This study aims to design the warehouse tata letak at PT MIKRON PRESISI INDONESIA that experiences post-relocation irregularities in production facilities, along with adjustments to the area area and available rental costs. The absence of a racking system and clear classification of goods has caused the storage, retrieval, and work safety processes to not run efficiently. The research approach uses a qualitative descriptive method by collecting data through field observations, interviews, and documentation. The tata letak design was carried out by implementing the Activity Relationship Chart (ARC) and Activity Relationship Diagram (ARD) methods, then tested through simulations using FlexSim software with two operator scenarios. The simulation results showed that the proposed alternative tata letak design was able to improve the workflow by grouping goods into seven categories and arranging shelves based on the level of proximity of activities. However, the utilization rate of operators is still relatively low, namely 17.65% and 19.78%, due to the periodic pattern of goods arrival and efficient search times due to a more structured storage system. These findings indicate opportunities for human resource optimization through reducing the number of operators or adjusting workloads. The resulting tata letak design makes a practical contribution to improving warehouse space efficiency and operations, and can be used as a reference for applications for companies with similar characteristics.

Logistics service providers (LSPs) face fierce competition despite increasing demand for third-party logistics (3PL). LSPs must tackle increasing costs and complexity, labor shortages, and scarce warehouse space while meeting individual customers' needs. Studies suggest modularity could provide LSPs a competitive edge, but few methods for modeling logistics service modules exist. Although there are numerous product design approaches, these are seldom applied to services. This study applies insights from product design literature to develop a top-down approach for modeling and modularizing warehouse services. To test the proposed approach, three case studies across seven warehouses were conducted at a world-leading LSP. The study shows the approach can identify and define warehouse service modules using the warehouse service variant master (WSVM) technique, which clarifies the variety of warehouse services in three domains: client, service, and resource. The study also suggests LSPs can reduce complexity by offering warehouse services from standardized service modules.

This study aims to determine and analyze Management Medication Logistics at the Pharmacy Installation of the Donggala District Health Office. The theory used in this study is Siagian's theory which states that management is an activity of planning, organizing, directing and supervising carried out by each organization coordinating various data sources owned to achieve the goals set effectively and efficiently. This research method uses qualitative research that produces descriptive data through in-depth interviews in the form of written or oral statements from people and their actions that can be observed. The location of this research is at the Donggala District Health Office. The selection of informants was 3 people using the purposive method. Data collection was carried out by interview, observation, and documentation. Based on the results of the study, it was carried out according to the management process starting from planning, organizing, directing and supervising. Planning in terms of procurement, storage and distribution of drugs that have been carried out has not gone well. Procurement of drugs in shipping takes 2-3 weeks because the expedition route from outside Palu City, so there is a shortage of drugs. Drug storage is not optimal with a warehouse space that is not large enough. Drug distribution is not optimal with empty drug stocks. Organization has been done effectively with human resources as expected. Direction is carried out by the head of the pharmacy section who coordinates with the health resources sector in providing direction to employees to work well. In terms of supervision, internal meetings and quarterly meetings are held in coordination with the health resources sector in reporting performance, achievements and obstacles that occur in the field.

Online shopping is widely believed to reduce demand for retail stores and presumably decrease energy consumption in the retail sector, yet this relationship has not been studied empirically. We address this gap by first developing a regression model that empirically links historical retail building space needs to in-store shopping time. The historical online shopping time is taken from the 2003–2023 American Time Use Survey, which is then extrapolated to 2030 under two scenarios: a slower growth scenario based on 2003–2023 trends, and a faster growth scenario based on 2015–2023, reflecting a more recent acceleration of online shopping. Future energy use in retail buildings is estimated by combining predicted building space demand with extrapolated trends in energy intensity. Monte Carlo analysis is used to quantify uncertainty. Results show that by 2030, retail building energy demand will decline by 6–12% under the slower growth scenario and by 11–20% under the faster growth scenario, relative to 2018. These changes correspond to reductions in total U.S. commercial building energy demand of 0.7–1.3% and 1.3–2.2%, respectively. While potential increases in warehouse space, delivery services, and residential energy use are not analyzed here, the findings have significant implications regarding e-commerce for retail space and urban energy demand.

Thailand is a major palm oil producer that serves domestic and international markets. With an increased demand, the need for polymer materials used in palm oil bottle production rises. To achieve efficient production processes and minimize the effects of dynamic changes in the market, effective inventory management is required to ensure smooth production with the lowest total inventory cost. This study utilizes a system dynamic (SD) modeling approach to examine the interrelationships of parameters affecting the total inventory cost of polymer material used in palm oil bottle production. The SD model considers parameters: order quantity, discount price, ordering cost, holding cost, warehouse cost, transportation cost, and product cost. The simulation results indicate that the product cost takes over 95% of the total inventory cost. Taking the biggest discount price with the largest order quantity provides the lowest inventory cost in the long term. The warehouse cost ranks second. By fully utilizing the warehouse space, the total inventory cost is reduced. The sensitivity analysis is performed to confirm the model’s validity and suggest strategies to minimize the total inventory cost in the long term. The results suggest that the companies negotiate with the suppliers to match the best order quantities with suitable prices. The selected order quantities should fully utilize the warehouse space to reduce the total inventory cost in the long term.

Abstract In a dynamic global economic landscape, logistics companies have to be able to respond quickly and flexibly to changes in demand. This is where the concept of On-Demand Warehousing (ODW) comes in; an emerging approach that promises to revolutionize the way companies manage their warehouse space. This approach allows companies with temporary excess capacity to offer their space to others, who want to cover short-term demand peaks. By this, this concept provides advantages over traditional models, such as dedicated storage facilities or long-term leasing. However, the dynamic nature of this system presents unique challenges, especially in terms of matching customer requests with available storage in real time. Unlike offline models, where future demands are known or estimated, the Online ODWP requires decisions to be made without prior knowledge of upcoming requests. Our work addresses online ODWP by proposing an innovative methodology that integrates Machine Learning methods with sequential stochastic optimization to enhance decision making processes in real time. In an extensive computational study, we show that the newly proposed approach outperforms state-of-the-art heuristics and yields near optimal solutions within very short run times. Detailed algorithmic analyses as well as managerial insights are derived. We, for instance, provide decision guidelines for platform providers facing acceptance or rejection decisions on dynamically arriving storage requests.

The current economic situation in Ukraine is characterised by profound structural changes caused by the consequences of martial law, which significantly affects the activities of most enterprises. Disruptions in logistics chains, rising transport costs, resource shortages, labour shortages, declining purchasing power of the population and inflationary processes create new challenges for the effective functioning of business. In such conditions, enterprises are forced to review their marketing approaches, sales management methods and pricing policies. That is why it is particularly important to improve the interaction between marketing and logistics systems, which directly affect the economic stability of a company. Marketing logistics is an integrated system for managing material, information and financial flows, aimed at satisfying consumer needs at minimal cost. It combines the analytical function of marketing with practical logistics tools, ensuring the optimisation of supply, production, warehousing and distribution processes. Effective marketing logistics allows companies to respond quickly to changes in market demand, improve customer service and develop competitive pricing policies. In particular, rational inventory management, reduced delivery times and lower logistics costs directly reduce the cost of production, creating the conditions for setting flexible and attractive prices for consumers. In a state of martial law, logistics processes take on particular importance, as they determine a company's ability to deliver goods to consumers on time and keep costs under control. Companies are facing transportation restrictions, a shortage of warehouse space, supply chain disruptions, and rising fuel costs. This necessitates the search for new approaches to pricing management – from flexible tariff strategies to the use of digital cost analysis tools. In such conditions, logistics models based on the principles of Just-in-Time, e-commerce, digital delivery management platforms, and the integration of CRM systems into logistics processes become particularly relevant. In addition, marketing logistics contributes to greater transparency in pricing, allowing companies to more accurately determine their cost structure and identify savings opportunities. In the post-war period, when the economy will be actively rebuilding, the role of logistics in supporting the marketing strategies and competitiveness of Ukrainian companies will only grow. The combination of marketing analytics with effective logistics management creates the basis for the implementation of innovative pricing methods focused on the consumer and real market conditions. Research into the impact of marketing logistics on pricing policy formation is extremely relevant and practically significant in today's reality. Its results will contribute to improving cost management efficiency, adapting marketing strategies to the conditions of the war and post-war period, and strengthening the competitive positions of Ukrainian enterprises in domestic and foreign markets.

Introduction: inadequate inventory management in commercial microenterprises at the global, regional, and local levels is a significant cause of economic losses, lack of liquidity, and business mortality, negatively affecting profitability and customer service. Objective: to characterize the importance of inventory management, the application of the ABC method, and the impact of digitalization. Method: a review of the available literature was conducted in databases such as SciELO, Scopus, and ClinicalKey, from which a total of 16 related articles were consulted. Empirical methods such as logical history and analysis and synthesis were used. Results: inventory management goes beyond mere physical counting. Effective management of products and sales is crucial to business success. Inventory management is affected by several variables, including demand, costs, service level, and lead times. The choice of the appropriate software should be based on scalability, initial cost, required functionality, and return on investment. Proper inventory management is vitally important for businesses, as its proper management and implementation help maximize sales, free up capital for investment in other areas, generate warehouse space, and minimize or eliminate losses. Conclusion: inventory management is a determining factor in the profitability and sustainability of microenterprises. Digitization offers a return on investment by reducing errors, improving operational efficiency, and professionalizing stock control.

The article presents the fundamental problems of optimizing supply processes. It presents: theoretical foundations of optimization, structure of the supply process, cost structure and methods of their estimation, as well as basic methods of optimizing supply costs. The use of the indicated methods does not provide immediate effects. However, it allows for a permanent reduction of costs in the future related to the size of the required warehouse space or capital frozen in inventories. The fundamental dilemma is the correct estimation of the costs of a single delivery and unit costs of maintaining the inventory. The article describes the methodology for their estimation in several variants. It seems the most logical, but its practical application can be quite complicated. The issue of optimizing supply costs should be viewed holistically. Because improving the efficiency of one sub-process can lead to a deterioration in the efficiency of the others.

PT SMART Tbk is one of the largest palm oil processing companies in Indonesia. In its production process, the company uses various types of chemicals stored in a dedicated chemical warehouse. Data shows that the pallet capacity at the chemical warehouse in the Marunda Plant frequently reaches full capacity or even overcapacity, which poses a risk of disrupting production flow, such as delays in the movement of materials and accumulation of stock. This study aims to identify the types of waste that contribute to overcapacity and determine the root causes of the problem. The research is limited to the chemical warehouse at the Marunda Plant, focusing on inbound activities for flavor products (code VO16745) and outbound activities for ingredients (code Salt 300265). Based on the analysis using Process Activity Mapping and Value Stream Mapping, the waste level in warehouse operations was found to be 56%, with a total cycle time of 3,528 seconds and a value-added time of 1,536 seconds. This waste directly contributes to overcapacity and decreases warehouse efficiency. Using the Lean Six Sigma DMAIC approach, the root causes were identified through Fishbone Diagram and Root Cause Analysis. The main contributing factors include large vendor packing sizes and minimum order quantities (MOQ), increasing annual demand, limited manpower, narrow warehouse space, small unloading doors, narrow aisles, and a lack of pallets and shelves. As a solution, it is recommended to expand the warehouse area, add more operators, enlarge the unloading doors, and increase the number of pallets and shelves to optimize operations and reduce the risk of overcapacity.

This study explores the internal dynamics and external influences affecting Lingkar Kacamata, aiming to identify key challenges and opportunities for business growth. The research examines the company’s core customer base, evaluates current marketing efforts, and assesses necessary strategic adjustments. Findings reveal that while Lingkar Kacamata benefits from strong brand recognition, loyal customers, and operational strengths—such as personalized service and customer relationships—it faces limitations due to inadequate warehouse space and a shortage of staff. Moreover, the company has been slow to adopt emerging digital marketing trends, including a delayed presence on platforms like TikTok. Externally, rapid technological developments and the shift to online shopping post-pandemic present both opportunities and threats. The study highlights that Lingkar Kacamata’s tech-savvy, youth-oriented target market values affordability and stylish eyewear. However, limited digital outreach hinders market expansion. Addressing these issues is crucial for strengthening the brand, increasing customer acquisition, and enhancing competitiveness in the digital age.

There are several critical issues in managing the inventory of drugs for the Referral Program (PRB) at the Zhafir Clinic in Palembang. Internal audit data from the clinic shows that during the period January-December 2023, there was a stockout of PRB drugs of 27% of the total demand, with an average stockout duration of 3-5 days per incident. This condition resulted in 42% of PRB patients having to receive alternative drugs that did not match the initial prescription from the specialist doctor. This study aims to identify and analyze the Inventory Control Strategy for the BPJS Referral Program Drug Stock at the Zhafir Clinic in Palembang. The research method is a qualitative descriptive study, with a case study approach. The results of the study show that the just in time strategy in managing the inventory of drugs for the Referral Program (PRB) at the Zhafir Clinic in Palembang can reduce or even eliminate unnecessary inventory. Therefore, the clinic does not store large quantities of drugs in the warehouse. That way, storage costs can be minimized and warehouse space can be optimized for other needs. Just in time strategy can reduce waste, such as inventory excess (excess inventory), waiting time (waiting time), and defects (expired drugs).

General Background: Managing spare parts inventory is crucial for maintaining operational efficiency in manufacturing industries. Specific Background: PT ABC experienced overstocking of steel plate materials, causing increased storage costs and exceeding warehouse capacity. Knowledge Gap: Current company methods lack optimization under space constraints, leading to inefficiencies. Aims: This study aims to determine the optimal order quantity that minimizes total inventory costs while considering warehouse space limitations. Method: The Lagrange Multiplier approach was applied to optimize inventory levels under capacity constraints, using data on demand, ordering and holding costs, and storage volume. Results: The proposed method reduced total inventory costs to Rp46,259,547, compared to Rp185,458,780 using the company’s approach, achieving a 75.05% reduction. Novelty: Unlike standard EOQ-based systems, this study integrates Lagrange Multipliers to handle physical constraints, producing feasible and cost-effective solutions. Implications: Implementing this approach in similar manufacturing contexts can significantly reduce storage costs and prevent inefficiencies caused by overstocking.Highlights: Lagrange Multiplier reduced inventory costs by over 75%. Optimal inventory levels aligned with warehouse limits. Forecasting enhanced ordering precision for future cycles. Keywords: Inventory Management, Lagrange Multiplier, Spare Parts, Warehouse Capacity, Cost Reduction

Intensive development of information technologies and e-commerce in recent years contributes to the rapid increase in demand for warehouse space. Starting from 2021, the growing interest of potential investors in warehouse real estate confirms the relevance of the chosen topic and the conducted research. In the article was used methods of synthesis, analysis of scientific literature on the selected research topic, correlation analysis, concretization, comparison. The article reveals the influence of e-commerce on the investment attractiveness of warehouse real estate. The hypothesis about the growth of rental rates for warehouse real estate objects depending on the growth of the e-commerce market on the example of the real estate market of St. Petersburg has been proved. For this purpose, a vertical and horizontal analysis of the e-commerce market in Russia was carried out, and the main trends in the warehouse real estate segment were identified. A correlation analysis was also carried out correlation analysis, which revealed the dependence of rental rates of warehouse rental rates to changes in the online commerce market.

This research highlights the significance of adopting eco-friendly methods in storage, which is a vital part of the manufacturing supply chain. Although green warehousing is still in its early stages, and there is less literature and empirical data on the subject, its potential for sustainable growth is enormous and requires additional exploration. The study's objective is to identify areas in research that have not been adequately addressed and to establish key performance indicators (KPIs) relevant to green warehousing. This will be achieved by a thorough invesstigation of existing literature. The research methodology employs well-regarded databases such as Publish or Perish, Proquest, and Google Scholar. It utilizes keywords such as "key performance indicators" and "green warehousing". A study of bibliographic data is performed using VOS Viewer to find patterns and concentration of research in this field. The results provide a range of KPIs for environmentally friendly warehousing, such as energy usage, waste management, efficiency in utilizing warehouse space, and monitoring of carbon emissions. The discussion emphasizes the importance of using eco-friendly warehousing practices to decrease greenhouse gas emissions and improve the efficiency of supply chains. The study suggests that researchers create practical case studies based on observation and participate in interdisciplinary research. For professionals in the industry, it recommends adopting quantifiable KPIs and allocating resources to develop environmentally friendly infrastructure.

This paper studies a flow hub location problem (FHLP) stemming from recent trends in network design for e-commerce businesses. Specifically, e-commerce companies are flexible and agile in reoptimizing their logistics networks, including supplier (origin) and customer zone (destination) decisions. Furthermore, a large number of commodities (flows) and a relatively small sales volume for each product incentivize e-commerce retailers to lease warehouse spaces as hubs, yielding a large number of hub location candidates. As such, the proposed FHLP determines the origin and destination of each flow simultaneously with the hub location and flow routing decisions in contrast to the classical hub location problems, where the origins and destinations of all flows are predetermined. To solve this large-scale optimization problem, we propose an optimization algorithm that combines Lagrangian relaxation and Benders decomposition. Novel acceleration techniques, such as a clustering-empowered multicommodity Benders reformulation, learning-empowered elimination tests, and variable reduction techniques, are further developed to improve the performance and convergence of the algorithm. The efficiency of the proposed algorithm is evaluated via extensive computational experiments. The numerical results show that when compared with five other benchmark methods, the proposed algorithm can achieve optimal solutions faster for small-sized test instances and reduce optimality gaps for large-sized ones. For example, the proposed method achieves optimal solutions for a set of 10 test instances, with node sizes ranging from 225 to 450, within 20 minutes on average. In comparison, the automatic Benders decomposition method implemented in the commercial CPLEX solver achieves an average optimality gap of 2% within one hour. History: Accepted by Russell Bent, Area Editor for Network Optimization: Algorithms & Applications. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0367 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0367 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

In this paper, we propose a software implementation to solve the mathematical problem of optimal placement of cargo units on the territory of a multimodal transport and logistics center. Warehouse management in intermodal and multimodal transportation is complicated by the problem of selecting an assortment of cargo in conditions of limited storage space. The solution to this problem should be mathematically correct, automatizable, and scalable, since different types of warehouses and different transport systems are concentrated in the territory of multimodal transport and logistics centers. We propose to apply the genetic algorithm as a mathematical apparatus for solving the above problem and a ready-made software implementation for the optimal placement of cargo units. The algorithm determines the optimal subset of cargo units that can be placed in the warehouse taking into account the weight and value priority constraints of the selected cargo units.

The study was conducted in order to optimize the production processes of NPF GENIX LLC, the leader of detergent and disinfectant production in the Republic of Mari El, by means of lean manufacturing basic tools. The main idea of lean manufacturing is to create a flow of values by exclusion from the production process components with zero contribution to the product manufacture. This approach allows discovering ways to reduce labor costs, new product developing time, product creating time, production and warehouse space reduction, and also guaranteeing the customer maximum quality at minimum cost. Based on practical analysis of the production of Nika Super detergent, the potential for improving the production process efficiency of NPF Genix LLC is analyzed. To do this, a test based on severity assessment of each factor related to losses in lean manufacturing: overproduction, excessive stocks, unnecessary transportation, excessive processing, unnecessary movements, and alteration, was conducted. Using lean manufacturing tools (mapping, “Spaghetti” diagram, inventory accumulation map,“5W+1H”, “Five Whys”), the process locations where the listed losses occur are identified and ways to improve the production process efficiency are proposed. Thus, using the value stream map, it was found that the process time was increased due to downtime and operations that do not create value; using the accumulation map, inventory accumulation sites were identified; due to the “Spaghetti” diagram, problems with a large extent of employee movements and incorrect layout of the work area were discovered.

Logistics warehouses are integral to supply chain management, enabling the efficient storage and movement of goods. However, the dynamic operational nature of these facilities, characterized by high product turnover, often results in suboptimal space utilization. This study addresses the inefficiency caused by partially filled pallets and the honeycombing effect, which leads to substantial storage capacity loss. Focusing on a third-party logistics warehouse managing apparel boxes, the uniqueness of each box introduces specific challenges in space optimization. To mitigate these issues, two integer linear programming models were developed. The first model is utilized for emptying and reallocating products from the predetermined low-capacity shelf cells to new locations. The second model simultaneously identifies both the shelf cells to be vacated and the optimal relocation destinations. Both models aim to minimize the total transportation costs. The first model is suited for rapid reallocation and efficient short-term solutions, whereas the second model offers a more holistic approach to long-term space optimization. These models provide a systematic, data-driven solution for enhancing warehouse space management. The problem is also considered bi-objective, with the objectives of maximizing the number of empty shelf cells and minimizing the total transportation costs. The bi-objective mathematical model was scalarized using the epsilon-constraint method and solved for different epsilon values. This process yielded 39 Pareto-optimal solutions. The results indicate that as more cells are emptied, both the total cost and cost per cell increase. Considering the problem in a bi-objective form has also been advantageous for offering the decision-maker not just one solution but a solution set with different numbers of cells to be emptied and different transportation costs.

Refining dimensional weight assessment is vital for improving warehouse management effectiveness, especially in the logistics and e-commerce sectors. Conventional approaches to calculating dimensional weight frequently cause either overestimation or underestimation, leading to higher shipping expenses and ineffective storage efficiency. This research seeks to create and apply a dimensional weight optimization algorithm that combines rule-based modeling with machine learning to enhance accuracy and storage efficiency, and lower operational costs. This study uses an experimental method, carried out in January 2025 in Malaysia, to evaluate the efficacy of the proposed algorithm in a Warehouse Management System (WMS). The algorithm is evaluated with live warehouse data, utilizing IoT and cloud computing technologies for smooth integration. Important assessment metrics consist of accuracy in dimensional weight assessment, effectiveness of warehouse storage, and decrease in logistics costs. The results indicate that the suggested algorithm reaches an accuracy of 97.2%, greatly exceeding the conventional method's 89.5%, while lowering the mean absolute error from 2.3 kg to 0.8 kg. Warehouse space usage rises from 75.4% to 89.6%, and processing efficiency grows by 37.5%, boosting total warehouse output. Moreover, operational expenses diminish because of enhanced weight evaluation and better space distribution. Future studies should emphasize the integration of deep learning models for enhanced optimization, experimentation with various product categories, and the inclusion of robotic automation to improve warehouse operations. This research highlights the significance of smart dimensional weight assessment in contemporary warehouse management systems.