Inventory Management Issues Research Articles

A micro-genetic algorithm for multi-objective scheduling of a real world pipeline network

The scheduling of activities to transport oil derivative products through a pipe network is a complex combinatorial problem that presents a hard computational solution. During the scheduling horizon, many batches are pumped from (or pass through) different areas. Pipes are shared resources. The balance between the demand requirements and the production campaigns, while satisfying inventory management issues and pipeline pumping procedures, is a difficult task. In order to reduce the complexity, this problem could be decomposed on three sub-problems according to the key elements of scheduling: assignment of resources, sequencing of activities, and determination of resource timing utilization by these activities. This work proposes a model to solve the sequencing and timing sub-problems, and its main objective is to develop a hybrid solution based on genetic algorithm (GA) and mixed integer linear programming (MILP) to drive batches of oil derivative products through the network. As both techniques (GA and MILP) can require significant computational efforts, we propose the use of micro-genetic algorithms (μGA) that generally guarantee good solutions with acceptable levels of computational effort. The μGA-MILP hybrid model has been implemented and tested on several practical cases of a Brazilian oil company. As a result, the model can provide a set of solutions that means different options of pipeline operations. This work contributes to the development of a tool to help the specialist solve the batch scheduling problem, which results in a more efficient use of the pipeline network.

Development of an integrated medical supply information system

The integrated medical supply inventory control system introduced in this study is a hybrid system that is shaped by the nature of medical supply, usage and storage capacity limitations of health care facilities. The system links demand, service provided at the clinic, health care service provider's information, inventory storage data and decision support tools into an integrated information system. ABC analysis method, economic order quantity model, two-bin method and safety stock concept are applied as decision support models to tackle inventory management issues at health care facilities. In the decision support module, each medical item and storage location has been scrutinised to determine the best-fit inventory control policy. The pilot case study demonstrates that the integrated medical supply information system holds several advantages for inventory managers, since it entails benefits of deploying enterprise information systems to manage medical supply and better patient services.

Genetic algorithm optimisation of an integrated aggregate production–distribution plan in supply chains

A production plan concerns the allocation of resources of the company to meet the demand forecast over a certain planning horizon and a distribution plan involves the management of warehouse storage assignments, transport routings and inventory management issues. A production–distribution plan integrates the decisions in production, transport and warehousing as well as inventory management. The overall performance of a supply-chain is influenced significantly by the decisions taken in its production–distribution plan and hence one key issue in the performance evaluation of a supply chain is the modelling and optimisation of the production–distribution plan considering its actual complexity. Based on the integration of Aggregate Production Plan and Distribution Plan, this article develops a mixed integer non-linear formulation for a two-echelon supply network (i.e. a production-distribution network) considering the real-world variables and constraints. Genetic Algorithm (GA), known as a robust technique for solving complex problems, is employed for the optimisation of the developed mathematical model due to its ability to effectively deal with a large number of parameters. To demonstrate the applicability of the methodology, a real-life case study will be finally studied incorporating the production of different types of products in several manufacturing plants and the distribution of finished products from plants to a number of end-users via multiple direct/indirect transport routes.

Breadth of range and depth of stock: forecasting and inventory management at Euro Car Parts Ltd.

This paper investigates inventory management issues in a distribution network. The study is motivated by examining the operation of a wholesaling car parts company. Customer service requirements are of paramount importance in this market sector. The nature of the demand facing the company is characterised. The breadth of range of stock keeping units (SKUs) held at a stocking location and the quantity of each SKU held are normally treated in isolation but in this case, the rule developed to select the range of SKU was extended to determine the level of stock to hold. It is intuitively obvious that these two factors should be linked, yet the authors have not found any other literature developing the connection in a practical context. Forecasting issues are explored as the rule on stock range depends on a forecast of the number of orders received for each SKU at each stocking unit. Some implementation issues and extensions are indicated.

Inventory Control Policy of Service Parts under the Background of Service-Embedded Manufacturing

This paper studied service parts inventory control policy of a large equipment manufacturer which is on the transformation to service-embedded manufacturing. In order to get win-win solution for both supplier and client, a new cooperation pattern of service parts management was proposed. And a service parts model of multi-echelon inventory system in the after-sale service market was build based on the analysis of service parts demand under servitizaion. Based on this model, many issues of service parts inventory management were studied. Finally the inventory control parameters were exploited which aims at the maximum profit of service parts inventory. With this method, the reduced inventory of suppliers and improved service level could be calculated quantitatively, which could work as a reference to set price of extensional services. Experiments showed the method proposed in this paper is valid for service-embedded manufacturing firms.

Optimizing Inventory Using Genetic Algorithm for Efficient Supply Chain Management

Problem statement: Today, inventory management is considered to be an important field in Supply chain management. Once the efficient and effective management of inventory is carried out throughout the supply chain, service provided to the customer ultimately gets enhanced. Hence, to ensure minimal cost for the supply chain, the determination of the level of inventory to be held at various levels in a supply chain is unavoidable. Minimizing the total supply chain cost refers to the reduction of holding and shortage cost in the entire supply chain. Efficient inventory management is a complex process which entails the management of the inventory in the whole supply chain and getting the final solution as an optimal one. In other words, during the process of supply chain management, the stock level at each member of the supply chain should account to minimum total supply chain cost. The dynamic nature of the excess stock level and shortage level over all the periods is a serious issue when implementation was considered. In addition, consideration of multiple products leads to very complex inventory management process. The complexity of the problem increases when more distribution centers and agents were involved. Approach: In present research, the issues of inventory management had been focused and a novel approach based on genetic algorithm had been proposed in which the most probable excess stock level and shortage level required for inventory optimization in the supply chain is distinctively determined so as to achieve minimum total supply chain cost. Results: The analysis provided us with an inventory level that made a remarkable contribution towards the increase of supply chain cost. We predicted the optimal inventory levels in all the supply chain members with the aid of these levels. Conclusion: We concluded that it is possible to minimize the supply chain cost by maintaining the optimal stock levels that we predicted from the inventory analysis. This will make the inventory management further effective and efficient thereby enhancing the customer servicing levels.

Issues in reverse supply chains, part I: end‐of‐life product recovery and inventory management – an overview

Recovery of used products has become a field of rapidly growing importance in reverse supply chain management. Product recovery includes collection, inspection/separation, disassembly, reconditioning/reuse, remanufacturing, and recycling. In real time situations, the collection of end‐of‐life (EoL) products from the customer and their return to the manufacturer is tedious and time consuming. Reduced product life cycles have increased the rate of product returns and disposals. Owing to shortened product economic life cycles, the recovery of value from EoL products is becoming a necessity. Companies have realised the value that they could recover by remanufacturing or recycling EoL products. Researchers have developed various models for product recovery network design, optimal inventory, production planning and control, remanufacturing, recycling, disposal, etc. The main purpose of this paper is to review the literature on EoL product recovery and inventory management issues in reverse supply chains and to outline some future directions for research on these issues.

A hybrid neural network and traditional approach for forecasting lumpy demand

Accurate demand forecasting is one of the most key issues in inventory management of spare parts. The problem of modeling future consumption becomes especially difficult for lumpy patterns, which characterized by intervals in which there is no demand and, periods with actual demand occurrences with large variation in demand levels. However, many of the forecasting methods may perform poorly when demand for an item is lumpy. In this study based on the characteristic of lumpy demand patterns of spare parts a hybrid forecasting approach has been developed, which use a multi-layered perceptron neural network and a traditional recursive method for forecasting future demands. In the described approach the multi-layered perceptron are adapted to forecast occurrences of non-zero demands, and then a conventional recursive method is used to estimate the quantity of non-zero demands. In order to evaluate the performance of the proposed approach, their forecasts were compared to those obtained by using Syntetos & Boylan approximation, recently employed multi-layered perceptron neural network, generalized regression neural network and elman recurrent neural network in this area. The models were applied to forecast future demand of spare parts of Arak Petrochemical Company in Iran, using 30 types of real data sets. The results indicate that the forecasts obtained by using our proposed mode are superior to those obtained by using other methods

Improvements to the Linear Programming Based Scheduling of Web Advertisements

We propose and evaluate a number of improvements to the linear programming formulation of web advertisement scheduling, which we have proposed elsewhere together with our colleagues [Langheinrich et al., 9]. In particular, we address a couple of important technical challenges having to do with the estimation of click-through rates and optimization of display probabilities (the exploration–exploitation trade-off and the issue of data sparseness and scalability), as well as practical aspects that are essential for successful deployment of this approach (the issues of multi-impressions and inventory management). We propose solutions to each of these issues, and assess their effectiveness by running large-scale simulation experiments.

Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model

Owing to the sporadic nature of demand for aircraft maintenance repair parts, airline operators perceive difficulties in forecasting and are still looking for superior forecasting methods. This paper deals with techniques applicable to predicting spare parts demand for airline fleets. The experimental results of 13 forecasting methods, including those used by aviation companies, are examined and clarified through statistical analysis. The general linear model approach is used to explain the variation attributable to different experimental factors and their interactions. Actual historical data for hard-time and condition-monitoring components from an airlines operator are used, in order to compare different forecasting methods when facing intermittent demand. The results confirm the continued superiority of the weighted moving average, Holt and Croston method for intermittent demand, whereas most commonly used methods by airlines are found to be questionable, consistently producing poor forecasting performance. We have, however, devised a new approach to forecasting evaluation, a predictive error-forecasting model which compares and evaluates forecasting methods based on their factor levels when faced with intermittent demand. A simple example is presented to illustrate the performance of the mathematical model. It is suggested that these findings may be applicable to other industrial sectors, which have similar demand patterns to those of airlines. Scope and purpose Demand forecasting is one of the most crucial issues of inventory management. Forecasts, which form the basis for the planning of inventory levels, are probably the biggest challenge in the repair and overhaul industry, as the one common problem facing airlines throughout the world is the need to know the short-term part demand forecast with the highest possible degree of accuracy. The high cost of modern aircraft and the expense of such repairable spares as aircraft engines and avionics constitute a large part of the total investment of many airline operators. These parts, though low in demand, are critical to operations and their unavailability can lead to excessive down time costs. Most airline materials managers deal with intermittent demand, which tends to be random and has a large proportion of zero values. In an effort to achieve this, the study has presented a model that could be of great benefit to airline operators and other maintenance service organisations. It will enable them to select in advance the appropriate forecasting method that better meets their cyclical demand for parts. This approach is consistent with the purpose of this study, which aims to compare different forecasting methods when faced with intermittent demand.

Inventory forecasting with a multiple criteria decision tool

The strategic role of logistics is increasingly being recognised, and inventory strategy is an essential element of logistics strategy. Demand forecasting is one of the most crucial issues of inventory management as forecasts form the basis for the planning of production, transportation and inventory levels. The traditional methods used for forecasting include time series methods and causal methods. In the paper, we present an Analytic Hierarchy Process-based approach to demand forecasting. The proposed decision support system offers many improvements compared to traditional methods, such as the possibility to include both tangible and intangible factors in the forecasting process, and the ability to make predictions about the future development of the environmental factors.

A clustering analysis of forecasting methods in a multi-objective inventory system

This study employs a descriptive model to relate key issues in inventory management — cost and demand estimation, lead time and performance measures of inventory systems. The model assumes a single item, uncertain demand, deterministic review order point and discrete lead time. Sixteen forecasting methods were examined and compared on empirical ground with three data sets. Cluster analysis was first used to group forecasting methods along four defined dimensions. The naive forecasting method was found to be distinctly different from the rest. Furthermore, the naive method consistently provided less accurate forecasts across the three data sets used. Cluster analysis was again used to put the other fifteen forecasting methods into groups. Results indicated that under general conditions regarding the demand pattern none of the forecasting methods have demonstrated the expected advantage relative to other methods. Thus more specific knowledge is required to assign a forecasting method to specific data. Such a model was recently studied by Reinstein, Gibson and Zidman under the naive forecasting method which calls for inventory based on the demand from the prior period. The results also have valuable insights for inventory management.

Manufacturing systems with forbidden early shipment: Implications for setting manufacturing lead times

SUMMARY The authors compare two well-known methods for setting manufacturing lead times (flow allowances) in a general job shop when early shipment of completed jobs is forbidden. One of the methods for setting a job's allowance is to make it proportional to the total processing time for the job. This method, referred to as TWK., is compared to a second method PPW. With the PPW method, a job's allowance is obtained by adding to the total job-processing time an allowance for waiting that is proportional to the number of operations that the job requires. The two allowance methods are compared using a computer simulation of an 8-machine job shop. The model is unique in that jobs are not permitted to leave the shop early. This feature of forbidding early shipment (FES) complicates the comparison between allowance methods because it draws the issue of finished-order inventory management into the analysis. Results of computer simulations over a wide range of average due-date difficulty suggest that TWK is the d...

Using Optimal Policy Surfaces to Analyze Aggregate Inventory Tradeoffs

The marginal cost information needed to implement traditional inventory models is not likely to be available in practice. The most important inventory management issues in practive involve aggregate objectives and constraints while the richest theoretical models deal with single item management. To help resolve these problems, the authors propose that inventory decisions be conceived as policy tradeoffs on a three dimensional response surface showing the optimal relationships among aggregate customer service, workload, and investment. We show that any optimal management decision must result in a point located on the surface. Computational results show that the methodology suggested can make improvements in management policy in four inventories that total more than 78,000 line items.