Safety Stock Levels Research Articles

Case Article—Humanitarian Supply Chains and Inventory Management: Planning Medical Supply at the International Committee of the Red Cross

This case is based on an actual project with the International Committee of the Red Cross. The rich context allows instructors to cover important topics in inventory management, supply chain management, and humanitarian logistics at a level appropriate for different student backgrounds. At a foundational level, the case presents opportunities to practice concepts like cycle stock, safety stock, and service level. Technical audiences can appreciate applications of machine learning and optimization for inventory management. Practice-oriented audiences learn about the challenges of applying commercial operations metrics and heuristics like ABC analysis to humanitarian logistics. Realistic data and models are provided. Funding: B. Thakur-Weigold acknowledges funding from the Engineering for Humanitarian Action initiative launched by the ICRC, ETH Zurich, and EPFL. International Committee of the Red Cross (ICRC), Eidgenössische Technische Hochschule (ETH) Zürich, and École Polytechnique Fédérale de Lausanne (EPFL). Supplemental Material: Supplemental materials are available at https://www.informs.org/Publications/Subcribe/Access-Restricted-Materials .

The Impact of Lead Time Variability on Supply Chain Management

Purpose: The impact of lead time variability on supply chain management (SCM) is a critical factor affecting operational efficiency, cost management, and service delivery. This study examines how variations in lead time affect key aspects of supply chain performance, including inventory management, production scheduling, and order fulfillment. Methodology: A mixed-methods approach is adopted, combining quantitative analysis through simulation modeling and qualitative insights derived from case studies of companies in the manufacturing and retail sectors. The simulation model accounts for different levels of lead time variability and its influence on stockouts, excess inventory, and overall supply chain responsiveness. Case studies are used to illustrate real-world challenges and strategies employed by businesses to mitigate the effects of lead time fluctuations. Findings: Findings indicate that higher lead time variability leads to increased inventory costs, stockouts, and delays in product deliveries. Companies that rely heavily on just-in-time (JIT) systems are particularly vulnerable to these fluctuations, whereas firms with flexible inventory strategies or buffer stock perform better in managing variability. Additionally, the study reveals that advanced demand forecasting and more resilient supplier relationships can significantly reduce the negative impact of lead time uncertainty. The analysis also highlights the importance of integrating lead time variability into the broader risk management framework of supply chains. Unique Contribution to Theory, Practice and Policy: Based on these findings, recommendations include implementing more robust demand forecasting systems, optimizing safety stock levels, diversifying suppliers, and using advanced inventory management technologies. Moreover, it is advised that businesses foster stronger communication and collaboration with suppliers to reduce variability and improve lead time predictability. Future research should explore the potential of real-time data and AI-driven supply chain management systems to further mitigate lead time variability's impact on SCM.

Forecasting and stock control for fast-moving humanitarian aid supplies

In Indonesia, effective disaster management is imperative due to the region's susceptibility to a wide array of natural and man-made disasters. The establishment of the National Disaster Management Agency (BNPB) underscores the significance of this issue. This study investigates the improvement of disaster logistics, with a particular emphasis on the challenges associated with fast-moving humanitarian supplies. The primary obstacle in disaster logistics lies in the unpredictability of demand after the disaster occurred, coupled with the potential for supply disruptions stemming from inadequate inventory in warehouses. A critical focus of this research is placed on fast-moving humanitarian aid supplies, which are essential for immediate relief efforts, even when lead times are short. The study's primary objective is the development of a safety stock level policy for BNPB's national warehouse, adjusted through the analysis of demand forecasts and lead times. Notably, the research results for the year 2022 reveal minimal errors in demand forecasts. This outcome highlights the effectiveness of the safety stock policy in proactively addressing spikes in demand and mitigating uncertainties related to supply timelines. The research underscores the critical role of accurate inventory management, particularly in the case of fast-moving humanitarian supplies, within the context of disaster relief efforts. It emphasizes the importance of forward-thinking planning to meet urgent needs, ultimately contributing to more efficient disaster response efforts and ensuring the timely delivery of vital relief items to affected populations.

Leveraging advanced technologies in Supply Chain Risk Management (SCRM) to mitigate healthcare disruptions: A comprehensive review

The Healthcare sector is undeniably one of the most critical sectors in any given economy and disruptions in this industry have dire consequences that affect patient care, the efficiency of healthcare systems and by far public health. This publication seeks to analyze and discuss the vital role of supply chain risk management in addressing disruptions, becoming resilient and ensuring that healthcare services function effectively. The pandemic (COVID-19) brought to fore just how critical it is to have a robust and agile system for supply chain risk management in healthcare, the sudden and unprecedented surge in demand for essential medical equipment like personal protective equipment (PPE) left millions of people at risk thereby jeopardizing their safety and health. This crisis echoed the urgent need for efficient and effective supply chain risk management strategies to mitigate such risks and ensure the availability of vital medical supplies. This review paper will be sharing different strategies and actionable tips to develop contingency plans, identify, reduce potential disruptions and recommended practices for risk management in the healthcare supply chain. This will highlight key areas such as inventory management and stockpiling, which focuses on maintaining safety stock levels to buffer against sudden disruptions, using advanced analytics to forecast demand and manage inventory levels. It will also examine risk assessment and identification by pinpointing potential attacks, threats, and vulnerabilities within the healthcare supply chain through thorough risk assessments. Additionally, it will cover supplier base diversification, emphasizing the importance of engaging multiple suppliers to reduce dependency on a single source and enhance supply chain resilience by balancing global and local suppliers to mitigate geographic disruption risks. Furthermore, it will explore the integration of technology, leveraging innovative solutions like blockchain for enhanced transparency, security, and traceability of supply chain transactions.

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

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

Neutrosophic Inventory Management: A Cost-Effective Approach

Abstract Classical inventory models (IM) serve as quantitative tools for determining the optimal order quantities, timing of orders, and safety stock levels for specific inventory items or item groups. Zadeh (1965. Fuzzy sets. Information and Control, 8, 338–353) introduced fuzzy theory and Dubois and Parade (1988. Fuzzy logic in expert systems: The role of uncertainty management. Fuzzy Sets and Systems, 28, 3–17) presented the study of fuzzy inventory model, which, however, exhibits limitations in effectively handling uncertainty, inaccuracies, and imprecise data. In 1999, Smarandache presented the idea of neutrosophic set theory to handle uncertainty. Using trapezoidal neutrosophic numbers, this study extends the idea of neutrosophic sets to inventory management, concentrating on resolving the uncertainty associated with holding costs, ordering costs, and shortage costs. First time within the literature of the neutrosophic set, our new method not only addresses existing problems but can also tackle other issues that no other authors have successfully resolved so far. Additionally, we conduct a comparative analysis of our proposed model against existing models in this article. Based on this comparative study, our findings assert the superior performance of our proposed model in relation to some of the existing models. In conclusion, we wrap up our research by presenting graphical, logical, and tabular comparisons with the existing methods.

EOQ Optimization Revolutionizes Inventory Management, Delivering Cost Savings Globally

Efficient inventory management is crucial for companies to minimize costs while ensuring adequate supply to meet demand. PT Samawa Tirta Alam, a bottled sweet drinking water company, faces significant inventory management issues, notably with refined sugar raw materials. High transportation costs and fluctuating demand contribute to excessive inventory accumulation and increased storage costs, necessitating an optimization strategy. This study implements the Economic Order Quantity (EOQ) method to address these challenges. Through a mixed-method approach, qualitative data were gathered via observations and interviews, while quantitative analysis utilized historical data to calculate optimal order quantities, frequencies, safety stock levels, and reorder points. Results demonstrate substantial cost savings, with optimal order quantities determined, reducing order frequencies from 12 to 2 times per year, and minimizing safety stock levels. The findings underscore the effectiveness of the EOQ method in enhancing cost-effectiveness and operational performance in raw material inventory management for similar companies. Highlights: 1. Cost Efficiency: EOQ method reduces total inventory costs effectively.2. Operational Precision: Minimizing safety stock enhances operational efficiency.3. Strategic Insight: Integrated approach provides practical solutions for inventory management challenges. Keywords: Inventory Management, Economic Order Quantity (EOQ), Cost Optimization, Raw Materials, Transportation Costs

Inventory Management Optimization: Balancing Cost and Service Levels

Efficient management of inventory plays a role, in the success of businesses across sectors. Finding the equilibrium between keeping inventory costs low and meeting customer needs is a challenge that requires constant exploration and creativity. This study delves into the complexities of optimizing inventory management focusing on strategies that effectively handle the trade offs between minimizing expenses and fulfilling customer expectations. By delving into an analysis of existing literature this study consolidates approaches, models and best practices implemented by companies to enhance the efficiency of inventory management. It thoroughly examines aspects such as improving demand prediction accuracy reducing lead times optimizing safety stock levels and integrating supply chains. Additionally it explores how advanced technologies like intelligence data analytics and automation are transforming inventory management processes.Moreover this research delves into how industry landscapes, market dynamics and business goals influence inventory management tactics. Through real world examples and empirical data analysis it offers insights, on how organizations can tailor their strategies to meet needs while balancing the objectives of cost control and service level improvement. In the end this study adds to what we know about improving inventory management by providing advice and tips for companies to gain lasting edges in todays ever changing market. KEYWORDS: Research Paper, Blockchain, Management, Risk Evaluation, Empirical Analysis

Demand Driven Material Requirements Planning: An Inventory Optimization Model

When dealing with stochastic multi-state production and distribution systems, determining the appropriate quantity of safety stocks on hand may be tricky. We often limit existing solutions for maximizing safety stock levels while keeping cost, objectives and service level constraints in mind to a subset of multi-stage inventory challenges, such as strictly serial architectures or specialist two-stage production/distribution systems. This may make achieving ideal stock levels while meeting cost objectives and sustaining service levels problematic. This is due to the underlying assumption in these systems that cost and service level are directly proportional to one another. Controlling unpredictability and satisfying customers are inventory management?s most important tasks. DDMRP (Demand-Driven Material Requirements Planning) dynamically adjusts inventories to handle unpredictability and improve customer service. This is done to improve crisis management. This achieves ?demand-driven material requirements planning.? This study presents a DDMRP-specific safety stock calculation. The inclusion of this formula aids in the preservation of mathematical consistency. When dealing with a model based on normal probability distributions, the major focus is discovering and applying the parts necessary for safety stock computation. One of the most well-known fast-moving consumer goods (FMCG) firms in South East Asia is the major focus of a case study to validate the proposed strategy. To build a scientific model, the study adopts a probabilistic strategy. The primary goal of this model is to aid in the process of identifying appropriate levels of safety stock and inventory at the distributor level, which will ultimately allow for a customer fill rate of 99%. A distribution network will be employed to achieve this purpose. The model provides a complete framework for estimating optimum safety stock levels and reorder points while accounting for the stochastic nature of demand and including critical elements. This enables more precise forecasting of future demand. This contributes to better inventory management practices and promotes customer satisfaction.. KEYWORDS :DDRMP, Demand level, Distribution network, Safety Stock Model.

Dynamic pricing strategies for efficient inventory management with auto-correlative stochastic demand forecasting using exponential smoothing method

The research presents a novel approach to inventory modelling, emphasizing stochastic demand and dynamic pricing strategies for a seasonal sales framework. The methodology divides the sales season into intervals, each associated with distinct pricing strategies influenced by stochastic factors. The study employ exponential smoothing for demand forecasting, optimizing inventory replenishment and dynamic pricing strategies through developed algorithms. Notably, the study determine the optimal smoothing parameter for demand forecasting, balancing responsiveness to recent demand patterns with long-term stability. Proposed research achieves a comprehensive framework empowering businesses to enhance competitiveness and profitability by addressing challenges of stochastic demand and dynamic pricing. Dynamic pricing strategies outperformed classical strategies, allowing businesses to respond promptly to demand fluctuations and optimize profit margins during sales seasons. Incorporating stochastic demand models enabled organizations to implement safety stock and buffer inventory levels effectively, mitigating risks associated with uncertain demand patterns. Real-time data analysis was crucial for adjusting price dynamics and making effective management decisions, leading to improved financial performance. The iterative nature of dynamic pricing strategies emphasized the importance of continuous refinement to adapt to evolving market dynamics. This approach enables data-driven decisions, adaptation to market fluctuations, and improved inventory management despite unpredictable demand. Ultimately, this study provides valuable insights and methodologies applicable across industries for efficient and profitable inventory management.

Inventory Management Improvement at PT Inalum, Kuala Tanjung, Sumatra Utara

This research aimed to analyze the inventory issue in PT Inalum. The method used in this research is a quantitative method to collect inventory historical data from January 2022 to December 2022 obtained from the company’s ERP SAP. The data contains information of 6028 items of the MRO inventory. The collected data was analyzed using Economic Order Quantity (EOQ), Reorder Point (ROP), Safety Stock (SS) and Average Inventory Level (AIL). By using Fixed Order Quantity model, the MRO inventory is calculated to compare the ideal and actual average inventory levels. From the analysis, the researcher found that the MRO inventory in Kuala Tanjung, PT Inalum, has a service level of 0.52, which is still far below the service level target of 0.95. At the same time, PT Inalum has the potential to reduce inventory by USD 5,402,389 or 58.2% of the inventory value. This research aims to provide guidance for the management of PT Inalum in making inventory policies that can increase service levels as well as minimize inventory costs.

Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels

In recent years, inventory has been critical due to the production cost and overstock risk related to the expiration date and the fluctuation price risk. This study's minimization of overstock and price fluctuation in the warehouse used a hybridized artificial neural network (ANN) and analytical hierarchy process (AHP) to produce an optimum model. The variables, such as average demand, reorder point, order quantity, factor service level, safety stock, and average inventory level, were used to obtain the optimal condition of the average inventory levels to maximize the profit. Then, the type of inventory system that guarantees the minimum risks in managing the inventory would be selected. The result shows that the data has a mean of 39.2 units, and the standard deviation (SD) was 12.9. This means that the order quantity is 20.2 units, the average inventory level is 57.3, and the average demand is 39. These conditions used the factor z, which is 97% service level. This study concludes that the optimum average inventory level is 91 units, the order quantity is 11 units with the maximum average profit is $1098, and the peak fluctuation condition maximum profit is $1463 when the average inventory level is 7.3, and the inventory policy system used to minimize the risk is the continuous review policy type. The study could be beneficial to reduce production costs and enhance overall profitability and operational efficiency in the sector by mitigating the risks associated with excessive inventory and price volatility while also minimizing the potential for expired inventory.

Use Case Diagram for Enhancing Warehouse Performance at PT. MDA Through the Implementation of 5S, Economic Order Quantity, Safety Stock, and Warehouse Management System

An industrial water pump importing company relies on a network of distribution warehouses to efficiently manage the storage and delivery of its products to clients. This paper delves into the operational intricacies of the company, with a primary focus on sustaining a superior level of service to meet customer demands, all while attempting to minimize costs and achieve optimal inventory control. The central aspects explored in this research encompass the meticulous determination of the number of pipes needed and the optimal ordering times. To address this, the Probabilistic Economic Order Quantity (EOQ) method is used and supported by 5S concept, recognized for its ability to provide reasonably accurate estimates crucial for pivotal decision-making in inventory management. The utilization of the Probabilistic EOQ method in this context reflects the company’s commitment to adopting sophisticated and proven methodologies to enhance decision-making accuracy and the warehouse area is more suitable by the 5S implementation principles. The research outcomes not only assist in refining the determination of Safety Stock levels but also contribute valuable insights into the precise quantities of goods that should be ordered. This strategic approach aligns with the company’s overarching goal of achieving cost-efficiency without compromising on the ability to efficiently meet customer demands. These upcoming insights could encompass innovative strategies, technological implementations, or advances in supply chain optimization.

Inventory Control Analysis Using Economic Order Quantity (EOQ) in the Pekalongan Batik Industry

One of the economic sectors that is able to provide a significant increase in Indonesia's economic growth is MSMEs, which operate in the creative industry sector. On the other hand, MSMEs are a business sector that is often faced with uncertainty. One of the batik MSMEs in Pekalongan was forced to face post-disruption effects such as a pandemic, which had an impact on decreasing demand and increasing supply. Therefore, strategies are needed to remain competitive in the market, one of which is inventory management. The EOQ model is used in this research to minimize raw material inventory costs by determining the economical order quantity (Q) in one of the Pekalongan batik industries. The results of this research provide a concrete reference for MSMEs to plan their supply of fabric raw materials, starting from determining order frequency, reorder points, and safety stock. The optimal order quantity for MSME raw materials is 10,189 meters, with an order frequency of 35 times in the planning horizon. The safety stock level that must be prepared is 3,988 meters, and the reorder point is 6,362 meters to meet demand during lead time. Through the proposed inventory planning, MSMEs can save inventory costs and allocate resources for other purposes.

Managing material shortages in project supply chains: Inventories, time buffers, and supplier flexibility

We consider a two‐stage project supply chain with a downstream project firm producing an engineer‐to‐order (ETO) complex product or a make‐to‐order (MTO), low‐volume, customized industrial product as a project, and an upstream contract supplier supplying a key material to the project. The project faces two uncertainties: project activity time uncertainty and material consumption uncertainty, which may be positively or negatively correlated. In anticipation of these uncertainties, the project firm has to carefully decide its promised project due date to its project customer, against which harsh penalties will be assessed, and his material order quantity to commit to the contract supplier in advance. In most practical settings, project firms order from contracted suppliers via a flexible wholesale price contract consisting of a discounted advance order price and a risk‐premium adjusted expedite order price. The discounted advance order price encourages the project firm to take more inventory risk in the supply chain, and the expedite order price incentivizes the supplier to bear more inventory risk by carrying safety stock in excess of the project firm's advance material order. We formulate an optimization model that solves the project firm's project due date and material order problem, which takes into account the supplier's strategic reaction to the project firm's material order under the flexible wholesale price contract. We show that for MTO projects, risk‐sharing with suppliers on project materials is less important to the project firm, with the project firm assuming ownership of all material inventory in the channel and setting a deliberate project due date being the key. On the other hand, for ETO projects, risk‐sharing with contracted suppliers assumes critical importance. Project firms managing ETO projects should fully exploit the flexibility in the material supply contract to optimally drive the supplier's safety stock level and set the project due date reflecting the shared risk in the supply chain.

Extensions to the guaranteed service model for industrial applications of multi-echelon inventory optimization

Multi-echelon inventory optimization (MEIO) plays a key role in a supply chain seeking to achieve specified customer service levels with a minimum capital in inventory. In this work, we propose a generalized MEIO model based on the Guaranteed Service approach to allocate safety stock levels across the network at the lowest holding cost. This model integrates several existing and some novel features that are usually present in pharmaceutical multi-echelon supply chains into a single model: review periods, manufacturing facilities, hybrid nodes (nodes with both internal and external demand), minimum order quantities (MOQ), and different service level performance indicators (fill rate and cycle service levels). We include a polynomial regression to approximate fill rates as a possible target measure to set safety stocks. To improve efficiency, we propose a nonlinear programming model to support decision making, which can be reformulated as a Quadratically Constrained Program (QCP), which leads to order of magnitude reductions in computational time. The performance of the model is evaluated by solving illustrative and real-world cases, and is validated with simulation.

Evaluating Inventory Management Policies of Platelets at Regional-Block Blood Centers in Japan

With the decreasing proportion of blood donors to patients undergoing transfusion in Japan, optimal blood-product management is becoming increasingly important. Accordingly, we evaluate blood-product inventory management by means of mathematical optimization techniques. Using numerical analyses, we determine the effect of differences in the priority of collections on the units of discarded platelets, and demonstrate the impact of safety-stock levels upon these units. Our results suggest that policymakers should implement the following measures to minimize their disposal rates. Regional-block blood centers with significant fluctuations in demand must generate schedules that minimize both the units of platelets collected, and those interchanged to the maximal extent. Conversely, centers with smaller fluctuations in demand must generate schedules wherein a constant number of platelets is collected every day. Safety-stock levels should be revised in accordance with demands. Furthermore, our policy proposals should be able to mitigate social damage to the blood-supply chain over the course of the COVID-19 pandemic.

A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality

Forecasting demand and determining safety stocks are key aspects of supply chain planning. Demand forecasting involves predicting future demand for a product or service using historical data and other external and internal drivers. Stockouts and excess production can be reduced by accurately forecasting demand. This allows companies to plan production, inventory, and logistics more effectively. Companies maintain safety stocks in their inventory to protect against unexpected changes in demand or supply. A company must find the appropriate safety stock level to meet customer demands while avoiding excess inventory and carrying costs. Forecasting demand and determining safety stocks work together to help companies reduce costs, improve customer service, and optimize inventory levels. Key Performance Indicators (KPIs) are commonly used to measure model performance. Classical forecasting models mostly concern themselves with minimizing forecast errors. However, the impact on inventory costs is not directly considered. In this paper, we introduce a Key Performance Indicator to be used in the demand forecasting process that produces more efficient results in terms of inventory costs. We also propose a novel approach to determining the best level for safety stock. This approach considers logistic network supply reliability and seasonality indices identified within historical demand patterns. We use real-life data and show that the proposed method can improve efficiency in forecasting and safety stock levels by reducing the risk of stockouts and excess inventory.

Determination of Economic Value using the EOQ and ROP Approaches in the Raw Material Control System

Inventory is one of the fundamental aspects of the company, not only the production process but also related departments need inventory. If the material inventory is too little, the production process is disrupted which in the end the delivery schedule is also disrupted, whereas if the material inventory is too much, the material storage costs incurred by the company will increase. The purpose of this research is to maximize material inventory and determine the right time for reordering raw materials. The methods used are EOQ, ROP, and ITO. Data collection was carried out quantitatively in food canning companies to determine economic orders. Based on the calculation results, the level of economical purchase of raw materials using the EOQ method approach is 54.64 kg per order and the recommended number of reorders is 45 kg per order with a safety stock level of 10.16 kg/day.

An applied mathematical model based on discrete regression algorithm for computational prediction of blood collection volume

Abstract To make blood collection more accurate, we propose a dynamic model of blood collection based on a discrete regression algorithm. This paper proposes a dynamic model of blood collection based on a discrete regression algorithm mathematical model. By defining the data parameters of the collection model and using the blood collection example data as a reference, we analyze the factors affecting the blood collection process, including blood inventory control, blood demand fluctuation, safety stock, and target stock level, to obtain the required blood collection volume. The analysis of the case data shows that moderately increasing the target stock level and safety stock is conducive to reducing the shortage, thus understanding that the current blood collection volume is not in high demand. Improving blood demand forecasting can improve blood security and can have a significant impact on the value of blood collection. Therefore, before blood collection, a blood collection dynamic model can be used to make correct and reasonable inventory control parameters and calculate a clear blood collection volume.