Safety Stock Management Research Articles

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.

Safety stock management in a two-stage supply chain with controllable lead time and batch shipments: A technical note

Safety stock management in a two-stage supply chain with controllable lead time and batch shipments: A technical note

A novel type-2 hexagonal fuzzy logic approach for predictive safety stock management for a distribution business

Safety stock is an important method to overcome variability in inventory management. The classical approach to safety stock decisions relies on historical demand and lead time statistical data, which may not capture the uncertainty and complexity of the real world. Human knowledge and experience are valuable assets for making better decisions, especially when facing unpredictable situations. The fuzzy method is widely used for employing human intuition for decisions. When fuzzy opinions are input, decisions can be made proactively rather than reactively while benefiting from future predictions. The paper aims to integrate human intuition using Hexagonal Type-2 Fuzzy Sets (HT2FS) for safety stock management. HT2FS is a generalization of Interval Type-2 Fuzzy Sets that can represent more uncertainty in the membership functions. Predictions may be integrated into the safety stock models using human intuition. The proposed model uses novel fuzzy approaches to integrate human intuition into the traditional safety stock model. Applying fuzzy sets to safety stock management allowed experts' opinions under fuzzy logic to be integrated into decision-making. The proposed novel approach uses the centre of gravity method of Polygonal Interval Type-2 Fuzzy sets for defuzzification, which is a computationally efficient method that can handle any shape of the footprint of uncertainty. A mathematical model is developed to validate fuzzy opinions that may replace historical data. The data is received from a real-life case, and human intuition is integrated using an expert’s input. After the validation, a real-life numerical example has been considered to illustrate the model and its validity compared to the classical model. The outcomes show that the proposed model may contribute to the classical models, mainly when experts' inputs offer good predictions. When expert opinion on HT2FS is used for a real-life case, the results show that the expert's better representation of future variances lowers total cost by 2.8%. The results, coupled with the sensitivity analysis, underline that the proposed approach may contribute to the literature on safety stock management.

The Impact of Diversity, Inclusion and Equity Product Offering and Forecast Bias on Supply Chain Decisions

U.S. demographic change brings new product offerings, production planning, and distribution challenges. While many companies embrace diversity, inclusion, and ethnicity product offering, their practice often renders mixed results, potentially negatively impacting their profitability. We offer an interdisciplinary marketing and supply chain perspective in addressing demand distribution change and its impact on inventory and distribution decisions. In addition, we demonstrate the importance of developing culturally adapted inventory and customer analytics models for manufacturing and supply chain distribution. We examine demand forecast bias in the apparel industry through the product demand distribution analysis at the product size level. We propose supply chain solutions to alleviate the situation by product size re-positioning, distribution channel choice, multistage execution plan, lead time and safety stock, and planning and revenue management approach. We also recognize the potential of addressing the problem by better understanding the consumer behavior change process and allowing a more extended period of marketing campaigns. In the apparel industry, the planning and supply chain decisions assume product demand follows a normal distribution. However, our research found that the demand distribution is highly skewed with significant kurtosis. Therefore, during the planning stage, inventory policy must be adjusted to count for the bias in the demand distribution. Moreover, managers can orchestrate other supply chain and marketing solutions such as centralized operation, leading time and safety stock trade-offs, and more extended marketing campaigns to match supply and demand better. Our work offers some analytical and practical approaches to increase a better supply and demand match.

A Vendor-Managed Inventory Mechanism Based on SCADA of Internet of Things Framework

In recent years, with the rise of the Internet of Things (IoT) and artificial intelligence (AI), intelligent applications in various fields, such as intelligent manufacturing, have been prioritized. The most important issue in intelligent manufacturing is to maintain a high utilization rate of production. On the one hand, for maintaining high utilization, the production line must have enough materials at any time; on the other hand, too many materials in stock would greatly increase the operating cost of the factory. Therefore, maintaining sufficient inventory while avoiding excessive inventory is an important key issue in intelligent manufacturing. After the factory receives the order, it would issue the manufacturing order to the production line for manufacturing. The capacities of different production lines are different. If the Supervisory Control And Data Acquisition (SCADA) system based on the IoT framework can be used to monitor the capacity of each production line, in addition to estimating the capacity, the usage of key materials can also be accurately estimated through AI; when the quantity of key materials is below the safety stock, the manufacturer can actively notify the supplier and request for replenishment. This is a Customer-to-Business (C2B) safety stock management model (i.e., the vendor-managed inventory, VMI), which combines AI and IoT. In particular, in the case of consumer electronics, because their life cycles are short and they are vulnerable to market fluctuations, the manufacturer must adjust the production capacity. This study will propose to construct a SCADA system based on the IoT, including the capacity of the production line, materials inventory, and downstream order requirements, and use the Artificial Neural Network (ANN) to accurately predict inventory requirements. In this study, through the factory, a SCADA system based on AI and IoT will be constructed to monitor the factory’s manufacturing capacity and predict the product sales of downstream manufacturers, for the purpose of facilitating the analysis and decision-making of safety stock. In addition to effectively reducing the inventory level, in essence, the purpose of this study is to enhance the competitiveness of the overall production and sales ecosystem, and to achieve the goal of digital transformation of manufacturing with AI and IoT.

Effects of Inventory Management on The Performance of State Corporations in Kenya

Purpose: The purpose of the study was to determine the effects of inventory management on the performance of state corporations in Kenya with an aim of making recommendations.Methodology: The study employed a descriptive research design. The researcher preferred this method because it allows an in-depth study of the subject. Data was collected using self-administered questionnaires. The study employed stratified random sampling technique in coming up with a sample size. Pilot study was carried out to establish the validity and reliability of the research instruments. The instruments were designed appropriately according to the study objectives. The data collected was analyzed by use of descriptive and inferential statistics. The study used multiple regression and correlation analysis to show the relationship between the dependent variable and the independent variables. The data generated was keyed in and analyzed by use of Statistical Package of Social Sciences (SPSS) version 24 to generate information which was presented using charts, frequencies and percentagesResults and conclusion: The regression equation above has established that taking all factors into account (inventory categorization, inventory control techniques, information technology integration and demand and supply forecasting) constant at zero, performance of state corporations in Kenya will be an index of 0.817.The findings presented also shows that taking all other independent variables at zero, a unit increase in inventory categorization will lead to a 0.537 increase in performance of state corporations in Kenya. The P-value was 0.000 which is less 0.05 and thus the relationship was significant. The study also found that a unit increase in inventory control techniques will lead to a 0.097 increase in performance of state corporations in Kenya. The P-value was 0.002 and thus the relationship was significant. In addition, the study found that a unit increase in information technology integration will lead to a 0.067 increase in the performance of state corporations in Kenya. The P-value was 0.000 and thus the relationship was significant. Lastly, the study found that a unit increase in demand and supply forecasting will lead to a 0.08 increase in the performance of state corporations in Kenya. The P-value was 0.001 and hence the relationship was significant since the p-value was lower than 0.05. The findings of the study show that, inventory categorization contributed most to the performance of state corporations in Kenya. The findings of the study indicated that; safety stock management, inventory control techniques, information technology integration and demand and supply forecasting have a positive relationship with performance of state corporations.Unique contribution to theory, policy and practice: Finally, the study recommended that public institutions should embrace inventory optimization practices so as to improve their performance and further researches should to be carried out in other public entities to find out if the same results can be obtained.

Safety stock management in a supply chain model with waiting time and price discount dependent backlogging rate in stochastic environment

In today’s dynamic global economy, with highly oscillated demand for any product, safety stock and lead-time management have become even more challenging. In this paper, we develop an integrated supply chain model under continuous-review inventory policy where the lead-time demand is uncertain and the lead-time consists of two components: production time and transportation time; the production time being dependent on ordered quantity and the transportation time being in a range between minimum and normal durations which can be crashed to minimum duration with some additional investment. The unsatisfied demands are partially backlogged with the backlogging parameter being dependent on the time the customers wait before receiving the item. The buyer provides a certain range of price discount to increase the backorder rate. Unlike the previous research, this study considers that the safety factor for the first shipment is different from the rest of the shipments. The model is formulated to find the optimal solution for order quantity, safety factors, price discount, transportation time, and the number of shipments from the vendor to the buyer so that the joint total cost attains the minimum value. Some theoretical results are derived to demonstrate the existence and uniqueness of the optimal solution. It is seen from the numerical study that lead-time reduction is more profitable when the backorder rate depends on both price discount and lead-time.

A novel approach to safety stock management in an integrated supply chain with controllable lead time and ordering cost reduction using present value

This paper presents a novel approach to safety stock management and investigates the impact of lead time reduction within an integrated vendor–buyer supply chain framework using present value where lead time and ordering cost reductions act dependently. In particular, the cost of the safety stock is determined by adopting a logistic approximation to the standard normal cumulative distribution. The service level is formulated in relation to the dimension of the single shipment, to the average demand of the buyer and to the number of admissible stockouts. We first discuss the case where the lead time and ordering cost reductions with linear function, and then consider the logarithmic functional relationship. Numerical examples including the sensitivity analysis with some managerial insights of system parameters is provided to validate the results of the supply chain models. The main contribution of this paper is introducing various types of ordering cost reduction in Bragliaet al.(Appl. Stoc. Mod. Bus. Ind.32(2016) 99–112) by handling a new approach.

EFFECT OF INVENTORY OPTIMIZATION ON PERFORMANCE OF STATE CORPORATIONS IN KENYA

Purpose: the general objective of was to determine role of inventory optimization on performance of state corporations in Kenya.Methodology: This research study adopted a descriptive research design approach targeting heads of procurement at the 187 state corporations. This method was preferred because it allowed an in-depth study of the subject. The study preferred this method because it allowed an in-depth study of the subject. To gather data, structured questionnaire will be used to collect data from 187 respondents. The research focused on primary data that was collected from questionnaires distributed to the target groups. This study collected both qualitative and quantitative data. After data collection the data was edited and coded in readiness for analysis by the researcher. The qualitative data collected was subjected to content analysis. On the other hand, the study used descriptive and inferential statistics to analyze the quantitative data. This study utilized the SPSS version 23 software to perform correlation and regression analysis on the collected data. The analyzed data was presented using statistical and graphical techniquesResults: R square value of 0.768 means that 76.8% of the corresponding variation in performance of state corporations in Kenya can be explained or predicted by (safety stock management, inventory control techniques, information technology integration and demand and supply forecasting) which indicated that the model fitted the study data. The results of regression analysis revealed that there was a significant positive relationship between dependent variable and independent variable at (β = 0.761), p=0.000 <0.05).Conclusion: The findings of the study indicated that; safety stock management, inventory control techniques, information technology integration and demand and supply forecasting have a positive relationship with performance of state corporationsPolicy recommendation: the study recommended that public institutions should embrace inventory optimization practices so as to improve their performance.

EFFECT OF INVENTORY OPTIMIZATION ON PERFORMANCE OF STATE CORPORATIONS IN KENYA

Purpose: the general objective of was to determine role of inventory optimization on performance of state corporations in Kenya.Methodology: This research study adopted a descriptive research design approach targeting heads of procurement at the 187 state corporations. This method was preferred because it allowed an in-depth study of the subject. The study preferred this method because it allowed an in-depth study of the subject. To gather data, structured questionnaire will be used to collect data from 187 respondents. The research focused on primary data that was collected from questionnaires distributed to the target groups. This study collected both qualitative and quantitative data. After data collection the data was edited and coded in readiness for analysis by the researcher. The qualitative data collected was subjected to content analysis. On the other hand, the study used descriptive and inferential statistics to analyze the quantitative data. This study utilized the SPSS version 23 software to perform correlation and regression analysis on the collected data. The analyzed data was presented using statistical and graphical techniquesResults: R square value of 0.768 means that 76.8% of the corresponding variation in performance of state corporations in Kenya can be explained or predicted by (safety stock management, inventory control techniques, information technology integration and demand and supply forecasting) which indicated that the model fitted the study data. The results of regression analysis revealed that there was a significant positive relationship between dependent variable and independent variable at (β = 0.761), p=0.000 <0.05).Conclusion: The findings of the study indicated that; safety stock management, inventory control techniques, information technology integration and demand and supply forecasting have a positive relationship with performance of state corporationsPolicy recommendation: the study recommended that public institutions should embrace inventory optimization practices so as to improve their performance.

Reducing stock-outs of essential tuberculosis medicines: a system dynamics modelling approach to supply chain management.

The under-performance of supply chains presents a significant hindrance to disease control in developing countries. Stock-outs of essential medicines lead to treatment interruption which can force changes in patient drug regimens, drive drug resistance and increase mortality. This study is one of few to quantitatively evaluate the effectiveness of supply chain policies in reducing shortages and costs. This study develops a systems dynamics simulation model of the downstream supply chain for amikacin, a second-line tuberculosis drug using 10 years of South African data. We evaluate current supply chain performance in terms of reliability, responsiveness and agility, following the widely-used Supply Chain Operation Reference framework. We simulate 141 scenarios that represent different combinations of supplier characteristics, inventory management strategies and demand forecasting methods to identify the Pareto optimal set of management policies that jointly minimize the number of shortages and total cost. Despite long supplier lead times and unpredictable demand, the amikacin supply chain is 98% reliable and agile enough to accommodate a 20% increase in demand without a shortage. However, this is accomplished by overstocking amikacin by 167%, which incurs high holding costs. The responsiveness of suppliers is low: only 57% of orders are delivered to the central provincial drug depot within one month. We identify three Pareto optimal safety stock management policies. Short supplier lead time can produce Pareto optimal outcomes even in the absence of other optimal policies. This study produces concrete, actionable guidelines to cost-effectively reduce stock-outs by implementing optimal supply chain policies. Preferentially selecting drug suppliers with short lead times accommodates unexpected changes in demand. Optimal supply chain management should be an essential component of national policy to reduce the mortality rate.

A novel approach to safety stock management in a coordinated supply chain with controllable lead time using present value

This paper considers the management of safety stock in a coordinated single‐vendor single‐buyer supply chain under continuous review and Gaussian lead‐time demand. The lead time is supposed controllable, and shortages are not allowed. We follow the present value criterion by considering both inflation and time value of money. Our aim is to present a novel approach to optimizing the safety stock in such system. Under the conditions considered, the safety stock is typically determined according to the value assigned to the safety factor, which is thus treated as a parameter of the model, and not as a decision variable. In this paper, we take a different perspective by putting the order quantity and the safety factor in functional dependence through the adoption of a specific parameter. More precisely, we express the service level as a function of the number of admissible stockouts per time unit and the order quantity. This allows optimizing the safety stock taking into account the constraint on the number of admissible stockouts per time unit. We present both exact and approximated minimization algorithms. Numerical examples are finally shown to illustrate the effectiveness of the approximation algorithm, and to investigate the sensitivity of the model with respect to variations in some fundamental parameters. Copyright © 2015 John Wiley & Sons, Ltd.

Safety stock management in single vendor–single buyer problem under VMI with consignment stock agreement

In this paper a new analytical approach to safety stock management, within single buyer–single vendor framework under VMI with consignment agreement, is presented. In particular, the cost of the safety stock is evaluated adopting a logistic approximation of the standard normal cumulative distribution. The service level is put in relation to the dimension of the single shipment, to the average demand on the buyer and to the number of admissible stockouts. Once the mean joint total cost function is defined, on the basis of the model of Braglia and Zavanella, (2003. Int. J. Prod. Res. 41, 3793–3808) modified according to Braglia et al. (2014. Int. J. Logist. Syst. Manage. forthcoming 2014), a minimum cost solution (within a predefined domain) is derived. Moreover, a numerical study is carried out. First, a sensitivity analysis is shown. Then, the model of Braglia and Zavanella, (2003. Int. J. Prod. Res. 41, 3793–3808), in its original expression, is compared with both Hill (1997. Eur. J. Oper. Res. 97, 493–499), Hill (1999. Int. J. Prod. Res. 37, 2463–2475) and Hill and Omar (2006. Int. J. Prod. Res. 44, 791–800). In particular, the comparison considers the equal-sized shipments case without delayed deliveries taking into account different reciprocal values for the stockholding costs.

Large Deviation Approach for Safety Stock Management for Correlated Demands(Theory and Methodology)

Large Deviation Approach for Safety Stock Management for Correlated Demands(Theory and Methodology)

Empty container reposition planning for intra-Asia liner shipping

This paper addresses empty container reposition planning by plainly considering safety stock management and geographical regions. This plan could avoid drawback in practice which collects mass empty containers at a port then repositions most empty containers at a time. Empty containers occupy slots on vessel and the liner shipping company loses chance to yield freight revenue. The problem is drawn up as a two-stage problem. The upper problem is identified to estimate the empty container stock at each port and the lower problem models the empty container reposition planning with shipping service network as the Transportation Problem by Liner Problem. We looked at case studies of the Taiwan Liner Shipping Company to show the application of the proposed model. The results show the model provides optimization techniques to minimize cost of empty container reposition and to provide an evidence to adjust strategy of restructuring the shipping service network.

Integrated safety stock management for multi-stage supply chains under production capacity constraints

In the petrochemical, chemical and pharmaceutical industries, supply chains typically consist of multiple stages of production facilities, warehouse/distribution centers, logistical subnetworks and end customers. Supply chain performance in the face of various market and technical uncertainties is usually measured by service level, that is, the expected fraction of demand that the supply chain can satisfy within a predefined allowable delivery time window. Safety stock is introduced into supply chains as an important hedge against uncertainty in order to provide customers with the promised service level. Although a higher safety stock level guarantees a higher service level, it does increase the supply chain operating cost and thus these levels must be suitably optimized. The complexities in safety stock management for multi-stage supply chain with multiple products and production capacity constraints arise from: (1) the nonlinear performance functions that relate the service level, expected inventory with safety stock control variables at each site; (2) the interdependence of the performances of different sites; and (3) finally the margin by which production capacity exceeds the uncertain demand. Given the complexities, the integrated management of safety stocks across the supply chain imposes significant computational challenges. In this research, we propose an approach in which the evaluation of the performance functions and the decision on safety stock related variables are decomposed into two separate computational frameworks. For evaluating the performance functions, off-line computation using a discrete event simulation model is proposed. A linear programming based safety stock management model is developed, in which the safety stock control variables (the target inventory levels used in production planning and scheduling models, base-stock levels for the base-stock policy at the warehouses) and service levels at both plant stage and warehouse stages are used as important decision variables. In the linear programming model, the nonlinear performance functions, interdependence of the performances, and the safety production capacity limits in safety stock management are properly represented. To demonstrate the effectiveness of the proposed safety stock management model, a case study of a realistically scaled polymer supply chain problem is presented. In the case problem, the supply chain is composed of two geographically separated production sites and 3–8 warehouses supplying 10 final products to 30 sales regions.