Mitigation of bulllwhip effect in Integrated Manufacturing-Remanufacturing Supply Chain (IMRSC) : system dynamics perspectives - Kays, H. M. Emrul (Kulliyyah of Engineering, International Islamic University Malaysia, 2018)
In contrast to the linear economy of 'Take, Make, and Dispose' model, the obligation of minimizing the resource input, waste, emission and energy leakage for today's circular economy has raised the sustainability issues for any manufacturer. As a major segment of global manufacturing, the strategic and tactical decisions of automotive industries cannot be endorsed without addressing such issues. The resource base has to be conserved and enhanced by salvaging the embedded values from End-of-Life (EoL) vehicles, their subassemblies and components for securing the sustainability goal. For this purpose, adoption of remanufacturing epitome within a manufacturing facility is deemed to be an attractive solution. But, management of such Integrated Manufacturing Remanufacturing Supply Chain (IMRSC) in real life is a great challenge. As the IMRSC, specifically a reverse chain, is most often coupled with numerous uncertainties in terms of quantity, quality and timing of return. Thus the stochastic and inconsistent nature of EoL product returns upsurges the operational complexities which, in turn, affects both the forward and reverse chains. Moreover, the multiplicative seasonality and changing growth rate in product demand incorporate supplementary intricacies in the predictive management of forward chain. Due to these unexpected but unavoidable operational complexities in forward and reverse chains, an amplification of variance of demand along its route of propagation is evident and consequently bullwhip effect becomes inevitable in an IMRSC. Certainly, this bullwhip effect, having interactions with the net stock amplification and fill rate, directly influences the entire industrial dynamics. To tackle such situations, the role of reliable and accurate data forecasting and sharing in the integrated supply chain is undeniable. Hence, this study is undertaken to develop a reliable, accurate demand forecasting and sharing techniques for the IMRSC. In this context, a simulation of the distribution patterns is conducted based on a real life automotive IMRSC to generate scenarios for choosing appropriate models for forward and reverse chains. In this regard, Automatic Pipeline, Inventory and Order-Based Production Control System (APIOBPCS) is adopted as an optimal production and inventory control policy for the considered SC. Meanwhile a comprehensive statistical analysis is carried out in the SPSS platform to investigate the impact of the level and the growth rate of the Multiplicative Holt-Winters (MHW) model on the Bull-whip effect (BWE). Thus, a responsive MHW approach is developed to predict the seasonal demand in the forward chain. A Neuro-fuzzy based product return model is also formulated, concerning product sales, failures, usage intensity and pattern of return. Finally, a system dynamics model is developed and simulated in the Vensim DSS platform to analyse the underlying dynamics of the forecasting models, the BWE, NSAMP and the fill rate. The obtained solution is assessed through different performance metrics. It is revealed that by using the proposed forecasting approaches, for any particular time instance, the BWE can be reduced (maximum) by 45%, 51%, and 38.7%, NSAMP by 86.59%, 82.94% and 37.86% at retailer, manufacturer and remanufacturer echelons respectively. While, the fill rate is found to be improved by 12.76%, 0.71% and 2% respectively for manufacturer, remanufacturer and retailer echelons. This research work is likely to propagate the concept of real time IMRSC management by tailoring the forecasting and optimal order confirmation models to enhance the economic and environmental sustainability of manufacturing sector.