The Effects of the Correlation of Electric Materials on Forecasting and Stock Control

Forecasting and stock control play an important role in the electric companies because outstanding forecasting and stock control increase service level obviously and decrease stock cost effectively. However, the majority of the electric materials are intermittent demand, resulting in poor forecasting and stock control performance. Therefore, exploring the reasons that affect forecasting performance and stock control is necessary. This paper explores the effects of the correlation of intermittent electric materials on forecasting and stock control. First, we divide the correlation into three categories: autocorrelation in demand sizes, autocorrelation in intervals and cross-correlation between demand size and interval. Forecasting by SBA approach and using periodic dynamic inventory strategy (T, S) to control stock, exploring the effects of these three correlations on forecast accuracy, stock cost and service level. The data shows that correlations of electric materials affect their forecasting and stock control, which will help company find more accurate forecast approach and lower the cost of stock in the future

A Simulink library for supply chain simulation

Despite the links between forecasting and inventory control, both areas have evolved separately. This work presents a supply chain simulation library de-veloped in Simulink to bridge that gap. To simulate a supply chain is important to define the number of companies/echelons that belong to the same supply chain and the policies that each company employs to demand forecasting and stock control. The potential user can find in this library forecasting and stock control blocks to simulate a supply chain in a modular design. We show how to imple-ment: i) Forecasting policies. For example, the widely used exponential smooth-ing; ii) Replenishment models. For instance, typical order-up-to-level stock control policies as (s,S); and finally, how to connect the forecasting and stock control blocks to describe the performance of a company and to extend such relation-ships to define the whole supply chain

An Integrated Approach to Managing Spare Parts Inventory: A Case Study

Spare parts have become crucial for the proper functioning of professional equipment and ensuring their availability in stock implies overcoming the trade-off between high service levels and low inventory costs. Since spare parts are usually highly varied –with different costs and demand patterns–, the proper categorisation of the relevant stock keeping units (SKUs) is essential to align the inventory management system with their distinctive attributes. Moreover, an important issue involved in the management of spare parts is that of coping with the demand intermittency that is characteristic of this industry in order to facilitate decision making with respect to forecasting and stock control. This paper presents a stocking approach integrating the classification, demand forecasting and stock control policies for the improvement of the current spare parts management system at a small reseller company in Mexico. To provide insight into the empirical utilisation of inventory management theory, the proposed categorisation, forecasting and inventory control techniques are evaluated using real data

Reliability-based economic model predictive control for generalized flow-based networks including actuators' health-aware capabilities

This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalized flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamically allocate safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators’ availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the considered case study.Peer ReviewedPostprint (author's final draft

Forecasting Sales of Slow and Fast Moving Inventories.

Adaptations of simple exponential smoothing are presented that aim to unify the task of forecasting demand for both slow and fast moving inventories. A feature of the adaptations is that they are designed to ensure that the resulting prediction distributions have only a nonnegative domain. A parametric bootstrap approach is proposed for generating empirical approximations for the so-called lead-time demand distribution, something required for inventory control calculations. The proposed methods are illustrated and their performance compared on real demand data for car parts.demand forecasting, inventory control, simulation, parametric bootstrapping, time series analysis.

Forecasting the Intermittent Demand for Slow-Moving Items

Organizations with large-scale inventory systems typically have a large proportion of items for which demand is intermittent and low volume. We examine different approaches to forecasting for such products, paying particular attention to the need for inventory planning over a multi-period lead-time when the underlying process may be non-stationary. We develop a forecasting framework based upon the zero-inflated Poisson distribution (ZIP), which enables the explicit evaluation of the multi-period lead-time demand distribution in special cases and an effective simulation scheme more generally. We also develop performance measures related to the entire predictive distribution, rather than focusing exclusively upon point predictions. The ZIP model is compared to a number of existing methods using data on the monthly demand for 1,046 automobile parts, provided by a US automobile manufacturer. We conclude that the ZIP scheme compares favorably to other approaches, including variations of Croston's method as well as providing a straightforward basis for inventory planning.Croston's method; Exponential smoothing; Intermittent demand; Inventory control; Prediction likelihood; State space models; Zero-inflated Poisson distribution

Demand categorization, forecasting, and inventory control for intermittent demand items

[EN] It is commonly assumed that intermittent demand appears randomly, with many periods without demand; but that when it does appear, it tends to be higher than unit size. Basic and well-known forecasting techniques and stock policies perform very poorly with intermittent demand, making new approaches necessary. To select the appropriate inventory management policy, it is important to understand the demand pattern for the items, especially when demand is intermittent. The use of a forecasting method designed for an intermittent demand pattern, such as Crostons method, is required instead of a simpler and more common approach such as exponential smoothing. The starting point is to establish taxonomic rules to select efficiently the most appropriate forecasting and stock control policy to cope with thousands of items found in real environments. This paper contributes to the state of the art in: (i) categorisation of the demand pattern; (ii) methods to forecast intermittent demand; and (iii) stock control methods for items with intermittent demand patterns. The paper first presents a structured literature review to introduce managers to the theoretical research about how to deal with intermittent demand items in both forecasting and stock control methods, and then it points out some research gaps for future development for the three topics.This research was part of the project GEMA, supported by the Ministerio de Educación y Ciencia (Ref. DPI 2007-65441).Babiloni Griñón, ME.; Cardós, M.; Albarracín Guillem, JM.; Palmer Gato, ME. (2010). Demand categorization, forecasting, and inventory control for intermittent demand items. South African Journal of Industrial Engineering. 21(2):115-130. https://doi.org/10.7166/21-2-54S11513021

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously. In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems

Forecasting intermittent demand

Methods for forecasting intermittent demand are compared using a large data-set from the UK Royal Air Force (RAF). Several important results are found. First, we show that the traditional per period forecast error measures are not appropriate for intermittent demand, even though they are consistently used in the literature. Second, by comparing target service levels to achieved service levels when inventory decisions are based on demand forecasts, we show that Croston's method (and a variant) and Bootstrapping clearly outperform Moving Average and Single Exponential Smoothing. Third, we show that the performance of Croston and Bootstrapping can be significantly improved by taking into account that each lead time starts with a demand