Inventory Budget Research Articles

Planning cost-effective operational forest inventories.

We address a Bayesian two-stage decision problem in operational forestry where the inner stage considers scheduling the harvesting to fulfill demand targets and the outer stage considers selecting the accuracy of pre-harvest inventories that are used to estimate the timber volumes of the forest tracts. The higher accuracy of the inventory enables better scheduling decisions but also implies higher costs. We focus on the outer stage, which we formulate as a maximization of the posterior value of the inventory decision under a budget constraint. The posterior value depends on the solution to the inner stage problem and its computation is analytically intractable, featuring an NP-hard binary optimization problem within a high-dimensional integral. In particular, the binary optimization problem is a special case of a generalized quadratic assignment problem. We present a practical method that solves the outer stage problem with an approximation which combines Monte Carlo sampling with a greedy, randomized method for the binary optimization problem. We derive inventory decisions for a dataset of 100 Swedish forest tracts across a range of inventory budgets and estimate the value of the information to be obtained.

ANALYSIS OF THE STRUCTURE OF THE LIST OF ANTIBIOTICS USED IN PHONG DIEN DISTRICT HEALTH CENTER IN 2019

Background: Antibiotics administered incorrectly may result in detrimental consequences, such as prolonging treatment duration, causing adverse effects, or even increasing mortality. Similar to surrounding underprivileged urban medical institutions, the Drug and Therapeutics Committee (DTC) at Phong Dien district's health center (PDHC) is still struggling to attain rationality in keeping track of and evaluating hospital medication formularies. It is commonly believed that these lists directly impact the DTC's decisions on drug selection, distribution, and order for supply, as well as other inventory issues such as assuring quality and minimizing extra expenses. Objectives: The research aimed to examine the characteristics of the antibiotics list utilized at PDHC in 2019. Methods: From January to December 2019, we investigated 99 different medicines from 8 different antibiotic families in retrospective research. The acquired data were analyzed statistically using descriptive analysis. Results: Foreign medications accounted for 19.3% of pharmaceutical expenditure, whereas domestic medicine accounted for 80.7%. Regarding route of administration, ratios of oral medicines dominated others in quantity and cost (67.67% and 82.73%, respectively). It also revealed that 97.27% of the inventory budget was spent on generic medications compared to under 3% of brand-name ones. Additionally, single-component products (78.79% of items and 71.65% of total expense) were used more often than those with multicomponents. β-lactam was the most widely utilized group among the 8 studied groups, which accounted for 72.61% of the monetary allocation. Conclusion: The results of this study showed that the use of antibiotics at the center was relatively adequate and followed the principle of drug consumption by the Ministry of Health (MOH) in drug utilization and prescription of antibiotics.

How to Change a Running System—Controlling the Transition to Optimized Spare Parts Inventory Policies

Inventory optimization approaches typically optimize steady‐state performance, but do not consider the transition of an initial state to the optimized state. In this study, we address this transition. Our research is motivated by a company that implemented an improved inventory policy for its spare parts division. The improved policy suggested new base stock levels for the majority of the parts. For parts with increased base stock levels, inventory increases were realized after the part lead times, but for low‐demand parts with decreased base stock levels, inventory reductions were slow. As a result, inventory cost increased over the first months after the new inventory policy had been introduced and exceeded the inventory budget substantially. To avoid such undesirable effects, base stock level changes must be phased in. We consider a multi‐item spare parts inventory system, initially operating under an item approach inventory policy that achieves identical fill rates for all parts. Our approach addresses the transition to a superior system approach inventory policy that maximizes the system fill rate. We model the inventory transition as a finite‐horizon optimization problem and apply column generation and a marginal analysis heuristic to determine transient base stock levels for all parts. Using data from the company that motivated our research, we illustrate how the transition can be controlled to quickly improve fill rates without exceeding the initial inventory budget.

Application of BIM Technology in the Construction Phase of Steel Structure Engineering

At present, the continuous development of the construction industry is inseparable from the support of BIM technology. In the construction industry, the construction of steel structures is particularly prominent. With the rapid development of steel structure engineering and the continuous advancement of technology, the technology of BIM has been produced. It is the integration of the two that led to the BIM technology. Only in this way can it play the key to ensuring quality in the construction of steel structures, so it can be said to be an indispensable technology. Therefore, the application of BIM technology in the current steel structure engineering construction can be very beneficial to improve work efficiency, quality assurance and project inventory budget. For this reason, this article analyzes and recommends the application of BIM technology in steel structures.

Finite inventory budgets in production capacity and safety stock placement under the guaranteed service approach

Following the guaranteed service approach (GSA), several studies have investigated the interaction between production and inventory decisions in multistage supply systems and developed efficient algorithms to solve the large-scale optimization problems that emerge. These works have largely ignored limited inventory budgets and their effect on production and inventory decisions. In reality, however, firms tend to limit the amount of capital tied-up in inventories, and there is usually a limited amount of storage space at a warehouse or a retailer. This study investigates the problem of production capacity and safety stock placement with both limited capacity and inventory budgets in general acyclic supply chains. This problem allocates capacity to production stages and sets inventory targets at logistics stages, with the objective to minimize the expected total supply chain cost while meeting a target customer service level.The present paper contributes to the GSA literature in several ways. First, we compare guaranteed and chance-constraint approaches to model limited inventory budgets and analyze their effect on safety stock placement. Second, we present two new equivalent formulations of the production capacity and safety stock placement problem. Solving these new models using a successive piecewise linear approximation algorithm outperforms existing solution procedures in terms of solution quality and time, in both cases of infinite and finite inventory budgets. Third, taking advantage of the structure of these formulations, we develop a two-phase heuristic, which finds optimal or near-optimal solutions and greatly improves CPU times. Finally, our numerical experiments show that inventory budgets affect production capacity and safety stock placement decisions, increasing both work in process (WIP) and safety stock levels.

БЮДЖЕТУВАННЯ ЯК ІНСТРУМЕНТ УПРАВЛІННЯ РЕСУРСАМИ В СИСТЕМІ БУХГАЛТЕРСЬКОГО ОБЛІКУ ПІДПРИЄМСТВА

A modern enterprise management system is impossible without the development and implementation of planned indicators, it should be based on the budget planning and the achievement of budgets realized within the budgeting. The approaches of scientists about the "budgeting" definition were considered in the article and they found that in the modern scientific literature this concept is considered firstly as a process, secondly, as a technology and thirdly, as planning. Therefore, the existing system of enterprise budgeting is aimed not only at the development and implementation of budgets, it permeates all stages of enterprise management. Also it has a significant impact on the organization of accounting, analysis and control, the proper functioning of which depends on the effectiveness of management decisions and the stable development of the enterprise as a whole. Completed researches have shown that the budgeting is used as a tool for managing enterprise resources, debt capital, as well as a tool of internal accounting, control and analysis. The following types of budgets should be developed in the company that are directly related to resource management and debt capital: capital (investment) budget, procurement budget, production budget, inventory budget of finished products, cash flow budget, cash income budget, credit budget, cash budget. No less important place in the enterprise management system is budgeting as a tool of internal accounting, control and analysis. An accounting of budget execution is appointed to reflect the results of individual services, departments, workshops, brigades and the costs incurred by them for the relevant period. Control over the execution of budgets provides verification and evaluation of the results of the operation of various budgeting subsystems. The analysis of budget execution should be understood as a set of methods for comparing the actual budget execution with the planned indicators for a certain period and identifying the factors and reasons that led to their non-execution. Thus, budgeting as a tool of resource management and debt capital of the enterprise involves the formation of plans and budgets for future operations that makes it possible to anticipate problems and make management decisions to eliminate them. Therefore, the introduction of a budgeting system is an objective necessity that has its advantages and contributes to the effectiveness of resource management of the enterprise, increasing its competitiveness and efficiency.

Nonparametric advertising budget allocation with inventory constraint

In this paper, we study the optimization problem of the advertising budget allocation for revenue management faced by a marketer. Besides the advertising budget, the marketer is subject to an inventory constraint during the promotion season. The marketer can affect sales by spending on advertising but does not initially know the relationship between the advertising expense and consequent sales. We propose a nonparametric learning-while-doing budget allocation policy for the problem. Specifically, we first conduct a sequence of advertising experiments to learn (predict) the market sales response through observing realized sales (exploration), then based on the learned sales function determine the following budget allocation planning (exploitation). In particular, during the exploration and exploitation phases, we need to balance the advertising and inventory budgets simultaneously. We show that our policy is asymptotically optimal as the size of the market increases. By constructing a worst-case example, we show that our policy achieves near-best asymptotic performance. We also provide numerical illustrations to show how our policy works, and discuss how its performance changes as the system parameters vary. We also glen some managerial implications of our model and policy from the numerical results.

Improving Order Fulfillment Performance through Integrated Inventory Management in a Multi‐Item Finished Goods System

Effective inventory management is fundamental to order fulfillment excellence and supply chain success. In this paper, we develop a strategic inventory management decision tool that integrates inventory classification and inventory control policy decisions for maximizing order fulfillment performance, while accounting for a constraint on inventory budget and the profit expectation of a firm. This inventory solution tool provides critical enhancements to current inventory planning software, which is developed upon the traditional inventory classification scheme and where practitioners have to balance service levels and safety stock decisions through trial‐and‐error. The model allows firms to assess whether the current inventory performance is Pareto optimal, quantify the trade‐offs between various performance measures, and identify the right inventory level according to the firms' strategic goals. In computational results, we demonstrate the trade‐off and positive relationships between key item‐ and order‐based inventory performance measures and short‐term profitability under different levels of inventory budget in a multi‐item finished goods inventory system.

Flexible ABC Inventory Classification

ABC inventory classification is a well-known approach to assign inventory item into A, B, and C groups based on their sales and usage volume. This helps inventory management become more efficient. Behind its advantage, it usually shows some problems with an inventory budget and warehouse space because the ABC assignment of SKUs are mad e without an inventory budget and space available involved. In this paper, the ABC group under restricted of an inventory budget and warehouse space to maximize the profit with optimal service level is presented. We establish this proposed model to enhance the existing ABC approach to be more applicable in real life, which has the limited inventory budget and warehouse space. Keywords: ABC Inventory Classification; Inventory Management

Integrated multi-period dynamic inventory classification and control

This paper investigates the dynamic integration and optimization of inventory classification and inventory control decisions to maximize the net present value (NPV) of profit over a planning horizon. A mixed-integer linear programming model is developed by explicitly accounting for various real-world complexities, such as nonstationary demand, arbitrary review period, and limited inventory budget. We apply the model to a 900-SKU experiment and observe an average 7.5% improvement in profit over the traditional ABC approach. A real-world application for a cheese manufacturer provides a nearly 3% improvement in company's profit with a 13% reduction in inventory capital compared to its current eight-class multi-criteria inventory classification scheme. Comprehensive computational experiments are performed to examine the individual and interactive effects of various parameters on the inventory performance. Results show that it is critical for a company to manage its inventory both dynamically in the face of nonstationary demand and holistically by integrating SKU classification and policy setting. Our work provides a practical decision-support tool that simultaneously optimizes multi-period inventory classification and control decisions under nonstationary demand. It contributes to the inventory management literature by bridging two distinct streams of inventory research, inventory classification and inventory optimization, within a practical inventory modeling framework. Our study also offers several managerial implications for manufacturers and distributors managing finished goods inventory.

Quantifying Spatiotemporal Greenhouse Gas Emissions Using Autonomous Surface Vehicles

Accurately quantifying total greenhouse gas emissions (e.g., methane) from natural systems such as lakes, reservoirs, and wetlands requires the spatial and temporal measurement of both diffusive and ebullitive (bubbling) emissions. Ebullitive emissions exhibit high spatial and temporal variability and as such are difficult to measure. Traditional manual measurement techniques provide only limited localized assessment of methane flux, often introducing significant errors when extrapolated to the whole‐of‐system. This is further exacerbated when whole‐of‐region estimates are developed for inclusion in global greenhouse gas inventories. In this paper, we directly address these current sampling limitations by comparing two robot boat‐based sampling systems with complementary sensing modalities to directly measure in real time the spatiotemporal release of methane to atmosphere across inland waterways. The first system consists of a single Autonomous Surface Vehicle (ASV) fitted with an Optical Methane Detector with algorithms to exploit the robot's mobility and transect repeatability for the accurate detection and quantification of methane bubbles across whole‐of‐system. The second system consists of multiple networked ASVs capable of persistent operation and scalable to whole‐of‐region monitoring. Each ASV carries a novel automated chamber‐based gas sampling system to allow simultaneous real‐time measurement of methane across the waterway. These ASV systems provide a foundation for persistent large‐scale spatiotemporal sampling allowing scientists to develop whole‐of‐region greenhouse gas estimates and greatly improve global inventory budgets. An overview of the single and multi‐robot sampling systems is presented, including their automated methane detection and sampling methodologies for the spatiotemporal quantification of greenhouse gas release to atmosphere. Experimental results are shown demonstrating each system's ability to autonomously navigate, detect, and quantify methane release to atmosphere across an entire inland reservoir.

Simulation-based assessment of sampling strategies for large-area biomass estimation using wall-to-wall and partial coverage airborne laser scanning surveys

Abstract Airborne laser scanning (ALS) has been demonstrated to be an excellent source of auxiliary information for increasing the precision of estimating stand-level attributes in forest inventories. It has also been proposed to use ALS for estimating biomass and carbon stocks under the United Nations Collaborative Program on Reduced Emissions from Deforestation and Forest Degradation in Developing Countries (UN-REDD). The benefits of REDD depend among other facts on the cost-efficiency of the carbon accounting systems, which should be economically feasible and highly accurate. Acquiring full-coverage ALS data would provide highly accurate estimates but might be too expensive for limited inventory budgets. As an alternative, the ALS data might be collected as a sample by acquiring data from a portion of the area of interest. However, in surveys involving complex multi-phase and multi-stage systematic sampling designs, the efficiency of ALS-based estimates is hampered by the ability of estimating the sampling variability correctly. It has been demonstrated recently that the precision of such complex analytical estimators may be largely underestimated. In order to make an informed decision, simulated sampling from artificial populations generated from empirical data may provide a means for assessing the cost-efficiency of various sampling strategies when analytical approaches fail. This study presents a simulation-based assessment of sampling strategies employing ALS with focus on large-area (27,400 km 2 ) biomass estimation. Simulated sampling mimicking the two contrasting cases “wall-to-wall” and two-phase ALS-aided surveys is exemplified using Norwegian National Forest Inventory data for creating an artificial population. The main results indicated that (1) the gain in precision (10%) when using “wall-to-wall” ALS data may not be worth the very high inventory costs, (2) using variance estimators based on higher-order successive differences produced correct confidence intervals for two-phase systematic sampling, and (3) two-phase ALS-aided systematic surveys are cost-efficient solutions for large-area biomass estimation.

Designing Response Supply Chain against Bioattacks

Bioattacks, i.e., the intentional release of pathogens or biotoxins against humans to cause serious illness and death, pose a significant threat to public health and safety due to the availability of pathogens worldwide, scale of impact, and short treatment time window. In this paper, we focus on the problem of prepositioning inventory of medical countermeasures (MCM) to defend against such bioattacks. We introduce a two-stage robust optimization model that considers the policymaker’s static inventory decision, attacker’s move, and policymaker’s adjustable shipment decision, so as to minimize inventory and life loss costs, subject to population survivability targets. We consider a heuristic solution approach that limits the adjustable decisions to be affine, which allows us to cast the problem as a tractable linear optimization problem. We prove that, under mild assumptions, the heuristic is in fact optimal. Experimental evidence suggests that the heuristic’s performance remains near-optimal for general settings as well. We illustrate how our model can serve as a decision support tool for policy making. In particular, we perform a thorough case study on how to preposition MCM inventory in the United States to guard against anthrax attacks. We calibrate our model using data from multiple sources, including publications of the National Academies of Sciences and the U.S. Census. We find that, for example, if U.S. policymakers want to ensure a 95% survivability target for anthrax attacks that simultaneously affect at most two cities (in the same or different states), the minimum annual inventory budget required is approximately $330 million. We also discuss how our model can be applied in other contexts as well, e.g., to analyze safety-stock placement in supply-chain networks to hedge against disruptions.

Financial Management Practices and Profitability of Modern Rice Milling Firms in Kangayam Cluster, Tamil Nadu

The study analyses the financial management practices adopted by modern rice milling firms in the Kangayam cluster of Tamil Nadu. For study, 40 firms were selected using simple random sampling method and data were collected through personal interviews with the owner/managers of the firm using a pre-tested interview schedule. The data pertaining to the financial performance were obtained from the records maintained by the firms for three financial years, from 2011–12 to 2013–14. The factor analysis and multiple regression analysis were carried out. Significant differences among the fully and partially modernized rice milling firms have been observed in the preparation of cash budget, setting-up credit policy, preparation of inventory budget and review of inventory turnover, capital budgeting in investment analysis and accounting practices. The measures of financial management practices explained 58.4 per cent (R-squared value) of the variation in the profitability measure return on equity.

The case for a limited distribution model

During a pharmacy and therapeutics committee meeting, discussion about new, high-cost hepatitis C treatments prompted me to consider the role of specialty pharmacy (SP) within the industry as a whole. A colleague found issue with limiting patients to an SP, arguing that face-to-face communications are most effective and that patients should be able to choose where SPECIALTY PHARMACY SECTION they receive their specialty pharmaceuticals.

Comparison of order-fulfilment performance in MTO and MTS systems with an inventory cost budget constraint

The make-to-order or make-to-stock decision is an important issue faced by firms in many industries. In the existing literature, optimisation models comparing the cost of making product to stock versus making it to order have been widely developed. Motivated by the problem faced by a machinery company, we examine the issue from a different perspective and formulate service-maximisation models with inventory cost budget constraints. We compare and analyse order-fulfilment performance measures for the two different production control systems. The goal is to identify the key influencing factors and devise a production strategy that maximises service performance subject to resource constraint. We show that the MTO production control system is preferred in the production system with low component values and long component processing times and high value and short lead time in the final assembly stage; while the MTS production control system is applicable to the production system with high component values and short component processing times and little value and long lead time in the final assembly stage.

A risk management-based approach for inventory planning of engineering-to-order production

Engineering-to-order has steadily increased shares of total production. By its own nature, the order specific products often come without pre-defined bills-of-materials which undermines the starting point of prevailing inventory planning methods. Manufacturers often have to confront with difficult, if not impossible, choices for meeting the highly responsive service level without investing in costly inventory, particularly for long lead time items.In this paper, a novel inventory planning approach is presented. Based on predetermined inventory budget, customer responsiveness can be optimized by considering risks associated with supply chain uncertainty, component commonality, substitution possibility, market intelligence, and other salient factors.

A comparison of simple heuristics for multi-product dynamic demand lot-sizing with limited warehouse capacity

The paper analyzes the problem of the replenishment of multiple products to satisfy dynamic demands when the warehouse capacity or the available inventory budget is limited. In this context the timing of replenishment lot-sizes has to be staggered to account for the capacity conflict and to provide an effective space sharing in addition to the solution of the trade-off between setup and inventory holding costs. We analyze three simple heuristics. First, we review a forward algorithm that successively builds lots by extending replenishments according to a cost-based priority rule. The second heuristic solves the lot-sizing problems independently for each product in a first step and then resolves capacity violations by a smoothing mechanism. Further, this paper adapts a heuristic for single-item uncapacitated lot-sizing that successively improves an initial lot-for-lot schedule by combining replenishments according to a cost savings-based priority rule to the multi-item capacitated problem. The performance of the three simple methods is compared in an extensive numerical study and benchmarked against the solution of a mixed-integer programming approach. The results show the different ability of the approaches to simultaneously account for the individual lot-sizing problems and the lot-staggering problem across multiple products. Especially the savings approach appears to provide better results for a broad range of problems, especially for large, tightly capacitated problems with high demand variability.

Genetic algorithm for multi-item inventory control problem

The main purpose of this research is to apply an approximation approach - genetic algorithm to resolve the inventory control problems which maturely developed during 1960s to 1990s. However, it is still a tough work to deal with the multi-item inventory control optimization problems. Under the constraints on inventory space or budget limitations, to solve the multi-item inventory control problem by traditional approach, it is certainly in difficulty to collect the inventory data and in complexity to compute. Fortunately, an approach is applied into this study without the constraints on multi-item inventory system. It is so-called “optimal inventory policy surface.” This study utilizes the model of “optimal inventory policy surface” and the genetic algorithms (GAs), because of easiness, to resolve the multi-item inventory control optimization problems. In this research, a systematically experimental design of Taguchi method is used to analyze the different settings of both parameters and different ranges of variables of “optimal inventory policy surface” model using GAs as the calculation approach. In addition, the response effects (i.e., the percentage of requisitions short (or inventory shortages)) analysis of “optimal inventory policy surface” is obtained by applying in different settings of GAs program running conditions through a serial analysis.

Inventory budget optimization: Meeting system-wide service levels in practice

The work described in the literature on inventory and supply chain management has advanced greatly over the last few decades and now covers many aspects and challenges of applied supply chain management. In this paper we describe an approach that combines many of these academic aspects in a practical way to manage the spare parts logistics at a German automobile manufacturer. The basic problem is a single-echelon inventory problem with a system-wide service-level requirement and the possibility of issuing emergency orders. There exist two related optimization problems: One is to maximize the system-wide service level under the constraint of a given budget; the other is to minimize the budget for a given system-wide service level. The most important requirements and constraints considered are a detailed cost structure, different packaging sizes, capacity constraints, several storage zones, the decision whether or not to stock a product, stochastic lead times, highly sporadic demands, and the stability of the optimization result over time. Our approach has been implemented successfully in an automotive spare parts planning environment. The complete solution package integrates into the mySAP ERP®, the SAP Enterprise Resource Planning system, and APO 4.0, the SAP Advanced Planning and Optimization system. A detailed description of the model is given and results are presented.