Inventory Costs Research Articles

An Inventory Model with Fuzzy Valued Inventory Costs under Inflation Price and Time Dependent Demand

An Inventory Model with Fuzzy Valued Inventory Costs under Inflation Price and Time Dependent Demand

Research on vehicle routing problem of automobile part Milk-Run with time windows constrained

Based on the parts inbound logistics as the research object, this thesis introduces Milk-run of third party logistics enterprise mode. Establish mathematical model under the premise of time window, through genetic algorithm optimize the distribution route, and using MATLAB to simulation, the results show that distribution vehicles reduced from 18 to 6. Transportation costs were reduced by 30.05%. Though the research on the circular pick-up mode of incoming logistics of parts and components, this thesis explores the strategy of optimizing its transportation path. On the one hand, it can solve the contradiction of reducing inventory cost but increasing transportation cost to a certain extent, and effectively reduce the cost of the nodes of the whole supply chain, which has certain theoretical value and significance for the research of vehicle path optimization; On the other hand, a reasonable distribution route can reduce driving distance and alleviate urban traffic pressure, while reducing distribution vehicles can reduce urban noise and automobile exhaust emissions, and contribute to the realization of low-carbon environmental protection.

A Trade-off between Overstock and Stockouts of a Perishable Raw Material with Uncertain Demands in a Cosmetics Industry

An inventory system is a crucial aspect of any business that deals with physical goods or products. It refers to the processes, software, and strategies put in place to track and manage the company’s inventory efficiently. In a fluctuating demand situation, the importance of an inventory system becomes even more pronounced. Managing inventory effectively becomes challenging when demand is unpredictable, and having a robust inventory system can make a significant difference. A cosmetics manufacturer produces four types of body scrubs from the same type of rice groats under fluctuating product demand conditions and limited raw material shelf life, resulting in high inventory costs due to unoptimized raw material purchasing. A probabilistic inventory system was proposed based on the newsvendor model, where demands were assumed to be normal distributed. Our computation showed that the optimal rice procurement was 2,235.69 kg/month resulting a service level of 90 percent. The proposed inventory policy was able to increase the expected monthly profit from Rp 11.3 million to Rp 14.2 million, equivalent to a 25 percent increase. Results and insights for future research were discussed.

Spatial layout and optimization of e-commerce logistics management based on combinatorial optimization algorithm

Abstract Warehousing space layout is crucial for e-commerce logistics. A refined and scientific approach to warehouse design enhances space utilization and operational efficiency. This minimizes unused space and idle time, lowers inventory costs, and strengthens the competitive edge of e-commerce logistics. In this study, we conceptualize the logistics space layout issue as a crate problem, harnessing the rapid optimization capabilities of genetic algorithms (GA) and simulated annealing (SA). We propose a hybrid algorithm where SA forms the core, using GA to generate initial solutions and new iterations. Design parameters for the combined operation process are systematically developed. This algorithm is evaluated using both datasets and real arithmetic cases, demonstrating superior performance in large-scale combinatorial optimization problems. It achieves a search accuracy of 0.5% to 3% higher than GA alone, converging more reliably to the global optimum, thus reducing search time and operational scope. This research offers vital theoretical insights for optimizing space layouts in e-commerce logistics management.

Improvement of Inventory Management and Demand Forecasting by Big Data Analytics in Supply Chain

Abstract Inventory management plays a very important role in the process of business operation, providing favorable backing for the smooth operation of production and sales. In this paper, LightGBM and PSO-LSTM models in big data technology are combined to improve inventory management and demand forecasting in supply chains. Then, the relationship between inventory, order, and forecast is elaborated, the two-level inventory cost components and the relationship between them are analyzed, the model constraints are formulated, and a mathematical model for two-level multi-cycle inventory control is constructed. Finally, the single demand forecasting model is compared with the improved model to explore the optimization effect of inventory management after the application of the LightGBM-PSO-LSTM model. The LightGBMPSO-LSTM model is the best fit and can be used for actual demand forecasting. After the optimization of inventory management, the inventory turnover ratio of Company H increased from 8.2 in 2022 to the maximum value of 9.2 in 2023, and the OTIF achievement rate of sales orders increased from 97.9% in 2022 to 99.3% in 2023. This paper provides a successful example of optimizing supply chain inventory management using big data analytics.

Improved cuckoo search optimization for production inventory control systems

The efficient use of the production-inventory control system is of great importance in the industry. In this paper, an investigation study of the impact of an optimal Integral minus Proportional Derivative (I-PD) controller on the dynamic behavior of the production-inventory system is given. The differential equations of the system are first formulated. Then, the I-PD controller is proposed to enhance the responsiveness of the inventory performance. Two swarm optimization techniques, Cuckoo Search Optimization (CSO) and an Improved CSO (ICSO), are employed to tune the adjustable design parameters of the I-PD controller. In order to represent a realistic environment, two simulations are conducted. The first one is when the system is subject to a unit step change in the demand and second is when the demand is randomly. In a comparative performance study for both tuning schemes, the Integral of Time Absolute Error (ITAE) and the Integral of Square Error (ISE) indices were used as evaluation measures. The simulation outcomes using MATLshow the superiority of the ICSO to tune the I-PD controller in terms of reducing the ITAE and ISE indices in comparison with the result obtained from traditional CSO. The result of this improvement indicates that the I-PD controller tuned by ICSO looks likely to better in terms of improving the performance of the system, particularly, by significantly reducing the inventory cost .

Low-carbon supply chain coordination through dual contracts considering pareto-efficiency

ABSTRACT Considering the growing green awareness and increasingly stringent emission regulations, heavy-emitting supply chains are required to re-schedule their operations for environmental responsibility. Although coordination helps the supply chains overcome the decentralised disadvantages to achieve desirable profits, literature considering option contracts under emission constraints, especially combining warehousing contracts, remains scarce. This paper fills this research gap with the novelty in the dual-contract-coordinated decision analysis for achieving profit maximum and emission reduction targets considering customers’ green awareness through option and warehousing contracts, based on the originality of using the Lagrange-Stackelberg optimisation method, which overcomes the difficulty in expressing the first-mover’s decisions and simplifies the problem-solving process. Analytical and numerical results show that Pareto-efficient coordination can be fully achieved by the option contract if the warehousing contract ensures the same inventory costs before and after coordination. Otherwise, partial coordination also raises insiders’ profitability only through the option contract. Purchasing extra emissions with green investment is the best in most cases. The contract-maker should deliberate its contract settings including the option and wholesale prices, as well as warehousing, to develop Pareto-efficient coordination. Sustainability comes at a cost, but coordination raises profitability and emission mitigation in a well-built ETS market.

The Tradeoff of Bullwhip Effect with Inventory Costs in a Supply Chain

The Tradeoff of Bullwhip Effect with Inventory Costs in a Supply Chain

Meta Analysis: Impact of Just in Time Implementation on Cost Efficiency and Profit

Excessive inventory results in losses for the industry due to capital being held back, increased storage costs, incurring tax and inventory insurance costs, the risk of falling prices and quality, and giving rise to the potential for damage and theft. Inventory shortages can also cause losses because they can disrupt the production process. This research identified and analyzed 20 journals related to the implementation and impact of just in time implementation. Implementing just in time can reduce inventory costs, increase time efficiency and production costs so that producers can focus more on quality and increase company profits. The implementation of just in time is accompanied by an increase in other higher costs, this can be caused by constraints in the supply chain and the risk of uncertain demand.

Inventory Control for Eyeglass Supply Using the P Model Based on Sales Products Sales Forecasting (Case Study: Merry Optic Bandung)

Inventory is a resource owned by the company to be used in the production process to meet consumer demand. Companies must be able to control inventory appropriately in order to avoid excess or shortage of inventory by using inventory control. Inventory control is a necessary part of a company that requires an appropriate inventory policy to meet uncertain needs. Based on this background, this study discusses the single item inventory model in the form of photochromic glasses at Merry Optik to find the optimal total inventory cost. In meeting the uncertain needs of the company, the Additive Decomposition forecasting method is used in order to find out the forecast sales data pattern in the future. Uncertain demand causes the inventory system to be probabilistic, so it is necessary to carry out probabilistic inventory control. The P model of the case of back orders was chosen because the range of ordering periods is fixed and the company can buy inventory when it runs out before the time the inventory order is made so that buyers can wait until the inventory arrives. By using Model P for the case of back orders, the company can obtain the period between orders, the total cost of inventory, and the optimal level of service. Based on the results of this study, a pattern of sales forecast data is obtained which repeats every 12 months. Companies must order glasses within a period of 32 days between orders so that it is optimal and able to provide a reduction in the total inventory cost of IDR 21,828,771 with a service level of 95%. Companies can save on inventory costs if they use shorter periods between orders. The total cost of inventory can be more optimal if the company reduces the cost of storing inventory in the warehouse.

Perbandingan Pengadaan Persediaan Bahan Baku Dengan Menggunakan Metode EOQ dan Min Max pada PT XYZ

PT XYZ is an industrial company that produces vehicle spare parts. So far, raw material inventory planning has been carried out based on estimates of past needs, methods like this will result in uncertainty raw material needs which will cause total inventory costs to increase, stockouts, and overstocks. This research aims to evaluate the company's raw material inventory procurement, to obtain optimal raw material inventory procurement. The research methods used are normality tests, forecasting, and comparison of the EOQ & Min method where the best method is proposed to be applied in the company. The data collection techniques used were observation, interviews, and documentation. The research results show that companies should use the EOQ method for steel raw materials and Min Max for copper raw materials, which will save inventory costs by 25.16% or IDR 9,340,698. Apart from that, the safety stock value, maximum inventory, and reorder point are also known to avoid stockouts and overstocks. By implementing inventory procurement methods, companies can find out the maximum inventory that should be in the warehouse, expedite production operational activities, and save space usage which of course benefits the company.

Energy-efficient production control of a make-to-stock system with buffer- and time-based policies

Increasing energy efficiency in manufacturing has significant environmental and cost benefits. Turning on or off a machine dynamically while considering the production rate requirements can offer substantial energy savings. In this work, we examine the optimal policies to control production and turn on and off a machine that operates in working, idle, off, and warmup modes for the case where demand inter-arrival, production, and warmup times have phase-type distributions. The optimal control problem that minimises the expected costs associated with the energy usage in different energy modes and the inventory and backlog costs is solved using a linear program associated with the underlying Markov Decision Process. We also present a matrix-geometric method to evaluate the steady-state performance of the system under a given threshold control policy. We show that when the inter-arrival time distribution is not exponential, the optimal control policy depends on both the current phase of the inter-arrival time and inventory position. The phase-dependent policy implemented by estimating the current phase based on the time elapsed since the last arrival yields a buffer- and time-based policy to control the energy mode and production. We show that policies that only use the inventory position information can be effective if the control parameters are chosen appropriately. However, the control policies that use both the inventory and time information further improve the performance.

Analisis Pengendalian Persediaan Bahan Baku Dalam Perencanaan Proses Produksi Pada Pabrik Tahu Ambon

This study aims to determine the control of soybean raw material inventory applied at the Ambon Tofu Factory and to determine the number of economic soybean raw material inventory orders at the Ambon Tofu Factory. The type of research used in this research is comparative descriptive method research. The data processed is in the form of inventory reports on the purchase and use of soybean raw materials at the Ambon Tofu Factory in 2017. The data analysis techniques used in this study are EOQ, Safety stock, and ROP. Based on the results of research using the EOQ method, the economic ordering amount of soybean raw materials are obtained at 10,480 kg, with a frequency of ordering 17 times, safety stock of 7,221 kg, ROP can be done when the soybean raw material in the warehouse is 1,497 kg, and the total inventory cost is Rp. 335,352. The conclusion of this study shows that the application of the Economic Order Quantity (EOQ) method shows a more economical number of orders to minimize the costs incurred at the tofu factory and can maximize the profit earned.
 Keywords: raw material control, economic order quantity (EOQ)

Inventory Control Analysis of Sticker Raw Materials Using Continuous (Q) and Periodic Review (P) Methods at PT Adiguna Label Indonesia

PT Adiguna Label Indonesia is one of the manufacturing industries engaged in producing label stickers. To produce label stickers, this company requires the main raw material, one of which is substrate. PT Adiguna Label Indonesia is presently having trouble managing the supply of substrate type Raflacoat 22.5 cm. This type of substrate often fluctuates in the amount of demand, so it is not uncommon to experience stockouts that cause many customers to cancel orders due to insufficient material supplies. To reduce the susceptibility to out-of-stock and overstock situations along with the overall cost of inventory at PT Adiguna Label Indonesia, this study intends to manage the inventory of raflacoat substrate 22.5 cm. The method used is Continuous Review (Q) Lost Sales and Periodic Review (P) Lost Sales. The calculation results obtained that the total cost of minimal inventory is the method of Continuous Review (Q) Lost Sales of IDR. 2,160,302,178 with the company method of IDR.2,326,322,927, resulting in cost savings of IDR. 166.020.750.- or 7.14%. After forecasting in March 2023 – February 2025, the total need reached 2304 rolls, then the inventory control of Raflacoat 22.5cm substrate raw materials obtained a number of orders of 193 rolls / one order with a total inventory cost of IDR. 2,739,423,128,-.

Analysis of Cocoa Bean Inventory Control with Continuous (Q) and Periodic (P) Review System Methods at Majapahit Chocolate Factory

Majapahit Chocolate Factory is one of the manufacturing industries that processes cocoa beans into various chocolate products called Majapahit Chocolate. In the production process, Majapahit Chocolate Factory has not controlled raw material inventory efficiently using any method so there is still a frequent mismatch between the amount of inventory and production needs. The higher the amount of inventory, the higher the inventory costs incurred. Based on these problems, this study aims to determine the control of cocoa bean inventory to minimize the total cost of inventory at the Majapahit Chocolate Factory. The results showed that the most minimal total inventory cost was the Continuous Review System method of IDR303,863,596.70 with a savings of 1% from the Periodic Review System method and 4% from the company method. After forecasting the need for raw materials for January-December 2023 using the Weighted Moving Average method, the total demand for cocoa beans during the 2023 period is 61752 kg. Meanwhile, the size of the order quantity is 5739 kg and the reorder point is 8234 kg, so the total inventory cost required in January-December 2023 is IDR1,278,081,375.44.

Integrated Multi-Vendor Single-Buyer Inventory System Model in Textile Waste Processing Industry

PT. Superbtex Nonwoven Division is one of the industries implementing a green manufacturing system in Bandung by using textile solid waste as raw material and processing it into value-added finished products. However, the solid waste generated by consumers and garment factories is very large and cannot be accommodated in its entirety by PT. Superbtex Nonwoven Division. Another problem arises in terms of inventory both for vendors and buyers; delivering waste from vendors to buyers in small quantities and frequently can lower inventory costs for buyers, but concurrently raise transportation expenses for vendors, and vice versa. Therefore this research was conducted to solve the problem of an integrated multi vendor-single buyer inventory system that considers independent transportation, sorting, and disposal to find the minimum total system cost by finding the optimal value of order lot size, delivery frequency, and proportion of order lot size. that each vendor needs to send to the buyer. Problem-solving is done using the Mixed Integer Non Linear Programming (MINLP) method with the LINGO 18.0 solver. The results obtained from this study stated that the sorting costs at vendor 1 (S1), the sorting costs at the buyer (Sb), the transportation variable costs (Fy), the delivery truck capacity (X), and the demand rate (D) have a significant effect to changes in total system cost (TC). So that the five parameters need to be estimated carefully to determine the optimal system cost.

Analisis Penerapan Metode Economic Order Quantity Sebagai Upaya Pengendalian Persediaan Bahan Baku Pada UD Imanuel Tompaso Baru

Economic Order Quantity is a method for determining the optimal purchase quantity so that there is no excess or shortage of raw materials. The aim of this research is to see how the Economic Order Quantity method is applied as an effort to control raw material supplies. The type of research used is descriptive qualitative. The research results show that there are quite large differences in the number of purchases, purchase time, and total inventory costs using the company's own method and the application of the Economic Order Quantity method. The number of purchases of raw materials according to the company is greater than using the Economic Order Quantity method, the company's purchasing frequency is more frequent than using the Economic Order Quantity method and there is a large comparison of total inventory costs. Therefore, it is hoped that companies can apply the Economic Order Quantity method to determine the optimal purchase quantity. The right repurchase time and at the same time can reduce total inventory costs.

Production costs and total completion time minimization for three-stage mixed-model assembly job shop scheduling with lot streaming and batch transfer

Assembly scheduling problems have broad applications in discrete manufacturing enterprises and hence gained extensive attention in academics. However, some important and actual factors including lot streaming, batch transfer of components and mixed-model assembly are yet not well studied. To adapt to actual production and enhance competitiveness, a three-stage mixed-model assembly job shop scheduling with lot streaming and batch transfer is investigated in this work. To tackle this problem, spatial-temporal links among three stages with differentiated production lot size are established and a novel mathematical model is formulated to minimize total completion time and production costs including setup costs, transport costs, and inventory costs. Besides, an improved multi-objective coevolutionary simulated annealing algorithm is developed. To promote the coevolution of processing, transfer, and assembly sub-problems, the multi-attribute combination rule, similarity rule, and consistency rule are respectively designed for each sub-problem. An adaptive execution strategy of sub-problems is trained by Q-learning to maximize the overall benefits. Experimental results show that both the discovered rule and the adaptive strategy are effective, and the proposed algorithm significantly outperforms seven competitive multi-objective algorithms in fixing the studied problem. More importantly, the developed production mode of “processing with lot streaming-batch transfer-mixed-model assembly considering pull production” has a better comprehensive performance than the other three actual production modes.

Fuzzy AHP TOPSIS Methodology for Multicriteria ABC Inventory Classification

Products’ classification according to their importance has been a topic addressed by academia and industry for many years, mainly due to the great importance of this process to obtain efficient inventory policies that reduce lost sales while reducing inventory maintenance costs. This research has to perform an ABC inventory classification in a medium-sized company that sells hardware goods and construction materials, considering multiple quantitative and qualitative criteria. AHP fuzzy TOPSIS multicriteria tool was used as a methodological approach which implies the definition and initial weighting of a set of relevant criteria for the study based on the AHP fuzzy methodology, to obtain an inventory products’ importance assessment according TOPSIS technique procedure. After applying the technique, it is possible to obtain that 0.26% of the inventory was classified as highly critical. Likewise, 5.45% represents products of medium relevance to the organization. Finally, it is observed that many of the products (approximately 94%) have little or almost no impact within the company under study. This methodology was used in a practical case where some criteria were taken from the reviewed literature. In addition, the criticality criterion was used from a financial perspective.

A self-learning knowledge-based MOEA/D for distributed heterogeneous assembly permutation flowshop scheduling with batch delivery

With the requirements of just-in-time and make-to-order, it is necessary to consider the batch delivery in distributed heterogeneous assembly flow shop scheduling as well as the minimization of inventory and tardiness costs. Hence, this work proposes a new mixed integer linear programming model and a self-learning knowledge-based multi-objective evolutionary algorithm based on decomposition. In this algorithm, 4 problem-specific properties are extracted and embedded in the designed solution representation, forward-reverse objective calculation and local search operators. A new hybrid initialization with six knowledge-based NEH heuristics and a random generation is developed to generate better initial solutions. A self-learning selection strategy is developed to coordinate the above knowledge-based local search operators. The experimental study, based on 900 small and 540 large instances, demonstrates that the designed components such as hybrid initialization and self-learning selection strategy are effective, and the proposed algorithm is superior to 6 existing algorithms in the literature in solving this new problem.