Total Inventory Cost Research Articles

Pengendalian persediaan berbasis economic order quantity pada UD Wenang Perkasa Manado

The economic order quantity is the number of goods ordered that will result in the lowest possible cost. This study will determine how inventory control is based on economic order quantity at UD Wenang Perkasa. This study employs a qualitative research method with a descriptive approach. The study results clearly show that the company's total merchandise inventory costs are higher than they would be if they used economic order quantity. This means that using economic order quantity will save the company costs and increase profits. The company must implement economic order quantity as a method for controlling its inventory because it will allow the company to order merchandise in optimal quantities with minimum total inventory costs, increasing the company's profits.

Study on Optimizing Inventory to Minimize Waste in the Fresh Produce Supply Chain

Abstract: The increasing demand for fresh produce, particularly greens, presents unique challenges in inventory management due to their short shelf life and susceptibility to spoilage. Inefficient management often results in significant waste, leading to financial losses for retailers and increased environmental burdens from the disposal of spoiled goods. Despite various strategies being employed in the agri-food sector, gaps remain in understanding how to optimize inventory levels to minimize waste at the retails level while maintaining high service levels for customers. This research aims to develop an inventory management model tailored to fresh produce that can mitigate these issues by balancing stock availability and minimizing wastage, particularly in large retail shops. To address the research problem, a mixed-integer linear programming (MILP) model was developed, focusing on three service levels—90%, 92%, and 95%—to evaluate the trade-offs between customer satisfaction and total inventory costs. The model incorporates key constraints, including stock balance, shelf-life limitations, and demand satisfaction. The model was validated using actual data, including stock, sales, and wastage records for the month of August, 2024. The objective function aimed to minimize total costs, encompassing holding costs, wastage, and lost revenue. The findings indicate that higher service levels, while reducing stockouts, significantly increase costs due to excess inventory and waste. The most cost-effective solution was observed at the 90% service level, which balanced waste reduction and customer satisfaction. The study also emphasizes the importance of integrating total cost reduction and minimize wastage. Future work should focus on incorporating consumer behavior patterns and extending the model to multi-echelon inventory systems, which include both distribution centers and retail outlets, to capture a more holistic view of the supply chain.

Analisis Just In Time (JIT) Persediaan Krudung di CV. Age Group

Every company always needs inventory, without having inventory, entrepreneurs will be faced with risks such as one time not being able to fulfill their customers' desires. This can happen, because goods are not always available at all times, which means entrepreneurs will lose the opportunity to gain the profits they should get. Therefore, the company must have an inventory management strategy in which the company must ensure that it is able to provide supplies with quality, quantity and time. Therefore CV. Age Groub must pay attention to storage capacity in order to meet demand for supplies of goods, and more attention must be paid to inventory management so that it remains well organized. By using the Just In Time approach. This research aims to optimize the efficient use of warehouse space to store goods with maximum capacity. The results using the Just In Time system approach are more efficient in calculating total inventory costs for the CV age group. The results obtained from analysis using the Just In Time method vary because they are based on various aspects. The greatest savings value is in the aspect of minimum inventory capacity from normal inventory with 66 deliveries which can save approximately 87% of the company's total costs, while from inventory which is added assuming damage, loss and defects of 5% with 73 deliveries can be achieved. save costs by 88%. So if you use the Just In Time system you will save costs of approximately 87-88%.

The impact of transshipment and staggered deliveries on pharmaceutical supply chain performance

ABSTRACT This paper studies the impact of proactive transshipment and staggered deliveries on the pharmaceutical supply chain performance. Therefore, a mathematical model considering transshipment and staggered deliveries is developed to minimise the total inventory management costs of a pharmaceutical supply chain under uncertain demand. To deal with this problem, a two-stage stochastic model is presented and solved using the sample average approximation. The proposed solution shows an important gain of 5% on average in the total inventory management costs when incorporating transshipment of drugs between hospitals and a gain of 32% on average when considering both transshipment and staggered deliveries.

ANALISIS PENGENDALIAN PERSEDIAAN SIMPLISIA PRODUK WEDANG UWUH (STUDI KASUS: CV SALAMA NUSANTARA)

CV Salama Nusantara had not conducted calculations regarding inventory, and there were still raw materials accumulating in the warehouse (overstock), which could result in a reduction in profit and an increase in company expenses. Additionally, it was observed that there was a shortage of raw materials (stockout), leading to production interruptions and the company incurring higher costs for reordering in smaller quantities. This research aimed to understand the inventory system when using ABC and EOQ methods and the difference in Total Inventory Cost (TIC) values between the company’s and EOQ methods The study showed that the ABC method in the simplisia inventory system classified category A with 66.58% of the investment value for one type of simplisia, requiring greater control compared to categories B and C. The use of EOQ for ginger simplisia resulted in the highest EOQ value of 134.72 kg with an ordering frequency of 7 times. The comparison of Total Inventory Cost (TIC) for simplisia such as red ginger, cardamom, sappanwood, cinnamon, cloves, and nutmeg showed that the EOQ method resulted in a lower TIC compared to the company’s policy, with differences of Rp2,365, Rp12,973, Rp14,116, Rp65,494, Rp4,768, Rp16,055, and Rp66,048, respectively.

A deterministic fluid model for production and energy mode control of a single machine

Improving machines’ energy efficiency through dynamic energy mode control to meet demand requirements with minimal energy consumption is a promising approach. This study considers a machine operating in working, idle, off, and warmup energy modes with different energy consumption in each mode. A deterministic fluid model is developed to analyze an energy mode control policy that determines when to keep the machine working, off or idle, and switch to other modes based on the inventory/backlog level to minimize the total energy, inventory, and backlog costs. This approach facilitates the derivation of closed-form expressions for the optimal thresholds and the associated costs. This modeling approach allows us to prove that a policy that operates the machine between the working and off modes or the working and idle modes is always better than a hybrid policy that operates the machine in working, off, and idle modes simultaneously. We use the solution of the deterministic fluid model to propose an approximate policy for machines with stochastic production, warmup, and demand processes. We compare the results of the proposed approximation method with the optimal solution of a stochastic system where the production and warmup times are exponential and the demand inter-arrival times have Erlang distribution. The optimal solution for the stochastic system is determined by solving a Markovian Decision Process (MDP). Our numerical experiments show that the proposed approximation method predicts the optimal policy type for the stochastic case with a 89.3% accuracy, and the average error between the optimal cost and the cost obtained with the approximation method is 1.37% for 729 different cases tested. Furthermore, the computational efficiency of the proposed approximation is around 250 times better than the effort to determine the optimal policy using an MDP approach. We propose this approximation method where the control parameters are given in closed form as an easy-to-implement and effective policy to control energy modes to minimize the total energy, inventory, and backlog costs. Furthermore, we present the deterministic fluid modeling approach as a versatile approach to analyze energy mode control problems.

A constraint programming approach for resource-constrained flexible assembly flow shop scheduling problem with batch direct delivery

Production and distribution are both crucial components of supply chains. Integrated production and distribution scheduling (IPDS) in the context of flexible assembly flow shop scheduling and batch delivery problems is often overlooked. A realistic problem inspired by the production and distribution processes of a dishwasher factory can be modeled as a resource-constrained flexible assembly flow shop scheduling problem with batch direct delivery (RCFAFSP-BDD). In this problem, order requirements are decomposed into several production tasks processed at different stages of the workshop, and are then delivered in batches via a third-party logistics provider to regional distributors at various locations. Auxiliary resource restrictions, hierarchical coupling constraints, machine eligibility restrictions and sequence-dependent setup times, are incorporated into the problem as operational constraints. To the best of our knowledge, this is the first attempt to solve this problem. This work formulates a mixed-integer linear programming (MIP) model to minimize total costs, including tardiness, inventory, and delivery costs. Given the problem’s strong NP-hard nature, the focus is on developing an efficient solution approach using constraint programming (CP). A CP model is proposed and enhanced with multiple redundant constraints. To reduce runtime, two branching strategies are designed for the CP model. Numerical experiments with varying instance scales reveal that the proposed CP model outperforms the MIP model in accuracy and efficiency within the given time limit. The redundant constraints and search strategy can reduce CP model runtime by up to 263.83%. Compared to manual scheduling at the studied factory, the CP model can cut costs by up to 26.59% for real data, offering viable alternatives for factory planners.

Perancangan Kebijakan Persediaan e-Commerce dengan Pertimbangan Permintaan Probabilistik dan Performance Score

Julis, a local company focused on the production and sale of perfumes through e-commerce platforms, is one of the SMEs facing such challenges. Julis has encountered difficulties in managing its product inventory, leading to high inventory costs and low customer order fulfillment rates (fill rate). These issues risk hindering the company's growth and diminishing consumer trust. This research aims to design optimal inventory policies for Julis by considering probabilistic demand and the company's performance score. This performance score is a measure of performance that integrates two key criteria: profit, calculated based on total inventory costs, and fill rate. With the right inventory policies, Julis is expected to improve inventory efficiency, reduce costs—particularly ensuring inventory costs meet the target of less than 40% of the company's total revenue or income—and achieve a minimum fill rate target of 95%, which is considered crucial for maintaining customer satisfaction and supporting business growth.The approach used in this research is an inventory optimization model that combines probabilistic methods in determining Order Quantity (Q) and Reorder Point (ROP). This design aims to address demand uncertainty, which often poses a major challenge in inventory management within the e-commerce sector. Additionally, this research introduces the integration of inventory classification based on performance score, enabling the company to prioritize certain products based on relevant performance criteria. As a result, each product category can be managed more efficiently and strategically according to business priorities. The results of this research indicate that the implementation of the designed inventory policies can significantly improve Julis' performance score. Specifically, in terms of cost efficiency, these policies can reduce inventory costs and increasing the fill rate, which directly positively impacts the company's profitability. Furthermore, the integration of inventory classification based on performance score has also proven effective in helping Julis identify the most critical products to prioritize in its inventory management.

Inventory Planning of Raw Materials for Furniture Products (Case Study: CV XYZ Furniture)

The burgeoning industrial sector in Indonesia has intensified competition among businesses vying for market share. The furniture industry is a prime example of this dynamic landscape. To expand their market reach, companies must demonstrate their ability to fulfill every consumer demand. Failure to meet these expectations will prompt consumers to seek alternatives. One crucial factor in meeting consumer demands is product availability, which is directly influenced by the availability of raw materials within the company. CV XYZ Furniture, a furniture company based in Padang City, exemplifies this challenge. The company produces a diverse range of furniture, primarily utilizing rattan as its raw material. However, CV XYZ Furniture currently lacks a structured inventory planning method for raw materials, resulting in excess inventory for each period. To address this issue, this research aims to establish an optimized inventory planning strategy for raw materials used in furniture production at CV XYZ Furniture. The continuous review system method serves as the foundation for this endeavor. The research outcomes encompass the determination of optimal demand quantity, reorder point, and safety stock. The implementation of the continuous review system model yielded a significant reduction in total inventory costs, amounting to Rp8,625,594 or 2.03%. It can be proven that the continuous review system model is suitable for calculating raw material inventory at CV XYZ Furniture for raw material planning in the following years.

Analisa Pengendalian Persediaan Bahan Baku pada CV. Es Mitra Jaya Surabaya

Manual data collection of raw materials causes inventory problems such as data collection errors, duplicate data, and warehouse loading processes that consume a lot of time. This research was conducted on a milktea boba frenchise company that is very maintaining quality. Raw materials are provided and arranged by the central company, the time of marking goods can only be ordered according to a predetermined schedule. By implementing the Qr-Code Scan system using the Scan-IT to Office application, the warehouse goods data collection process becomes more effective. The results of system implementation were evaluated using the PIECES method with an average satisfaction of 3.58 (SATISFIED) from the six domains tested. The results of the total cost calculation that have been carried out prove that the decrease in inventory costs using Material Requirement Planning (MRP) with the Economic Order Quantity (EOQ) method has decreased. The calculation carried out by the company shows the total inventory cost of Rp.32,346,426.25. After calculations using the MRP method, it decreased by Rp.1,460,780.23.

Sustainable Hybrid Manufacturing and Refurbishing Systems with Substitution

This study considers the planning decisions in a production facility that receives returned products as a contribution to sustainability through the reduction in waste from used products. It involves three processes: the refurbishing and disassembly of returned items and the manufacturing of new items. The process is driven by the demands of new items and collected secondhand items. In this study, we consider a downward substitution, in which new items could be used to meet the demand for secondhand items. The objective is to determine the best production planning schedule to satisfy all demands while minimizing the total costs of production and inventory. We propose and analyze different configurations distinguished by their level of integration and whether they allow substitution. Integration is mainly achieved through the sharing of the inventory of returns between the refurbishing line and the disassembly line, which responsible for feeding the manufacturing process with “as good as new” parts. The best configuration is identified based on the total cost and environmental impact. Four heuristics based on combining relax-and-fix and fix-and-optimize approaches are proposed. Numerical experiments have shown that the heuristics are very efficient, achieving gaps of less than 1% from the optimum in short CPU times for most instances. Numerical experiments have also shown that integration through the sharing of returns inventories leads to considerable cost and environmental benefits.

Supply Chain Management Optimization and Cost Reduction Based on Data Mining Algorithm

Supply Chain Management (SCM) is an evolving industry that constantly evolves, but one constant is the significance of effective inventory control in optimizing profits and customer satisfaction. The primary objective of the research is to identify the most effective method for a business to supply several products while maintaining customer service and keeping inventory costs low. The main aim of this paper's Stochastic Gradient Descent (SGD)-enhanced Support Vector Machine (SVM) approach is to predict ideal order and lack quantities, considering into account historical demand, level of inventory, vendor performance, and cost data. The key goal is to reduce the total cost of inventory (which includes holding, placing orders, and supply-chain costs) while maintaining excellent customer service. In order to accomplish this, the proposed system combines SGD's efficiency to enhance predictions and SVM's predictability. A consumer products business chain's real-world dataset was employed to train and test the algorithm. The dataset provides three years' worth of revenue, stock levels, vendors, and costs. Parameters like Mean Absolute Error (MAE), Inventory Turnover Ratio (ITR), and Service Level Achievement (SLA) have been employed to test the methodology, and parameters were optimized using a 5-fold cross-validation. Based on the study findings, the simulation model obtains an average MAE of 1.81 following 100 training epochs, substantially boosting ITR and SLA. As it effectively and accurately balances inventory levels with customer needs, the findings demonstrate that the SVM model with SGD is an excellent match for IMO.

A Sustainable Inventory Model with Advertisement Effort for Imperfect Quality Items under Learning in Fuzzy Monsoon Demand

In this paper, we proposed a sustainable inventory model with a learning effect for imperfect quality items under different kinds of fuzzy environments like crisp, general fuzzy, cloudy fuzzy, and monsoon fuzzy. We divided the mathematical model into three parts under the learning effect according to the real-time fuzzy components (crisp, cloudy, and monsoon environments) of the demand rate of the items. We minimized the total inventory cost with respect to cycle length in each environment under the proposed assumptions. The non-linear optimization technique is applied for the algorithm and the solution method to find the decision variable. Finally, we compared the total inventory cost under different fuzzy environments and our finding is that the fuzzy monsoon environment is a more effective fuzzy environment than crisp and cloudy fuzzy environments. We have presented a numerical example for the validation of the proposed model and have shown the impact of the inventory input parameters on the cycle length and total inventory fuzzy cost. The managerial insights and future scope of this proposed study have been shown in the sensitivity analysis and conclusion. The limitations, application, future extension and scope, and social implementation have been shown in this research study.

Optimizing Raw Material Inventory Control with Continuous and Periodic Review

PT XYZ is a company that operates in the field of plywood processing with various variations that produce products every day. Plywood or plywood is the product with the most demand every month. This company experiences problems, namely ordering raw materials that are uncertain, ordering raw materials because of probabilistic demand with an uncertain period and the quantity of raw materials that does not match the needs used in the production process, so that it often experiences overstock and stockouts. This research aims to control the optimal supply of sengon wood at PT. XYZ by comparing the company's method with the continuous review (Q) back order method and the periodic review (P) back order method to obtain the minimum total inventory costs. Based on calculations using the company method, the total inventory cost is IDR 32,502,564,700 and the minimum total inventory cost is obtained using the continuous review (Q) back order method of IDR IDR 28,829,527,844. The forecasted demand for sengon wood from May 2024 to April 2025 is 42,109 m3. The company made a total order of 944 m3 and a reorder point of 146 m3, so the total inventory cost generated was IDR 36,151,230,302.

MULTI-ITEM PROBABILISTIC INVENTORY MODELS CONSIDERING DISCOUNT, EXPIRATION, WAREHOUSE CAPACITY, AND CAPITAL CONSTRAINTS

In general, companies have inventory stored in warehouses to be used or sold in the future. To optimize inventory, companies require a model to determine the appropriate order quantity and optimal reorder timing to minimize total inventory costs. To stay within the set capacity, several factors need to be considered in inventory management, such as a product's shelf life, warehouse capacity, and capital. On the other hand, suppliers may offer discounts, and companies tend to take advantage of them. However, they must consider the warehouse capacity and capital availability. This paper constructs two probabilistic multi-item inventory models, considering discounts, expiration, warehouse capacity, and capital constraint. The first model considers all-unit discounts, while the second deals with incremental discounts. We consider three items to be managed and examine three replenishment policies, namely individual order, joint order, and a combined policy of individual and joint order, to minimize the total inventory cost. Sensitivity analysis is performed on the joint order policy to determine the influence of parameter values' changes on the reorder time and total inventory cost.

Implementation of the ABC Analysis to the Inventory Management

In an ever-changing market, inventory management is becoming a major problem that requires thorough planning, supervision, and control. If companies are well managed and have good planning and control, they can last a long time. Poor inventory management is very detrimental to the business as it can cause the company to fail to meet customer or consumer demands, which can lead customers to choose other companies that can meet their demands. Plan your stockpiles to maintain balance, avoid excess or deficiency, and control storage costs. ABC analysis, one of the most common inventory management techniques, divides inventory into three categories (A, B, and C) based on total annual product usage or total inventory cost based on the Pareto principle. The goal of this study is to use ABC analysis in the company's inventory management to identify products in categories A, B, and C based on profitability and sales factors. The research phases include data collection, data preprocessing, exploratory data analysis, ABC analysis, key product analysis, and explanatory analysis. According to the research findings, Segment A accounts for 18% of the total product and generates 78% of the overall profit. Segment B contributes 23% of the total number of items and 16% of all revenue.

Analysis of E-Flute Raw Material Inventory Control using the Material Requirement Planning (MRP) Method in a Cardboard Packaging Company

Cardboard packaging companies are companies thatproduces various types of packaging products including Master Boxes, Archive Boxes, Mosnon Boxes, Small Cartons, Big Cartons). Cardboard packaging companies have not implemented good inventory management when purchasing E-Flute raw materials, soInventory control in these companies often experiences problemsnamely ordering and using inappropriate raw materials, sometimes in excess and sometimes in shortage. Optimum material requirements planning is carried out using the Material Requirement Planning (MRP) method. This method begins by forecasting the number of requests for the future. This forecasting is carried out using the Moving Average, Exponential methodSmootingand Linear Regression. After knowing the price of raw materials, data on material requirements, and costs for inventory and material storage, then a comparison of inventory planning costs is carried out using the Lot For Lot (LFL) method.AndEconomic Order Quantity (EOQ). From the calculation results of the two methods, the method that produces the minimum inventory costs is selected. The Lot For Lot (LFL) technique produces the lowest total inventory costs, namely Rp4.800.000,-

Enhancing Inventory Management through Safety-Stock Strategies—A Case Study

Efficient inventory management, including optimal safety-stock levels, is crucial for operational continuity and cost-effectiveness in various industries. This study seeks the optimal inventory management strategy to minimize costs and determine ideal safety-stock levels. It compares five approaches: the company’s (STAR) current “number of days” method, two alternative models from the literature (the theory of constraints (TOC) replenishment model and the service-level approach), and two newly developed hybrid methodologies (the TOC replenishment model with ABC–XYZ classification and the service-level approach with ABC–XYZ classification). The analysis focused on financial performance, considering inventory holding and shortage costs. Monthly production plans were established and fixed as constant based on predetermined optimum month-end inventory levels derived from each method. Through simulation, actual month-end inventory levels were assessed, comparing total inventory costs (TICs). While unit holding costs (UHCs) were documented in financial records in the company, unit shortage costs (USCs) were not; thus, USCs were examined in three scenarios. The results show that the second proposed hybrid model consistently outperformed the other four methods, including the company’s current approach, significantly reducing TIC. The analysis emphasizes the importance of demand variation in setting safety stocks and demonstrates the second hybrid methodology’s effectiveness in optimizing safety-stock strategies and improving overall inventory management efficiency.

Designing A Multi-item Product Inventory Policy to Reduce Stockouts on CV VLJ

CV VLJ is a distributor of mineral water and bottled water in Cilegon area, Indonesia selling various water brands and is supplied by four suppliers. CV VLJ often runs out of goods in the warehouse when consumers want to buy the goods they are looking for. As a result, consumers are disappointed and buy it at other distributors. This leads to lost sales. Therefore, this study aims to reduce the loss of sales and minimize the total inventory cost at each supplier. The determination of the order quantity and period is calculated using the EOQ, POQ, EOQ multi-item and multi-item EOI methods. The inventory total cost and lost sales can be calculated through the inventory table simulation. Based on the simulation results, sales losses can decrease by 88-99% for all four suppliers and total costs decrease by an average of around 33% compared to actual conditions.

Multi objective optimization approach for multi-item inventory control: A case study in leather industry

PT ASA is a leather tanning company. Almost 65% of the company's assets are allocated for the procurement of raw leather, which consists of goat and sheepskin. In addition to being expensive, the availability of raw leather is also very limited. The company faces a trade-off where, on one hand, the raw material is easily decayed, but on the other hand, its availability is extremely limited, and if there is not enough inventory, the production process will be disrupted. In this research, a multi-objective optimization model is developed for controlling the inventory of raw leather using the Fuzzy Goal Programming approach. The objectives to be achieved are to minimize the total inventory cost, maximize the total quantity of raw leather that meets standards, and minimize the total cost of losses due to decayed raw leather. Based on the calculations, the fuzzy goal programming membership function value is obtained at 0.9155, with a total inventory cost over the planning horizon of IDR 10,341,630,000, a total of 1,279,542 sq ft of raw leather meeting standards, and a total loss cost due to decayed raw leather of IDR 142,911,691.