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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.

The objective of this paper is to investigate the impact of products' characteristics on the performance of three distribution strategies: traditional warehousing (TW), cross-docking pick by line (XDPL) and cross-docking pick by store (XDPS).

Based on a case study of an FMCG “Fast Moving Consumer Goods” company and a major French retailer, we empirically analyse the impact of the products' characteristics on the performance of the three distribution strategies. We consider a three-echelon supply chain composed of one supplier DC, one retailer DC and multiple retailer stores. The inventory at each echelon is controlled according to an order-up-to (OUT) level policy. The demand is forecasted by means of a single exponential smoothing method. A sensitivity analysis is also conducted to analyse the impact of the supply chain parameters on the comparative performance of the strategies when the parameters' values deviate from the empirical base case.

The empirical investigation shows that the use of XDPL results leads to an increase in the supply chain total cost, whereas XDPS reduces the cost. Moreover, we show that for a service-level target, cross-docking strategies should be selected for products with low variability, high shelf space, low value and short lead-time. For an inventory reduction target, these strategies should be selected for products with high demand volume. We also propose a managerial framework for choosing the right strategy for each product.

This paper fills a gap in the literature by presenting empirical results based on a real business case of a multi-echelon supply chain. Both cost and service are used to evaluate the performance of the strategies.

Our work has the limitation to ignore the transportation cost implications when selecting the right distribution strategy. Hence, including such cost in the analysis would constitute an interesting extension of this work. Moreover, our empirical analysis represents a practical rich context that makes the scope for transferability of findings learned from this article substantial. However, for the generalisability of the findings, larger datasets in the retail supply chain would be interesting to consider

Spare parts have become ubiquitous in modern societies and managing their requirements is an important and challenging task with tremendous cost implications for the organisations that are holding relevant inventories. An important operational issue involved in the management of spare parts is that of categorising the relevant stock keeping units (SKUs) in order to facilitate decision‐making with respect to forecasting and stock control and to enable managers to focus their attention on the most “important” SKUs. This issue has been overlooked in the academic literature although it constitutes a significant opportunity for increasing spare parts availability and/or reducing inventory costs. Moreover, and despite the huge literature developed since the 1970s on issues related to stock control for spare parts, very few studies actually consider empirical solution implementation and with few exceptions, case studies are lacking. Such a case study is described in this paper, the purpose of which is to offer insight into relevant business practices.

The issue of demand categorisation (including forecasting and stock control) for spare parts management is addressed and details reported of a project undertaken by an international business machine manufacturer for the purpose of improving its European spare parts logistics operations. The paper describes the actual intervention within the organisation in question, as well as the empirical benefits and the lessons learned from such a project.

This paper demonstrates the considerable scope that exists for improving relevant real word practices. It shows that simple well‐informed solutions result in substantial organisational savings.

This paper provides insight into the empirical utilisation of demand categorisation theory for forecasting and stock control and provides some very much needed empirical evidence on pertinent issues. In that respect, it should be of interest to both academics and practitioners.

Research in the area of forecasting and stock inventory control for intermittent demand is designed to provide robust models for the underlying demand which appears at random, with some time periods having no demand at all. Croston’s method is a popular technique for these models and it uses two single exponential smoothing (SES) models which involve smoothing constants. A key issue is the choice of the values due to the sensitivity of the forecasts to changes in demand. Suggested selections of the smoothing constants include values between 0.1 and 0.3. Since an ARIMA model has been illustrated to be equivalent to SES, an optimal smoothing constant can be selected from the ARIMA model for SES. This chapter will conduct simulations to investigate whether using an optimal smoothing constant versus the suggested smoothing constant is important. Since SES is designed to be an adapted method, data are simulated which vary between slow and fast demand.

The purpose of this paper is to construct a fuzzy logic model that acts as a decision support system to minimize inventory-related costs in the field of industrial maintenance. Achieving a balance between the unavailability and over-storage of spare parts is a problem with potentially significant consequences. That significance increases proportionally with the ever-increasing challenge of reducing overall cost. Either scenario can result in substantial financial losses because of the interruption of production or the costs of tied-up capital, also called the “solidification of capital.” Moreover, there is that additional problem of the expiry of parts on the shelf.

The proposed approach relies on inputs from experts with consideration for incompleteness and inaccuracy. Two levels of decision are considered simultaneously. The first is whether a part should be stored or ordered when needed. The second involves comparing suppliers with their batch-size offers based on user-determined criteria. A mathematical model is developed in parallel for validation.

The results indicate that the fuzzy logic approach is accurate and satisfactory for this application and that it is advantageous because of its limited sensitivity to the inaccuracy and/or incompleteness of data. In addition, the approach is practical because it requires minimal user effort.

To the best of the authors’ knowledge, the exploitation of fuzzy-logic altogether with limited sensitivity experts' inputs were never combined for the solution of this particular problem; however, this approach's positive impact is expected to be highly significant in solving a chronic problem in industry.

Information sharing is one of essential collaboration methods for building effective system-level disruption responses and communication for supply chain resilience. However, supply chain members are often reluctant to share the members' business information for fear of losing competitiveness. To facilitate the cooperation among these members, the supply chain members' should be made aware of the value of information. As a result, the purpose of this paper is to quantify the benefit of information sharing and evaluate its magnitude under various factors.

In this paper, information sharing is measured in a two-stage supply chain containing a manufacturer and a retailer. A demand function is constructed as a linear combination of a first-order autoregressive [AR(1)] process, the retail and reference prices. The values of information sharing are quantified for four scenarios: (1) no information sharing, (2) full information sharing, (3) limited information sharing and (4) partial information sharing. Based on the four scenarios, the conditions for valuable information sharing are determined. In addition, the impact of several demand parameters on the usefulness of information sharing is analyzed.

When the demand function is a pure AR(1) process (i.e. there is no impact from the retail and reference prices), information sharing is always valuable regardless of the autoregressive coefficient. Under the influence of the retail price and consumer behavior via the reference price, information sharing is not always beneficial. The boundaries for useful information sharing are analytically constructed. In addition to full information sharing, this study also quantifies the value of information under a partial sharing scheme. The results indicate that the information is more valuable as long as the information is inducible.

This study highlights several specific conditions for a beneficial information sharing agreement in consideration of consumer behaviors. These conditions enable supply chain members to design a sustainable partnership.

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.

Despite the importance of collaborative planning, forecasting, and replenishment (CPFR), its influence on supply chain innovation capability (SCIC) and supply chain performance (SCP) has not been sufficiently examined. The purpose of this paper is to examine the antecedence of SCP through CPFR and SCIC.

Through cluster and stratified random sampling, 286 responses from top managers of 1,574 Nigerian manufacturing companies were analyzed. Data analysis was performed using structural equation modeling with AMOS graphics.

The results show that SCIC has a full mediating effect on the relationship between CPFR and SCP. Specifically, CPFR has a significant relationship with both SCP and SCIC, and SCIC also relates significantly to SCP.

This study offers implications for manufacturers in developing countries in general, and in Nigeria in particular, by providing a guideline on how to improve SCP through CPFR.

The paper contributes to the limited studies on CPFR and SCP by extending this line of study into the realm of innovation capability and innovation. It integrates the social exchange theory and the dynamic capabilities theory to examine the collaborative processes of CPFR in the supply chain context. This study stressed the importance of boundary theoretical spanning by extending CPFR and SCP into the domain of innovation capability.

Different information sharing mechanisms and online platform information sharing to different charging models are compared and analyzed.

This paper uses the Stackelberg game model to study the demand information sharing and pricing decisions.

The results show that: (1) the retailer's pricing strategy is the highest when both of them obtain information, while the manufacturer's pricing strategy is affected by the related attributes of different products, such as the sensitivity of consumers to product prices; (2) in the online platform sales model, the demand information data sharing owned by the online platform can bring more expected profits to the whole supply chain and the members of the supply chain, and the higher the accuracy of the information, the higher the expected profit; (3) when the cost of obtaining demand information is zero, that is, the online platform shares the information data about market demand free of charge, the retailer and manufacturer tend to obtain information; (4) for the online platform, charging a certain fee can achieve higher expected profits than free sharing.

Based on the single platform online sales model, this paper uses the Stackelberg game model to study the demand information sharing and pricing decision of a manufacturer and a retailer selling products through the same online platform.

The purpose of this paper is to design a model that can accurately forecast the supply chain sales.

This paper proposed a new model based on lightGBM and LSTM to forecast the supply chain sales. In order to verify the accuracy and efficiency of this model, three representative supply chain sales data sets are selected for experiments.

The experimental results show that the combined model can forecast supply chain sales with high accuracy, efficiency and interpretability.

With the rapid development of big data and AI, using big data analysis and algorithm technology to accurately forecast the long-term sales of goods will provide the database for the supply chain and key technical support for enterprises to establish supply chain solutions. This paper provides an effective method for supply chain sales forecasting, which can help enterprises to scientifically and reasonably forecast long-term commodity sales.

The proposed model not only inherits the ability of LSTM model to automatically mine high-level temporal features, but also has the advantages of lightGBM model, such as high efficiency, strong interpretability, which is suitable for industrial production environment.