Inventory Cost Savings Research Articles

Optimization of machine schedules and vehicle routes in the mobile additive manufacturing mode

Online shopping giant Amazon proposes to use 3D printers mounted on vehicles to print customer purchases on the fly and deliver them immediately. Because of the overlap between production and transportation processes, such a mobile manufacturing mode offers potential savings in lead times and inventory costs. However, the decisions associated with the two processes are intertwined, making it challenging to achieve satisfactory machine schedules and vehicle routes. To address the situation and fully understand the benefits of the mode, we study a related optimisation problem of finding routes for vehicles and production schedules for 3D printers mounted on them with minimum travel costs. First, we construct a mixed-integer programming model to formulate the problem. Then, we develop an exact algorithm to solve it. Specifically, we propose two propositions about the optimal production schedule of machines in the route and develop a labelling algorithm for the pricing problem. And we devise a problem-specific heuristic labelling algorithm to speed up column generation. We use two sizes of instances with five problem parameter settings (including 290 instances) to validate the effectiveness of the algorithm. In addition, we provide some managerial insights for decision-makers by comparing the novel mode with the traditional manufacturing mode.

Retail store-SKU level replenishment planning with attribute-space graph recurrent neural networks

Large retail chains routinely make replenishment decisions for inventory at the store Stock-Keeping-Unit (SKU) level, relying heavily on accurate demand forecasting to meet target service levels. We critically examine the current state-of-the-art in demand forecasting, revealing a notable gap in existing models, specifically the neglect of intricate cross-SKU promotional effects. To address this gap, we introduce an innovative solution: the Attributed Space Graph Recurrent Neural Network (ASG-RNN), which encodes demand history and drivers' history using a recurrent neural network, and captures cross-SKU promotional interactions through a graph convolution network. The graph convolutions operate on an attribute space graph constructed using K-nearest neighbor algorithm based on SKU-level static attributes. Initial empirical tests were conducted on a dataset comprising 200 SKUs in the Coffee category from a retail chain with 93 stores. The analysis demonstrates that ASG-RNN, on average, reduces loss by 4.1% compared to the best benchmark, resulting in average inventory cost savings of 4.94%. Notably, for promoted SKUs, ASG-RNN reduces loss by 13% on average, and it can achieve a 30% reduction in loss when the target service rate is high. Further experiments conducted on four additional product categories, including Beer, Laundry Detergent, Milk, and Mayo, yield similar results, showcasing the robustness of ASG-RNN's performance. These findings underscore the practical significance and cost-effectiveness of our innovative approach in enhancing demand forecasting and optimizing replenishment decisions for large retail chains.

Analysis of Raw Material Inventory Control Using the Economic Order Quantity (EOQ) Method: Study of Simping MSMEs, Purwakarta Regency, Indonesia

Raw material inventory is an important aspect in managing small and medium enterprises (SMEs). Effective inventory control can help SMEs increase efficiency and profitability. This research aims to analyze raw material inventory control at Simping MSMEs in Purwakarta Regency, Indonesia, using the economic order quantity (EOQ) method. Data was collected through a survey of 30 Simping MSMEs. The research results show that the EOQ method can help Simping MSMEs in determining the optimal number of raw material orders and minimizing inventory costs. Implementing the EOQ method can result in average inventory cost savings of 15% compared to the method currently used.

ANALYSIS OF CONTROL FOR PRIMARY RAW MATERIAL INVENTORY USING EOQ, POQ, & LFL METHODS

Inventory is the goods that a company plans to own, acquired through purchase or production, with the aim of being marketed to consumers. Companies must be able to overcome challenges in controlling goods to achieve the desired target by minimizing the costs incurred and maximizing the profits obtained by the company. There are three methods that can be used to manage raw material inventory, namely EOQ, POQ, and LFL. This research aims to analyze, using the EOQ, POQ, and Lot For Lot methods, the possibility of supply chain resilience for a company operating in the short-lived perishable food industry. The results show that inventory cost savings for raw materials using the POQ method can achieve the largest savings of up to 6.78% compared to the company's current method. Savings in ordering costs with the EOQ method result in the highest savings, namely 88.68% compared to the company's current method. As for savings in storage costs, the method that was applied showed that the Lot For Lot (LFL) method is 0.6% more cost-effective compared to the company's current method, as well as the POQ and EOQ methods. The POQ method is identified as the one that minimizes raw material inventory costs, amounting to Rp 375,005,043.

Analisis Metode Economic Order Quanity Guna Melakukan Efisiensi Persediaan Bahan Baku dan Efisiensi Biaya di Industri Bakery

One of the companies in East Bekasi is a company engaged in the bread industry, with the product being sandwiches. The purpose of this research is to perform raw material efficiency and cost savings of raw material inventory. In this study, identified the bread production process for 1 period (3 months). It is known that this company ordered raw materials, namely 2100 kg of flour and 480 kg of granulated sugar. However, in reality, only 1537 kg of wheat flour was used and 379 kg of granulated sugar. There is a difference between ordering raw materials and using raw materials so that the costs incurred in carrying out raw material inventory exceed the limits of the costs that have been determined by the company. The method used is the Economic Order Quantity (EOQ) method. The goal is to control the number of raw material orders made by this company and indirectly to save raw material inventory costs. The findings are comparing calculations by companies using EOQ, the efficiency of raw materials after using the EOQ method for wheat flour is 542 kg and granulated sugar is 93 kg and savings in raw material inventory costs for wheat flour is Rp. 156,437 and granulated sugar is Rp. 212,092.

THE IMPLEMENTATION OF INVENTORY CONTROL USING ECONOMIC ORDER QUANTITY METHOD IN IMPROVING THE COST EFFICIENCY OF RAW MATERIALS AND INVENTORY TURNOVER OF THE COMPANY (CASE STUDY IN PT HERLINAH CIPTA PRATAMA)

Inventory has an important role in the smooth production process of the company. Inventory control becomes necessary to do so that the company's operations run according to plan and operational costs can be minimized. This study focuses on knowing the cost efficiency of raw material inventory and inventory turnover at PT HerlinahCiptaPratama by using the Economic Order Quantity (EOQ) method so that the optimal order quantity and the right time in the procurement of raw material inventory can be known. The type of research used is descriptive quantitative with case study method. Sources of data used are primary data obtained through interviews and observations, and secondary data obtained through documentation from archives, records, and company reports. The results of this study indicate that the application of the Economic Order Quantity method in inventory control can increase the cost efficiency of the company's raw material inventory in 2018-2020. This cost efficiency is reflected in the total cost of inventory using the Economic Order Quantity method, which is lower than the total cost of the conventional method of inventory, thus indicating a savings in inventory costs. However, the application of the Economic Order Quantity method does not fully increase the inventory turnover value of all PT HerlinahCiptaPratama raw materials in 2018-2020 because the accumulation of purchases according to the EOQ method in a certain period is greater than the conventional method so that the ending inventory is higher.

Implementasi Metode Period Order Qantity Dalam Pengendalian Persediaan Bahan Baku Crude Palm Oil

PKS Pagar Merbau is a plantation company engaged in palm oil processing. Due to the unpredictable level of demand, determining the timing and number of orders becomes difficult, resulting in increased inventory costs. By using the Period Order Quantity Method, it aims to be able to control Crude Palm Oil raw material inventory which will minimize inventory costs. The Period Order Quantity (POQ) method is a development of the Economic Order Quantity (EOQ) method where the number of orders can minimize the total cost of inventory. The results of applying this method, obtained the optimal number of orders of 1,208,798.12 kg for one order with a frequency of 101 times a year with a safety stock of 3,000,200.61 kg and a reorder point of 3,961,816.26 kg. The total inventory cost according to company policy is Rp. 3,159,298,770.65 while the total inventory cost according to the Period Order Quantity method is 2,879,961,532.42. Then the total inventory cost savings of Rp. 279,337,238.23 or 8.86%

Modeling Inventory Cost Savings and Supply Chain Success Factors: A Hybrid Robust Compromise Multi-Criteria Approach

Determining success factors for managing supply chains is a relevant aspect for companies. Then, modeling the relationship between inventory cost savings and supply chain success factors is a route for stating such a determination. This is particularly important in pharmacies and food nutrition services (FNS), where the advances made on this topic are still scarce. In this article, we propose and formulate a robust compromise (RoCo) multi-criteria model based on non-linear programming and time-dependent demand. The novelty of our proposal is in defining a score that allows us to measure the mentioned success factors in a simple way, in meeting together all three elements (RoCo multi-criteria, non-linear programming, and time-dependent demand) to state a new model, and in applying it to pharmacies and FNS. This model relates inventory cost savings for pharmacy/FNS and success factors across their supply chains. Savings of inventory costs are predicted by lot sizes to be purchased and computed by comparing optimal and true inventory costs. We utilize a system that records the movements and costs of products to collect the data. Factors, such as purchasing organization, economies of scale, and synchronized supply, are assumed using the purchase system, with these factors ranked on a Likert scale. We consider multilevel relationships between savings obtained for 79 pharmacy/FNS products, and success factor scores according to these products. To deal with the endogeneity bias of the relationships proposed, internal instrumental variables are employed by utilizing generalized statistical moments. Among our main conclusions, we state that the greatest cost savings obtained from inventory models are directly associated with low-success supply chain factors. In this association, the success factors operate as endogenous variables, with respect to inventory cost savings, given the simultaneity of their relationship with cost savings when inventory decision-making.

Aplikasi metode Material Requirement Planning (MRP) pada CV. Kana permadi dalam merencanakan kebutuhan bahan baku

The global pandemic of the Covid-19 virus has resulted in high demand for face masks as a means of personal protection against the transmission of the virus. Several Micro, Small, and Medium Enterprises (MSMEs) have started to produce their cloth masks because it is increasingly challenging to obtain surgical masks/medical masks. The planning of raw materials can determine the efficiency of production costs and profits from this business. Material Requirement Planning (MRP) is a system or concept used to manage raw material demand planning to meet the expected quantity of raw materials in production is called Material Requirement Planning (MRP). In this research, the author wants to help SMEs, especially CV. Kana Permadi is engaged in making cloth masks to manage the use of their raw materials optimally. They can reduce the cost of raw material inventory and obtain maximum profit by applying the MRP method. In addition, the authors want to know the effect of MRP on the planning of the raw material inventory of the cloth mask product. This study uses the MRP method with the EOQ technique to predict the number of raw material needs for the next period. Exponential Smoothing and Least Squares methods are used. The study results obtained the total cost of planning raw materials CV. Kana Permadi, with the conventional way from April 2020 to March 2021, is IDR 258,763,200 while using the MRP method with the EOQ technique, and the inventory cost is only IDR 46,827,179. So that the application of EOQ techniques on inventory management CV. Kana Permadi in producing cloth masks resulted in inventory cost savings of Rp.211,936,021. In addition, this study proves that the MRP technique can be used in various industries, including MSMEs engaged in convection, such as the production of cloth masks.

Exploiting Random Lead Times for Significant Inventory Cost Savings

Taking Advantage of the Lead Time Randomness in Supply Chains Randomness in lead times is a major—and increasingly important—issue of inventory management, as a variety of risk factors motivate companies to diversify their supply sources and rely on distributed networks of suppliers. In “Exploiting Random Lead Times for Significant Inventory Cost Savings,” A. Stolyar and Q. Wang show that, surprisingly, instead of being a damaging factor to supply chain performance, randomness may be harnessed for potentially very substantial reductions of inventory costs. Specifically, the theoretical analysis and simulation results in the paper demonstrate that, under certain conditions, appropriately designed novel policies can significantly outperform the conventional base stock policies.

Implementation Of The Just In Time (JIT) Method To Efficiency Raw Material Supply Costs

Inventory control is carried out to obtain savings in inventory costs by keeping the inventory level at an optimal level. One way to reduce inventory levels is to use the Just In Time (JIT) method. The Just In Time (JIT) method reduces inventory levels so asto reduce costs as efficiently as possible based on the fact that Just In Time (JIT) lowers the shipping lot, as a means of applying Just In Time (JIT) within the scope of a large Economic Order Quantity (EOQ) Lot. Based on this research, it can be seen that with the same data but using the Just In Time (JIT) method, the cost of raw material inventory issued is smaller than the costs incurred previously. The total inventory cost incurred before using the method is IDR 32,359,000.00 with the frequency of ordering each raw material 24 times in one period. Meanwhile, with the Economic Order Quantity (EOQ) method, the costs incurred amounted to IDR 17,767,620.00 with the frequency of ordering soybean and palm sugar raw materials 13 times and 20 times in one period. Using the Just In Time (JIT) method costs Rp8,883,810.00 with a frequency of ordering each raw material 4 times in one period. Based on the results of these calculations, the Just In Time (JIT) method costs more efficient inventory.

Application of SMART Method in Insurance Selection Decision Support System

The background of making the system is that there are still many people who still think that insurance is not useful due to their lack of understanding of the need for proper insurance. The purpose of making this system is to make it easier for people who want to register as insurance policy holders or just to get information on what type of insurance is suitable for them. The making of this system is based on data obtained from the Yogyakarta branch of the Prudential company which is then used as the basis for calculating the system using the SMART (Simple Multi Attribute Rating Technique) method. the calculation of the questionnaire which reached 77

ANALISIS PENGENDALIAN PERSEDIAAN KELAPA SAWIT DENGAN METODE ECONOMIC ORDER QUANTITY(STUDI KASUS PADA PTPN IV UNIT USAHA ADOLINA)

The aims of this study is to determine whether Economic Order Quantity method can be applied in Control of Oil Palm Inventory at PTPN IV Unit Usaha Adolina. This research was conducted by comparing between inventory controlaccording to company policy with the Economic Order Quantity method. The results of the study prove that there are total inventory cost savings of Rp. 274,320,143 in 2017, Rp. 302,087,136 in 2018 and Rp 330.090.690 in 2019 if PTPN IV Unit Usaha Adolina used the Economic Order Quantity method in controlling oil palm inventory. Ordering costs by using EOQ is more economical because the frequency of ordering supplies is reduced so as to reduce costs when ordering. However, companies cannot apply the EOQ method for several reasons, namely oil palm cannot be stored for a long time. If the oil palm is stored for too long it will increase the levels of Free Fatty Acid (ALB) which causes the quality of the oil palm to decrease. The second reason, demand for oil palm varies and is not constant in one period.

Ordering Size Optimization of Raw Material to Minimize Inventory Costs using Wagner-Within Algorithm and Silver-Meal Methods

Inventory is one of the current assets that can be classified as company assets. But in reality, inventory often turns into an operational problem that can affect the company's overall performance. This problem occurs due to the incorrect determination of the amount of inventory. Both excess and shortage of inventory can lead to cost overruns. This study applies the Wagner Whitin Algorithm and the Silver-Meal method in the lot-sizing process and the ABC (Always Better Control) method in determining the classification of raw materials at PT. X. Using the ABC method, it is known that the ADC-12 Ingot item has a very significant effect to inventory. Inventory optimization using the Wagner Whitin Algorithm and the Silver-Meal method resulted in the optimal frequency of ordering ADC-12 Ingots is 9 times and the total inventory cost is 994,098,000 IDR as well as inventory cost savings of around 18,403,180 IDR or 1.8% of the cost of existing inventory.

Category Inventory Planning With Service Level Requirements and Dynamic Substitutions

We study a single‐period inventory planning problem for a category of substitutable products. This is an important practical problem facing category managers who have to maintain high service levels for constantly expanding product catalogs. We formulate the problem as a stochastic optimization model that minimizes the total stocking cost subject to service level requirements, which consist of product‐specific and category‐wide targets for inventory availability (ready rates) through the selling season. Our model accounts for stochastic customer arrivals, captures stockout‐based substitutions, and determines initial stocking quantities jointly for all products. Recognizing the challenges that these aspects pose in solving the problem, we propose an optimization‐based method that estimates the ready rates using a deterministic approximation and discretizes the selling season into a finite number of time intervals. This novel modeling approach permits us to recast the stochastic optimization model as a deterministic mixed integer linear program that can accommodate several common stockout‐based substitution schemes. We characterize the worst‐case behavior of this approach to develop performance guarantees. We also implemented and applied this model to randomly generated numerical instances featuring different types of product differentiation and varying in parameter values. We observe that the approach is robust to changes in problem parameter values and yields solutions very quickly, outperforming an enumeration‐based alternative, a practical heuristic, and an approach based on extant literature. Finally, we applied our approach to data from a re‐seller of Information Technology products. Results illustrate that our approach scales well and has the potential to generate savings in inventory costs.

Pengendalian Persediaan Bahan Baku Impor dengan Menggunakan Metode EOQ Probabilistik pada Perusahaan Cat Industri

This research was carried out in a manufacturing company producing industrial paints for steel roofing and walling. The level of sales of industrial paint varies and fluctuates depending on product demand from customers. Due to fluctuating demand and forecasting methods for current product demand that is less accurate based only on demand from the previous period, the inventory of imported raw materials has not been optimal. The ARIMA method is used to forecast and then control imported raw material inventories using the probabilistic Economic Order Quantity (EOQ) method to determine the optimal order quantity, safety stock, re-order points and order frequency so as to minimize the total inventory cost. With the probabilistic EOQ method that implements the existence of safety stock will be able to minimize the risk of a shortage of imported raw materials so that the burden of shortage costs can be minimized. The results shows that the proposed method provides a total inventory cost savings of Rp.493,832,731 or obtained a 66% reduce cost compared to the company method

Improving hospital pharmacy inventory management using data segmentation.

This case study describes the development and empirical validation of an easy-to-implement practical framework for improving hospital pharmacy inventory management. Research suggests various inventory optimization models, which can lead to cost reductions while maintaining adequate service levels; however, they are facing limited adoption in healthcare settings. The main barriers appear to be the high effort and complexity of implementation, the dependence on data that are not available or might not be in the right form, and the one-size-fits-all approach often followed without addressing healthcare sector-specific particularities. A research framework was developed by adapting relevant inventory models to the healthcare context using the concept of data segmentation on the basis of a three-dimensional classification of hospital pharmacy inventory items based on their relative importance, clinical criticality, and consumption pattern. Suitable replenishment policies were assigned to high-impact classes, and an integrated performance-measurement component assesses the framework's effectiveness. The suggested approach was implemented and empirically tested at the pharmacy of a large public hospital using longitudinal data. The results demonstrate substantial improvements with respect to all of the selected key performance indicators and translate into inventory cost savings due to reduced stockholding costs and better synchronization of inventories to demand. Use of standard software functionalities combined with targeted data segmentation efforts significantly improves hospital pharmacy inventory cost performance.

The layout design in reconfigurable manufacturing systems: a literature review

The layout is an important issue in the design of manufacturing systems. In conventional systems, the layout rarely changes after the initial design. However, as the market demands are changing more frequently, layout configurations must be capable of reconfiguring the arrangement of resources to suit new production requirements, while minimising material handling and relocation costs and maximising savings in material flow and inventory costs. This paper presents a literature review on the layout design of reconfigurable manufacturing systems (RMS), focusing on reconfigurable layouts, which have been attracting increasing attention in recent years. A systematic literature network analysis was applied to identify trends, evolutionary trajectories and key issues that are influencing the development of knowledge in this field of study. The results are analysed and discussed using a bibliometric and a chronological citation network analysis. The major findings of this research includes (1) the layout design of RMS must be integrated in the process of RMS design, which, in turn, should be considered as a cyclic process, instead of divided into phases. (2) The core characteristics of reconfigurability and the layout design cannot be dissociated. (3) Operational performance measures are rarely considered in the reconfigurable layout problem, despite their importance. (4) Optimisation approaches have been widely used to solve the reconfigurable layout problem. However, they might not be the most suitable approach to deal with the uncertainty and variability present in manufacturing environments in which reconfigurable layouts are required. Finally, this paper identifies gaps in the literature and suggests directions for future research.

Product platforms design, selection and customisation in high-variety manufacturing

Product platforms represent an effective strategy implemented by manufacturers to cope with dynamic market demands, decrease lead-time and delay products differentiation. A decision support system (DSS) for product platforms design and selection in high-variety manufacturing is presented. It applies median-joining phylogenetic networks (MJPN) for the platforms design and phylogenetic tree decomposition for platforms selection by determining the product family phylogenetic network and defines the platforms at various levels of assembly corresponding to different trade-offs between number of platforms (variety) and number of assembly/disassembly tasks (customisation effort). Product platforms are reconfigured and customised to derive final product variants. The phylogenetic tree is decomposed in multiple levels, from the native platforms to the final variants. New Platforms Reconfiguration Index (PRI) and Platforms Customisation Index (PCI) were developed as metrics to evaluate the platforms customisation effort. A case study of a large family of plastic valves is used to demonstrate the DSS application. It shows reduction of 60% in platforms variety and increases in platform customisation assembly/disassembly tasks by only 20% leading to significant production and inventory efficiencies and cost savings. This methodology supports companies in the design and selection of best product platforms for high-variety to reduce cost and delivery time.

Business intelligence for service parts logistics management

Many leading companies now offer global customers better spare parts services for system maintenance through a complex service logistics network, which extends traditional on-site stocking management to boost profit margins. The major challenge that spare parts service providers face is how to achieve desired service levels at a low cost and minimise excess inventory in the global spare parts supply chain. To address this issue, we demonstrate an inventory redeployment strategy that transforms a conventional spare parts supply chain into a closed-loop, multi-echelon service network capable of redeploying inventory from overstocked to understocked facilities, and thus reducing purchases of high-value spare parts. To assess the quality of our novel approach, we used a network flow optimisation model to analyse the proposed excess inventory redeployment strategy based on an international company's service parts operations. Significant inventory cost savings were identified.