M. ZIKRI ALFAIZ NASUTION

Coordinating supplier-retailer using multiple common replenishment epochs with retailers’ choices, To obtain competitive advantages, supply chain partners often seek coordination to improveperformance of the supply chain. As one of the most important supply chains in the modernretail industry, chain convenience stores have devoted significant efforts to improve supplycoordination. As of 2012, the three famous convenience store chains, 7-Eleven, FamilyMart,and Couche-Tard, operate around 43,000, 17,560 and 6,000 convenience stores worldwiderespectively (7-Eleven, 2012; FamilyMart, 2012; Alimentation Couche-Tard, 2012). The hugeand continuously growing number of stores in chain convenience stores creates a persistentdrive to improve their supply coordination. Often, a better coordination in the supply chainsupports the products with an enhanced competitive advantage. In practice, the retailingindustry with multiple stores would divide the distribution channels into various modules basedon geographic areas, replenishment epochs, or some other managerial considerations toenhance logistic efficiency.This paper studies a single-supplier, multiple-retailer supply chain for a single commodity. Thedemand of the commodity occurs only at the retailers with constant rates, and the supplier isresponsible for replenishing all the retailers’ requests to satisfy their demands. The supplier isthe leader of the supply chain, and it provides a sequence of prescheduled epochs for all theretailers to choose their order replenishment timings. The cost disadvantages of the retailersfor joining the replenishment timings shall be compensated by the supplier through quantitydiscounts. In addition, the costs considered in the supply chain include setup and deliverycosts of the supplier, as well as holding and ordering setup costs of the retailers. The objectiveof the supply chain is to minimize the total cost of the supplier.

The importance of coordination in the supply chain is evidenced by the ample growth in thecoordination literature. Fugate, Sahin, and Mentzer (2006) summarize the benefits ofcoordination including cost reduction, risk reduction, profit increase and competitiveadvantages enhancement. Interested readers can refer to Goyal and Gupta (1989), SarmahAcharya and Goyal (2006, 2008), Arshinder, Kandaa and Deshmukh (2008), Chan and Chan(2010), and Chan (2011) for the review of the buyer-vendor coordination models.Replenishment timing is one of the essential issues in inventory management. Heterogeneousretailers pursuing their own optimizations may order at various times. Nevertheless, thevariations on retailers’ replenishment timings significantly increase the cost of the supplier forhandling these replenishment orders. If replenishment orders can be synchronized, therelevant inventory costs can be reduced. Hence, replenishment coordination is one of the areasthat research focuses on supply chain coordination, e.g., Yao and Chiou (2004), Chen and…

Simulation guided value stream mapping and lean improvement : A case study of a tubular machining facility, The ultimate goal of lean manufacturing is to reduce waste in manpower, inventory, time to market, to become highly responsive to customer demand while producing quality products ina most efficient and economical manner (Womack & Jones, 1996). It is well known that seventypes of waste generally occur (Sullivan, McDonald & Van Aken, 2002), as listed in Table 1.Visualizing the flow creates the ability to see where, when, and how both the information andproduct flows through, and consequently recognize and eliminate the source of waste.Unfortunately, many manufacturers have failed to fully understand this in their initialexcitement and eagerness to start with the lean approaches. The implementation of theseapproaches involves more than just applying individual concepts like Kanban, layout planning,visual control, and takt time calculations (Barker, 1994). Typically, such organizationssporadically carry out these activities without linking their efforts to a systematic framework. A critical assessment of many lean tools suggests that a key weakness is absence of visualnature, i.e., the ability of people with knowledge of lean techniques to explain the currentdynamics of the organization, and to communicate an action plan that would be understood by all key stake-holders. There is a need to develop a more systematic means to help guide theselean tools and activities. Value stream mapping (VSM) is vigorously capable of undertaking thisrole. It not only highlights process inefficiencies, transactional and communication mismatches,but also guides the improvement area.A value stream is a collection of all actions (both value-added and non-value-added) that arerequired to bring a product or a product family that use the same resources through the mainflows, starting with raw material and ending with the customer (Childerhouse & Towill, 2002).The VSM is defined as ‘the simple process of directly observing the flows of information andmaterials as they now occur, visually summarizing them, and then envisioning a future statewith much better performance’ (Jones & Womack, 2000). The primary objective of the VSM isto identify all kinds of waste in the value stream and to take actions to eliminate these (Rother& Shook, 1999). While researchers have created numerous lean tools to optimize individualoperations, most of them fall short in linking and visualizing the material and information flowthroughout the entire process (Pavnaskar, Gershenson & Jambekar, 2003). The VSM creates acommon basis for the process, thus facilitates more thoughtful decisions to improve it(McDonald, Van Aken & Rentes, 2002). This helps plan and link lean initiatives throughsystematic data capture and analysis. The VSM has emerged as the preferred way toimplement lean approaches, both inside facilities and at the supply chain level linking thosefacilities (Hines & Rich, 1997). This unique mapping method facilitates visualization of the cycletimes, inventory at each stage, human effort and information flow. The current or ‘as is’ statusis mapped to capture a snapshot of how things are done and where the improvement solutionslie, and the future or ‘to be’ state map is then built to show how things should be doneconsidering potential requirements.For traditional manufacturers, the reluctance to implement lean tools arises since theirdistinctive requirements recurrently make it hard to predict the gain magnitude achieved bydoing so. It is an endeavor also due to the difficulty in changing aspects including raw materialprocurement, inventory management, production control, and facility layout. Therefore,managerial decisions often rely on the reported results of others who have implemented, orheuristic rules of thumb on the expected benefits. It is always an insufficient justification, andlacks the quantifiable evidence needed to convince them to adopt lean (Detty & Yingling,2000). While in some situations the future state map can be evaluated with relatively modesteffort, it is not as straightforward to do so in many others. In general, a complementary tool isimperative for the VSM to quantify the gains during planning and assessment stages.

Integration of Graph Theory and Matrix Approach with Fuzzy AHP for Equipment Selection, The equipment selection problem is essential in manufacturing today because improperequipment selection can negatively affect the overall performance and productivity of amanufacturing system. The outputs of manufacturing system (i.e., the rate, quality and cost)mostly depend on what kinds of properly selected and implemented equipment are used.Selecting the new equipment is a time-consuming and difficult process, requiring advancedknowledge and experience deeply. So, the process can be a hard task for engineers andmanagers, and also for equipment manufacturer or vendor, to carry out. For a proper andeffective evaluation, the decision maker may need a large amount of data to be analyzed andmany factors to be considered (Ayag & Ozdemir, 2006). Although equipment selection plays animportant role in the design of an effective manufacturing system, the publications on thissubject are limited (Kulak, Durmusoglu & Kahrama, 2005). The studies performed could beclassified in to two groups as equipment selection and machine selection. One of the recentstudies is by Standing, Flores and Olson (2001) which uses multi-attribute utility theory toquantify the contribution of various structural and infrastructural factors for an equipmentselection decision. Tabucanon, Batanov and Verma (1994) developed a decision supportsystem for multi-criteria machine selection problem for flexible manufacturing systems (FMS),and used the AHP technique for the selection process. Chen (1999) develops an integerprogramming model and a heuristic algorithm to solve the problem of multiple time periods.Lagrange an relaxation is used to generate lower bounds for the integer programming model toevaluate the quality of the heuristic solution. Machine selection from fixed number of availablemachines is also considered by Atmani and Lashkari (1998), who developed a model formachine tool selection and operation allocation in FMS. Wang, Shaw and Chen (2000)proposed a fuzzy multi-attribute decision making model to assist the decision maker to dealwith the machine selection problem for a FMS. Dellurgio, Foster and Dickerson (1997) presentsa Monte Carlo simulation model for designing and selecting integrated circuit (IC) inspectionsystems and equipment choices. Beaulieu, Gharbi and Kadi (1997) consider the cell formationand the machine selection problems for the design of a new cellular manufacturing systemusing a heuristic algorithm. In addition, the articles for an equipment replacement decisionsare presented by Oeltjenbruns, Kolarik and Kirschner (1995) and Sullivan, Mcdanold and VanAken (2002). Yilmaz and Dagdeviren (2011) used a combined approach for equipmentselection. Their approach is based on F-PROMETHEE method and zero–one goal programming.Safari, Fathi and Faghih (2011) applied fuzzy analytic hierarchy process (AHP) and the fuzzytechnique for order preference by similarity to ideal solution (TOPSIS) methods for theselection of Machine. Integration of Graph Theory and Matrix Approach with Fuzzy AHP , The proposed methods have been applied to Machine selection problemof an Electerofan company in Iran. Li, Wang, Hu, Lin and Abell (2011) utilizes such ahierarchical composition in generating system configurations with equipment selection foroptimal assembly system design. A recursive algorithm is developed to generate feasibleassembly sequences and the initial configurations including hybrid configurations. The

An inventory model of instantaneous deteriorating items with controllable deterioration rate for time dependent demand and holding cost, Inventory System is one of the main stream of the Operations Research which is essential inbusiness enterprises and Industries. Interest in the subject is constantly increasing, and itsdevelopment in recent years closely parallels the development of operations research ingeneral. Some authors even claim that “More operations research has been directed towardsinventory control than toward any other problem area in business and industry” and amongthese the deteriorating items inventory have gain large emphasis in last decade. The inventorysystem for deteriorating items has been an object of study for a long time, but little is knownabout the effect of investing in reducing the rate of product deterioration. So in this paper, aninventory model is developed to consider the fact that the uses of preservation technologyreduce the deterioration rate by which the retailer can reduce the economic losses, improvethe customer service level and increase business competitiveness. Inventory of deteriorating items first studied by Within (1957), he considered the deteriorationof fashion goods at the end of prescribed storage period. Ghare and Schrader (1963) extendedthe classical EOQ formula with exponential decay of inventory due to deterioration and gave amathematical model of inventory of deteriorating items. Dave and Patel (1981) developed thefirst deteriorating inventory model with linear trend in demand. He considered demand as alinear function of time. Goyal and Giri (2001) gave recent trends of modeling in deterioratingitems inventory. They classified inventory models on the basis of demand variations andvarious other conditions or constraints. Ouyang, Wu and Cheng(2005) developed an inventorymodel for deteriorating items with exponential declining demand and partial backlogging.Alamri and Balkhi (2007) studied the effects of learning and forgetting on the optimalproduction lot size for deteriorating items with time varying demand and deterioration rates.Dye and Ouyang(2007) found an optimal selling price and lot size with a varying rate ofdeterioration and exponential partial backlogging. They assume that a fraction of customerswho backlog their orders increases exponentially as the waiting time for the nextreplenishment decreases. In (2008) Roy Ajanta developed a deterministic inventory model when the deterioration rateis time proportional, demand rate is function of selling price and holding cost is timedependent. Skouri, Konstantaras, Papachristos and Ganas(2009) developed an Inventory models with ramp type demand rate, partial backlogging and Weibell’s deterioration rate.Hsu, Wee and Teng(2010)develop adeterioratinginventory policy when the retailer investson the preservation technology to reduce the rate of product deterioration.Mishra and Singh(2010) developed a deteriorating inventory model with partial backlogging when demand anddeterioration rate is constant. They made Abad (1996, 2001) more realistic and applicable inpractice. He, Wang and Lai(2010) gave an optimal production inventory model fordeteriorating item with multiple market demand. Mandal (2010) gave an EOQ inventorymodel for Weibull distributed deteriorating items under ramp type demand and shortages.Chang, Teng and Goyal(2010) gave optimal replenishment policy for non instantaneousdeteriorating items with stock dependent demand. Dye and Ouyang (2011) Studiedadeterioratinginventory system with fluctuating demand and trade credit financing andestablish a deterministic economic order quantity model for a retailer to determine itsoptimal selling price, replenishment number and replenishment schedule with fluctuatingdemand under two levels of trade credit policy. Hung (2011) gave an inventory model withgeneralized type demand, deterioration and backorder rates. Mishra and Singh (2011)developed deteriorating inventory model for time dependent demand and holding cost withpartial backlogging. Leea and Dye (2012) formulate a deteriorating inventory model withstock-dependent demand by allowing preservation technology cost as a decision variable inconjunction with replacement policy. Maihami and Kamalabadi (2012) developed a jointpricing and inventory control system for non-instantaneousdeterioratingitems and adopt aprice and time dependent demand function.The deterioration rate of items in the above mentioned papers is viewed as an exogenousvariable, which is not subject to control. In practice, the deterioration rate of products can becontrolled and reduced through various efforts such as procedural changes and specializedequipment acquisition. The consideration of PT is important due to rapid social changes andthe fact that PT can reduce the deterioration rate significantly. By the efforts of investing inpreservation technology we can reduce the deterioration rate. So in this paper, we made themodel of Mishra and Singh (2011) more realistic by considering the fact that the usepreservation technology can reduce the deterioration rate significantly which help the retailersto reduce their economic losses.

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