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Intermittent demand appears sporadically, with some time periods not even displaying any demand at all. Even so, such patterns constitute considerable proportions of the total stock in many industrial settings. Forecasting intermittent demand is a rather difficult task but of critical importance for corresponding cost savings. The current study aims to examine the empirical outcomes of three heuristics towards the modification of established intermittent demand forecasting approaches.

First, optimization of the smoothing parameter used in Croston's approach is empirically explored, in contrast to the use of an a priori fixed value as in earlier studies. Furthermore, the effect of integer rounding of the resulting forecasts is considered. Lastly, the authors evaluate the performance of Theta model as an alternative of SES estimator for extrapolating demand sizes and/or intervals. The proposed heuristics are implemented into the forecasting support system.

The experiment is performed on 3,000 real intermittent demand series from the automotive industry, while evaluation is made both in terms of bias and accuracy. Results indicate increased forecasting performance.

The current research explores some very simple heuristics which have a positive impact on the accuracy of intermittent demand forecasting approaches. While some of these issues have been partially explored in the past, the current research focuses on a complete in‐depth analysis of easy‐to‐employ modifications to well‐established intermittent demand approaches. By this, the authors enable the application of such heuristics in an industrial environment, which may lead to significant inventory and production cost reductions and other benefits.

The study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.

This work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.

This research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.

This research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.

This research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.

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.

These days vehicles' spare parts (SPs) are a very big market, and there is a very high demand for these parts. Forecasting vehicles' SPs price and demand are difficult because of the lack of data and the pricing of the SPs is not following the normal value chain methods like normal products.

A proposed model using multiple linear regression was developed as a guide to forecasting demand and price for vehicles' SPs. A case study of selected hybrid vehicle is held to validate the results of the research. This research is an original study depending on quantitative and qualitative methods; some factors are generated from realistic data or are calculated using numerical equations and the analytic hierarchy process (AHP) method; online questionnaire and expert interview survey.

The price and demand for SPs have a linear relationship with some independent variables is the hypothesis that is tested. Even though the proposed models are generally recommended for predicting demand and price, in this research the linear relationship models are not significant enough to calculate the expected price and demand.

This research should concern both academics and practitioners since it provides new intuitions on the distinctions between scientific and industrial world regarding SPs for vehicles as it is the first study that investigates price and demand of vehicles' SPs.

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 recent decades there has been much interest and activity in the application of mathematical ideas for controlling inventory. However most of this has been related to the control of stock products whose demand is smooth and continuous. When demand is lumpy these methods are inefficient in their attempts to minimise thé operating cost. A simple regression model is developed for computing optimal (s, S) policies for items with lumpy demand patterns. Continuous review of inventory level is assumed and the lead time demand is approximated by the stuttering Poisson distribution. A grid of 864 known optimal policies has been used to provide the data for the calibration of the regression models. Numerical models are used to illustrate this approach. Extensive computational results show that this model provides excellent performance in estimating the optimal values of the control parameters s and S for wide ranges of demand and cost parameters.

Presents a new taxonomy for the non make‐to‐stock sector to enable a like‐with‐like comparison, arguing that existing taxonomies within the literature are inadequate for strategic research purposes. Presents empirical evidence which has been collected from 22 companies in three European countries – the UK, Denmark and The Netherlands. The data support the structure of the proposed new taxonomy and provide insights into competitive advantage and customisation issues in the non make‐to‐stock sector. Finally, two new labels for this sector of industry are proposed. “Versatile manufacturing company” is used to describe those manufacturers which are involved in a competitive bidding situation for every order which they receive, customisation by individual order. In contrast, the “Repeat business customiser” may only be in this position for the first of a series of similar orders from a particular customer, customisation by contract.

Real operational data are used to optimise the performance measurement of air cargo capacity demand management at Virgin Atlantic Cargo by identifying the best KPIs from the range of outcome-based KPIs in current use.

Intelligent fuzzy multi-criteria methods are used to generate a ranking order of key outcome-based performance indicators. More specifically, KPIs used by Virgin Atlantic Cargo are evaluated by experts against various output criteria. Intelligent fuzzy multi-criteria group making decision-making methodology is then applied to produce rankings.

A useful ranking order emerges from the study albeit with the important limitation that the paper looked solely at indices focussing exclusively on outcomes while ignoring behavioural complexity in the production of outcomes.

This paper offers a practical overview of the development of performance measures useful for air cargo capacity demand management.

This paper conducts a Systematic Literature Review (SLR) of Machine Learning (ML) in Supply Chain Management through bibliometric and network analysis, the authors are able to grasp key features of the contemporary literature. The study makes use of state-of-the-art analytical framework based on a unified approach to reveal insights from the present body of knowledge and the potentials for future research developments.

Unlike standard literature reviews, in SLR, a structured approach is followed. The approach enables utilizing contemporary tools and software packages such as R-package “bibliometrix” and Gephi for exploratory and visual analytics. A number of clustering methods are employed to form clusters. Later, multivariate analysis methodologies are adopted to determine the dominant clusters for the influential co-cited references.

Using contemporary tools from Bibliometric Analysis (BA), the authors identify in an exploratory analysis, the influential authors, sources, regions, affiliations and papers. In addition, the use of network analysis tools reveals research clusters, topological analysis, key research topics, interrelation and authors’ collaboration along with their patterns. Finally, the optimum number of clusters computed for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis.

Modern-day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent ML algorithms. Through a BA followed by a detailed data visualization in a network analysis enabled grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure.

The purpose of this paper is to identify the strengths and weaknesses of the current spare parts replenishment system of the Army. This exercise is being done with an aim to assess the capability of the current system to implement a time separated lean-agile system of spare parts replenishment.

The paper is based on a survey conducted on people in managerial ranks, working in the field of military logistics. The survey is thereafter summarised to ascertain the current status of spare parts replenishment system in the Army. The findings of the survey are elaborated at the end of the paper.

The strengths of the current spare parts replenishment system are highlighted. This is followed with the weaknesses of the system in implementing a dynamic lean-agile replenishment system.

The paper is aimed at assessing the capability of the current spare parts replenishment system and its ability to adapt to a novel replenishment system that is lean in peacetime to save money and agile during war to increase reliability of equipment achieved by a certainty of supply. The survey conducted on the persons actually involved in this logistics reveals areas that need emphasis in order to achieve such a time separated lean-agile replenishment system.