Green and Resilient Supplier Selection Model and Inventory Management under uncertainty for Cement Industry

Abstract

The study explores two sections of supply chain management namely Inventory Management (IM) and Procurement Management (PM). Both have direct on profitability of firms. Profitability of an organization is influenced by Inventory Metrics. Inventory faces many risks that includes the impact of exchange rate fluctuations, high storage costs, obsolescence and poor demand forecasting. The Total Inventory Value is indirectly influenced by good supplier selection as well. The suppliers need to be cost as well time and quality conscious in order to positively impact inventory. In line with growing concern for the environmental impact as well growing global supply chain risks the supplier have to be green and resilient as well. The combination of good supplier materials entering inventory in correct forecasted quantity will lead to lower inventory costs. First part of the study explores minimization of forecasted total inventory costs under exchange rate fluctuation scenarios. Total Inventory value forecasting is carried out using grey forecasting techniques by obtaining previous secondary historical data of 10 years. Forecasting/Estimation is carried out for next 5 years. GM (1, 1) Markov technique is used to obtain the initial forecast. The error in the forecast is optimized by using Markov Chain technique. The forecasted data is used as an input for inventory cost function with the assumption that all consumed inventory is replenished within the same year and prices of spares remains the same throughout the year. The overall inventory cost function includes the share of purchase cost in local currency and foreign currency as well as impact of insurance, obsolescence and storage costs. The cost function is analyzed by plugging in multiple scenarios of currency fluctuations and percentage share of local versus import spares in buying/replenishment. The forecast accuracy is high as per low MPE which is acceptable for implementation. The novelty of the research lies in adopting the attitude to use complete inventory value instead of demand forecasting for each spare part or raw material. Moreover, in previous studies, neither specific cement plant inventories have been studied in detail nor capacity utilization has been considered to improve forecasting. Impact of this approach on cement plants in Pakistan is discussed along with some recommendations for industrial practitioners. Cement plant management can plan and save accordingly by considering the findings. The second part of the study explores Green and Resilient Supplier Selection. This evaluation is a considerable strategic solution for minimizing environmental impact, operational costs and continuously improving the resilience and competitive advantage of the supply chain of the organization. Adding green factors and resilience factors to supplier selection process will have iii a positive impact on manufacturing plant inventories. This research aims to develop a hybrid model for supplier selection while incorporating the environmental performance criteria and resilience requirements by integrating expert opinion. The framework is based on a business quintet of cost, quality, time, resilience, and green score. Cost and time objective functions includes the forecasted (GM (1, 1)-Markov Model) demanded quantities. Quality objective function is built upon fuzzy numbers, in our case triangular. Green and resilience objective functions are grounded on Quality Function Deployment using input from experts by utilizing Delphi Technique. All the objectives are converted to single objective using multi-objective fuzzy weighted goal programming, the relative weight of each is obtained from expert opinion by utilizing Delphi Technique. The originality of the research lies in adopting the method combining green and resilient criterion to use at cement plants that were previously are not well studied.