This paper deals with the co-ordination of a single producer, multi distributors and multi retailers for a supply chain management to get the maximum profit at minimum investment when shortages is permitted at the retailer‘s end and they are the partially backlogged. Most previous studies on supply chain have dealt with a moderately simpler chain with a single producer and a single buyer. The requirement of the producer is directly proportional to demand of the distributor, while the demand of the distributor is dependent on retailers’ requirement. This passes on rationally to the whole supply chain. The proposed model of this paper considers deteriorating items where the deterioration rate is considered as constant.

Traditional economic production quantity (EPQ) model assumes that the production products are perfect. However, this assumption does not hold for many real production systems due to several weaknesses. This paper considers production inventory model with defective items for deteriorating items. In this paper, production rate is considered to be greater than demand rate. Mathematical model is developed for finding optimal order quantity, cycle time and total profit. Moreover, a numerical example is provided to illustrate the proposed model. Next, sensitivity analysis is established to demonstrate the model developed. Finally, some conclusions and future research directions are proposed.

Authors developed a two-period buyback pricing model which shows a competition between independent repair shop, third party remanufacturer (TPR) and original equipment manufacturer (OEM) for market share in spare parts business after the end of warranty period. Remanufacturing is a profitable option for OEM rather than producing new parts after finishing the warranty period for satisfying the demand of spare parts. OEM acquires damaged/broken parts from local independent repair shop to remanufacture those parts. But if there existed any Third Party Remanufacturer (TPR) then it would lead competition and would decrease the market share of OEM in sales of spare parts. TPR is basically independent remanufacturer. OEM has no control over the activity of the TPR for selling remanufactured spare parts after finishing warranty period of the products. In this paper, authors considered a supply chain model, where independent repair shop is responsible for handling the repair process and both OEM and TPR are remanufacturing spare parts. Repair shop may procure spare parts from both OEM and TPR. A discount is given on the price of the spare parts by TPR which attracts the customers. Repair shop also tries to sell repaired parts at an attractive discounted price. Both TPR and OEM need to collect broken/damaged parts to remanufacture them for maintaining an inventory of spare parts. This paper aimed to develop a deterministic framework for finding optimal buyback price for the OEM and the impact of different parameters on the profitability of spare parts management for individual players of supply chain management.

Planning is one of the most important components of gas industry production. Most big gas companies usually look for effective planning approaches to accomplish the organizational objectives including cost and time reduction as well as enhancing quality and efficiency. For planning gas refinery production, important parameters including production time, production volume, production cost and storage need to be considered. Planning different units ought to be integrated and coordinated with other departments. This article presents an intensive arithmetic model to determine the production of gas derivatives. The proposed model of this paper is formulated as a mixed integer programming and the resulted problem is solved using NSGA-II algorithm and a hybrid method called BBO/NSGA-II. The problem is also applied for a real-world case study and the results are discussed.

This paper presents a unified multi items general inventory model for integrated production of new items and remanufacturing of returned and defected items for a finite planning horizon. In this paper, a production model that takes into account learning, instantaneous deterioration rate and inflation is proposed. In addition, we also consider that the holding cost is a non-negative, non-decreasing and continuous function of time. In this model, the preservation technology is used to reduce the rate of product deterioration. A theory is developed to find the optimal solution of the proposed model; it is then exemplified with the help of several numerical examples. An efficient solution procedure is also provided to find the optimal strategy. Finally, sensitivity of the optimal solution to changes in the values of different parameters of the system and the convexities of the cost functions are also studied and represented through the graphs.

Supply chain management is important for companies and organizations to improve their business and lead competitiveness in the global marketplace. But demand variations in the supply chain are significant problem for most practitioners, planners, demand managers, and operations managers. Demand variations make forecasting and inventory management more difficult and tend to increase inventory levels. The supply chain (SC) profitability can be affected by the cost associated with large inventories, transportation, and production due to the bullwhip effect. Only bullwhip effect can lead to reduce the supply chain profitability in great amount. This paper represents a computational intelligence approach, which addresses the bullwhip effect in multistage supply chain. As a computational intelligence approach, Genetic Algorithm (GA) is employed to reduce the bullwhip effect. Through this approach, optimal order quantity in each stage is to be calculated by considering cost associated with bullwhip effect. Distorted information from one end of a supply chain can lead to tremendous inefficiencies to other end. In this paper it is shown that if each player of the supply chain orders or transfers optimum quantities for the upcoming period then the bullwhip effect can be reduced significantly.

Manufacturing industries are consistently working on improving their operational performance to remain competitive in the market. LM is a well-recognized approach for improving the overall performance. It contains several elements covered under a few lean attributes. This paper presents the application of Graph Theory and Matrix Approach (GTMA) for the identification of relative importance of different lean attributes in a lean environment using qualitative and quantitative factors. The Lean Manufacturing Attributes (LMA’s), affecting the overall LM environment, of a manufacturing industry were identified and analyzed for the implications on the managerial decisions. .In this proposed study, The GTMA approach is applied to prioritize the LMA’s based on their relative importance.

Bullwhip effect is one of the serious problems caused by uncertainty in supply chain management. Graph theoretical approach has been utilized by some of the authors towards evaluation of various phenomena performance. This paper has been an attempt to develop bullwhip effect mitigation index (BWEMI), which is a single numerical benchmarking index utilizing graph theory and matrix (GTM) method. Review of literature and subsequent discussions with experts to obtain their valuable opinions regarding managing supply chains have been utilized to complement and supplement the variables. Digraph theory has been used to develop various matrices showing interactions among bullwhip effect mitigation factors. The tool so developed may help supply chain managers to analyze and quantify efforts towards bullwhip effect mitigation.

Customer Demand Information (CDI) sharing plays a vital role in reducing the bullwhip effect as well as in improving the performance of a supply chain. The objective of the present research is to identify the best form of CDI sharing experimentally for a four-stage serial supply chain under lost sales business environment. A supply chain role play game software package is developed for conducting suitable experiments. Different forms of CDI sharing tested in this research are periodic CDI, history of CDI and CDI in the form of distribution. It is found that all forms of CDI sharing have significant impact on the reduction of bullwhip effect compared to non-sharing of information and the upstream stages in the supply chain are benefited the most under CDI sharing. The statistical analysis also confirms that sharing CDI in the form of distribution is the most effective among the various forms of information sharing studied. The percentage reductions in magnitude of order variance under the most benefitted information sharing at distributor and factory stages are 64.43 and 66.04, respectively. It is also found that the performance of a supply chain depends on the degree of customer demand information shared among the stages in the supply chain.