Safety Stock Levels Research Articles

Robust simulation optimization for supply chain problem under uncertainty via neural network metamodeling

Real-world supply chain management problems are highly complicated such that their optimization procedure is computationally expensive due to the extensive dimensions and uncertainty of their critical variables. Simulation optimization is a commonly applied technique to determine the optimal variables since the problem is too complex. Due to the uncertain nature of the real-world systems, it is also worthy to consider the robustness of the optimal solutions. To address this issue, this study investigates the problem of determining near-optimal safety stock levels in a multi-product supply chain with regard to deviations of its overall cost. A new framework is proposed to define the decision as well as environmental variables. This novel framework results in a significant reduction of the solution space while maintains the essential supply chain control parameters. The prediction performance of artificial neural networks (ANNs) with different structural settings are compared, and the best-fitted ANNs are selected to obtain the robust solutions. Consequently, we employ a robust metamodel-based simulation optimization approach based on Taguchi’s view and optimize the multi-objective supply chain problem with respect to supply chain operational costs and customer satisfaction criteria.

Optimization of Inventory Controlling System Using Integrated Seasonal forecasting and Integer Programming

Ethiopia's industrial development strategy is characterized by manufacturing-led and expansion labor-intensive industrialization. The country expects to generate more income from the exported market. However, the case company is still known not to become productive as much as possible due to different reasons. One of the big challenges of the company has the problem with holding inappropriate inventory and with determines their optimal cost due to poor production planning. So that to solve this problem objective of the paper is to minimize total cost through the integration of seasonal forecasting and integer programming model without violating demand fulfillments. This technique improves resource utilization and enhances inventory control or stock control system. Currently, the company produces different kinds of products grouped into four common types of products (knitted garment, knitted fabric, woven garment, and woven fabric). The data survey system was both primary and secondary system and classified the products using A B C (always better classification) classification. The optimal solution was settled through the integration of seasonal forecasting and integer programming. As the Sensitivity analysis indicated the a big gap between production capacity and actual demand of the products. As the optimized solution indicated that total cost of production cost and inventory cost was minimized and the optimal production plan as well safety stock levels in each quarter was settled. Seasonal demand forecasting is a key activity for a garment which more or less controls all activities of production processes since garment products are affected by seasonal. As the result and discussion have shown that after optimized increase profit of the company through minimizing production cost and inventory costs since both costs are the big constraint of the company. Based on the optimized solution finding annually total cost needs for each A, B, and C – categories products are 57,225,920 BIRR 4,733,013 BIRR, 8,229,309 BIRR, respectively for production and inventory costs. The optimized solution indicated that if the company implemented exactly the proposed solution it will get an additional,4,219,788.8 BIRR,772,055.8 BIRR,2,119,824.2 BIRR respectively for A, B, C categories products totally around 7,111,668.8 BIRR profit per year will get. To end, it was concluded that this remarkable profit increment of the case company can certainly enhance its productivity and worldwide competitiveness. This research will create further pathways for other researchers to accomplish substantial studies on other garment sectors or other manufacturing industries based on local and international perspectives.

PENGENDALIAN PERSEDIAAN DENGAN MENGGUNAKAN METODE CONTINUOUS REVIEW SYSTEM DAN METODE PERAMALAN PADA PT XYZ

This study aims to determine the optimal number of orders, estimated costs, reorder points, and safety stock. This study uses quantitative methods and aims to provide inventory control proposals using Continuous Review System analysis and Forecasting with QM software for windows. So as to obtain optimal order results A3 + Glossy 8060 pack stickers with a cost of Rp. 545,508,208.43. A3 + Matte stickers 3,238 pack with a fee of Rp 237,554,470.91. A3 + Transprent 4,181 pack stickers at a cost of Rp. 294,737,067.25. Furthermore, a Reorder Point point of 1,520 packs for Glossy, 687 packs for Matte, and 732 packs for Transparent and Safety Stock Level for Glossy of 189 packs was obtained. Matte of 117 packs, Transparent of 97 packs. Based on the results of these calculations it can be concluded that using the Continuous Review System method can control inventory and estimate the cost to be incurred more effectively and efficiently by the company. (NT, CC, JK)

Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data

Supply chain management depends heavily on uncertain point forecasts of product sales. In order to deal with such uncertainty and optimize safety stock levels, methods that can estimate the right part of the sales distribution are required. Given the limited work that has been done in the field of probabilistic product sales forecasting, we propose and test some novel methods to estimate uncertainty, utilizing empirical computations and simulations to determine quantiles. To do so, we use the M5 competition data to empirically evaluate the forecasting and inventory performance of these methods by making comparisons both with established statistical approaches and advanced machine learning methods. Our results indicate that different methods should be employed based on the quantile of interest and the characteristics of the series being forecast, concluding that methods that employ relatively simple and faster to compute empirical estimations result in better inventory performance than more sophisticated and computer intensive approaches.

Analisis Perbandingan Metode Regresi Linear dan Metode Exponential Smoothing Dalam Peramalan Penjualan Keripik Ubi Kayu dan Keripik Ubi Rambat

Forecasting plays a crucial role in the decision-making process of any company or business organization. The aim of this study is to minimize potential losses through effective production planning. The study utilizes linear regression and exponential smoothing as forecasting methods. The results obtained from the study are specific to the cases in UD. Mustika Tip and it concludes that linear regression is the most effective prediction method. For cassava chips, the study reports a Mean Absolute Deviation (MAD) of 3,060.19 MAD, a method exponential smoothing (MSE) of 12,742,472.70, and a Mean Absolute Percentage Error (MAPE) of 0.52%. For yam chips, the MAD is 272.65, the MSE is 248,835.91, and the MAPE is 0.12%. Furthermore, the study recommends safety stock levels for both cassava chips and yam chips. The suggested safety stock for cassava chips is 211 kg, while for yam chips, it is 28 kg.

Optimal stock and capital reserve policies for emergency medical supplies against epidemic outbreaks

Killing tens of thousand of people and bringing tremendous loss, epidemics have been one of the most huge and urgent threats for all human kind. Given the abruptness of epidemic outbreaks, quick access to sufficient medical supplies is essential. Most existing research focuses on reserving physical stocks only, while some authors also consider reserving production capacity. This study is the first to consider capital reserves besides physical stocks, a practice that is for instance used in China. We characterize in closed form and graphically under what conditions a certain reserve policy (no reserves, safety stock only, capital reserve only, mixed reserves) is optimal. A key insight is that increased demand uncertainty sometimes leads to lower safety stock levels, although the combined reserves do increase. In such situations, to limit the risk of obsolescence, it is optimal to rely less on safety stocks and more on capital reserves.

An integrated queuing-stochastic optimization hybrid Genetic Algorithm for a location-inventory supply chain network

We consider a location-inventory optimization model for supply chain (SC) configuration. It includes a supplier, multiple distribution centers (DCs), and multiple retailers. Customer demand and replenishment lead time are considered to be stochastic. Two classes of customer orders, priority and ordinary, are assumed based on their demand. The goal is to find the optimal locations for DCs and their inventory policy simultaneously. For this purpose, a two-phase approach based on queuing theory and stochastic optimization was developed. In the first phase, the stock level of DCs is modeled as a Markov chain process and is analyzed, while in the second phase, a mathematical program is used to determine the optimal number and locations of DCs, the assignment of retailers to DCs, and the order quantity and safety stock level at DCs. As solving this problem is NP-hard, a hybrid Genetic Algorithm (GA) was developed to make the problem computationally tractable.

Optimizing inventory management costs in supply chains by determining safety stock placement

For a production business, the measurement of safety inventory to be maintained during each step of a supply chain is a key concern and requires providing the clients an irregular state of management. The stock held should be small to reduce holding costs and capacity while maintaining the capacity to service customers in time to satisfy much, if not all, the demand. This paper discusses this issue by using a deterministic time structure and provides a measure of the security position of stock in supply chains for the overall network. First, prove that the overall problem is NP-hard. Then set up a couple of parameters that characterize an optimal overall network structure. To take care of these problems, a polynomial approximation is considered. An arrangement of computational tests to survey the execution of the general-network calculation and to decide how to set different parameters for the calculation is selected. In addition to the general network case, the two-layer network issues are considered. Also, a nonlinear model for determining the level of safety stock in different components of the supply chain to minimize the related safety stock costs is developed.

Two-echelon supply chain network design with trade credit

This study considers a two-echelon supply chain that is comprised of an outside vendor, multiple distribution centers (DCs), and multiple retailers. The retailers have access to trade credit offered by upstream suppliers. We propose an integrated DC-retailer network design model that optimizes trade credit terms and safety stock levels, in addition to the decisions of DC locations, DC-retailer assignments, and inventory replenishment policies typically considered in the literature. The operating and handling cost is concave and non-decreasing to capture economies of scale whereas most existing studies simply assume that such cost is linear in demand. Trade credit financing cost is characterized in a way that preserves the important mathematical structure of the classic warehouse retailer network design model. Leveraging on the submodularity property of the cost components, we developed a polymatroid cutting-plane solution algorithm, which is effective for practically sized problem instances in numerical experiments. The results show that incorporating trade credit financing into supply chain network design may substantially reduce the total cost. Further, our study suggests a more consolidated supply chain network when either the safety stock or financing cost increases. Interestingly, as financing cost rises, a high-volume and low-frequency reorder pattern is favored, but the opposite is recommended when financing gain increases. The variation of each individual cost component is also analyzed for an in-depth understanding of the impact of operational and financial parameters on supply chain optimization.

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.

The principal-agent problem in supply chain management – the simulation based framework

Abstract This research deals with a phenomenon well-known in socio-economic studies and referred to as the Agency Theory: the principal-agent problem. The agent is designated to act on behalf of the company owner, i.e., the principal, in the domain of supply chain management in the face of a supplier’s default uncertainty. Each of the players is gain-oriented, but their objective functions and the degree of risk-bearing may significantly differ, leading to an agent’s decisions not being optimal in terms of the principal’s outcome. This article proposes a multi-period supply chain simulation model that can be used to perform a supply chain optimization and comparison between the agent and the principal. The proposed framework allows for differentiating the model parametrization depending on the industry, in particular the consequences of the inability to deliver the end product, costs of keeping safety stocks, or the uncertainty regarding the suppliers’ delivery failure. As players’ objective functions, we consider expected profit-based indicators and measures taking the inter-period variance into account. We find that the agent’s actions may diverge from the principal’s optimum if the agent’s incentive system is not selected correctly. We also propose a solution that unifies the goals of the players. The recent COVID-19 pandemic amplifies the importance of such research. Many companies had to limit production capacities due to global lockdowns and, per the JIT production strategy, the prior safety stock levels were low.

A mathematical model for the joint planning of maintenance and safety stock in deteriorating imperfect manufacturing systems

In this article, a mathematical model is proposed for the joint planning of maintenance policies and inventory control in a deteriorating production system. A safety stock is maintained to meet the demand during the conduction of maintenance actions and to avoid shortages. The optimal planning of maintenance and inventory improves the productivity of the manufacturing system. In a deteriorating production system, the process has two operational states including in-control and out-of-control states as well as a non-operational state, or failure mode. The time for the transition among the states follows a general continuous distribution. The time duration of maintenance actions is also considered as a random variable. The purpose of this study is to optimize the safety stock level and the time to conduct maintenance actions so that the expected total cost per time unit can be minimized. To verify the efficiency of the model, some numerical examples are solved with a genetic algorithm, and validation is conducted for the solutions. Finally, sensitivity analyses are performed on the critical parameters.

Determination of The Safety Stock Level and Reorder Point in Hospitals with Probabilistic Inventory Model: An Example of a Public Hospital

Bu araştırmanın konusu diğer işletmeleri olduğu gibi hastaneleri de çok yakından ilgilendiren etkili stok yönetimidir. Bu bağlamda kamu hastanelerinde tıbbi malzemelerin stok kontrolünde önem derecelerine göre gruplandırılması ve devamında belirlenen gruplardan rastgele örneklem yöntemi ile seçilen tıbbi malzemelerin hastanede dönem başında elde bulunması gereken emniyet stok miktarlarının belirlenmesi hedeflenmiştir. Araştırmanın gerçekleştirildiği hastaneden, seçilen tıbbi malzemelere dair veriler alınarak ABC, VED yöntemleri ile analiz edilmiştir. Analiz sonuçlarından yararlanarak IBM SPSS 20 paket programı aracılığıyla A-V grubu malzemelerin talep dağılımının normal dağılım gösterdiği saptanmıştır. Olasılıklı stok modelindeki varsayımlara uygun matematiksel denklem kullanılarak dönem başında elde bulundurulması gereken emniyet stoku miktarları ve yeniden sipariş noktası hesaplanmıştır. Ardından belirlenen servis düzeylerinde toplam emniyet stok tutma maliyetleri de hesaplanarak karar vericiye her bir servis düzeyinde maliyet tabanlı alternatif çözümler sunulmuştur.

Optimal lot-size and Price of Perishable Goods: A novel Game-Theoretic Model using Double Interval Grey Numbers

Industrial supply chain management is facing major challenges associated with supplying perishable products, particularly healthy and high-quality foods with a limited shelf-life that require specific inventory holding conditions. Consumers usually prefer product freshness over its price when making buying decision since they will be able to keep it for a longer term. Demand is considered to be a function of the selling price and reference price as well as the freshness of products associated with their expiry dates and safety stock levels. The model is developed taking advantage of Double Interval Grey Numbers (DIGN) to more accurately formulate consumer behavior and enhance quality of the analytical results in practical decision-making. Moreover, the optimal retailer decision provided by the model is discussed when losing the market share and when there is no consistency between product freshness and price. The present study is conducted to develop and propose a game-theoretic model for the joint decisions made on pricing and lot-sizing by retailers of perishable goods. Numerical experiments confirm the consistency of the optimal pricing and inventory strategies and show that the system was uniquely balanced for different demand scenarios. Sensitivity analysis is also presented to identify the structural characteristics of the problem and understand the impact of different parameters on optimal decision-making.

Exploring the nonlinear dynamics of the lost-sales order-up-to policy

With most inventory theory investigating linear models, the dynamics of nonlinear inventory systems is not well understood. We explore the dynamics of the order-up-to policy under lost-sales for the case of i.i.d. normally distributed demand and unit lead times. We consider the ideal minimum mean-squared error forecast and two alternative scenarios: partial demand observation and dynamic demand forecasting, providing a broad understanding of the operational performance of lost-sales systems. In each scenario, we obtain analytical expressions for the order, inventory, and satisfied demand distributions. This allows us to quantify the Bullwhip and inventory variance amplification ratios as well as the fill rate and the inventory cover. We show the lost sales nonlinearity induces complex behaviours in inventory systems. Interestingly, lost sales smooth supply chain dynamics, significantly affecting the trade-off between service level and average inventory holding. We also reveal the inventory downsides of demand censoring and the production damages induced by dynamic forecasts. We identify a key parameter, the relative safety margin, that characterises the performance of lost-sales systems. We finish by offering some prescriptive results for the optimal safety stock and capacity level in both a retail and a manufacturing lost-sales setting.

The effect of economic uncertainty on inventory and working capital for manufacturing firms

This paper develops an empirical model to investigate the impact of macro-economic risk on working capital and the various types of inventory. Our analysis helps manufacturing firms anticipate the implications of high macro-economic risk, measured through the economic policy uncertainty (EPU) index, on their operations. Using a sample of 6503 US manufacturing firms during the period 1990–2018, we show that EPU drives high levels of inventory, thus corroborating existing theories about random disruptions raising safety-stock levels. We also show that increased economic uncertainty yields higher trade credits, payables, and working capital, thus requiring firms to tie more capital to their operations. Our results are statistically significant, yet these effects are small at the firm level. The results are robust when applying the monetary policy uncertainty (MPU) index to the subsample of the data from 1990 to 2007, instead of the EPU index to the same data set.

A Non-Parametric Approach for Setting Safety Stock Levels

In practice, lead time demand (LTD) can be non-standard: skewed, multi-modal or highly variable; factors that compromise the validity of the classic approaches for setting safety stock levels. Motivated by encountering this problem at our industry partner, we develop an approach for setting safety stock levels using the bootstrap, a widely-used statistical procedure. We extend prior research that has used the bootstrap for quantile estimation to address the multi-parameter estimation of safety stocks. We develop a multivariate central limit theorem for the bootstrap mean and bootstrap quantile -- components of the safety stock calculation -- highlighting why the generalization of these bootstrap methods is critical for inventory management. These results provide a theoretical underpinning for the bootstrap estimator of safety stock and permit the construction of confidence intervals for safety stock estimates, allowing decision makers to understand the reliability with which the desired service level will be achieved. Building on our theoretical results, and supported by numerical experiments, we provide insights on the behavior of the bootstrap for various LTD distributions, which our results demonstrate are critical when employing the bootstrap method. Implementation results with our industry partner indicate our approach is quite effective in setting safety stock levels.

Supply chain management and market responsiveness: a simulation study

PurposeThis study aims to provide a practical solution to the relationship between supply chain (SC) integration and market responsiveness (MR). A method is proposed to integrate SC and MR parameters, namely, product supply and demand in the context of low-value commodities (e.g. cement).Design/methodology/approachSimulation and forecasting approaches are adopted to develop a potential procedure for addressing demand during lead time. To establish inventory measurements (safety stock and reorder level) and increase MR and the satisfaction of customer’s needs, this study considers a downstream SC including manufacturers, depots and central distribution centers that satisfies an unbounded number of customers, which, in turn, transport the cement from the industrialist.FindingsThe demand during lead time is shown to follow a gamma distribution, a rare probability distribution that has not been considered in previous studies. Moreover, inventory measurements, such as the safety stock, depending on the safety factor under a certain service level (SL), which enables the SC to handle different responsiveness levels in accordance with customer requests. In addition, the quantities of the safety stock and reorder point represent an optimal value at each position to avoid over- or understocking. The role of SC characteristics in MR has largely been ignored in existing research.Originality/valueThis study applies SC flexibility analyzes to overcome the obstacles of analytical methods, especially when the production process involves probabilistic variables such as product availability and demand. The use of an efficient method for analyzing the forecasting results is an unprecedented idea that is proven efficacious in investigating non-dominated solutions. This approach provides near-optimal solutions to the trade-off between different levels of demand and the SC responsiveness (SLs) with minimal experimentation times.

FIXED TIME PERIOD MODELLING FOR INVENTORY CONTROL SYSTEM IN REGIONAL WAREHOUSING

Inventory control modelling is utilized to optimize warehousing cost and the availability of product and raw material while keeping customer satisfaction level. A formulation of fuzzy time series based on raw material planning for fixed-time period safety stock model was proposed to enhance forecasting accuracy in limited data availability. Safety stock levels were computed in monthly fashion to optimizing the most appropriate capacity in each regional warehouse. Forecasting accuracy was based on MAPE indicator. By deploying forecasted data, the calculation of product quantity and capacity level were set to correspond with the target of product box quantity to produce. The result showed that the forecasting of six products in four regions showed the range of MAPE values with minimum at 0.29% and maximum at 1.94% level. The implementation of formula on a real field application had increased the data flow transaction and mobility amount among stakeholders. Keywords: formulation, fuzzy tim series forecasting, inventory control, modelling, MAPE

Risk Mitigation Strategies on Supply Chain PT. X

Every supply chain activity in a company is not separated from risk, therefore risk management in a company is strongly required for risk handling. PT. X is a manufacturing company that produces food flavors. Dependence with suppliers and consumers causes uncertainty in the company’s supply chain flow. This uncertainty can lead to adverse effects called risk. Risk can be minimized by carrying out risk management. This study aimed to design a risk management by identifying risk events from the company’s business processes based on Supply Chain Operation Reference (SCOR), to determine the causes of priority risks and then identifying risk mitigation strategies using House of Risk stage 1 (HOR1), and ranking risk mitigation strategies by considering company criteria in determining risk mitigation strategies using the Analytical Hierarchy Process (AHP). From the results of the study, 21 risk events dan 13 priority risk causes’ identified, and 9 risk mitigation strategies’ determined. Risk mitigation strategies’ rank sequentially were conducting briefings and evaluations every day, coordinating with suppliers continuously, making signboards at the warehouse, tightening the implementation of Standard Operating Procedures (SOP), providing ongoing training, evaluating supplier performance, monitoring more frequently, demand management with considering safety stock and service levels, and had supplier reserves. So, this study suggests PT.X to implement the risk mitigation strategies for minimizing the risks in the company.