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The purpose of this paper is to propose a new integrated method for evaluating inventory of slow-moving items by introducing the application of fuzzy AHP method with interval Type-2 fuzzy sets (IT2FSs) and ABC analysis.

In the study, fuzzy analytic hierarchy process (AHP) method with IT2FSs is employed to set the importance of criteria. The weights obtained from IT2 fuzzy AHP are used to classify slow-moving items in ABC analysis. In the application part, a real-life case study is presented.

The result of this study indicates that an integrated approach utilizing IT2 fuzzy AHP and ABC analysis can be used as a supportive tool for classification of slow-moving items. The problem is solved under fuzzy environment to handle uncertainties and lack of information about slow-moving items.

Actual data are provided from an automotive company for prioritizing a various criteria to evaluate and classify stocks and a hypothetical model integrated with IT2 fuzzy AHP and ABC analysis is demonstrated.

Apart from inventory classification literature, the study integrates fuzzy AHP method by employing interval IT2FSs and ABC analysis to solve the real-life inventory classification problem.

One aspect of forecasting intermittent demand for slow-moving inventory that has not been investigated to any depth in the literature is seasonality. This is due in part to the reliability of computed seasonal indexes when many of the periods have zero demand. This chapter proposes an innovative approach which adapts Croston's (1970) method to data with a multiplicative seasonal component. Adaptations of Croston's (1970) method are popular in the literature. This method is one of the most popular techniques to forecast items with intermittent demand. A simulation is conducted to examine the effectiveness of the proposed technique extending Croston's (1970) method to incorporate seasonality.

A Bayesian approach to demand forecasting to optimize spare parts inventory that requires periodic replenishment is examined relative to a non-Bayesian approach when the demand rate is unknown. That is, optimal inventory levels are decided using these two approaches at consecutive time intervals. Simulations were conducted to compare the total inventory cost using a Bayesian approach and a non-Bayesian approach to a theoretical minimum cost over a variety of demand rate conditions including the challenging slow moving or intermittent type of spare parts. Although Bayesian approaches are often recommended, this study’s results reveal that under conditions of large variability across the demand rates of spare parts, the inventory cost using the Bayes model was not superior to that using the non-Bayesian approach. For spare parts with homogeneous demand rates, the inventory cost using the Bayes model for forecasting was generally lower than that of the non-Bayesian model. Practitioners may still opt to use the non-Bayesian model since a prior distribution for the demand does not need to be identified.

A new approach to the problems of product line strategy and inventory investment decisions

Control charts are designed to be effective in detecting a shift in the distribution of a process. Typically, these charts assume that the data for these processes follow an approximately normal distribution or some known distribution. However, if a data-generating process has a large proportion of zeros, that is, the data is intermittent, then traditional control charts may not adequately monitor these processes. The purpose of this study is to examine proposed control chart methods designed for monitoring a process with intermittent data to determine if they have a sufficiently small percentage of false out-of-control signals. Forecasting techniques for slow-moving/intermittent product demand have been extensively explored as intermittent data is common to operational management applications (Syntetos & Boylan, 2001, 2005, 2011; Willemain, Smart, & Schwarz, 2004). Extensions and modifications of traditional forecasting models have been proposed to model intermittent or slow-moving demand, including the associated trends, correlated demand, seasonality and other characteristics (Altay, Litteral, & Rudisill, 2012). Croston’s (1972) method and its adaptations have been among the principal procedures used in these applications. This paper proposes adapting Croston’s methodology to design control charts, similar to Exponentially Weighted Moving Average (EWMA) control charts, to be effective in monitoring processes with intermittent data. A simulation study is conducted to assess the performance of these proposed control charts by evaluating their Average Run Lengths (ARLs), or equivalently, their percent of false positive signals.

Here's how to eliminate slow‐moving inventory to increase customer service.

Enterprise resource planning (ERP) systems provide functions to calculate safety stock (SS), make demand forecast and determine reorder point (ROP) for each item contained in the database based on the item’s demand history. Most ERP systems are ill‐equipped to deal with the demand of slow moving items such as spare parts. Based on data from a Fortune 500 company, presents the development and evaluation of a spare parts inventory control model. Compares the proposed model with the results achieved using the forecasting and inventory management modules of a popular ERP system. Tested with computer simulation, the proposed model significantly outperforms the commercial ERP model on both measures of service level and expected total annual cost.

In today’s competitive retail industry the most critical success factor is customer service which is indicated by product availability. It is argued that in the retail industry, product availability is an important measure of quality. The single most vital decision that every retailer needs to make is, how to maximize service level while keeping minimum inventory level. The purpose of this paper is to explain and demonstrate the relationship between inventory level and customer service level.

This study examines an inventory system utilizing a simulation model based on company data obtained from a retail fast-moving-consumer goods chain operating in Thailand.

The results suggest that the achievement of a responsive service level is dependent on managing an efficient supply chain in addition to logistics cost reductions. The findings also reveal the effect the inventory level has on the service level. From the findings of this study, demand variability and service level have been found to have the most significant influence on the inventory level. From the findings, it can also be shown that real and accurate information is very important for service supply chains.

The paper promotes the importance of having an appropriate inventory management policy for a retail chain which should be driven by retail companies in order to better balance inventory and service levels.

The relationship between the inventory level and customer service level lead to different outcomes at different combinations of inventory and service levels. Significant relationships were found between inventory and service levels.

The purpose of this paper is to approximate the impact that additive manufacturing (AM) will have on firm inventory performance (IP) and customer satisfaction (CS) when it is applied within the spare parts (SP) supply chain of manufacturing organisations. This research also explores the influence of customer sensitivity (CSy) to price and delivery lead time and supply risk (SR) within those approximations.

An online survey was used to collect the primary data for this research. Data were collected from 69 respondents working for organisations in two industrial segments within the UK manufacturing sector: “Industrial and Commercial Machinery and Computer Equipment” and “Measuring, Analysing and Controlling Instruments, Photographic, Medical and Optical Instruments”. The respondents worked for entities that were categorised in three groups: customers, suppliers, and entities that were both customers and suppliers. The groups that were self-identified as “customers” or “suppliers” answered 20 survey items each and the group that was identified as both “customers” and “suppliers” answered 40 survey items.

The results revealed that AM was considered a suitable vehicle for the fulfilment of SP demand. However, AM appeared to make no material difference to CS; the scenario used improved delivery time of SP but increased price. Also, AM was thought to improve IP through less reliance on buffer stock to manage SR and spikes in demand and less carrying of SP at risk of obsolescence.

The respondents worked for entities within two manufacturing industry segments within the UK and the insights garnered may not be indicative of similar organisations competing in other manufacturing industry segments within the UK or in other countries. In addition, approximately 82 per cent of the surveyed respondents worked for small organisations with fewer than 100 employees and the results may differ for larger organisations. Further limitations were the relatively small sample size and lack of open-ended questions used in the survey. Larger sample size and the usage of open-ended survey questions may lead to more reliable and valuable responses and feedback.

The findings from this research are considered to be of interest to practitioners contemplating adoption of AM and to developers of AM wishing to increase market share due to the positive reaction of entities within the industrial and commercial machinery and computer equipment, and measuring, analysing and controlling instrumentation industrial segments. This research raises awareness to the possible risks and rewards – from a range of perspectives, of AM to practitioners considering its adoption in the spare parts supply chain (SPSC).

The paper takes a novel perspective on AM in SPSCs by illuminating the supplier and buyer perspective based on empirical data. This research provides new insights about the appreciation of the use of AM in SPSCs of mostly small sized manufacturing companies located in the UK. This paper also gives new insights about the willingness/conditions of manufacturing companies in the UK to adopt AM for the provision of SP. The originality of this research is twofold: it broached the applicability of AM in the supply chains of the two targeted industrial segments, and as far as the authors are aware, the influence of CSy (e.g. to price or lead time) and SR on SPSC players’ attitude to AM is yet to be considered. Finally, this research adopted a systems theory lens and considered system-wide impact of AM introduction.

The purpose of this paper is to describe and evaluate the potential approaches to introduce rapid manufacturing (RM) in the spare parts supply chain.

Alternative conceptual designs for deploying RM technology in the spare parts supply chain were proposed. The potential benefits are illustrated for the aircraft industry. The general feasibility was discussed based on literature.

The potential supply chain benefits in terms of simultaneously improved service and reduced inventory makes the distributed deployment of RM very interesting for spare parts supply. However, considering the trade‐offs affecting deployment it is proposed that most feasible is centralized deployment by original equipment manufacturers (OEMs), or deployment close to the point of use by generalist service providers of RM.

The limited part range that is currently possible to produce using the technology means that a RM‐based service supply chain is feasible only in very particular situations.

OEMs should include the consideration of RM in their long‐term service supply chain development.

The paper identifies two distinct approaches for deploying RM in the spare parts supply chain.