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The article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of parts used to repair equipment acquired over many decades. Demand is intermittent, procurement lead times are long, and the total inventory investment is significant.

Demand exists for repair kits, and a repair cannot start until all required parts are available. The cost model includes holding cost to carry the part being modeled as well as shortage cost that consists of the holding cost to carry all other repair kit parts for the duration of the part’s lead time. The model combines deterministic and stochastic approaches by assuming a fixed ordering cycle with Poisson demand.

The results show that optimal service levels vary as a function of repair demand rate, part lead time, and cost of the part as a percentage of the total part cost for the repair kit. Optimal service levels are higher for inexpensive parts and lower for expensive parts, although the precise levels are impacted by repair demand and part lead time.

The proposed model can impact society by improving the operational performance and efficiency of public transit systems, by ensuring that home repair technicians will be prepared for repair tasks, and by reducing the environmental impact of electronic waste consistent with the right-to-repair movement.

The optimization model is unique because (1) it quantifies shortage cost as the cost of unnecessary holding other parts in the repair kit during the shortage time, and (2) it determines a unique service level for each part in a repair kit bases on its lead time, its unit cost, and the total cost of all parts in the repair kit. Results will be counter-intuitive for many inventory managers who would assume that more critical parts should have higher service levels.

The weapon system of The Navy is the small quantity producing system on multiple kinds. It is consisted of various equipment and the subordinate parts of those which can repair the damaged part. The operating procedure concerning warship's repair parts managed under these systems is as follows. Firstly, if demand of repair parts occurs from warship which is the operating unit of weapon, then the Fleet(the repair & supply support battalion) is in charge of dealing with these requests. If certain request from warship is beyond the battalion's capability, it is delivered directly to the Logistic Command. In short, the repair and supply support system of repair parts can be described as the multi-level support system. The various theoretical researches on inventory management of Navy's repair parts and simulation study that reflects reality in detail have been carried out simultaneously. However, the majority of existing research has been conducted on aircraft and tank's repairable items, in that, the studies is woefully deficient in the area concerning Navy's inventory management. For that reason, this paper firstly constructs the model of consumable items that is frequently damaged reflecting characteristics of navy's repair parts inventory management using ARENA simulation. After that, this paper is trying to propose methodology to analyze optimal inventory level of each supply unit through OptQuest, the optimization program of ARENA simulation.

This paper aims to address the supplier selection (SS) problem under dynamic business environments to optimize the procurement cost of spare-parts in the context of a mining equipment company (MEC). Practically, involved parameters’ value does not remain constant as planning periods due to fluctuation in the demand and their market dynamics. Therefore, dynamicity in the parameter is considered as an important factor when a company forms a responsive chain through most eligible suppliers with respect to planning periods. This area of study may be considered for their complexities to the approaches toward order-allocations with bi-products of unused and repair spare-parts.

An integrated methodology of analytic hierarchy process (AHP) and mixed-integer non-linear programming (MILP) is implemented in the two stages during each planning periods. In the first stage, AHP is used to obtain the relative weights with respect to each spare-parts of each criterion and based on that, the ranking is evaluated in accordance with case considered. And in the second stage, MILP is formulated to find the allocations of each spare-part with two distinct approaches through Model-1 and Model-2 separately. Moreover, Model-1 and Model-2 are outlined based on the ranking and efficient parameters-value under cost, limited capacities, quality level and delay lead time respectively.

The ranking and their optimal order-allocation of potential suppliers are obtained during consecutive planning periods for both unused and repair spare-parts. Subsequently, sensitivity analysis is conducted to deduce the key nuggets with the comparison of Model-1 and Model-2 in the changing of capacity, demand and cost per spare-parts. From this analysis, it is found that suppliers who have optimal parameter settings would be better for order-allocations than ranking during the changing planning period.

This paper points out the situation-specific approach for SS problem for a mining industry which often faces disruptive supplying environments. The managerial implication between ranking and parameters are highlighted through Model-1 and Model-2 by sensitivity analysis.

It provides useful directions for managers who are involved in the procurement of spare-parts in the mining environment. For this, suppliers are selected for order-allocation by using Model-1 and Model-2 in the dynamic business environment. The solvability of the model is presented using LINGO 17. Furthermore, the case company selected in this study can be extended to other sectors.

Although the adoption of metal additive manufacturing (AM) for production has continuously grown, in-house access to production grade metal AM systems for small and medium enterprises (SMEs) is a major challenge due to costs of acquiring metal AM systems, specifically powder bed fusion AM. On the other hand, AM technology in directed energy deposition (DED) has been evolving in both: processing capabilities and adaptable configuration for integration within existing traditional machines that are available in most SME manufacturing facilities, e.g. computer numerical control (CNC) machining centers. Integrating DED with conventional processes such as machining and grinding into Hybrid AM is well suited for remanufacturing of metal parts. The paper aims to discuss these issues.

Classical facility location models are employed to understand the effects of SMEs adopting DED systems to offer remanufacturing services. This study identifies strategically located counties in the USA to advance hybrid AM for reverse logistics using North American Industry Classification System (NAICS) data on geographical data, demand, fixed and transportation costs. A case study is also implemented to explore its implications on remanufacturing of high-value parts on the reverse logistics supply chain using an aerospace part and NAICS data on aircraft maintenance, repair and overhaul facilities.

The results identify the candidate counties, their allocations, allocated demand and total costs. Offering AM remanufacturing services to traditional manufacturers decreases costs for SMEs in the supply chain by minimizing expensive new part replacement. The hubs also benefit from hybrid AM to repair their own parts and tools.

This research provides a unique analysis on reverse logistics through hybrid AM focused on remanufacturing rather than manufacturing. Facility location using real data is used to obtain results and offers insights into integrating AM for often overlooked aspect of remanufacturing. The study shows that SMEs can participate in the evolving AM economy through remanufacturing services using significantly lower investment costs.

In this paper, the authors are concerned with a maintenance workshop (MW) centralizing all corrective maintenance activities. The purpose of this paper is to propose a methodology for designing a central maintenance workshop, enabling the evaluation of performance in terms of cost and sojourn time, for a given budget.

The authors propose a modeling framework based on queuing networks. The aim is to maximize operational availability of the production workshop, by reducing the sojourn time of failed equipment in the MW.

The proposed methodology leads to a maintenance decision support tool enabling to give the structure of the MW, performing at a higher level, but at a reasonable configuration cost. Simulation results illustrate the influence of different parameters, such as the number of stations and the level of spare parts in the MW, on the sojourn time of the equipment.

Only corrective maintenance is taken into account and only equipment that can be taken out of the production workshop are considered. The preventive replacement of some equipment items can be taken into account by the repair process by considering them as failed.

The work falls within a more general framework for optimizing maintenance costs, in the context of integration of multi-site services in a distributed context. The paper is concerned with centralized maintenance, and proposes to integrate the so-called repair by replacement technique in a MW, used for a multi-site production workshop.

A study was conducted on a leading US‐based computer maker to examine its reverse logistics operations in the Asia‐Pacific region. This US company had set up a spare parts business unit in Singapore to take care of the Asia‐Pacific customers for its products, which were still under warranty or service contracts. Defective parts were sent to its US headquarters for refurbishment and repair, and subsequently return to the Asia‐Pacific region. The study revealed a number of interesting findings. These included: about 50 percent of the products returned to the USA cost less than half the reverse logistics costs; the current information technology systems supporting the reverse logistics operations are not used in assisting the company’s managers in making critical decisions but in data collection; and decision making on reverse logistics at each of the company’s Asia‐Pacific offices was inconsistent and lacked standardization. Recommendations were subsequently made to overcome some of the inefficiencies in managing the reverse supply chain.

This paper seeks to present a decision‐making model for manufacturers to maximize their profits in reverse logistics operations.

A system dynamic model has been developed to complement with prior models and is validated using data collected from a computer company manufacturer handling returns with volumes transacted over a period of two years.

The results from the model indicate that part replacements from suppliers are more profitable than refurbished computer parts. In addition, transportation delay and supplier delay in processing returns have a significant impact on the viability of reverse logistics regardless of return volumes.

The current model is not designed for third‐party logistics (3PL) offering reverse logistics services. However, this can be accomplished by resetting some of the parameters in the model. The other limitations are exchange rate fluctuation and product depreciation which are not incorporated in the model. This is important in Asia where each country has its own currency which fluctuates with time.

This dynamic model will assist decision‐makers to test new policies related to reverse logistics, for example, liberal versus conservative return policy from supplier, shipment consolidation (longer delays) versus direct shipment, batch (longer delays) versus JIT remanufacturing, pricing of new parts versus re‐condition parts, as well as to examine its long‐term viability.

Using system dynamics to understand the profitability of reverse logistics for both replacement parts to suppliers and refurbished parts to manufacturers.

A Paper presented at the Conference of the International Federation of Airworthiness, the theme of which was ‘maintenance and economics’, and which took place in an atmosphere shadowed by the effects of world oil shortages and escalating oil prices on the costs — and indeed on the whole structure — of airline operations. If this is the most obvious influence upon costs it is certainly not the only factor involved. Raw material prices continue to rise, and certain alloying elements such as cobalt which are essential ingredients of today's high temperature metals are becoming increasingly difficult to obtain. Environmental pressures continue to exercise considerable influence upon the airlines and the aerospace industry, and whilst none would argue the importance of preserving our environment, the task of reducing noise and other forms of pollution from aircraft gas turbine engines cannot be fulfilled without considerable cost to both parties.

The purpose of this paper is to outline the application of a Six Sigma (SS) methodology as a means of reducing supply chain risk in aerospace maintenance repair and overhaul (MRO) functions. In this contribution the LSS method is used to estimate the economic impact on the selection of the most appropriate maintenance strategy pertaining to aircraft display units (DUs) as well as providing a reduction in turn round time (TRT) variation of the DUs.

The paper develops a SS approach which includes the development of the Monte Carlo technique as a mechanism to identify the most cost effective MRO strategy whilst simultaneously reducing variability in TRT servicing of the DUs. This application enabled the authors to obtain further proof of concept and also to apply a number of focused quality improvement techniques to systematically reduce TRT variation.

An effective development of the SS approach is proposed and the effectiveness of the method is subsequently evaluated highlighting the benefits to the host organisation. The SS methodology demonstrates that it is possible to identify the most cost effective MRO strategy and thus suggests a suitable DU replacement policy which in turn allows engineers to develop the appropriate maintenance schedules for the company.

The design, development and implementation of this SS methodology offers an approach to achieving a more cost effective MRO strategy whilst reducing TRT variability which can lead to greater predictability of operations which in turn enables the company to effectively synchronise supply with demand. The paper offers practicing maintenance managers and engineers a practical example for increasing productive efficiency and output.

This SS strategy contributes to the existing knowledge base on maintenance systems and subsequently disseminates this information in order to provide impetus, guidance and support towards increasing the development companies in an attempt to move the UK manufacturing sector towards world class manufacturing performance.

Service part inventories cannot be managed by standard inventory control methods, as conditions for applying the underlying models are not satisfied. Nevertheless, the basic questions of inventory control have to be answered: Which parts should be stocked? Where should they be stocked? How many of them should be stocked? Presents a case study in which a pragmatic but structured approach is followed – a framework is developed and built into a spreadsheet. The resulting tool has been tested in a real‐life situation, indicating that considerable amounts of money can be saved.