A dynamics approach for modeling inventory fluctuations of the pharmaceutical supply chain in covid 19 pandemic
الموضوعات :
Parvaneh Tavakol
1
,
Bijan Nahavandi
2
,
Mahdi Homayounfar
3
1 - Department of Industrial Management, Bandar-e- Anzali International Branch, Islamic Azad University, Bandar-e-Anzali, Iran
2 - Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
الکلمات المفتاحية: Supply Chain, Bullwhip Effect, System Dynamics, Inventory management,
ملخص المقالة :
Considering the importance of inventory management in the pharmaceutical industry, especially during the Covid-19 pandemic, this paper investigates a five-echelon pharmaceutical supply chain including component suppliers, manufacturers, retailers, distributors and consumers in order to identify various variables of the inventory management systems and analyze their behavior. Conducting the research, first, based on the reviewing the literature, 29 drivers of bullwhip effect (BWE) in supply chain were extracted. Next, systems dynamics as a powerful approach for modeling complex systems, especially supply chains, is applied to simulate the dynamics of the pharmaceutical supply chain. So, the interactions of the main variables of the system were translated to the dynamic hypotheses and constitute the causal loop diagram. Then, stock and flow diagram was formulated in form of the differential equations. To validate the proposed model, some structural and behavioral validation test were implemented which indicated model’s accuracy. Finally, 4 potential scenarios based on the extent of improvement in information quality, safety stock and lead time were developed and manipulated to analyze their effects on inventory gap, as the main indicator of BWE. The results indicate that the best scenario for the component supplier and manufacturer is 5% increase in the information quality, 10% increase in the safety reserve and 5% decrease in lead time. While for the medicine distributor and medicine retailer; 5% increase in information quality, 5% increase in the safety reserve and 10% reduction in lead time, minimizes stock gap in the shortest time.
Aslani Khiavi, S., & Skandari Dastghiri, S. (2021). The Design of Inverse Network DEA Model for Measuring the Bullwhip Effect in Supply Chain with Uncertain Demands. Journal of Optimization in Industrial Engineering, 14(1), 93-104.
Brandimarte, P., & Zotteri, G. (2007). Introduction to distribution logistics, John Wiley & Sons.
Braz, A.C., Mello, A.M., de Vasconcelos Gomes, L.A., & Nascimento, P.T. (2018). The bullwhip effect in closed-loop supply chains: A systematic literature review. Journal of Cleaner Production, 202, 376-389.
Cannella, S., Bruccoleri, M., & Framinan, J.M. (2016). Closed loop supply chains: What reverse logistics factors influence performance? International Journal of Production Economics, 175, 35-49.
Chen F., Drezner, Z., Ryan, J.K., & Simchi-Levi, D. (2000). Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting. Management Science, 46(3), 436-443.
Chong, L. (2013). Controlling the bullwhip effect in a supply chain system with constrained information flows. Applied Mathematical Modelling, 4, 1897-1909.
Costantino, F., Gravio, G.D., Shaban, A., & Tronci, M. (2015a). The impact of information sharing on ordering policies to improve supply chain performances. Computers & Industrial Engineering, 82, 127–142.
Costantino, F., Gravio, G.D., Shaban, A., & Tronci, M. (2015b). A real-time SPC inventory replenishment system to improve supply chain performances. Expert Systems with Applications, 42, 1665–1683.
Costantino, F., Gravio, G.D., Shaban, A., & Tronci, M. (2016). Smoothing inventory decision rules in seasonal supply chains. Expert Systems with Applications, 44, 304-319.
Corum, A., Vayvay, Ö., & Bayraktar, E., (2014). The impact of remanufacturing on total inventory cost and order variance. Journal of Cleaner Production, 85, 442-452
Dai, H., Li, J., Yan, N., & Zhou, W. (2016). 3Bullwhip effect and supply chain costs with low- and high-quality information on inventory shrinkage. European Journal of Operational Research, 250(2), 457-469.
de Lima, D.P., Fioriolli, J.C., Padula, A.D., & Pumi, G. (2018). The impact of Chinese imports of soybean on port infrastructure in Brazil: A study based on the concept of the Bullwhip Effect. Journal of Commodity Markets, 9, 55-76.
Dominguez R., Cannella S., & Framinan, J.M. (2015). On returns and network configuration in supply chain dynamics. Transportation Research Part E: Logistics and Transportation Review. Transportation Research Part E: Logistics and Transportation Review, 73, 152-167.
Dominguez, R., Ponte, B., Cannella, S., & Framinan, J.A. (2019). On the dynamics of closed-loop supply chains with capacity constraints. Computers & Industrial Engineering, 128, 91-103.
Dominguez, R., Cannella, S., Ponte, B., & Framinan, J.A. (2020). On the dynamics of closed-loop supply chains under remanufacturing lead time variability. Omega, 97, 102106.
Duc Tai, P., Ton Hien Duc, T., & Buddhakulsomsiri, J. (2018). Measure of bullwhip effect in supply chain with price-sensitive and correlated demand. Computers & Industrial Engineering, 127, 408-419.
Fu, D. (2015). Quantifying and mitigating the bullwhip effect in a benchmark supply chain system by an extended prediction self-adaptive control ordering policy. Computers & Industrial Engineering, 81, 46-57.
Gaalman, G., Disney, S.M., & Wang, X. (2019). When the Bullwhip Effect is an Increasing Function of the Lead Time. IFAC Papers OnLine, 52-13, 2297–2302.
Gavirneni, S. (2006). Price fluctuations, information sharing, and supply chain performance. European Journal of Operational Research, 174, 1651-1663.
Giri, B.C., & Glock, C.H. (2022). The bullwhip effect in a manufacturing/remanufacturing supply chain under a price-induced non-standard ARMA (1,1) demand process. European Journal of Operational Research, 301(2), 458-472.
Goodarzi, M., & Farzipoor Saen, R. (2020). How to measure bullwhip effect by network data envelopment analysis? Computers & Industrial Engineering, 139, 105431.
Haines, R., Hough, J., & Haines, D. (2017). A Metacognitive Perspective on Decision Making in Supply Chains: Revisiting the Behavioral Causes of the Bullwhip Effect. International Journal of Production Economics, 184, 7-20.
Holland, W., & Sodhi, M.S. (2004). Quantifying the effect of batch size and order errors on the bullwhip effect using simulation. International Journal of Logistics Research and Applications, 7(3), 251-261.
Hosseini Bamakan, S.M., Malekinejad, P., Ziaeian, M., & Motavali, A. (2021). Bullwhip effect reduction map for COVID-19 vaccine supply chain. Sustainable Operations and Computers, 2, 139-148.
Hu, Q. (2019). Bullwhip effect in a supply chain model with multiple delivery delays. Operations Research Letters, 47(1), 36-40.
Hussain, M., & Drake, P.R. (2011). Analysis of the bullwhip effect with order batching in multi‐echelon supply chains. International Journal of Physical Distribution & Logistics, 41(8), 797-814.
Jiang, Q., & Ke, G. (2019). Information Sharing and Bullwhip Effect in Smart Destination Network system. Ad Hoc Networks, 87(1), 17-25.
Kadivar, M., & Akbarpour Shirazi, M. (2018). Analyzing the Behavior of the Bullwhip Effect considering Different Distribution Systems. Applied Mathematical Modelling, 59, 319-340.
Ghaffari, M., Javadian, N., & Tavakkoli-Moghaddam, R. (2014). Controlling the Bullwhip Effect in a Supply Chain Network with an Inventory Replenishment Policy Using a Robust Control Method. Journal of Optimization in Industrial Engineering, 16, 75-82.
Khosroshahi, H., Moattar Husseini, S.M., & Marjani, M.R. (2016). The bullwhip effect in a 3-stage supply chain considering multiple retailers using a moving average method for demand forecasting. Applied Mathematical Modelling, 40(21–22), 8934-8951.
Kumara, A., Shankarb, R., & Aljohani, N.R. (2020). A big data driven framework for demand-driven forecasting with effects of marketing-mix variables. Industrial Marketing Management, 90, 493-507.
Lee, H.L., Padmanabhan, V., & Whang, S. (1997). The bullwhip effect in supply chains. Sloan Management Review, 38, 93–102.
Lee, H.L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43, 546–555.
Li Q., Disney, S.M., & Gaalman, G. (2014). Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy. International Journal of Production Economics, 149, 3-16.
Lin, W., Ren, H., Peng, Q., Ye, S., Jiang, Z., Wang, K. (2019). An Analysis of the Bullwhip Effect in Multi-echelon Hybrid Supply Chain. IFAC Papers OnLine, 52(13), 2419–2424.
Ma, J., & Bao, B. (2017). Research on bullwhip effect in energy-efficient air conditioning supply chain. Journal of Cleaner Production, 143, 854-865.
Malekinejad, P., Ziaeian, M., & Hosseini Bamakan, S.M. (2022). A communication model for reducing the bullwhip effect in closed-loop supply chain. Advances in Industrial and Manufacturing Engineering, 5, 100086.
Matamoros. O., Mauricio, M., Ricardo, F.C., Isaías, T.P., & Ignacio, C.M. (2011). Fractal characterization of an after-sales spare parts supply chain in telecom industry. International Journal of Physical Sciences, 6(10), 2462-2469.
Metters, R. (1997). Quantifying the bullwhip effect in supply chains. Journal of Operations Management, 15, 89–100.
Michna, Z., Disney, S.M., & Nielsen, P. (2020). The impact of stochastic lead times on the bullwhip effect under correlated demand and moving average forecasts. Omega, 93, 102033.
Minner, S., & Transchel, S. (2017). Order variability in perishable product supply chains. European Journal of Operational Research, 260(1), 93-107.
Mirab Samiee, Z., Rostamzadeh, M., & Fatahi Valilai, O. (2020). An Analysis of the BWE-Associated Costs: The issue of Demand Forecasting Accuracy. IFAC Papers online, 53(2), 10836-10842.
Nakade, K., & Aniyama, Y. (2019). Bullwhip Effect of Weighted Moving Average Forecast under Stochastic Lead Time. IFAC Papers OnLine, 52(13), 1277–1282.
Nguyen, D.T., Adulyasak, Y., & Landry, S. (2021). Research manuscript: The Bullwhip Effect in rule-based supply chain planning systems–A case-based simulation at a hard goods retailer. Omega, 98, 102121.
Ojha, D., Sahin, F., Shockley, J., & Sridharan, S.V. (2019). Is there a performance tradeoff in managing order fulfillment and the bullwhip effect in supply chains? The role of information sharing and information type. International Journal of Production Economics, 208, 529-543.
Pastore, E., Alfieri, A., Zotteri, G., & Boylan, J.E. (2020). The impact of demand parameter uncertainty on the bullwhip effect. European Journal of Operational Research, 283 (1), 94-107.
Pastore, E., Alfieri, A., & Zotteri, G. (2019). An empirical investigation on the antecedents of the bullwhip effect: Evidence from the spare parts industry. International Journal of Production Economics, 209, 121-133.
Ponte, B., Dominguez, R., Cannella, S., & Framinan, J.M. (2022). The implications of batching in the bullwhip effect and customer service of closed-loop supply chains. International Journal of Production Economics, 244, 108379.
Ponte, B., Framinan, J.M., Cannella, S., & Dominguez, R. (2020a). Quantifying the Bullwhip Effect in closed-loop supply chains: The interplay of information transparencies, return rates, and lead times. International Journal Production Economics, 230, 107798.
Ponte, B., Puche, J., Rosillo, R., & de la Fuente, D. (2020b). The effects of quantity discounts on supply chain performance: Looking through the Bullwhip lens. Transportation Research Part E, 143, 102094.
Ponte, B., Sierra, E., de la Fuente, D., & Lozano, J. (2017). Exploring the Interaction of Inventory Policies across the Supply Chain: An Agent-based Approach, Computers and Operation Research, 75, 335-348.
Potter, A., & Disney. S.M. (2006). Bullwhip and batching: an exploration. International Journal Production Economics, 104(2), 408-418.
Sadeghi, A. (2014). Measuring and Analyzing the Bullwhip Effect in a Two-Product and Two Echelon Supply Chain Using Control Theory Approach. Journal of Optimization in Industrial Engineering, 15, 55-63.
Scarpin, M.R.S., Scarpin, J.E., Musial, N.T.K., & Nakamura, W.T. (2022). The implications of COVID-19: Bullwhip and ripple effects in global supply chains. International Journal of Production Economics, 251, 108523.
Shaban, A., & Shalaby, M.A. (2018). Modeling and Optimizing of Variance Amplification in Supply Chain Using Response Surface Methodology. Computers & Industrial Engineering, 120, 392-400.
Shabany Moghadam, F., & Fazel Zarandi, M.H. (2022). Mitigating bullwhip effect in an agent-based supply chain through a fuzzy reverse ultimatum game negotiation module. Applied Soft Computing, 116, 108278.
Sterman, J.D. (1989). Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision-Making Experiment. Management Science, 35, 321-339.
Sy, C. (2017). A policy development model for reducing bullwhips in hybrid production-distribution systems. International Journal Production Economics, 190, 67-79.
Trapero, J.R., & Pedregal, D.J. (2016). A novel time-varying bullwhip effect metric: An application to promotional sales. International Journal of Production Economics, 182, 465–471.
Udenio, M., Fransoo, J.C., & Peels, R. (2015). Destocking, the bullwhip effect, and the credit crisis: Empirical modeling of supply chain dynamics. International Journal of Production Economics, 160, 34-46.
Wang, X., & Disney, S.M. (2016). The bullwhip effect: Progress, trends and directions. European Journal of Operations Research, 250(3), 691–701.
Wang, X., & Disney, S.M. (2017). Mitigating variance amplification under stochastic lead-time: The proportional control approach. European Journal of Operational Research, 256(1), 151-162.
Yang, Y., Lin, J., Liu, G., & Zhou, L. (2021). The behavioural causes of bullwhip effect in supply chains: A systematic literature review. International Journal of Production Economics, 236, 108120.
Yuan, X.G., & Zhu, N. (2016). Bullwhip Effect Analysis in Two-Level Supply Chain Distribution Network Using Different Demand Forecasting Technology. Asia-Pacific Journal of Operational Research, 33(3), 1650016.
Zhang X., & Burke G.J. (2011). Analysis of compound bullwhip effect causes. European Journal of Operational Research, 210, 514-526.
Zhao, S., & Zhu, Q. (2018). A risk-averse marketing strategy and its effect on coordination activities in a remanufacturing supply chain under market fluctuation. Journal of Cleaner production, 171, 1290-1299.
Zhao, Y., Cao, Y., Li, H., Wang, S., Liu, Y., Li, Y., & Zhang, Y. (2018). Bullwhip effect mitigation of green supply chain optimization in electronics industry. Journal of Cleaner Production, 180, 888-912.
Zhou, L., & Disney, S.M. (2006). Bullwhip and inventory variance in a closed loop supply chain. Spectrum, 28(1), 127-149.
Zhou, K., Wei, S., & Yang, S. (2019). Time-of-use pricing model based on power supply chain for user-side microgrid. Applied Energy, 248, 35–43.
Zhu, T., Balakrishnan, J., & da Silveira, G.J.C. (2020). Bullwhip Effect in the Oil and Gas Supply Chain: A Multiple-case Study. International Journal of Production Economics, 224, 107548.
Zotteri, G. (2013). An empirical investigation on causes and effects of the Bullwhip-effect: Evidence from the personal care sector. International Journal of Production Economics, 143(2), 489-498.
