Project Abstract

<p> As the bar for service excellence keeps rising, especially in the request of shorter lead times, higher service levels, lower costs and better customer service support, the conventional models of spare parts inventory control are increasingly becoming inadequate. Therefore, to tackle this challenge, in this study, three novel models of spare parts inventory control have been formulated, developed and packaged into a multi-model and multi-purpose engineering computer software, called U-SPIC. Model 1 used mathematical analysis to integrate 7 spare parts inventory policies together. Model 2 integrated the same inventory policies of Model 1, using stochastic simulation while Model 3 expanded Model 2 by considering bulk demand and supply using stochastic simulation. Chi-square goodness of fit inference statistical technique was employed in the preliminary design to check the reasonableness of using Poisson distribution for the demands and it gave 86% success. Composite stepwise two dimensional graphical representations of the models were formulated, which captured the stochastic demands and stepwise state transitions. The inverse transform algebraic method was applied for the generation of random numbers while next event method was used for the time advancement of the simulation clock. Traces and structured walk through were utilized for debugging the stochastic simulation models. Batch mean method was used in determining the confidence intervals of the simulation models, with 105 days run time and 100 replications each. The developed models results were validated with the case study (ANAMMCO) software package called IDIS which uses standard (r;Q) inventory policy. Beyond that, the models results were compared via an extensive simulation approach. 19 sensitivity parameters were varied in the study, where at each instance of variation, the behaviour of the fill rate of demands, as well as backorders (i.e. with regard to its average number, mean response time and maximum queue length) were analysed. On the average a saving of 18.51% demands in comparison with the conventional models was found, which indeed will result in huge cost savings in absolute terms. Beyond that, the insights from these models will increase the overall efficiency of spare parts inventory control. <br></p>

Project Overview

<p> </p><p>INTRODUCTION</p><p>1.1 Spare Parts Inventory Control – Meaning<br>To establish a common understanding, ‘Spare parts’ refers to the<br>parts requirement for keeping both owned equipment/machine or<br>service needs of customers in healthy operating condition by<br>meeting repair and replacement needs imposed by breakdown and<br>preventive maintenance. The term spare parts in this study<br>therefore, is used to connote both spare parts and service parts<br>as applied to a firm handling both internal and<br>external spare and service needs. On the other hand, ‘Inventory<br>Control’ refers to the management of the supply, storage and<br>accessibility of items, in this case spare parts, in order to<br>ensure an adequate supply without excessive supply.</p><p>Spare parts inventory models differ substantially from regular<br>inventory models. The key reason for this difference is that<br>spare parts provisioning is not an end in itself, but a means to<br>guarantee up-time of equipment. With respect to spare parts<br>inventory, the customer’s sole interest is that his systems are<br>not down due to lack of spare parts because equipment downtime is<br>lost production capacity.<br>1.1.1 Large Revenue and Investment on Spare Parts Inventory<br>1<br>2<br>In today’s technological environment, the importance of after<br>sales service which basically concerns the use of spare parts for<br>maintenance purposes, is high. Lost revenues due to disservice<br>are enormous. Not only is after-sales service valuable as a<br>competitive advantage for manufacturers and service providers,<br>direct revenues in this service are also remarkably high.<br>Companies that provide the after-sales service have to invest a lot on spare parts inventory. In 2006, Koudalo1 investigated<br>revenues of spare parts in the service business over a period of<br>one year, and he reports combined revenues of more than $1.5 trillion. Flint2 stated that the world’s spare parts inventory in<br>the aviation industry in 1995 amounted to $45 billion at that<br>time. Any means to downsize this stock, without decreasing<br>customer service, would be more than welcomed by the aviation<br>industry. Also in other industries, large amounts of money are<br>invested in spare parts inventory and this has increased over the years. Heather3 reported that the spare parts market of U.S.<br>represents $700 billion and 8 percent of the U.S. gross domestic<br>product. Many manufacturers find that profit margins for services<br>can top 40 percent, whereas margins for finished goods top out at around 13 percent. Cohen et al4 and AberdeenGroup5 also report<br>that profitability in service is much higher than profitability<br>for initial products. Because of these large amounts of money<br>involved, savings of a few percent only constitute large cost<br>savings in absolute terms.</p><p>The above indicates that the control of spare parts for after<br>sales service deserves substantial corporate attention, which is<br>even more true, since customer requirements have tightened. AberdeenGroup5 indicates that 70% of the respondents in its study<br>have seen service response times as required in service level<br>3<br>agreements shrinking to 48 hours or less, and Koudalo1 states<br>that customers keep raising the bar for service excellence by<br>requesting shorter lead times, higher service levels, lower<br>costs, and better customer service support.</p><p>1.1.2 Overview of the Case Study<br>The first insight on the importance of spare parts inventory<br>control by the researcher was made while carrying out another study, Okonkwo6, on stochastic queueing behaviour of vehicles in<br>a maintenance workshop which eventually resulted in the<br>development of a computer software: Ugoo Multi-Purpose Computer<br>Qeueuing Model Simulator (Ugoo MC-QMS).</p><p>However, the primary motivation that finally triggered off this<br>research is an experience with the spare parts complex of a<br>leading motor assembling/manufacturing company in Nigeria. The<br>Anambra Motor Manufacturing Company (ANAMMCO) Enugu, Nigeria –<br>This company is a product of a joint venture of the Federal<br>Government of Nigeria and Daimler-Chrysler of Germany, and was<br>commissioned in 1980. The spare parts complex of ANAMMCO provides<br>considerable after-sales service which is impacted significantly<br>by the spare parts control. The company has a very large spare<br>parts complex that stores and manages spares various models of<br>Mercedes Benz heavy duty vehicles. Specifically, besides the<br>selling of vehicles, the spare parts of various models of heavy<br>duty vehicles listed below are stored and managed by the company.</p><p>Trucks: MB-711, MB-1418, MB-1520, MB-1518, MB-1720, MB-1620,<br>MB-1718, MB-1634, MB-2423<br>Actros: MB-2031, MB-2035, MB-3340, MB-4031<br>4<br>Freightliner: MB-M21126X4, MB-M21124X2<br>Axor: MB-1823<br>Buses: MB-712, MB-812, MB-1721, MB-O400, MB-O500</p><p>The management of these models which is complex was further<br>complicated by the vast number of parts required in each model.<br>In fact, more than 30,000 active parts needed to be controlled.<br>The management of these parts can only be done with the aid of a<br>computer, hence the spare parts complex has a computerized spare<br>parts inventory database. Each of the parts that is supplied or<br>replenished is continuously keyed into the computer and the<br>inventory stock parameters are updated automatically.</p><p>The company uses two software for its inventory control. The<br>first is the Electronic Parts Catalogue (EPC) which is used to<br>identify the part number of the spare parts. Once the engine and<br>chassis number is inputted, it invokes a dialogue box from where<br>spare parts section is selected and from the pull down menu, the<br>particular spare part is chosen. The software will search and pop<br>up the part number of the spare part, a 3-D AutoCAD drawing of<br>the required part, a CAD drawing guide on how it can be fixed<br>into the vehicle and in some cases an alternative part to be used<br>in case the said part is out of stock. From the part number, the<br>location of the spare parts in the stock room is identified. The<br>second software is Integrated Dealer Importer System (IDIS). It<br>is a software that determines the stock level for each part in<br>the stock complex. It has a database showing the orders and<br>replenishments that have been made. It also indicates when to<br>replenish and the quantity. It uses continuous review (r,Q)<br>inventory policy. It should also be noted that the complex<br>5<br>observes the well known A-B-C classification in its spare parts<br>inventory.</p><p>The company faces two major demands of spare parts from the<br>complex, the first is demand from the maintenance section of the<br>company. The second is from the external customers that directly<br>buy spare parts from the complex for their personal use. Demand<br>from the maintenance section is as a result of spare parts<br>demands for maintaining their vehicles, for maintaining after<br>sales service of vehicles whose owners had service level<br>agreement with the company as well as those that just take their<br>vehicles to their maintenance workshop for either regular<br>servicing or for repairs when they have broken down completely.</p><p>Notwithstanding the fact that the company’s inventory system is<br>computerized, yet the computerized system does not observe<br>service differentiation through rationing and demand lead time.<br>However, in some exceptional cases, the company observes demand<br>lead time manually though, But, more than ever before, this<br>method can no longer withstand the challenges of modern standards<br>of spare parts inventory control. These standards have risen to<br>such levels that it is difficult, if not impossible to attain it<br>by manual form of optimization.</p><p>Therefore, this study provides improved models which when<br>implemented, find solution to the company’s spare parts network<br>challenge. These models will not only provide immediate and<br>significant benefit to the company under study, but can be<br>adapted to very many other systems.</p><p>1.1.3 Introduction to Service Differentiation<br>6<br>In spare parts inventory, just as different customers may require<br>different product specifications, they may also require different<br>service levels. For instance, for a single product, different<br>customers may have different stockout costs and/or different<br>minimum service level requirements or different customers may<br>simply be of different importance to the supplier by similar<br>measures. Therefore, it can be imperative to distinguish between<br>classes of customers thereby offering them different services. In<br>this setting, different product demands from different customers<br>can no longer be handled in a uniform way. This, in turn, gives<br>rise to multiple demand classes and customer differentiation.</p><p>In this system of multiple demand classes the easiest policy<br>would be to use different stockpiles for each demand class. This<br>way, it would be very easy to assign a different service level to<br>each class. Also the practical implementation of this policy<br>would be relatively easy and will require less mathematical<br>analysis. But the drawback of this policy is that there is no<br>advantage from the so-called portfolio effect. In other words,<br>the advantage of pooling demand from different demand sources<br>together would no longer be utilized. Therefore, as a result of<br>the increasing variability of demand, more safety stock would be<br>needed to ensure a minimum required service level which in turn<br>means more inventory.</p><p>On the other side, one could simply use the same pool of<br>inventory to satisfy demand from various customer classes without<br>differentiating them. In this case, the highest required service<br>level would determine the total inventory needed and thus the<br>inventory cost. The drawback of this policy is that higher<br>service level will be offered to the rest of the demand classes,<br>7<br>a deficiency that would lead to increased inventory costs.<br>Critical level policy essentially lies between these two<br>extremes. It requires complex mathematical analysis, but the<br>gains outweigh the task involved.</p><p>In the existing practice, the company studied failed to exploit<br>service differentiation (demand classes) of the various<br>customers. The company targets to achieve the maximum of the<br>service level requirements while considering the aggregated<br>demand. Moreover, the company does not recognize the possible<br>demand lead times (the difference between requested date and<br>shipment date of the request) for lead time orders. This study<br>develops spare parts inventory models that recognize the demand<br>lead times, multiple demand classes, allow for providing<br>differentiated service levels through rationing, as well as<br>optimizes the generated policy parameters, notwithstanding the<br>complex analysis that it entails.</p><p>1.2 Statement of the Problem<br>The complexities and the growing criticality of spare parts<br>inventory control in manufacturing and service operations are on<br>the increase. Factors like demand unpredictability, parts<br>indigenization, high service levels, large investments on and<br>revenues from parts, the imperative to accurately forecast spare<br>parts requirements and to optimize existing inventory policies<br>require significant decision support. This decision support can<br>only be achieved from the results generated from more efficient<br>novel decision models.<br>8</p><p>Unfortunately, many researchers from the third world shy away<br>from developing this type of models. Those who delve into it<br>limit themselves to the development of spare parts inventory<br>control database, using conventional models. These conventional<br>models are increasingly becoming ineffective in tackling spare<br>parts inventory control problems. On the other hand, the<br>advanced countries that have done a lot of work with regards to<br>developing novel spare parts inventory control models have not<br>been able to integrate either the 7 spare parts inventory<br>policies as was done in Models 1 and 2 of this study, or 9 spare<br>parts inventory policy as was done in Model 3 of this study, in<br>any of their developed models. The spare parts inventory policies are listed in the objective of the study in section 1.3.</p><p>9<br>1.3 Objective of the Study<br>The objective of the study embraces the following:  Development of a novel analytical model (Model 1)<br>This integrates 7 spare parts inventory policies together.<br>The policies are continuous review, one to one lot, service<br>differentiation and rationing, backordering, demand lead<br>time, priority clearing mechanism and bounded enumerative<br>optimization.  Development of novel stochastic simulation models (Models 2 and 3)<br>Model 2 integrates the same 7 spare parts inventory policies<br>of model 1 using stochastic simulation while model 3 expands<br>model 2 by considering in addition to the to the policies of<br>modal 2, bulk demand and bulk replenishment of spare parts<br>using stochastic simulation.  Showcasing new insight in the behaviour of backorders of spare parts inventory This is with regards to its maximum queue length,<br>mean response time and average number in the system.  Establishment of the magnitude of cost savings<br>This is done by the application of service differentiation<br>through rationing and demand lead time.  Formulation of composite graphical representations of the models This is for pedagogical purposes.  Proposal of the models to the Management of ANAMMCO This is for possible interfacing with their already existing<br>computer spare parts inventory model.  Uploading the active software to the internet<br>This is for easy subscription, access and run, from any part<br>of the world.<br>10<br>1.4 Significance of the Study<br>The envisaged significance of this study is laid on its applied<br>nature mainly. That is, the output results from these models have<br>foreseeable potentialities for immediate practical applications<br>to the on-going challenge of achieving above 99% service level at<br>minimum stock.</p><p>Specifically, the models can easily be applied in spare parts<br>inventory control Industries/Companies and even Institutions, for<br>the following purposes:</p><p>1. The models can be applied to the management of the spare<br>parts inventory system, requiring both preventive and<br>breakdown demands of spare parts.</p><p>2. Industries that have contractual agreements for servicing<br>machines/vehicles/airplanes with some of its customers can<br>also find the models very useful for the management of its<br>inventory. An example is an airline industry that has<br>contractual agreement with its major and minor airlines of<br>differentiated service levels.<br>3. Spare parts inventory systems that do not recognize both<br>service differentiation and positive demand lead time can<br>equally make use of these models. What is required is just<br>to remove the service differentiation and demand lead time<br>by setting critical service level and demand lead time to<br>zero value, accomplished through pressing few clicks on the<br>graphical user interface of the multi-model software<br>package.<br>4. Managing inventories for spare parts of equipments of<br>different criticality can make use of the models. In this<br>11<br>case the equipment criticality will determine the service<br>level.<br>5. The building blocks that will be provided in this study can<br>be adapted to solving other real-life spare parts inventory<br>control problems.<br>6. Finally, it can be very useful as an effective and<br>interesting spare parts inventory pedagogical tool, both in<br>the academic and commercial Institutions.</p><p>1.5 Scope and Limitations<br>The study of the operating realities of the Spare Parts Complex<br>of Anambra Motor Manufacturing Company Limited (ANAMMCO) informed<br>this work.</p><p>The thrust was on the continuous review inventory models, which<br>in any case are better than periodic review for spare parts<br>inventory. For effective control, continuous review models<br>require companies whose inventory database systems are<br>computerized.</p><p>The study did not set out to develop database inventory models<br>but the decision-support models using mathematical and simulation<br>approaches. These models will be compatible with the case study<br>inventory databases and indeed should be easily interfaced with<br>standard databases of leading software manufacturers Like<br>Microsoft and Oracle Corporations.</p> <br><p></p>