Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Spare parts management: Linking distributional assumptions to demand classification

Spare parts are known to be associated with intermittent demand patterns and such patterns cause considerable problems with regards to forecasting and stock control due to their compound nature that renders the normality assumption invalid. Compound distributions have been used to model intermittent demand patterns; there is however a lack of theoretical analysis and little relevant empirical evidence in support of these distributions. In this paper, we conduct a detailed empirical investigation on the goodness of fit of various compound Poisson distributions and we develop a distribution-based demand classification scheme the validity of which is also assessed in empirical terms. Our empirical investigation provides evidence in support of certain demand distributions and the work described in this paper should facilitate the task of selecting such distributions in a real world spare parts inventory context. An extensive discussion on parameter estimation related difficulties in this area is also provided

Demand categorisation in a European spare parts logistics network

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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

On the empirical performance of (T, s, S) heuristics

The periodic ðT; s; SÞ policies have received considerable attention from the academic literature. Determination of the optimal parameters is computationally prohibitive, and a number of heuristic procedures have been put forward. However, these heuristics have never been compared in an extensive empirical study. Such an investigation on 3055 SKUs is carried out in this paper. Our study provides insights into the performance of ðT; s; SÞ heuristics, also in relation to demand forecasting. The results show that Naddor’s heuristic is best able to minimize the total cost. However, the normal and power approximations achieve more efficient solutions in that backorder volumes are smaller at the same inventory levels, indicating the potentially superior performance of these methods if the balancing of holding and backorder costs can be improved. The results also show that, for all heuristics, the SBA variant of the Croston forecasting method significantly outperforms Croston as well as Single Exponential Smoothing (SES)

Forecasting and inventory performance for a two-stage supply chain with ARIMA (0,1,1) demand : theory and empirical analysis

The ARIMA(0,1,1) demand model has been analysed extensively by researchers and used widely by forecasting practitioners due to its attractive theoretical properties and empirical evidence in its support. However, no empirical investigations have been conducted in the academic literature to analyse demand forecasting and inventory performance under such a demand model. In this paper, we consider a supply chain formed by a manufacturer and a retailer facing an ARIMA(0,1,1) demand process. The relationship between the forecasting accuracy and inventory performance is analysed along with an investigation on the potential benefits of forecast information sharing between the retailer and the manufacturer. Results are obtained analytically but also empirically by means of experimentation with the sales data related to 329 Stock Keeping Units (SKUs) from a major European superstore. Our analysis contributes towards the development of the current state of knowledge in the areas of inventory forecasting and forecast information sharing and offers insights that should be valuable from the practitioner perspective

Forecasting and stock control : a study in a wholesaling context

Wholesalers add value to the products they deal with by essentially bringing them closer to the end consumers. In that respect, the effective control of stock levels becomes an important measure of operational performance especially in the context of achieving high customer service levels. In this paper, we address issues pertinent to forecasting and inventory management in a wholesaling environment and discuss the recommendations proposed in such a context in a case study organization. Our findings demonstrate the considerable scope that exists for improving current practices and offers insights into possible managerial issues

Dynamic re-order point inventory control with lead-time uncertainty : analysis and empirical investigation

A new forecast-based dynamic inventory control approach is discussed in this paper. In this approach, forecasts and forecast uncertainties are assumed to be exogenous data known in advance at each period over a fixed horizon. The control parameters are derived by using a sequential procedure. The merits of this approach as compared to the classical one are presented. We focus on a single-stage and single-item inventory system with non-stationary demand and lead-time uncertainty. A dynamic re-order point control policy is analysed based on the new approach and its parameters are determined for a given target cycle service level (CSL). The performance of this policy is assessed by means of empirical experimentation on a large demand data set from the pharmaceutical industry. The empirical results demonstrate the benefits arising from using such a policy and allow insights to be gained into other pertinent managerial issues

INVENTORY CONTROL MODELS FOR SPARE PARTS IN AVIATION LOGISTICS

Effective inventory management has a direct influence on monetary savings, high customer service level and product quality and thus plays an essential role in a company's economic and strategic performance. Forecasting and inventory models for aviation logistics are essential in commercial aviation. The objective of this paper is to study the problem of identifying the optimal order quantity of aircraft spare parts and the demand periods using the Order-Up-To (OUT) inventory model in conjunction with the Negative Binomial Distribution (NBD) and the (s, S) inventory model with Revised Power Approximation Method. These models are compared and contrasted via a real-world paradigm. The analysis reveals that the OUT inventory model in conjunction with the Poisson distribution allows ordering the lowest order quantity. However, the (s, S) inventory model with the Revised Power Approximation outperforms it in terms of average total inventory costs