Inventory Lot Sizing Issues - Assignment Example

Inventory Lot Sizing Student’s Name: Instructor’s Name: Course: Date: Table of Contents i.Introduction 3 1.0.Background information 3 1.1.Inventory Management 4 1.2.Single Product Model 6 1.3.Multiple Product Model 8 1.4.Quantity Discount Model 9 2.0.Model Solution Techniques 10 2.1.Heuristic / Mathematical Algorithms Solutions 10 2.2.Non-Linear Optimisation 11 2.3.New Lagrangian Heuristic Solution 12 3.0.Conclusion 13 Inventory Lot Sizing i. Introduction 1.0. Background information Researches on inventory lot sizing remain multifaceted and scope of findings on the topic has captured different aspects including single item economic lot-sizing problem with regard to bounded inventory as well as lost-sales. Previous studies that attempted to contextualize inventory lot sizing within supply chain management included Nia, Far and Niaki (2014) who called for the need to bring together different facets of distribution facilities and options that can help productivities to obtain materials and undertake processing into the final product. Contrariwise, background information on inventory lot sizing stretches beyond what Nia, Far and Niaki (2014) proposed. Previous studies from Jaber and Bonney (1996) noted that from history, the concept has emanated from the globalized economy as well as liberation of market place. Studies such as Gutiérrez et al. (2003) recognized that lean productions were interested in development of coordination of different processes including item production material outsourcing and returning processes. As a result, inventory lot sizing was viewed to have sprung due to globalized economy as well as liberation of market thus enabling productions to incorporate different production uncertainties as well as financial risk into firms’ inventory control decisions and different levels of production. On the other hand, background information from scholars who have looked at inventory lot sizing from the perspective of lean production argue that the concept was motivated by the challenges of scheduling the shutting down of nuclear reactors when systems undergo maintenance (Chen and Thizy, 1990; Federgruen and Tzur, 1991). While there is paucity of research data to support this position, it forms background information in understanding elements such as mathematical algorithms solutions which are important factors in inventory management. Conversely, researches that have evaluated inventory lot sizing for the last three to four decades have been motivated primarily with the need to establish a model that saves cost for enterprises who for long, were seen to be struggling to maintain relevant inventory levels so as to boost their images through customer satisfaction and to cope with stochastic customer demands (Atamtürk and Küçükyavuz, 2005; Balakrishnan et al., 2004). Still, there is need to establish the stretch of inventory lot sizing by integrating supply chain as a network of companies or firms producing, selling and delivering services or products to a predetermined market segment. As a result, this report provides succinct understanding of supply chain, providing the location of inventory as part of what forms the basis of supply chain. 1.1. Inventory Management Inventory management is the precursor of inventory lot sizing and further provides an understanding of figures that show place of location of inventory. From research based evidences, inventory management is the concept in supply chain management that permeates processes of decision-making in different firms (Van Den Heuvel and Wagelmans, 2008). It is for this matter that management of relationships between sellers and buyers remain central in supply chain. As matter of fact, studies that have focused on the need to keep this relationship concluded that it is the core practice in supply chain management (Bowersox, 2002). Despite this position, the argument presented by Bowersox (2002) fails to inculcate the aspect of place location of the inventory as one factor that involves a set of decisions aimed at matching firms’ demands with the supply of products as well as materials over space and time. Currently, inventory management is focusing more attention to ways of attaining a given service level objectives, operations, observing product and demand characteristics. These new developments call for establishment of role of inventory in supply chain management. In such connectedness, figure 1 below provides an understanding of the relationship between different variables of inventory management including the place of the inventory. Figure 1: Product Process from Raw to Finished Materials Source: ioscm.com Figure 1 above provides further understanding on the role played by inventory in the management of supply chains. From the one hand, it establishes the stability of demand and supply. On the other hand, it provides baseline for organization as they can deal with demands from customers and suppliers thus managing onward and backward on inventory in processes of supply chain (Christopher, 2016). Contrariwise, the position Christopher (2016) holds may not conform to specific needs of inventory management including patters of demand which is the main intervening factor in inventory management. 1.2. Single Product Model Over years, single product model has been looked in terms of inventory models of products that perish (Arslan et al., 2007; Pasandideh et al., 2011). The challenge with these scholars, and Arslan et al. (2007) in particular, is drawing many assumptions including one stating that, ‘the product is having a fixed lifetime and if there is lack of demand occurring for that product within its life time then such product will be considered perished or removed from the inventory’ (Arslan et al., 2007 p. 216). One of the weaknesses that can be drawn from the perspective these scholars are having is that while they recognize deteriorating or decaying inventory models, they do not consider the fact that the decaying or deteriorating items are having one essential kind of interaction on the processes of demand in the sense that, additional to the demand that has been there, there may be a separate level of demand for some items that are slightly decayed in quality especially if the cost is seen to be comparatively lesser in a new one. Zeballos et al. (2013) have attempted to argue in this line, there points of consideration have merely been in terms of advancement in technology and competition which they recognize to be playing important role in shrinking life cycles. Conversely, single product model are becoming sophisticated owing to the fact that firms are now seen to be implementing strategies aimed at explaining and combining their supplier base. Studies that have argued in this direction have noted that in most cases, vendor initiate programs to create a single point strategy for products (Duan et al., 2014; Gaiardelli et al., 2014). Taking a case study from firms such as Coca-Cola, Gaiardelli et al. (2014) argue that this approach has allowed firms to come up with robust and concerted relationships that provide different benefits including reduced cost through developed bargaining power. There is a challenge however with Gaiardelli et al. (2014) arguments in the sense that these scholars are mainly concerned with a single point strategy forgetting that a continuous review of decaying inventory models has been found to be with the assumption that if there is lack of demand for a given product in inventory then the same product can pass through two distinct phases before it perishes: an item in phase one is fresh and in phase two it is considered slightly perished. In as much, the argument regarding a single point strategy has been put forward well by scholars who agree that the approach allows firms to build robust and concerted relationships that provide several benefits including reduced costs through developed bargaining power; better innovation and collaboration of design; enhanced information exchange and plan harmonization; better responsiveness of supplier (Swink et al., 2014). Based on these researches, there is possibility of drawing two distinct conclusions. Firstly, there is possibility of developing a single-product inventory model that can serve multiple demand classes and still, differ in their service-level requirements or shortage cost. Secondly we conclude that a single product inventory model serving several market classes could be one such model used in supply chain management. 1.3. Multiple Product Model Unlike single product model, multiple product model needs development of a set of unique functions that captures aspects such as nonlinear link between the lot sizes, output and available work in process inventory levels pertaining to all products in the system. Studies that have focused on multiple product model have premised their thesis statements on risks in supply chain management. In specific, Srinivasan and Swink (2015) argue that multiple suppliers provide ground for less dependency on a single vendor thus reducing different risks. This view has also been supported by Gaiardelli et al. (2014) who found that multiple product model allows for approximation of optimal solutions to be obtained and further compare them to aspects such as convectional lot sizing model without creating congestion. However, these views may not conform to current changes in multiple product model including the need to consider multi-product dynamic lot sizing model. Further to this point, Srinivasan and Swink (2015) even fail to recognize the fact that their view ignores increase in other risks of supply chain such as management or contractual risks, and quality. Contemporary studies now assess multiple product model with regard to whether dependency is mutual or lopsided (Kim et al., 2014). Kim et al. (2014) posit that for decades, firms have adopted strategies that help them rationalize as well as consolidate their supplier base. To that extent such firms have ended up creating a single sourcing. As a result, stronger collaborative relationships have been developed to deliver a range of benefits including improved innovations, improved supplier responsiveness decreased efforts in tracking performance. The positions Kim et al. (2014) have augur well with processes of globalization that has since changed the phase of multiple product model. For example, if an issue arose and the company lacks capacity, it is likely that the supplier will respond to other customers first. There is agreement among studies that multiple sourcing may help curb the risks making it more advantageous to the buyer as demand would spread through many suppliers that would be more able to respond to the purchaser collectively (Vugrin et al., 2015; Kang et al., 2014). However, Bajwa et al. (2016) are having different findings on whether mutual dependency helps the customer and supplier have high regard to the relationship. The bottom line of the argument is straightforward; that incase demand changes, the supplier will respond better and will be more flexible to address the needs of the buyer. The findings from Vugrin et al. (2015); Kang et al., (2014); Bajwa et al. (2016) may not be applicable in all situations as these studies ignore the fact that diverting and splitting demands will make it difficult for a supplier to commit resources to enhancing the quality of products or capacity matters. Their arguments further fail to challenge the fact that in case partners have a lopsided relationship, a multiple supplier sourcing strategy will lessen the risk of the supply chain. 1.4. Quantity Discount Model The manifestation of quantity discount model is two-fold; all-units discounts and incremental discounts. Buyer company offer discounts upon considerations of benefits of the discount of the purchase price and the lesser orders against the high costs in transport caused by higher inventories (Taleizadeh et al., 2013). The point of concern that researchers have had regarding this model is its modification of the processes of economic order quantity in the identification of areas in which discounts are available. One concept that stands out with regard to quantity discount model is that it adopts the aspect of economic order quantity. One challenge is overreliance on economic order quantity rather than giving attention to the fact that in this model, the unit price highly depends on quantity that has been ordered. 2.0. Model Solution Techniques Ranging research topics that have been witnessed for the last few decades regarding model solutions techniques is the solution on problems related with supply chains with some thesis statement reflecting on mathematical realm (Yin et al., 2015; Golicic and Smith, 2013). The views from these studies have been concentrated on models that offer solutions to getting the product to the customer cost effectively. As a result, mathematical algorithms have been suggested with the view of finding the balance between scientists view point and practitioners. In as much as Golicic and Smith (2013) integration of mathematical algorithms abodes well with challenges inherent in supply chain management, models of solution techniques requires an approach that leans more on heuristics rather than linear programming since doing so will provide an opportunity to solve problem using an array of equations and apply computer technology to solve different problems. This view has been supported by Wisner et al. (2014) adding that heuristics eventually finds the solution to the problem that has been inherent in model of solution techniques. Wisner et al. (2014); Golicic and Smith (2013) fail to mention that linear programming is rigorous thus making their point of argument invalid owing to the fact that many supply chain issues are not straight line but non-linear. 2.1. Heuristic / Mathematical Algorithms Solutions From the definition provided by Srinivasan and Swink (2015), heuristic/mathematical algorithms solutions refers to a level of artificial intelligence or mathematical optimization process that has been designed to offer solutions promptly as compared with the classic approaches deemed to be slow. Unlike model solution techniques, heuristic provides succinct link between problems inherent in supply chain and non-linear nature of supply chain (Ellram and Cooper, 2014). Studies agree that heuristic / mathematical algorithms solutions provide advantage when finding a good solver that abides by all the challenges and constraints that are involved in businesses (Vugrin et al., 2015; Srinivasan and Swink, 2015). These studies have even focused on the aspect of ‘possible combination’ thus adding that this model is fit for organisations looking for quick solutions. There is however, paucity of information from studies such as Vugrin et al. (2015) who fails to mention the result effect of this model when one applies a discrete rule or decision. In as much as Srinivasan and Swink (2015) present that when discrete rule is applied, the algorithm may overlook solutions that might be better, its applicability in supply chain is not captured by Srinivasan and Swink (2015) owing to the fact that long-term strategic business problems use linear programming methods whereas short-term business planning problems use heuristics. 2.2. Non-Linear Optimisation Within the context of economics, Cacchiani et al. (2013) define non-linear optimization as the approach of providing economic solutions from problems that have been defined by the system as constraints. These constraints are related with unknown real constants or variables in economics. The agreement that studies have reached regarding non-linear optimization is that if there is a bunch of work orders that require running on that asset, each of the orders will have different attributes the traders should be able to use an algorithm to identify the best sequence (Shabani and Sowlati, 2013; Golicic et al., 2013). However, this conclusion does not reflect the scope of non-linear optimization in the sense that these studies merely base such conclusion on features of the orders. Functionalities of non-linear optimization need to ascertain issues such as priorities and requirements from customers. It is known basing on Shabani and Sowlati (2013) argument that additional constraints will increase the problem making processes non-linear. However, these studies need to ask questions such as is labor or special materials required…do the customers have special priorities or requirements? As such questions may invalidate their argument that more constraints will require more rehearsals on testing. 2.3. New Lagrangian Heuristic Solution From the one hand, this approach addresses the main features of economic problem. On the other hand, Cacchiani et al. (2013) defines New Lagrangian Heuristic Solution as the relaxation method which can approximate a different difficult economic problem of constrained optimnisation. Development of automobile manufacturing industry has led to the development of task allocation problem (TAP) thus task allocation problem comes from different computing systems. As Cacchiani et al. (2013) put it; task allocation problem assigns a group of tasks to processors to enable them in the optimization of costs. The view presented by Cacchiani et al. (2013) needed to collaborate programming algorithm especially for problems that have been associated with capacitated economic lot size with piecewise linear production as well as general holding costs. There is need to apply this method in general problems as it is able to solve different problems. It is for this case that algorithm can solve many problems at reasonable time. 3.0. Conclusion This study sought to critically provide deeper understanding of inventory lot sizing basing on aspects such as single product model, multiple product models and quantity discount model among other concepts. Based on different research findings, evidences provide that uncertainty affects the performance of a company negatively. On the other hand, this uncertainty can be curbed if the firm establishes relationships with suppliers hence implementing strategies that allow them to handle environmental uncertainties to perform adeptly. There is need for further research into issues such as quantity discount model since factors like government support have become hindrance to supply chain management. 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