (2011). Integrated analysis of quality and production logistics performance in manufacturing lines. International Journal of Production Research, 49(2), 485-518.
• Drucker, P. (2012). The practice of management. Routledge.
• Florek-Paszkowska, A., Ujwary-Gil, A., & Godlewska-Dzioboń, B. (2021). Business innovation and critical success factors in the era of digital transformation and turbulent times. Journal of Entrepreneurship, Management, and Innovation, 17(4), 7-28. doi: 10.7341/20211741
• Gunasekaran, A., & Kobu, B. (2007). Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications. International Journal of Production Research, 45(12), 2819-2840.
• Halaška, M., & Šperka, R. (2019). Performance of an automated process model discovery–the logistics process of a manufacturing company. Engineering Management in Production and Services, 11(2), 106-118.
• Ivanov, D. (2018). Structural dynamics and resilience in supply chain risk management. Berlin: Springer International Publishing.
• Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2017). Global supply chain and operations management. A Decision-Oriented Introduction to the Creation of Value. 2nd Edition. Springer.
• Jabilles, E. M. Y., Cuizon, J. M. T., Tapales, P. M. A., Urbano, R. L., Ocampo, L. A., & Kilongkilong, D. A. A. (2019). Simulating the impact of inventory on supply chain resilience with an algorithmic process based on the supply-side dynamic inoperability input–output model. International Journal of Management Science and Engineering Management, 14(4), 253-263.
• Kozma, T. (2017). Cooperation in the supply chain network. Forum Scientiae Oeconomia, 5(3), 45-58. doi: 10.23762/FSO_vol5no3_17_3
• Kucukaltan, B., Irani, Z., & Aktas, E. (2016). A decision support model for identification and prioritization of key performance indicators in the logistics industry. Computers in Human Behavior, 65, 346-358.
• Lambert, D. M., & Cooper, M. C. (2000). Issues in supply chain management. Industrial Marketing Management, 29(1), 65-83.
• Lohman, C., Fortuin, L., & Wouters, M. (2004). Designing a performance measurement system: A case study. European journal of operational research, 156(2), 267- 286.
• Maestrini, V., Luzzini, D., Maccarrone, P., & Caniato, F. (2017). Supply chain performance measurement systems: A systematic review and research agenda. International Journal of Production Economics, 183, 299-315.
• Makris, S., Zoupas, P., & Chryssolouris, G. (2011). Supply chain control logic for enabling adaptability under uncertainty. International Journal of Production Research, 49(1), 121-137.
• Mandal, S. (2016). An empirical competence-capability model of supply chain innovation. Business: Theory and Practice, 17(2), 138-149. doi: 10.3846/btp.2016.619
• Marziali, M., Rossit, D. A., & Toncovich, A. A. (2021). Warehouse Management Problem and a KPI Ap-proach: a Case Study. Management and Production Engineering Review, 12(3), 51-62.
• Maulina, E., & Natakusumah, K. (2020). Determinants of supply chain operational performance. Uncertain Supply Chain Management, 8(1), 117-130.
• Neely, A. (Ed.). (2007). Business performance measurement: Unifying theory and integrating practice. Cambridge University Press.
• Neely, A., Richards, H., Mills, J., Platts, K., & Bourne, M. (1997). Designing performance measures: a structured approach. International Journal of Operations & Production Management, 17(11), 1131-1152.
• Nurakhova, B., Ilyashova, G., & Torekulova, U. (2020). Quality control in dairy supply chain management. Polish Journal of Management Studies, 21(1), 236- 250. doi: 10.17512/pjms.2020.21.1.18
• Osadolor, V., Agbaeze, E. K., Isichei, E. E., & Olabosinde, S. T. (2021). Entrepreneurial self-efficacy and entrepreneurial intention: The mediating role of the need for independence. Journal of Entrepreneurship, Management, and Innovation, 17(4), 91-119. doi: 10.7341/20211744
• Parmenter, D. (2015). Key performance indicators: developing, implementing, and using winning KPIs. John Wiley & Sons.
• Rafele, C. (2004). Logistic service measurement: a reference framework. Journal of Manufacturing Technology Management, 15(3), 280-290.
• Sangwan, K. S. (2017). Key activities, decision variables and performance indicators of reverse logistics. Procedia CIRP, 61(1), 257-262.
• Shiri, H., Rahmani, M., & Bafruei, M. (2020). Examining the impact of transfers in pickup and delivery systems. Uncertain Supply Chain Management, 8(1), 207-224.
• Steiner, G. A. (2010). Strategic planning. Simon and Schuster.
• Sujová, A., Marcineková, K., & Simanová, Ľ. (2019). Influence of modern process performance indicators on corporate performance – the empirical study. Engineering Management in Production and Services, 11(2), 119-129.
• Vollmann, T. E., Berry, W. L., Whybark, D. C., & Jacobs, R. (2005). Manufacturing Planning and Control for Supply Chain Management. 5th Edition. McGraw-Hill/Irwin.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).