Warehouse Management Problem and a KPI Approach: a Case Study

• Autorzy: Marziali Micaela , Rossit Daniel Alejandro , Toncovich Adrián

Identyfikatory

DOI

10.24425/mper.2021.138530

• Języki publikacji: EN

Abstrakty

EN

Warehouse and inventory management is a recurring issue in many of the different supply chains in diverse industries, where the constant changes in the markets have a direct impact on the management of warehouses and inventories, either generating over-stocks or shortages. This paper presents a case study on warehouse and inventory management control. The company under study was having problems in this area, where over-stocks were generated frequently, leading to various incidents, such as having to store finished and packaged product in unsuitable places, with the associated risk of deterioration. To deal with this problem, control tools based on the KPI (Key Performance Indicator) concept were developed. To this end, the corresponding problem and the information management process within the Supply Chain department had to be analyzed. In this case, it was observed that the databases were not synchronized, therefore strategies were proposed to systematize the collection and updating of data. In addition, to summarize the information, we proceeded to the implementation of an interactive form that facilitates the visualization and interpretation of the evolution of the process, and to be able to apply an efficient control on it, and thus to propose corrective actions supported by evidence.

Słowa kluczowe

EN

stock control; logistics; operations management; key performance indicators; warehouse management; control function

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2021
• Tom: Vol. 13, No. 3
• Strony: 51--62
• Opis fizyczny: Bibliogr. 40 poz., rys., tab., wykr.

Twórcy

autor

Marziali Micaela

• Departamento de Ingeniería, Universidad Nacional del Sur, Bahía Blanca, Argentina

autor

Rossit Daniel Alejandro

• Departamento de Ingeniería, Universidad Nacional del Sur, Bahía Blanca, Argentina
• INMABB – CONICET, Universidad Nacional del Sur, Bahía Blanca, Argentina

autor

Toncovich Adrián

• Departamento de Ingeniería, Universidad Nacional del Sur, Bahía Blanca, Argentina

Bibliografia

• Agyabeng-Mensah, Y., Ahenkorah, E., Afum, E., Dacosta, E., and Tian, Z. (2020). Green warehousing, logistics optimization, social values and ethics and economic performance: the role of supply chain sustainability. The International Journal of Logistics Management, 31, 3, 549–574. DOI: 10.1108/IJLM10-2019-0275.
• Braglia, M., Castellano, D., and Frosolini, M. (2016). A novel approach to safety stock management in a coordinated supply chain with controllable lead time using present value. Applied Stochastic Models in Business and Industry, 32, 1, 99–112.
• Broz, D.R., Rossit, D.A., Rossit, D.G., and Cavallin, A. (2018). The Argentinian forest sector: opportunities and challenges in supply chain management. Uncertain Supply Chain Management, 6, 375–392.
• Buonamico, N., Muller, L., and Camargo, M. (2017, April). A new fuzzy logic-based metric to measure lean warehousing performance. Supply Chain Forum: An International Journal, Taylor & Francis, 18, 2, 96–111.
• Chen, F., Drezner, Z., Ryan, J.K., and Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management science, 46, 3, 436-443.
• Chiang, D.M.H., Lin, C.P., and Chen, M.C. (2011). The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres. Enterprise Information Systems, 5, 2, 219–234.
• Chou, Y.C., Lu, C.H., and Tang, Y.Y. (2012). Identifying inventory problems in the aerospace industry using the theory of constraints. International journal of production research, 50, 16, 4686–4698.
• Christopher, M. (2011). Logistics and supply chain management: creating value-adding networks, 4th edn. Dorchester, Financial Times: Prentice Hall.
• Colledani, M. and Tolio, T. (2011). Integrated analysis of quality and production logistics performance in manufacturing lines. International Journal of Production Research, 49, 2, 485–518.
• Dev, N.K., Shankar, R., Gupta, R., and Dong, J. (2019). Multi-criteria evaluation of real-time key performance indicators of supply chain with consideration of big data architecture. Computers & Industrial Engineering, 128, 1076–1087.
• Drucker, P. (2012). The practice of management. Routledge.
• Gunasekaran, A. and 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.
• Ikeziri, L.M., Souza, F.B.D., Gupta, M.C., and de Camargo Fiorini, P. (2019). Theory of constraints: review and bibliometric analysis. International Journal of Production Research, 57, 15–16, 5068–5102.
• Ivanov, D., Sokolov, B., and Dolgui, A. (2014). The Ripple effect in supply chains: trade-off ‘efficiencyflexibility-resilience’ in disruption management. International Journal of Production Research, 52, 7, 2154–2172.
• Ivanov, D., Tsipoulanidis, A., and Schönberger, J. (2017). Global supply chain and operations management. A Decision-Oriented Introduction to the Creation of Value. 2nd edn, Springer.
• Ivanov, D. (2018). Structural dynamics and resilience in supply chain risk management, 265, Berlin: Springer International Publishing.
• Kucukaltan, B., Irani, Z., and 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.
• Laosirihongthong, T., Adebanjo, D., Samaranayake, P., Subramanian, N., and Boon-itt, S. (2018). Prioritizing warehouse performance measures in contemporary supply chains. International Journal of Productivity and Performance Management, 67, 9, 1703–1726.
• Lee, C.K.M., Lv, Y., Ng, K.K.H., Ho, W., and Choy, K.L. (2018). Design and application of Internet of thingsbased warehouse management system for smart logistics. International Journal of Production Research, 56, 8, 2753–2768.
• Lohman, C., Fortuin, L., and Wouters, M. (2004). Designing a performance measurement system: A case study. European journal of operational research, 156, 2, 267–286.
• Mason, S.J., Ribera, P.M., Farris, J.A., and Kirk, R.G. (2003). Integrating the warehousing and transportation functions of the supply chain. Transportation Research Part E: Logistics and Transportation Review, 39, 2, 141–159.
• Nantee, N. and Sureeyatanapas, P. (2021). The impact of Logistics 4.0 on corporate sustainability: a performance assessment of automated warehouse operations. Benchmarking: An International Journal, ahead-of-print. DOI: 10.1108/BIJ-11-2020-0583.
• Neely, A. (Ed.). (2007). Business performance measurement: Unifying theory and integrating practice. Cambridge University Press.
• Parmenter, D. (2015). Key performance indicators: developing, implementing, and using winning KPIs. John Wiley & Sons.
• Pereira, C.M., Anholon, R., Rampasso, I.S., Quelhas, O.L., Leal Filho, W., and Santa-Eulalia, L.A. (2020). Evaluation of lean practices in warehouses: an analysis of Brazilian reality. International Journal of Productivity and Performance Management 70, 1, 1–20.
• Popović, V., Kilibarda, M., Andrejić, M., Jereb, B., and Dragan, D. (2021). A New Sustainable Warehouse Management Approach for Workforce and Activities Scheduling. Sustainability, 13, 4. DOI: 10.3390/su13042021.
• Porras, E. and Dekker, R. (2008). An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods. European Journal of Operational Research, 184, 1, 101–132.
• Purba, H.H., Fitra, A., and Nindiani, A. (2019). Control and integration of milk-run operation in Japanese automotive company in Indonesia. Management and Production Engineering Review, 10, 79–88.
• 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.
• Sharma, S. and Shah, B. (2016). Towards lean warehouse: transformation and assessment using RTD and ANP. International Journal of Productivity and Performance Management, 65, 4, 571–599.
• Silver, E.A., Pyke, D.F., and Peterson, R. (1998). Inventory management and production planning and scheduling. New York: Wiley.
• Srisuk, K. and Tippayawong, K.Y. (2020). Improvement of raw material picking process in sewing machine factory using lean techniques. Management and Production Engineering Review, 11, 1, 79–85. DOI: 10.24425/mper.2020.132946.
• Staudt, F.H., Alpan, G., Di Mascolo, M., and Rodriguez, C.M.T. (2015). Warehouse performance measurement: a literature review. International Journal of Production Research, 53, 18, 5524–5544.
• Tokat, S., Karagul, K., Sahin, Y. and Aydemir, E. (2021). Fuzzy C-Means Clustering-Based Key Performance Indicator Design for Warehouse Loading Operations. Journal of King Saud University-Computer and Information Sciences, in press. DOI: 10.1016/j.jksuci.2021.08.003.
• Toncovich, A., Rossit, D.A., Frutos, M., and Rossit, D.G. (2019). Solving a multi-objective manufacturing cell scheduling problem with the consideration of warehouses using a simulated annealing based procedure. International Journal of Industrial Engineering Computations, 10, 1, 1–16.
• Torabizadeh, M., Yusof, N.M., Ma’aram, A., and Shaharoun, A.M. (2020). Identifying sustainable warehouse management system indicators and proposing new weighting method. Journal of Cleaner Production, 248, 119–190.
• Torbacki, W. and Kijewska, K. (2019). Identifying Key Performance Indicators to be used in Logistics 4.0 and Industry 4.0 for the needs of sustainable municipal logistics by means of the DEMATEL method. Transportation Research Procedia, 39, 534–543.
• Villalba-Diez, J., Ordieres-Meré, J., Molina, M., Rossner, M., and Lay, M. (2018). Lean dendrochronology: complexity reduction by representation of kpi dynamics looking at strategic organizational design. Management and Production Engineering Review, 9, 4, 48–55.
• Vollmann, Thomas E., Berry, William L., Whybark, D.C. and Jacobs R. (2005). Manufacturing Planning and Control for Supply Chain Management. McGrawHill/Irwin. 5th Edition.

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)