A Dynamic inventory optimization method applied to printer fleet management

Current optimization methods for inventory management of toner cartridges for printer fleets typically focus on aggregate cartridge demand. However, with the development of printer technology, toner consumption algorithms are being developed which can accurately quantify the amount of toner that has been consumed over time, based on print job characteristics. This research introduces a dynamic inventory optimization approach for a fleet of printers over a rolling time horizon. Given, the consumption algorithm for the printer system, the cumulative toner consumed per cartridge per printer can be tracked. A forecasting method is developed which utilizes this toner consumption data for individual printers to forecast toner cartridge replacement times. Taking into account the uncertainty related to demand, demand forecast and lead time, an optimization model has been developed to determine the order placement times and order quantities to minimize the total cost subject to a specified service level. An experimental performance evaluation has been conducted on the parameters of the dynamic inventory management algorithm. Based on the results of this evaluation, the implementation of this dynamic inventory optimization methodology could have a positive impact on printer fleet management

Intermittent demand forecasting: A guideline for method selection

Intermittent demand shows irregular pattern that differentiates it from all other demand types. It is hard to forecasting intermittent demand due to irregular occurrences and demand size variability. Due to this reason, researchers developed ad hoc intermittent demand forecasting methods. Since intermittent demand has peculiar characteristics, it is grouped into categories for better management. In this paper, specialized methods with a focus of method selection for each intermittent demand category are considered. This work simplifies the intermittent demand forecasting and provides guidance to market players by leading the way to method selection based on demand categorization. By doing so, the paper will serve as a useful tool for practitioners to manage intermittent demand more easily.Q1WOS:0005179795000012-s2.0-8507967227

An inventory control project in a major Danish company using compound renewal demand models

We describe the development of a framework to compute the optimal inventory policy for a large spare-parts’ distribution centre operation in the RA division of the Danfoss Group in Denmark. The RA division distributes spare parts worldwide for cooling and A/C systems. The warehouse logistics operation is highly automated. However, the procedures for estimating demands and the policies for the inventory control system that were in use at the beginning of the project did not fully match the sophisticated technological standard of the physical system. During the initial phase of the project development we focused on the fitting of suitable demand distributions for spare parts and on the estimation of demand parameters. Demand distributions were chosen from a class of compound renewal distributions. In the next phase, we designed models and algorithmic procedures for determining suitable inventory control variables based on the fitted demand distributions and a service level requirement stated in terms of an order fill rate. Finally, we validated the results of our models against the procedures that had been in use in the company. It was concluded that the new procedures were considerably more consistent with the actual demand processes and with the stated objectives for the distribution centre. We also initiated the implementation and integration of the new procedures into the company’s inventory management systemBase-stock policy; compound distribution; fill rate; inventory control; logistics; stochastic processes

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

SKU classification: A literature review and conceptual framework

Purpose - Stock keeping unit (SKU) classifications are widely used in the field of production and operations management. Although many theoretical and practical examples of classifications exist, there are no overviews of the current literature, and general guidelines are lacking with respect to method selection for classifying SKUs. The purpose of this paper is to systematically synthesise the earlier work in this area, and to conceptualise and discuss the factors that influence the choice of a specific SKU classification. Design/methodology/approach - The paper structurally reviews existing contributions and synthesises these into a conceptual framework for SKU classification. Findings - How SKUs are classified depends on the classification aim, the context and the method that is chosen. In total, three main production and operations management aims were found: inventory management, forecasting and production strategy. Within the method three decisions are identified to come to a classification: the characteristics, the classification technique and the operationalisation of the classes. Research limitations/implications - Drawing on the literature survey, the authors conclude with a conceptual framework describing the factors that influence SKU classification. Further research could use this framework to develop guidelines for real-life applications. Practical implications Examples from a variety of industries and general directions are provided which managers could use to develop their own SKU classification. Originality/value - The paper aims to advance the literature on SKU classification from the level of individual examples to a conceptual level and provides directions on how to develop a SKU classification

Applying Inventory Control Practices Within the Sisters of Mercy Health Care Supply Chain

This research lays a foundation for the better understanding of the application and acceptance of more advanced inventory control practices within the health care supply chain. The demand characteristics and optimal control policies for pharmaceutical items within a multi-echelon provider network are examined within the framework of a case study. Demand forecasting algorithms were applied to forecast demand for inventory control procedures. A spreadsheet-based inventory planning tool was used to minimize the inventory holding and ordering costs subject to fill rate constraints. The costs of inventory control models are compared to the current ordering and inventory control strategies to document potential cost savings using both a single echelon analysis and a multi-echelon analysis. The results indicate that there is great potential for significant cost savings within the provider network. It is likely that if other providers adopt such practices that they will be able to better control material supply costs

Forecasting Demand for Optimal Inventory with Long Lead Times: An Automotive Aftermarket Case Study

Accuracy in predicting customer demand is essential to building an economic inventory policy under periodic review, long lead-time, and a target fill rate. This study uses inventory and customer service level as a stock control metric to evaluate the forecast accuracy of different simple to more complex predictive analytical techniques. We show how traditional forecast error measures are inappropriate for inventory control, despite their consistent usage in many studies, by evaluating demand forecast performance dynamically with customer service level as a stock control metric that includes inventory holdings costs, stock out costs, and fill rate service levels. A second contribution includes evaluating the utility of introducing more complexity into the forecasting process for an automotive aftermarket parts manufacturer and the superior inventory control results using the Prais-Winsten, an econometric method, for non-intermittent demand forecasting with long-lead times. This study will add to the limited case study research on demand forecasting under long lead times using stock control metrics, dynamic model updating, and the Prais-Winsten method for inventory control