Safety Stock Levels Research Articles

A multi-agent system model for supply chains with lateral preventive transshipments: Application in a multi-national automotive supply chain

In this study, a multi-agent system model is developed to observe the effects of ordering parameters on a supply chain with lateral preventive transshipments. The proposed model involves ordering and premium freight bidding processes of the agents. The model is implemented to a multi-national supply chain considering both supply and demand side uncertainties. Effects of safety stock and supplier flexibility levels on performance are examined by simulation from both agent and system-wide perspective. The results reveal the viability of the proposed model.

Construction Material Safety-Stock Determination Under Nonstationary Stochastic Demand and Random Supply Yield

Nonstationary stochastic demand and random supply yield have led to a great challenge in construction material management. Appropriate safety stocks can cope with uncertainty in both demand and supply yield, and improve the construction cost and productivity significantly. But the safety stock is usually determined by planner's experience or rough evaluation in management practice, which results in overstock or understock situations frequently. For changing this situation, this paper focuses on analysis of the stochastic inventory control problem of construction material, and develops a general safety-stock determination approach under nonstationary stochastic demand and random supply yield. Based on the linear-order release rule and modified base-stock definition, the inventory balance equation is developed. Then, a coverage random variable, which incorporates the complete information of outstanding orders, is defined to represent the outflows and inflows to stock over risk periods. Meanwhile, the fixed-point iteration method is adopted to calculate the time-varying safety-stock level under predefined service-level (nonstockout probability) constraint. Finally, a numerical analysis is conducted to investigate the relative performance of the proposed safety-stock approach and days of supply rule. The result demonstrates a considerable service improvement and inventory decrease of the safety-stock approach.

Group Identity to Manipulate Social Preferences in Supply Chains with Costly E ffort

We analyze a supply chain in which a demand planner provides demand forecasts to a production planner. The production planner uses the forecasts to decide on the production quantities and needs information about the accuracy of the forecasts to determine the appropriate safety stock levels. The accuracy of the forecast depends on the effort that the demand planner invests in forecasting. The production planner cannot observe the effort and has to form a belief about the demand planner’s effort level. If the actual effort of the demand planner and the belief of the production planner are not aligned, the overall performance of the supply chain suffers. We develop a game theoretic model to show how social preferences affect the alignment between the demand planner and the production planner. Using laboratory experiments we find support for the theoretical predictions: Some demand planners invest effort, and production planners anticipate this effort. We further show that group identity can be used to increase social preferences, thereby increasing the incentives for the demand planners to invest effort, which is anticipated by the production planners and leads to increased supply chain profitability.

Inventory control for returnable transport items in a closed-loop supply chain

An inventory control model for returnable transport items (RTI) where the manager selects the optimal length for inspection, repair, and purchase cycles is described. Repaired and newly obtained RTI are used in combination to satisfy current production requirements. Uncertain returns are incorporated into the model by determining a satisfactory safety stock level to buffer the inventory of used and repairable containers. The minimum cost solution is obtained when inspection and repair runs begin simultaneously. Cycle times are a function of the expected return rate and repairable percentage, while variability in these random assumptions affects the required safety stock.

Optimizing Safety Stock Levels in Modular Production Systems Using Component Commonality and Group Technology Philosophy: A Study Based on Simulation

Modular production and component commonality are two widely used strategies in the manufacturing industry to meet customers growing needs for customized products. Using these strategies, companies can enhance their performance to achieve optimal safety stock levels. Despite the importance of safety stocks in business competition, little attention has been paid to the way to reduce them without affecting the customer service levels. This paper develops a mathematical model to reduce safety stock levels in organizations that employ modular production. To construct the model, we take advantage of the benefits of aggregate inventories, standardization of components, component commonality, and Group Technology philosophy in regard to stock levels. The model is tested through the simulation of three years of operation of two modular product systems. For each system, we calculated and compared the safety stock levels for two cases: (1) under the only presence of component commonality and (2) under the presence of both component commonality and Group Technology philosophy. The results show a reduction in safety stock levels when we linked the component commonality with the Group Technology philosophy. The paper presents a discussion of the implications of each case, features of the model, and suggestions for future research.

Safety stock placement in supply chains with demand forecast updates

Supply chains are exposed to many types of risks and it may not be obvious where to keep safety stocks in the supply chain to hedge against those risks, while maintaining a high customer service level. In this paper, we develop an approach to determine the safety stock levels in supply chain systems that face demand uncertainty. We model customer demand following the Martingale Model of Forecast Evolution (MMFE). An extensive body of literature discusses the safety stock placement problem in supply chains, but most studies assume independent and identically distributed demand. Our approach is based on a simulation study in which mathematical models are solved in a rolling horizon setting. It allows determining the safety stock levels at each stage of the supply chain. Based on a numerical study, we find that a big portion of the safety stocks should be placed downstream in the supply chain to achieve a high customer service level.

A tutorial to set safety stock under guaranteed-service time by dynamic programming

In this paper, we provide a tutorial to solve the problem of minimising the safety stock levels under guaranteed-service time over a supply chain (SC) using the dynamic programming (DP) algorithm proposed by Graves and Willems (2000a). We solve a small instance to exemplify the steps of the DP algorithm, then we solve two bigger instances by a Java-based application. As the DP algorithm has some insights that must be explained in detail to carry it out, the novelty and helpfulness of this tutorial lies in the fact that we account for those insights which lead researchers to develop new algorithms to solve bigger instances. This problem is included in some state-of-the-art SC books but none gives details about the solution algorithm as we do in this tutorial.

Analytical insights into two-stage serial line supply chain safety stock

Effective inventory management is one of the most significant challenges facing today׳s global supply chains. Businesses are observing significant profitability gain by optimizing their inventory. This paper optimizes safety stock inventory in a two-stage serial line supply chain, inspired by real-life Cisco supply chains, under guaranteed-service safety stock model assumptions. We analytically show that the optimal safety stock levels depend on the cost and leadtime parameters of the supply chain. Intuitively, it is only worthwhile to hold safety stock inventory at the upstream stage when cost at the upstream stage is relatively low or its leadtime is relatively long. We also show that total supply chain safety stock cost can be reduced when cost allocated at the upstream stage is reduced or leadtime at the upstream stage is increased.

정책적 안전재고의 비용 최적화 : 제록스 소모품 유통공급망 사례연구

Inventory, cost, and the level of service are three interrelated key metrics that most supply chain organizations are striving to optimize. One way to achieve this goal is to create a simulation model to conduct sensitivity analysis and optimization on several different supply chain policies that can be implemented in actual operation. In this paper, a case of Xerox global supply chain modeling and analysis to assess several what if scenarios for the consumable policy safety stock is presented. The simulation model, combined with analytical cost model and optimization module, is used to optimize the policy safety stock level to achieve the lowest total value chain cost. It was shown quantitatively that the policy safety stock can be reduced, but it is offset by the inbound premium transportation cost to expedite supplies in shortage, and the outbound premium transportation cost to send supplies to customers via express shipment, requiring fine balance.

A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting

In the retail stage of a food supply chain, food waste and stock-outs occur mainly due to inaccurate forecasting of sales which leads to incorrect ordering of products. The time series sales in food retail industry are characterized by high volatility and skewness, which vary by time. So, the interval forecasts are required by the retail companies to set appropriate inventory policy (reorder point or safety stock level). This paper attempts to develop a seasonal autoregressive integrated moving average with external variables (SARIMAX) model to forecast daily sales of a perishable food. The process of fitting a SARIMAX model in this study involves: (i) the development of Seasonal Autoregressive Integrated Moving Average (SARIMA) model and (ii) combining the SARIMA model and the demand influencing factors using linear regression. As the SARIMAX using multiple linear regression (SARIMA-MLR) model produces only mean forecast, the possibility of underestimation and overestimation is very high due to high service level, peak, and sparse sales in food retail industry. Therefore, a hybrid SARIMA and Quantile Regression (SARIMA-QR) is developed to construct high and low quantile predictions. Instead of extrapolating the quantiles from the mean point forecasts of SARIMA-MLR model based on the assumption of normality, the SARIMA-QR model directly forecasts the quantiles. The developed SARIMA-MLR and SARIMA-QR models are applied in modeling and forecasting of sales data, i.e., the daily sales of banana from a discount retail store in Lower Bavaria, Germany. The results show that the SARIMA-MLR and -QR models yield better forecasts at out-sample data when compared to seasonal naïve forecasting, traditional SARIMA, and multi-layered perceptron neural network (MLPNN) models. Unlike the SARIMA-MLR model, the SARIMA-QR model provides better prediction intervals and a deep insight into the effects of demand influencing factors for different quantiles.

Safety stock calculations and inventory analysis: a practical approach for the FMCG case in a South-East Asian country

With cut-throat competition in the marketplace, companies today have to look towards new ways to reduce their costs to survive. Inventory is one of the biggest areas tying up a firm’s working capital. This article discusses how a firm should look to optimize its existing inventory and how should it go about setting its safety stock levels. Our major emphasis is on the different parameters that are required for safety stock calculations using a probabilistic normal distribution model. For validation, we have taken a case of a major fast-moving consumer goods (FMCG) company and its operations in South East Asia. We have applied the model to calculate safety stock levels of over 100 SKUs at this company and shown how the model helps achieve inventory optimization effectively and efficiently. The resulting cost benefits are also shown.

A comparative study of production–inventory model for determining effective production quantity and safety stock level

This study presents a comparative study to determine ideal stock levels of a multi-national tire manufacturing company. The conventional inventory models can not be sufficient to optimize the production, the inventory quantity and the backorder simultaneously. Therefore, it is not possible to obtain a production policy by considering these objectives for all produced parts concurrently. In this paper, a production problem with three objectives is solved with mathematical modeling, greedy algorithm and genetic algorithm considering production constraints of a company. While existing inventory models based on conventional methods were applied for safety stock level determination, our proposed model uses the mathematical programming based optimization methods based on mathematical programming. Furthermore, the production planning policy is obtained with the optimum production amount and the stock is determined by considering the constraints defined by the firm. Finally, in our numerical results, we compare each solution methodology with respect to each objective criteria.

Safety stock levels in modular product system using commonality and part families

Despite the importance and potential for companies to minimize costs by reducing the safety stock levels using the modular product system, this has not been extensively studied; its study has been limited by the inherent complexity of the production system. The modular product system arises to try meeting the increasing demand of customized products, which are products manufactured with virtually the same costs, lead times and high quality of products made with mass production. This paper develops a new method to calculate and to reduce the safety stock levels for modular product system under the Make to Order strategy, where, besides considering its reduction using commonality in modules and components, it also considers a substitutability factor devised from Group Technology theory. The results show that with the application of the proposed model in a basic example, the safety stock levels in modular product system has been reduced.

Management of demand uncertainty in supply chain cost planning

Cost effective supply chain management under various market, logistics and production uncertainties is a critical issue for companies in the current development. Reservations in the supply chain usually increase the variance of profits (or costs) to the company, increasing the likelihood of decreased profit. Demand uncertainty, in particular, is an important factor to be considered in the supply chain design and operations. To hedge against demand uncertainty, safety stock levels are commonly introduced in supply chain operations as well as in supply chain design. In this paper, the authors proposed the use of deterministic planning and scheduling models which incorporate safety stock levels as a means of accommodating demand uncertainties in routine operation.

Optimal lockout/tagout, preventive maintenance, human error and production policies of manufacturing systems with passive redundancy

Purpose – The analysis of the optimal production and preventive maintenance with lockout/tagout planning problem for a manufacturing system is presented in this paper. The considered manufacturing system consists of two non-identical machines in passive redundancy producing one type of part. These machines are subject to random breakdowns and repairs. The purpose of this paper is to minimize production, inventory, backlog and maintenance costs over an infinite planning horizon; in addition, it aims to verify the influence of human reliability on the inventory levels for illustrating the importance of human error during the maintenance and lockout/tagout activities. Design/methodology/approach – This paper is different compared to other research projects on preventive maintenance and lockout/tagout. The influence of human error on lockout/tagout as well as on preventive maintenance activities are presented in this paper. The preventive maintenance policy depends on the machine age. For the considered manufacturing system the optimality conditions are provided, and numerical methods are used to obtain machine age-dependent optimal control policies (production and preventive maintenance rates with lockout/tagout). Numerical examples and sensitivity analysis are presented to illustrate the usefulness of the proposed approach. The system capacity is described by a finite-state Markov chain. Findings – The proposed model taking into account the preventive maintenance activities with lockout/tagout and human error jointly, instead of taking into account separately. It verifies the influence of human error during preventive maintenance and lockout/tagout activities on the optimal safety stock levels using an extension of the hedging point structure. Practical implications – The model proposed in this paper might be extended to manufacturing systems, but a number of conditions must be met to make effective use of it. Originality/value – The originality of this paper is to consider the preventive maintenance activities with lockout/tagout and human error simultaneously. The control policy is obtained in order to find the solution for the considered manufacturing system. This paper also brings a new vision on the importance of human reliability during preventive maintenance and lockout/tagout activities.

The impact of information sharing and inventory control coordination on supply chain performances

The lack of coordination in supply chains can cause various inefficiencies like bullwhip effect and inventory instability. Extensive researches quantified the value of sharing and forecasting of customer demand, considering that all the supply chain partners can have access to the same information. However, only few studies devoted to identify the value of limited collaboration or information visibility, considering their impact on the overall supply chain performances for local and global service level. This paper attempts to fill this gap by investigating the interaction of collaboration and coordination in a four-echelon supply chain under different scenarios of information sharing level. The results of the simulation study show to what extent the bullwhip effect and the inventory variance increase and amplify when a periodic review order-up-to level policy applies, noting that more benefits generate when coordination starts at downstream echelons. A factorial design confirmed the importance of information sharing and quantified its interactions with inventory control parameters, proving that a poor forecasting and definition of safety stock levels have a significant contribution to the instability across the chain. These results provide useful implications for supply chain managers on how to control and drive supply chain performances.

Meta-heuristics for placing strategic safety stock in multi-echelon inventory with differentiated service times

The computational resolution of multi-echelon safety stock placement problems has attracted ample attention in recent years. Practitioners can obtain good solutions for large supply networks with general structure using the guaranteed service model. The mainstream assumption in this model is that a stock point quotes identical service times to its successors. In business, it is common to assign customers to different customer- and service classes, as it can yield significant cost improvements. Nevertheless, differentiated service times have rarely been considered in computational methods of safety stock placement. We relax the assumption of identical service times in the guaranteed service approach and allow stock points to prioritize between their successors. This increases the complexity of the problem considerably, so that meta-heuristics become the methods of choice. Meta-heuristics need a mapping between safety stock levels in the supply network (from which they compute the holding cost) and an internal representation of stocking decisions (from which they generate new solutions). The design of a representation is non-trivial because of complex interactions between stocking decisions and stock levels. We propose a representation for the safety stock allocation problem with differentiated service times that can be used in general-acyclic supply networks. We apply a local search, a simple genetic algorithm and a problem-adjusted simulated annealing to 38 general-acyclic real-world instances. Results suggest that service time differentiation indeed decreases total holding cost in the network. Simulated annealing outperforms the other meta-heuristics within the set of tested methods with respect to speed and solution quality.

Setting safety stocks for stable rotation cycle schedules

In the process industries, specialized equipment and production processes often necessitate the manufacture of products in a pre-determined sequence to minimize changeover time and to simplify scheduling complexity; these types of schedules are referred to as pure rotation schedules, or product wheels, where the circumference of the wheel is the production cycle length. In these industries changeover times between the production of individual products can consume considerable time as well as raw materials and it is therefore often desirable to stabilize the production cycles in order to minimize unplanned changeovers as well as quote accurate lead times to customers. Materials requirements planning (MRP) systems are often used to plan and coordinate production and supply resources with demand in these environments. Central to the effectiveness of the MRP system is the dependability of the lead time parameters. In this paper, we introduce an optimization model to determine safety stock levels that minimize long run expected costs where a stable, cyclic schedule is used. Our model may be used strategically to assess inventory investment requirements as a function of capacity investment, product mix, production technology, demand volatility, and customer service levels. It may be used tactically to optimize item-level planning parameters such as lot size, safety stock and lead time in an MRP system and to support sales and operations planning (S&OP) processes where knowing the future costs associated with current decisions is highly desirable.

Determining supply chain safety stock level and location

Purpose: The lean methodology and its principles have widely been applied in supply chain management in recent decades. Manufacturers are one of the most important contributors in a supply chain and inventory plays a paramount role for them to become lean. Therefore, there should be appropriate management of inventory and all of its drivers in accordance with a lean strategy. Safety stock is one of the main drivers of inventory; it protects against increasing the stretch in the breaking points of the supply chain, which in turn can result in possible reduction of inventory. In this paper an optimization model and a simulation model are developed and applied in a real case to optimize the safety stock level with the objective of logistics cost minimization. Design/methodology/approach: In order to optimize the safety stock level while minimizing logistics costs, a nonlinear cost minimization safety stock model is developed in this paper and then it is applied in a real world manufacturing case company. A safety stock simulation model based on appropriate metrics in the case company’s supply chain performance is also provided. Findings: These models result in not only the optimum levels but also locations of safety stock within the supply chain. Originality/value: In this research, two models of cost minimization and simulation have been developed and also applied in a real case company to result in not only optimized levels but also optimized locations of safety stock across the whole supply chain. In addition, the appropriate supply chain performance measurement metrics have been introduced in this paper and the simulation model is developed based on those.

Coordinating Inventory Control and Pricing Strategies Under Batch Ordering

In this paper we investigate joint pricing and inventory control problems in a finite-horizon, single-product, periodic-review setting with certain/uncertain supply capacities. The demands in different periods are random variables whose distributions depend on the posted price exhibiting the additive form. The order quantity in each period is required to be of integral multiples of a given specific batch size (denoted by Q). Inventory replenishment incurs a linear ordering cost. Referred to as the cost-rate function, the sum of holding and backorder costs can either be convex or quasi-convex. The objective is to determine a joint ordering and pricing decision that can maximize the total expected profit over the planning horizon. We first consider the case in which the cost-rate function is convex and show that the modified (r, Q) list-price policy is optimal for the system with certain and limited capacities, a special case of which is the (r, Q) list-price policy when capacities become unlimited. As supply capacities become random, the optimal policy follows a new structure wherein the optimal order-up-to level and posted price must be coordinated to make the optimal safety stock level follow the (r, Q) policy. We further consider the case of a quasi-convex cost-rate function, which may arise when a service level constraint is used as a surrogate for the shortage cost. We demonstrate that the (r, Q) list-price policy is optimal for the system without supply capacity constraints. In addition, extensions to several other models are discussed. The enabling technique is based on the notion of Q-jump convexity and its variants.