Search

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The inventory management of medical items in humanitarian operations is a challenging task due to the intermittent nature of their demand and long replenishment lead-times. An effective response to humanitarian emergencies is often associated with excess inventories, which leads to high costs. Henceforth, using accurate demand forecasts to control the inventories of these medical items is shown to be a significant lever to reduce inventory costs while keeping high service levels. This paper investigates the effectiveness of parametric and non-parametric demand forecasting methods that are commonly considered to deal with items characterised with intermittent demand patterns. Moreover, we propose a new hybrid deep learning method that combines the long short-term memory neural network and the support vector regression. Such combination enables to deal with complex and non-linear data. We conduct an empirical investigation by means of data related to 523 medical items managed in two warehouses of a major humanitarian organisation. The forecast accuracy and the inventory efficiency of the methods are analysed. The empirical results show the high performance of the deep learning methods. These insights are valuable not only in the context of medical items in humanitarian operations, but also for any items with intermittent demand patterns. [ABSTRACT FROM AUTHOR]

We study a production lot-sizing problem inspired by a veterinary pharmaceutical plant in which demands are uncertain. First, we develop a deterministic capacitated lot-sizing model for the production of animal pesticides, performed in three machine-specific stages. Second, we propose a traditional robust optimisation formulation following the popular budget-of-uncertainty approach. Third, we derive a novel fragility-based approach that circumvents well-known issues with traditional robust optimisation approaches, such as the estimation of budgets of uncertainty, the over-conservatism of robust solutions and the sensitivity of solutions to the decision maker's risk attitude. The fragility-based approach is grounded in the idea of minimising violations, over the full uncertainty support, from a user-specified cost target. It avoids the estimation of budgets of uncertainty and produces less conservative solutions via explicit modelling of constraint violation. We demonstrate the effectiveness of our approach on instances built upon real data provided by our industrial partner, a major player in the Brazilian veterinary pharmaceutical sector. The results show that our fragility-based approach reduces average total costs across all instances and maintains greater model stability under different target estimations. It also preserves cost savings when bottlenecks are introduced in production and when inventory costs and capacities are varied. [ABSTRACT FROM AUTHOR]

To optimise production planning, maintenance strategies, and quality control in imperfect manufacturing systems, most existing works set the buffer stocking time at the cycle start time. However, excess inventory holding costs will be incurred if the buffer is stocked too early. This means that while the abovementioned setting can make the optimisation model simple, it might increase costs. Taking the uncertain practical buffer stocking time of an imperfect manufacturing system into account, this study aimed to find the production cycle, maintenance frequency, quality inspection cycle, and number of inspections that will minimise the expectation unit cost of the system. To this end, we developed a new model to optimise production, maintenance, and quality control considering timely replenishment. First, excess inventory holding and shortage costs were considered and the production process was divided into five scenarios based on the buffer stocking time and inspection time for assignable causes. Second, the link between production, maintenance, and quality was addressed by capturing the dynamic and random behaviour of production systems. An x-bar control chart was integrated into the model to monitor quality. A case study and sensitivity analysis were undertaken to verify the effectiveness and superiority of the proposed optimisation strategy. [ABSTRACT FROM AUTHOR]

This study deals with a closed-loop supply chain where inventory levels are controlled by an order-up-to inventory policy. The system under consideration is the cylinder-packaged gas supply chain of Air Liquide company, where empty cylinders used by customers are returned to be filled again by company plants. First, we examine the goodness of fit for demand distributions based on company real data. This enables us to better characterise demands pertaining to different classes of products. Then, we formulate the multi-echelon serial inventory model to be optimised and propose a heuristic to compute the target inventory levels that helps in achieving the desired customer service level while minimising the total inventory cost. The proposed heuristic is easy to implement in the field and gives results close those obtained using a simulation-optimisation approach that is more time-consuming. Finally, we perform a numerical analysis based on company real data and compare several methods that can be used to compute the target inventory levels by varying mainly two assumptions: parameters regarding demand distributions and metrics used to assess customer service levels. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Internet of Things (IoT) provides new opportunities to improve manufacturing lines' performance and in-plant logistic processes. The digital milk-run system represents the new frontier to optimize material handling strategies but is still not fully exploited to address material distribution depending on the time slots required by the manufacturing lines. Therefore, to fill this gap, this paper investigates the actual integration of the milk-run system with an IoT system. An analytical model for planning a dynamic routing strategy for tugger trains to deliver the materials to different workstations of a production line has been developed. The proposed model provides a materials distribution consistent with the time slot required by the manufacturing line, ensuring the minimisation of the total distance of the routes. An algorithm developed in Python is proposed to solve the NP-hard problem (nondeterministic polynomial time problem). The model has been applied to a real case of a worldwide automotive company to validate and prove its efficacy and efficiency. Indeed, compared to the current in-plant logistic strategy, (i) the inventory stock of each workstation was ensured, (ii) the average utilization rate of the tugger trains' fleet was improved, and (iii) the daily path was minimized. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to enlarge the scope of existing EOQ-type models by incorporating uncertain product returns that may include time or quantity restrictions. In this inventory control problem, the item's demand is deterministic and occurs at a known constant rate over time. Product returns arrive according to a Poisson process. The amount returned to the seller at each return epoch is a random or fixed fraction of the accumulated number of units (net of previous returns) that have been demanded so far within a stock cycle. The ordering policy is a continuous review policy that orders a fixed quantity every time an order is placed. The replenishment lead time is instantaneous, and shortages are not allowed. The objective is to find optimal order quantities by minimising the long-run average total costs per unit time. Costs include a fixed plus proportional cost per order, and a holding cost per unit held in inventory per unit time. A level-crossing approach is used to derive a closed-form expression for the expected ordering and inventory holding costs as a function of the order size and other relevant parameters. [ABSTRACT FROM AUTHOR]

This paper focuses on the impact of return decisions on the bullwhip effect in the online closed-loop supply chain. We investigate the optimal return modes when the inspection system is undertaken by the logistics centre or the remanufacturer. Then, we optimise the online retailers' return decisions of minimising the inventory cost and bullwhip effect under different supply chain circumstances. Distinctive from previous conclusion that the inspection operation is usually undertaken by the remanufacturer to sort the returned products, the analysis results reveal that the optimal decision of mitigating the bullwhip effect in most cases is to set the inspection system on logistics centre. Besides, consumers' return and exchange behaviours in e-commerce have different impacts on the supply chain efficiency. The product return can not necessarily mitigate the information distortion in online closed-loop supply chain. The research provides insights for managers to strategise about return policies and resource allocation in different e-commerce contexts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In order to support a low-carbon economy and manage market competition, location–inventory–routing logistics management must play a crucial role to minimize carbon emissions while maximizing customer satisfaction. This paper proposes a bi-objective mixed-integer nonlinear programming model with time window constraints that satisfies the normal distribution of stochastic customer demand. The proposed model aims to find Pareto optimal solutions for total cost minimization and customer satisfaction maximization. An improved non-dominated sorting genetic algorithm II (IMNSGA-II) with an elite strategy is developed to solve the model. The model considers cost factors, ensuring that out-of-stock inventory is not allowed. Factors such as a carbon trading mechanism and random variables to address customer needs are also included. An entropy weight method is used to derive the total cost, which is comprised of fixed costs, transportation costs, inventory costs, punishment costs, and the weight of carbon emissions costs. The IMNSGA-II produces the Pareto optimal solution set, and an entropy–TOPSIS method is used to generate an objective ranking of the solution set for decision-makers. Additionally, a sensitivity analysis is performed to evaluate the influence of carbon pricing on carbon emissions and customer satisfaction. [ABSTRACT FROM AUTHOR]

In this paper, we aim to investigate a model for a single item production system (economic production quantity) under the assumption that the demand rate is triangular fuzzy number. The other parameters, the rate production, fixed cost production and inventory cost are to be constant. Hence, the length cycle production is fuzzy number. Furthermore, the holding cost per cycle is fuzzy. Here, we need fuzzy integral to compute fuzzy holding cost. Accordingly, the annual total cost is also fuzzy. Actually, we can not formulate membership function for fuzzy holding cost and fuzzy annual total cost. The fuzzy ranking cannot be used in this model. However, we will do it numerically by alpha-cut approach to plot the fuzzy costs. [ABSTRACT FROM AUTHOR]

Micro, Small and Medium Enterprises (MSMEs) have an important role in economic growth in Indonesia. Inventory management is one of the key aspects in the operational sustainability of MSMEs, especially raw materials used in the production process. One of them is XYZ MSMEs which is a producer of meatball pentol and meatball tofu products which uses several types of raw materials in its production process. The problem currently being faced is that there are often shortages and excesses in purchasing raw materials during procurement. This research aims to analyze the cost of raw material inventory at XYZ MSMEs using five different Lot Sizing methods, namely Economic Order Quantity (EOQ), Period Order Quantity (POQ), Silver Meal (SM), Least Unit Cost (LUC), and Part Period Balancing (PPB). This research uses historical data from MSME XYZ to identify differences in raw material inventory costs between the five MRP methods used. The results of this research show that the Silver Meal (SM) method produces the lowest total inventory costs for beef and tapioca starch as raw materials. Meanwhile, the Part Period Balancing (PPB) method produces the lowest total inventory costs for MSG raw materials. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The choice of inventory cost flow assumption is a key financial reporting decision. The resurgence of inflation has renewed interest in this option. We derive algebraic models of the LIFO‐FIFO difference in cost of goods sold and the LIFO tax savings as a function of inflation, turnover, inventory increments, and tax rates. Managers and researchers can use these models to estimate the likely financial and tax impacts of LIFO adoption. Historic trends in public company LIFO usage since 1971 are consistent with the changing impacts estimated by our model. We infer an approximate cost level of using LIFO based on historic public company behavior. In the 1971–2022 period, LIFO adoptions were only common when the tax benefit in our model exceeded about 0.35% of COGS. Even when inflation rose in 2021 and 2022, the tax savings remained below this threshold, and there were far more LIFO abandonments than adoptions. [ABSTRACT FROM AUTHOR]

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 MATLAB show 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. [ABSTRACT FROM AUTHOR]

We study an online distribution problem in which a producer has to send a load from an origin to a destination. At each time period before the deadline, they ask for transportation price quotes and have to decide to either accept or not accept the minimum offered price. If this price is not accepted, they have to pay a penalty cost, which may be the cost to ask for new quotes, the penalty cost for a late delivery, or the inventory cost to store the load for a certain duration. The aim is to minimize the sum of the transportation and the penalty costs. This problem has interesting real-world applications, given that transportation quotes can be obtained from professional websites nowadays. We show that the classical online algorithm used to solve the well-known Secretary problem is not able to provide, on average, effective solutions to our problem, given the trade-off between the transportation and the penalty costs. Therefore, we design two classes of online algorithms. The first class is based on a given time of acceptance, while the second is based on a given threshold price. We formally prove the competitive ratio of each algorithm, i.e., the worst-case performance of the online algorithm with respect to the optimal solution of the offline problem, in which all transportation prices are known at the beginning, rather than being revealed over time. The computational results show the algorithms' performance on average and in the worst-case scenario when the transportation prices are generated on the basis of given probability distributions. [ABSTRACT FROM AUTHOR]

This paper presents a profit optimization model for substitute products in a competitive, time-sensitive market with scarcity and shifting user preferences. The model maximizes profit, considering production costs and inventory maintenance. It uses a discrete choice model to represent demand, sensitivity to price, availability, and changing preferences. A two-phase PSO-type metaheuristic solution tackles the nonlinear, recursive model, efficiently managing inventories and evolving consumer preferences. The model integrates production decisions, inventories, and sales prices, considering scarcity conditions and user preferences. It uses a multinomial logit for the consumers' demand function with soft exogenous constraints, which influence utility and change consumption preferences and choices. This research offers a tool for companies to manage stock, production, and pricing in a context where goods are substitutes, providing a new perspective on business strategy. [ABSTRACT FROM AUTHOR]

The raw material purchasing (RMP) problem is to determine the purchasing quantities of raw materials in given periods or cycles. Raw material purchasing optimization is crucial for large-scale steel plants because it is closely related to the supply of raw materials and cost savings. The raw material purchasing of large-scale steel enterprises is characterized by many varieties, large quantities, and high costs. The RMP objective is to minimize the total purchasing cost, consisting of the price of raw materials, purchasing set-up costs, and inventory costs, and meet product demand. We present a mixed integer linear programming (MILP) model and a column generation (CG) solution for the resulting optimization problem. The column generation algorithm is the generalization of the branch and bound algorithm while solving the linear programming (LP) relaxation of MILP using column generation (CG), and its advantage is to handle large-sized MILPs. Experimental results show the effectiveness and efficiency of the solution. [ABSTRACT FROM AUTHOR]

Every industry on average spends 70 percent on raw materials (inventory). Therefore, there is a need to know the raw material cost and also there is great importance to understand the inventory management system of this industry. The study helps a log to various departments to take steps to control the inventory process. In this competitive business world, each and every business organization need inventory management system for determining what to order, when to order, where and how much to order so that purchasing and storing costs are the lowest possible without affecting production and sales. Thus, inventory management control incorporates the determination of the optimum size of the inventory-how much to be ordered and when after taking into consideration the minimum inventory cost. The overall inventory management includes design and inventory control organization with proper accountability establishing procedure for inventory handling disposal of scrap, simplification, standardization and codification of inventories, determining the size of inventory holdings, maintaining record points and safety stocks, economic order quantity, Economic Order Quantity analysis and VALUE analysis and finally framing an inventory manual. [ABSTRACT FROM AUTHOR]

This study aims to develop a sustainable freight transportation network considering capacitated cross-docks for minimising the overall supply chain costs, including carbon emission cost. The problem is inspired by a major retail company based in India, which would like to expand its product portfolio in the new region. A mathematical model is developed to minimise total costs encompassing transportation cost, pipeline and retailers inventory cost, fixed cost of cross-dock and carbon emission costs. The deterministic time dependant demand, multiple products and multiple sourcing and distribution are some of the challenges faced by the retail industry. A two-level self-adaptive variable neighbourhood search algorithm is applied to solve a computationally complex problem. The results based on a two-level self-adaptive variable neighbourhood search algorithm are compared with the variable neighbourhood search algorithm to test the robustness of the developed model. Results reveal that an increase in retailers over suppliers significantly influences the number of open cross-docks. A multiple-case scenario approach captures the implications of varying capacity on the number of open cross-docks; thus, supporting the freight distribution managers in making sustainability-driven decisions. [ABSTRACT FROM AUTHOR]

In supply systems, maintaining stock levels is a major challenge. The purpose of this article is to determine the number of orders in a continuous review model. The demand rate is erratic, as it is dependent on promotional advertising on an ambiguous basis. This article considers learning and memory in an unpredictable setting. The fundamental crisp model is extended with a fuzzy formulation, and learning is included. Demand promotion is included with the complete backlog in models, which are based on finite-horizon time scales. The memory present in real-world situations is represented using Caputo's style of nonintegral ordered calculus. It focuses on the percentage of each process that is devoid of scarcity as well as the overall cycles of replenishment. After analyzing each model numerically, a parametric sensitivity analysis is performed to show the trade-offs and efficacy of the decision criteria. Longer memory and higher learning rates have been found to result in a cheaper total cost. The number of fuzzy learning orders and the overall cost shift in an inversely proportionate manner as learning increases.The fuzzy learning case's total cost decreases rather than the fuzzy case's if the differential fractional order rises within the range. The crisp model, which derives minimal total cost, is relevant as a mathematical bound for the other two realistic models that incorporate demand volatility and are the main goals of this simulation. These models ameliorate the burden of implausibly perfect demand knowledge. This simulation is used to analyze the increases in the inventory system's output for each model. With learning, the inventory and total cost are less than those of a pure fuzzy system: the fuzzy model's delta over the crisp limiter is reclaimed by 62% for the order size and by 39% for the cost over the considered horizon. This accurately simulates how experience leads to better decision-making. A move to shorter memory has twin manifestations in order size: differential memory index generates linear reduction with a mean rolling fall of 10.5% per 0.1 change but the integral index causes a stronger (20%) quadratic reduction. For the total cost, their roles are reversed: both cause quadratic increase with shorter memory but the differential index has the stronger impact - a mean rolling rise of 11% per 0.1 change compared to 6.5% for the integral index. [ABSTRACT FROM AUTHOR]

A location‐allocation problem faced by a company that aims to locate warehouses to supply products to a set of customers is addressed in this paper. The company's objective is to minimize the total cost of locating the warehouses and the cost due to inventory policies. However, these inventory decisions are made by a different decision‐maker. In other words, once the company makes the location decisions, the decision‐maker associated with each warehouse must determine its own order quantity. Warehouses are allowed to have a certain maximum number of backorders, which represents an extra cost for them. This situation can be modeled as a bilevel programming problem, where the upper level is associated with the company that needs to minimize the costs related to location‐allocation and the total orders of each warehouse. Each warehouse is associated with an independent lower level, in which a warehouse manager aims to minimize the total inventory cost. The bilevel problem results in a single‐objective upper‐level problem with non‐linear, multiple independent lower‐level problems, making it generally challenging to find an optimal solution. A population‐based metaheuristic under the Brain Storm Optimization algorithm scheme is proposed. To solve each non‐linear problem associated with the lower level, the Lagrangian method is applied. Both decision levels are solved in a nested manner, leading to obtaining bilevel feasible solutions. To validate the effectiveness of the proposed algorithm, an enumerative algorithm is implemented. A set of benchmark instances has been considered to conduct computational experiments. Results show that optimality is achieved by the proposed algorithm for small‐sized instances. In the case of larger‐sized instances, the proposed algorithm demonstrates the same efficiency and consistent results. Finally, interesting managerial insights deduced from the computational experimentation and some proposals for future research directions are included. [ABSTRACT FROM AUTHOR]

This paper deals with the more realistic version of the lot sizing and scheduling problem where we have a single machine that process a number of different products and for each product we have a set of delivery dates and the quantities to deliver at each of these dates. The objective is to minimise the sum of setup costs and inventory holding costs including the holding cost of products from the finish time of each production lot to the next delivery date. In addition, a setup can be carried over from a production run to the immediately succeeding one if the same product is processed during these two runs. A mathematical formulation of this problem is given as well as two specially designed solution heuristics. As the literature does not provide any other method to solve this problem, only the results obtained by these two heuristics are compared. [ABSTRACT FROM AUTHOR]

Currently, there are many mathematical models and tools for inventory management and optimization. Based on the literature review, the authors conclude that inventory management tools and models are mostly one-sided and focused on the cost aspect of the inventory management process. The research goal was to propose a set of criteria for inventory optimization as a basis for the development of an inventory management model whose structure would consider both the economic aspect and the stochasticity of demand and the reliability of delivery. Optimization criteria are based on extensive analyses of influences and correlations between logistic chain subsystems in relation to inventory. The interactions of the subsystems are then mathematically expressed, which represents the main output of the created set of optimization criteria. The defined criteria can be used as a basis for creating a new optimization model of inventory management. [ABSTRACT FROM AUTHOR]

The primary objective of this article is to develop a mathematical model and determine the optimal policies of an inventory system involving power demand and controlled deterioration through preservation technology. This model comes in handy in a power demand-oriented inventory system with demand high at the end of the period. The model incorporates backlogged shortages and linear holding cost. The triangular neutrosophic numbers (TNN's) are used for a nuanced representation of uncertain and imprecise inventory-related expenses. An efficient algorithm is constructed to minimize the total cost, and obtain optimal positive inventory time, optimum cycle time and minimum preservation technology investment. Few numerical examples are used to illustrate and validate the model. The comparative study conducted between models with and without preservation technology investment reveals a significant reduction in total inventory costs facilitated by the preservation facility. Also, the numerical results obtained in crisp and neutrosophic environment are compared. Specific previously obtained results are discussed to illustrate the theoretical findings. Sensitivity analysis of the model provides managerial insights replicating reality. [ABSTRACT FROM AUTHOR]

We study a shipment consolidation problem commonly faced by companies that outsource logistics operations and operate in a commit-to-delivery mode. It involves delivering a given set of orders to their destinations by their committed due times using multiple shipping methods at the minimum total shipping and inventory cost. The shipping cost is generally nonlinear in shipping quantity and can be represented by a subadditive piecewise linear function. We investigate two shipping scenarios, one involving long-haul shipping only and the other involving joint long-haul and short-haul shipping. We develop analytical results and solution algorithms for the shipment consolidation problem under each shipping scenario. The problem under the first shipping scenario is shown to be strongly NP -hard. We find that a simple policy, called the First-Due-First-Delivered (FDFD) policy, which assigns orders with earlier delivery due times to shipping methods with earlier destination arrival times, is very effective. This policy enables us to develop a polynomial time algorithm, which not only solves the problem under the concave shipping cost structure optimally but also achieves a performance guarantee of 2 for the problem under the general subadditive shipping cost structure. For the problem under the second shipping scenario, we extend the FDFD policy for long-haul shipping and derive another policy, called the No-Wait policy, for short-haul shipping. We use these policies to develop a polynomial time algorithm and analyze its performance guarantee. Our computational experiments show that the algorithm significantly outperforms a commercial optimization solver, and its performance is robust across different parameter settings that reflect various practical situations. This paper was accepted by Jeannette Song, operations management. Supplemental Material: The data and e-companion are available at https://doi.org/10.1287/mnsc.2023.4835. [ABSTRACT FROM AUTHOR]

Problem definition: Medical operations require a large volume and variety of consumable supplies that are kept in hospital inventory and replenished on a regular basis. Stringent requirements on the availability of these supplies, together with high variability in their daily usage, contribute to the high inventory costs of the surgical departments in hospitals. We investigate the value of utilizing Advance Booking Information (ABI) on elective surgeries—which are often booked up to months in advance—in reducing inventory costs. Methodology/results: We study a single-item, periodic-review, stochastic inventory control problem, where the item demand in each period is driven by the number and type of surgeries requiring the item, and with the available information on elective surgeries integrated into the ordering decisions. Given that item usage from each case is uncertain and only realized after the surgery, ABI provides imperfect information on future demand. Through exact analysis of a simplified version of the problem, as well as extensive numerical experiments using synthetic and real data, enabled using a state aggregation technique, we provide insights on and quantify the value of using ABI as a function of the number of periods of ABI integrated into the ordering decisions. We identify a relevant parameter regime—namely, high backlog (relative to holding) costs and when surgeries are booked sufficiently in advance—where the value of using ABI could be significant and the majority of the benefits can be gained through incorporating only one period of ABI beyond the order lead time. In a case study conducted using real data, we observe up to 26% reduction in average inventory levels, without violating the service levels. Managerial implications: By incorporating readily available elective surgery schedules into replenishment decisions of surgical supplies, hospitals could significantly reduce inventory costs without compromising the availability of the supplies. Funding: This work was partially funded by The Ontario Ministry of Government and Consumer Services (MGCS). The views expressed in the paper are the views of the authors and do not necessarily reflect those of the Province. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0063. [ABSTRACT FROM AUTHOR]

This study examines the inventory management (IM) practices, reasons, influencing factors and impact of IM on the performance of micro, small and medium enterprises (MSMEs). Primary data was collected using a survey questionnaire among 40+ MSMEs in Baddi Industrial Area, Himachal Pradesh, India. The study reveals that MSMEs generally follow poor IM practices and non-scientific forecasting methods, leading to capital being tied up due to excess inventory or stockouts, delayed delivery, customer dissatisfaction, reduced profits, operational inefficiencies, and diminished competitiveness. Factors such as limited awareness, lack of access to technology and finances, and a shortage of skilled manpower contribute to inadequate implementation of scientific IM practices. The study establishes that poor IM practices and unscientific forecasting practices have an impact on the inventory cost, which in tun impacts the firm's performance. To address these challenges, the study makes many recommendations. [ABSTRACT FROM AUTHOR]

In this paper, we develop a two-level supply chain model with low carbon emissions for defective deteriorating items under learning in fuzzy environment by using the double inspection process. Carbon emissions are a major issue for the environment and human life when they come from many sources like different kinds of factories, firms, and industries. The burning of diesel and petrol during the supply of items through transportation is also responsible for carbon emissions. When any company, firm, or industry supplies their items through a supply chain by using of transportation in the regular mode, then a lot of carbon units are emitted from the burning of petrol and diesel, etc., which affects the supply chain. Carbon emissions can be controlled by using different kinds of policies issued by the government of a country, and lots of companies have implemented these policies to control carbon emissions. When a seller delivers a demanded lot size to the buyer, as per demand, and the lot size has some defective items, as per consideration, the demand rate is uncertain in nature. The buyer inspects the received whole lot and divides it into two categories of defective and no defective deteriorating items, as well as immediately selling at different price. The fuzzy concept nullifies the uncertain nature of the demand rate. This paper covers two models, assuming two conditions of quality screening under learning in fuzzy environment: (i) the buyer shows the quality screening and (ii) the quality inspection becomes the seller's responsibility. The carbon footprint from the transporting and warehousing the deteriorating items is also assumed. The aim of this study is to minimize the whole inventory cost for supply chains with respect to lot size and the number of orders per production cycle. Jointly optimizing the delivery lot size and number of orders per production cycle will minimize the whole fuzzy inventory cost for the supply chain and also reduce the carbon emissions. We take two numerical approaches with authentic data (from the literature reviews) for the justification of the proposed model 1 and model 2. Sensitivity observations, managerial insights, applications of these proposed models, and future scope are also included in this paper, which is more beneficial for firms, the industrial sector, and especially for online markets. The impact of the most effective parameters, like learning effect, fuzzy parameter, carbon emissions parameter, and inventory cost are shown in this study and had a positive effect on the total inventory cost for the supply chain. [ABSTRACT FROM AUTHOR]

The research focuses on multi-retail distribution with a strategic distribution network. Challenges include intense competition, logistical and transportation complexities requiring robust infrastructure like warehouses and efficient supply chain management, as well as operational inefficiencies and distribution costs. To address these issues, a model is applied to make strategic decisions, such as determining the necessary number of facilities to minimize total supply chain network operational costs and infrastructure for retail distribution. The outcome is a model that introduces a novel approach to enhancing supply chain efficiency and effectiveness from production to distribution stages, thereby reducing system costs, including ordering costs and inventory handling. Costs and loss costs resulting from remaining products produced can be minimized by considering networks, multiple suppliers, multiple warehouses, Distribution Centers (DC), multiple retailers, and multiple products, factoring in the distances between facilities in the network. Subsequently, comprehensive testing of inspection, distribution, and retail parameters is conducted, with a focus on specific periods and product types. When applying this model, certain characteristics need to be considered regarding the importance of selecting efficient suppliers of goods, such as procurement, performance improvement, and the number of supply chains and supply chain systems. This research introduces novelty in production methods that can lead to increased customer satisfaction, sales, market share, profit margins, more effective brand advertising, and revenue streams. In this process, the research undergoes a training, testing, and validation process in forming a strategic multi-retail distribution network model, spanning a total of 52 epochs. This process yields accuracy values for training at 90 %, testing at 92 %, and validation at 94 % [ABSTRACT FROM AUTHOR]

In today's environment, organizations utilise Machine Learning based-models to keep stocks depending on the demand for a particular type of product. This article develops an inventory model considering the imperfect deteriorating product in a fuzzy environment. The shortages are allowed and partially backlogged. Since the deterioration rate and defective percentage in quantity in the received lot may not be predicted precisely because it depends on many uncertain situations, therefore both are considered fuzzy variables. This study aims to determine the optimal ordering quantity and replenishment period to optimize (minimize) the average overall cost with carbon emission cost. The defuzzification process is done using the sign distance approach method. A methodology based on Machine Learning is used to demand forecast seasonally. Some numerical examples are taken to validate the proposed mathematical model. The findings demonstrate the generation of direct month-wise predicted demand for deteriorating products based on the input of the month value, enabling organizations to optimize their inventory management according to forecasted demand. A comparative analysis is conducted between fixed and month-wise forecasted demand by highlighting the advantages of machine learning-based forecasting approaches. Sensitivity analysis performs to examine the behaviour of several parameters on an optimal solution and provides some managerial insights. [ABSTRACT FROM AUTHOR]

In a context of economic volatility, multiple crises, and growing environmental concerns, the optimization of physical and financial flows has become imperative for businesses. In a classical Inventory Routing Problem (IRP), only the physical flow of goods is considered. This paper introduces a link between a two-echelon Inventory Routing Problem and the financial flows of the Working Capital Requirement, where the products are dispatched from warehouses to fulfill customer demand. We propose a two Echelon Inventory Routing Problem under Working Capital Requirement (2E-IRP-WCR) model which allows us to evaluate the integration of physical and financial flows so as to further reduce the total cost, where the total cost consisting of ordering and inventory costs, transportation costs and financial costs. Numerical tests, established for multi-suppliers, multi-warehouses, multi-customers, single-product and finite capacity cases, are presented to demonstrate the pertinence of our approach. [ABSTRACT FROM AUTHOR]

The present chapter developed a preservation-based inventory model under the condition of carbon emissions and the effect of learning on perishable goods. Carbon emissions exit from many sources and affect the greening level of the environment. It is considered that carbon emissions exit due to electricity and also for many more reasons. Finally, total inventory cost will be minimized concerning the cycle length. For the validation of this model, a numerical example has been given and briefly justified with the help of sensitivity analysis. The effect of inventory parameters has shown separately in the sensitivity section. [ABSTRACT FROM AUTHOR]

By constructing the consumers’ utility function, this article calculates the optimal order quantity and optimal expected profit of a fresh retailer under the centralized and decentralized inventory strategy in an omni-channel supply chain. In addition, the impact of consumers’ channel preference, consumers’ demand transfer behavior, and the "buy-online-pickup-instore" (BOPS) service on retailers’ inventory strategy are analyzed. The results show that opening a BOPS service is not always beneficial to retailers. However, it is always beneficial for retailers to eliminate the differences between channels. Under the centralized inventory strategy, the optimal order quantity and expected profit are only affected by the freshness-keeping cost, while under the decentralized inventory strategy, they are affected by various consumer behaviors. A reasonable inventory management strategy should be adopted according to the product type, inventory cost, consumer preferences, and order quantity. [ABSTRACT FROM AUTHOR]

In this article, we present a novel methodology for inventory management in the pharmaceutical industry, considering the nature of its supply chain. Traditional inventory models often fail to capture the particularities of the pharmaceutical sector, characterized by limited storage space, product degradation, and trade credits. To address these particularities, using fuzzy logic, we propose models that are adaptable to real-world scenarios. The proposed models are designed to reduce total costs for both vendors and clients, a gap not explored in the existing literature. Our methodology employs pentagonal fuzzy number (PFN) arithmetic and Kuhn–Tucker optimization. Additionally, the integration of the naive Bayes (NB) classifier and the use of the Weka artificial intelligence suite increase the effectiveness of our model in complex decision-making environments. A key finding is the high classification accuracy of the model, with the NB classifier correctly categorizing approximately 95.9% of the scenarios, indicating an operational efficiency. This finding is complemented by the model capability to determine the optimal production quantity, considering cost factors related to manufacturing and transportation, which is essential in minimizing overall inventory costs. Our methodology, based on machine learning and fuzzy logic, enhances the inventory management in dynamic sectors like the pharmaceutical industry. While our focus is on a single-product scenario between suppliers and buyers, future research hopes to extend this focus to wider contexts, as epidemic conditions and other applications. [ABSTRACT FROM AUTHOR]

In case of formulating more realistic inventory models, two factors of the problem have been raising attention of the researchers, one being the change in the demand rate and the other being the different types of trade credits. Keeping in view of these, this study includes an exponential demand rate, where demand is proportional to the derivative of inventory level, under the single level trade credit, which attracts more customers, leading to minimization of total cost. Optimum result for the time cycle and minimum total cost corresponding to the inventory model problems in crisp as well as cloudy and intuitionistic fuzzy situations are obtained by taking a suitable numerical example and the obtained results are presented in graphical form. Sensitivity analysis is carried out corresponding to some key parameters involved in the model problem using the Matlab-15 software and finally it ends with conclusion and future scope for research. [ABSTRACT FROM AUTHOR]

The iron and steel industry is one of the leading sectors contributing to the economic power of countries. It is incorporated into numerous fields, including automotive, construction, manufacturing, agriculture, defense, and healthcare. Today, the increasing supply and demand balance in iron and steel products makes the stock management and control of the products crucial. The large quantity and variety of products in the iron and steel industry make stock management and control difficult. Creating stock for the business is a very costly process and is one of the important elements of the business. Successful stock management can make the business financially advantageous. This study aims to estimate the stock costs of different products and quantities according to different dates found in the business by using data mining. For this purpose, data mining classifier models are used, and estimated costs are found. By establishing a stock tracking system throughout the supply chain, the business should register all inventory movements of the products. It should work in an integrated way with stock management costing. Thus, unforeseen decreases and financial losses in products can be detected. The iron and steel sector, a key player in enhancing competition among nations and shaping the economic landscape, caters to the needs of various industries through its commitment to sustainable steel production. The industry has encountered challenges due to the swift advancements in recent years, impacting the costs associated with iron and steel products and posing issues for the sector. Tackling these challenges is essential for fostering national development and enhancing the competitiveness of businesses. This research delves into the elements influencing the expenses within the iron and steel industry, emphasizing the importance of minimizing factors leading to elevated costs. An automation system was developed using image processing techniques, and iron and steel products were analyzed. In the automation system, k-means and twostep, one of the clustering analysis methods, was applied.A decision support system defined as SDSS (Steel Decision Support System) and BIPS (Burak's Image Processing System)has been developed to determine the inventory costs of iron and steel products, adopting a data mining approach and including clustering methods to reveal similar inventory costs. Measurements with the SDSS-BIPS automation system were 94.4% successful. Withcost levels were determined by matching the products in the iron and steel business database with their costs. [ABSTRACT FROM AUTHOR]

In this paper, an Economic Order Quantity (EOQ) model of a fractional order with time-varying holding cost is discussed in detail with the help of numerical computations. As is well known, financial variables possess memories, which makes it appropriate for developing fractional modeling to describe the effect of memory in the financial system. This paper is concerned with the issues of deterioration of the items of the inventory system where the importance of time-varying holding cost on the memory-dependent inventory model has been studied. For the inclusion of the memory effect, fractional order derivative and fractional order integration have been used. Illustrative numerical examples and graphical presentations are exploited to verify the efficiency of the effect of fractional rate of change of inventory model and memory-affected costs. The numerical result shows that the output of memory-affected systems is different from the output of memory-less and low-memory-affected systems. Sensitivity analysis has been carried out in long memory effect as well as low memory effect to highlight the critical parameters of the two different systems. Sensitivity analysis clears that in the long memory effect, a small change in deterioration rate can disturb the system but in the low memory effect, such type of disturbances does not arises. At last, some conclusions are recommended. [ABSTRACT FROM AUTHOR]

Effective inventory management is crucial for businesses to balance minimizing holding costs while optimizing ordering strategies. Monthly or sporadic orders over time may lead to high ordering or holding costs, respectively. In this study, we introduce two novel algorithms designed to optimize ordering replenishment quantities, minimizing total replenishment, and holding costs over a planning horizon for both partially loaded and fully loaded trucks. The novelty of the first algorithm is that it extends the classical Wagner–Whitin approach by incorporating various additional cost elements, stock retention considerations, and warehouse capacity constraints, making it more suitable for real-world problems. The second algorithm presented in this study is a variation of the first algorithm, with its contribution being that it incorporates the requirement of several suppliers to receive order quantities that regard only fully loaded trucks. These two algorithms are implemented in Python, creating the software tool called "Inventory Cost Minimizing tool" (ICM). This tool takes relevant data inputs and outputs optimal order timing and quantities, minimizing total costs. This research offers practical and novel solutions for businesses seeking to streamline their inventory management processes and reduce overall expenses. [ABSTRACT FROM AUTHOR]

Commodity inventories are the key state variable determining the magnitude of commodity price responses to supply and demand shocks. Many firms in commodity supply chains use storage, but we know little about which firms and why. The economic theory of storage asserts that firms in a competitive market for inventories will store based on current and expected market prices and the per‐unit cost of storing. We empirically test at the firm‐level the importance of one major cost of storage: the opportunity cost of capital used to value foregone revenue from deferred commodity sales. To do so, we use panel data from thousands of Illinois farms who hold inventories of corn and soybeans. Although interest rates as a measure of capital costs are unlikely to vary widely across firms in this context, we exploit variation in the weighted average cost of capital due to cross‐firm differences in capital structure. Using two‐way fixed effects regressions, we find a statistically significant average effect of capital costs on inventory that masks notable heterogeneity across firms. Panel quantile regressions reveal two groups of firms: one whose inventory holdings are responsive to changes in the opportunity cost of storage and another whose are not. Our results suggest some farms behave like the profit‐maximizing ones from theory but substantial inframarginal commodity inventories are held by farms for other reasons. [ABSTRACT FROM AUTHOR]

Many retailers have recently integrated online and offline channels to give consumers a seamless shopping experience. Our study considers an omnichannel retailer fulfilling order delivery by adopting two common strategies, ship‐to‐store (STS) and ship‐from‐store (SFS), when one of the channels is in short supply. We develop a newsvendor model to investigate the effects of these two omnichannel fulfillment strategies on the retailer's order decisions and expected profits. The analysis reveals the key roles of the external market and inventory cost of different channels in adopting an omnichannel strategy. An interesting finding is that the retailer may adopt the STS (SFS) strategy and take the offline (online) channel as a showroom (virtual webroom) to obtain higher profits than without adopting an omnichannel strategy. [ABSTRACT FROM AUTHOR]

Background: A logistics network plan could be a major key issue due to its effect on supply chain effectiveness and responsiveness. This study aims to investigate the inventory location in the humanitarian logistics response stage using a three-level logistics network to integrate location–allocation problems such as warehouse location and shelter allocation to each facility, and then determine the inventory level in each warehouse. Methods: In this research, the center and its distribution, as well as the reduction in service-level costs due to inventory deficit, have been considered to increase the level of shelter services. In order to investigate the network, in this study, bi-objective mixed-integer linear programming (BOMILP) is presented. Results: The first objective is to reduce location costs and inventory costs that take into account probable demand, consumption factors, and transportation costs, and the second objective is to raise the level of services offered to victims in the model. The software programs GAMS win32, 25.1.2 and MATLAB have been utilized with numerical examples in various dimensions. Conclusions: To maximize the efficiency and quality of the service, first, the model was numerically solved, and then the location where the most commodities could be transported at the lowest possible cost was identified. [ABSTRACT FROM AUTHOR]