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Performance analysis of some supply chain replenishment strategies
International Journal of Logistics Research and Applications
Abstract
Proper inventory management reduces the distribution expenses and improves the performance of supply chains (SCs). In this study, the performance of some replenishment strategies (RSs) in terms of bullwhip effect (BWE), SC fill rate and total cost of SC, under impulse and average demand with standard error demand processes (DPs) is analysed by simulation. The results show that the performance of fixed-order quantity and average demand strategies is better with respect to BWE and SC fill rate, and the total cost of SC under (s, S) is the best when compared with other strategies studied. The number of impulses (NI) and the magnitude of impulses (MIs) have shown an impact on the performance of the RS. The BWE is decreased when variation in customer DP is increased in inventory position-based strategies. The analysis also shows that the impulse DP can be approximated with the average DP having standard error.
... Wadhwa et al. [19] have used an impulse demand to analyze the inventory position-based policies performance, OUT, (s, S) and (s, Q) in a serial supply chain. Pillai et al. [20] have studied same inventory-based policies against impulse and average demand process. In their work, they have applied an inventory policy at a four-stage serial supply chain to determine the order size, and the performance criteria of the supply chain. ...
... Recent researches [8] [21] [22] showed that Statistical Process Control (SPC)-based policies result in better bullwhip performance and inventory level compared to the traditional inventory position-based policies. Examples of these type of policies are Average Demand Strategy (ADS), Fixed Order Quantity (FOQ), Demand Flow Strategy (DFS) [19] [20], ] and EOQ-based policy [23]. The supply chain performance can be influenced by many factors such as review period and the lead time [24], also the customer demand pattern affects the performance [3] [25]. ...
... To our best of knowledge, realistic situations such as supplier capability to provide better lead time with small order quantity has not been considered, which may lead to better fill rate and bullwhip effect if it has been considered at the ordering time. Number of previous studies [2] [3] [8] [19], and [20] analyzed the supply chain performance under the effect of each performance measure separately. However, their effect needs to be incorporated to select the best performing policy. ...
... Qualitative performance measures are those measures for which there is no direct numerical measurement (such as customer satisfaction, products quality, SC vulnerability, SC resilience) and quantitative measures can be described numerically (such as fill rates, costs, inventory levels, resource utilization) [10]- [12]. The various performance measures noticed in the literature to analyse SC's are: SC fill rate [3], [13], order variance and Bullwhip Effect (BWE) [14], [15], order variance ratio [13] and Bullwhip Slope (BwSl) [13], [16], inventory variance and zero replenishment phenomenon [17], risk of shortage [18] and Total Cost of the Supply Chain (TCSC) [13], [19]. ...
... Qualitative performance measures are those measures for which there is no direct numerical measurement (such as customer satisfaction, products quality, SC vulnerability, SC resilience) and quantitative measures can be described numerically (such as fill rates, costs, inventory levels, resource utilization) [10]- [12]. The various performance measures noticed in the literature to analyse SC's are: SC fill rate [3], [13], order variance and Bullwhip Effect (BWE) [14], [15], order variance ratio [13] and Bullwhip Slope (BwSl) [13], [16], inventory variance and zero replenishment phenomenon [17], risk of shortage [18] and Total Cost of the Supply Chain (TCSC) [13], [19]. ...
... Qualitative performance measures are those measures for which there is no direct numerical measurement (such as customer satisfaction, products quality, SC vulnerability, SC resilience) and quantitative measures can be described numerically (such as fill rates, costs, inventory levels, resource utilization) [10]- [12]. The various performance measures noticed in the literature to analyse SC's are: SC fill rate [3], [13], order variance and Bullwhip Effect (BWE) [14], [15], order variance ratio [13] and Bullwhip Slope (BwSl) [13], [16], inventory variance and zero replenishment phenomenon [17], risk of shortage [18] and Total Cost of the Supply Chain (TCSC) [13], [19]. ...
In the present global market, the competition is not between the companies but between the supply chains. The comparison of performance measures of supply chains helps to identify a good supply chain. The best performing supply chains may survive over a long period. The performance of a supply chain is affected by various internal and external factors. The objective of this paper is to identify and conduct a detailed study of the major factors affecting the supply chain performance. The factors are identified by reviewing various literature in the supply chain field. In this respect, a total of 54 literature are reviewed. The major factors identified as supply chain structure, inventory control policy, information sharing, customer demand, forecasting method, lead time and review period length. The optimum selection of parameters of these factors improves the supply chain performance.
... Wadhwa et al. [22] analyzed the inventory performance of inventory position-based policies (OUT, (s, S), (s, Q)) in a serial supply chain under impulse demand. Pillai et al. [21] studied the performance of inventory position-based policies (OUT, (s, S), (s, Q)) under impulse demand and average demand processes. In their study, an inventory policy is used at each stage of a four-stage serial supply chain for deciding the order size, and the performance of the supply chain is analyzed in terms of supply chain fill rate, bullwhip effect, and TCSC. ...
... Some of the non-inventory position-based policies found in the literature are EOQ-based policy [32] and fixed Order Quantity (fOQ), Average demand Strategy (AdS) and demand flow Strategy (dfS). [21,22] performance measure more prominently. The ordering procedure of the proposed policy helps to have smoothened orders compared to OUT policy. ...
... The proposed policy takes into account this performance measure more prominently. It may be noted that in the studies involving analysis of inventory policies [2,3,13,21,22], the performance of supply chain is analyzed under different measures, but all these studies consider each performance measure independently. An inventory policy can be chosen for the operation in a supply chain considering several performance measures together. ...
This paper proposes an inventory position-based policy, and its performance is assessed in terms of supply chain fill rate, bullwhip effect and total cost of supply chain under different scenarios. The scenarios are developed considering the different levels of supply chain structures, demand patterns, and information sharing strategies. Under each scenario, the performance of the proposed policy is compared with the other inventory position-based policies such as Order-Up-To, Order-Up-To smoothing, (s, S) and (s, Q) using spreadsheet-based simulation methodology. The multi-attribute performance comparison, grey relational analysis, shows that the proposed policy is the best compared to other policies tested because of the better performance in supply chain fill rate and order smoothing. The simulation study provides sufficient insights to managers in selecting a suitable inventory policy based on the operating conditions of a supply chain and multiple performance measures.
... Different studies and scholars measure and define the performance of SC using different criteria. Pillai et al. [27] defined SCP in terms of cost, and they further segmented these costs into purchase order cost, setup cost, transportation cost, carrying cost, major cost, and shortage cost. On the other hand, customer satisfaction served as a metric for SC success and was considered to be a significant predictor of performance [28]. ...
... Warehousing and inventory-holding cost [27] Customer satisfaction (on-time delivery record to customers) [31] Flexibility in product design, product delivery [30] Satisfying customers' requirements [29] Ability of suppliers to quickly respond to changes in market demand [31] ...
Background: Supply chain performance (SCP) is impacted by complexity brought about by static and dynamic drivers. This study aims to investigate the effects of supply chain complexity (SCC) on SCP and ascertain whether additive manufacturing best practices have moderating effects on this relationship. Methods: Using data from 29 Ethiopian footwear industries and 205 respondents, the relationship established in the theoretical framework was validated using structural equation modelling (SEM). Results: The study’s findings provided several important insights. First, upstream supply chain complexity (USSCC), midstream supply chain complexity (MSSCC), and downstream supply chain complexity (DSSCC) negatively affect SCP. Second, additive manufacturing best practices have significant moderation effects between supply chain complexity and supply chain performance. Third, the negative impacts of USSCC and MSSCC on SCP are reduced at a higher level of additive manufacturing adaptation. The findings of this study also revealed that the effects of DSSCC on SCP have no difference at both low and high levels of additive manufacturing best practices. Conclusions: This work offers the first empirical investigation to which the detrimental effects of SCC on SCP are mitigated or improved through the moderating role of additive manufacturing best practice.
... Vrijhoef and Koskela (2000) have claimed four significant supply chain management roles in their study, and they allege that the centre of attention can be shifted to the supply chain or the site or on both based on the project. Many studies in this area conclude that information sharing and lead time directly impact the supply chain , Mani et al., 2016, Pillai et al., 2014. It is found that the construction sector is reluctant in implementing SCM; the reasons identified include fear of loss of control, inability to share information between stakeholders, project's complexity, inappropriate knowledge about SCM and its benefits in construction (Love 2000, Hope 2012, Benton and McHenry (2010), Battula et al., 2020) Highway construction projects are considered in this study, which involves various linear activities compared to projects like high-rise building construction, which has a nonlinear nature (Vorster et al., 1992). ...
The construction sector is a significant contributor to the Gross Domestic Product of a developing country. Infrastructure improvement plays a vital role in this wherein highway construction is a dynamic sector requiring proper planning and scheduling multiple resources. Appropriate integration among various associated stakeholders is essential for a project’s success, aided by supply chain management. Resource planning is one of the basic concepts in supply chain management, with material and equipment management being the critical area. The main objective of this study is to develop a conceptual supply chain simulation model using ARENA, to analyze the equipment idling and utilization rate, keeping inter-arrival time for dispatch, the number of equipment, and working hours as constant. This model employs the real-time ‘best fit’ material utilization data as input. Material utilization data collected from 62 construction projects are analyzed to arrive at a ‘best fit’ probability distribution. This study’s conceptual supply chain simulation model helps formulate suitable material and equipment delivery plans to lessen risk in construction projects.
... In particular, the upward/downward shifts in demand can be attributed to price changes, discounts, promotions or disruptions. The demand shocks/impulse can be the result of advance demand ( Ciancimino et al., 2012;Lee et al., 1997a,b;Pillai, Talari, & Elluri, 2014 ). ...
Extensive research has shown that collaboration has substantial impact on supply chains performance. However, most of previous research has focused on information sharing-based collaboration models that require considerable effort to be implemented, such as information exchange supply chain (IESC). This paper introduces a new efficient collaboration model (IS-OUT) for multi-echelon supply chains. The IS-OUT model relies upon the ordering mechanism of the classical order-up-to policy (OUT). In traditional supply chains (TSC), although OUT replenishment orders include two pieces of information: demand forecast and inventory position balance, they are transferred to upstream echelons as single-quantity orders. In IS-OUT, the order information is transferred to the upstream echelons as two component parts of information to provide better coordination in supply chains. In this paper, the mathematical formulation of IS-OUT is presented, and simulation is adopted to compare the performance of TSC, IS-OUT, and IESC under special conditions and assumptions, considering various performance metrics. Although the results indicate that IESC model offers higher performance mostly than IS-OUT, IS-OUT outperforms or at least is comparable with IESC under a few conditions. Since IS-OUT model is easier to implement than IESC, IS-OUT provides a compromise between extent and information sharing requirements, and performance efficiency. The results should help a decision maker to select the model that optimizes its operating environment.
... Other important operational causes are replenishment policies, the presence of price discounts, the imbalance between cost factors and the effect of production smoothing strategies (e.g. Dejonckheere et al. 2003;Disney and Towill 2003b;Wu and Zhang 2007;Childerhouse, Disney, and Towill 2009;Cantor and Katok 2012;Su and Geunes 2012;Madhusudanan, Pamulety, and Pratap 2014). In this stream of topics, (Niranjan, Metri, and Aggarwal 2009) used the Beer Game to show that the ratio of backorder cost vs. ...
In this paper, data from a Beer Game simulation have been used to investigate whether education on the inventory theory makes a difference, in terms of inventory planning performance. The data from the present experiment show that education on models that are difficult to apply to the Beer Game do in fact make a difference, since education increases a planner's ability to keep orders relatively constant, and to match supply with demand. The analyses show that the inventory theory matters, even when it cannot be used directly.
... Ordering a FOQ rather than the quantity determined by inventory position is found to be more efficient. Pillai et al. [4] analyse the performance of a single product four-echelon serial SC under various inventory replenishment strategies by simulation in terms of bullwhip effect (BWE), SC fill rate and total cost of SC. They consider various periodic system-based replenishment strategies such as: DFS, FOQ strategy, OUL strategy, (r, S) strategy, (r, Q) strategy and ADS. ...
The objective of this paper is to develop a spreadsheet-based simulation methodology to evaluate the performance of a serial supply chain under different inventory position based ((r, Q) and (r, S)) and non-inventory position based (Fixed Order Quantity (FOQ) and Demand Flow Strategy (DFS)) periodic review inventory policies. The performance measures comprises of Fill Rate, Risk of Shortage, Bullwhip Effect and Total Cost of Supply Chain. An experimental study with four scenarios are carried out for two customer demand distributions under information sharing and no information sharing scenarios to determine performance measures in lost sales environment. The best performing inventory control policy for each scenario is found by conducting Grey Relational Analysis and the analysis show that one of non-inventory position based policy, FOQ is the better policy compared to other three policies.
This article addresses a reorder point method with an extended inventory position. The inventory position is decreased by near future known demand spikes. This advance demand information helps to improve the performance of the reorder point method. The proposed extension leads to a hybrid model: make-to-stock for demand less than the spike threshold and make-to-order for demand greater than the spike threshold. Furthermore, the number of cycles is modeled more generally and takes into account that the replenishment order has lot-size Q or an integer multiple of Q. An iterative solution procedure is developed for the proposed model, and the numerical results are shown to underpin the usefulness of the approach.
This chapter discusses the bullwhip effect performance of serial and divergent supply chains under statistical process control (SPC)-based and order-up-to (OUT) policies. The performance of the supply chains is evaluated in terms of order rate variance ratio and bullwhip slope under four realistic customer demand models such normal, normal with a sudden change in mean, normal with seasonality, and normal with seasonality and a sudden change in mean. The impact of sharing of customer demand among the other members of the supply chain and the introduction of order smoothing parameter on both policies are also studied. The results show that under non-stationary customer demand models the difference in the performance of serial and divergent supply chains is noticeable. The bullwhip slope under OUT policy is significantly differing from the bullwhip slope under the SPC-based policy. OUT policy with order smoothing performs better than the SPC-based policy with order smoothing. This finding provides proper guidelines for a supply chain manager to make a decision in a practical scenario.
Organizations nowadays need to navigate the transformation from a traditional supply chain to an advanced and efficient adaptive supply chain. The adaptive supply chain network has an intelligent adaptive capability to the changing market. Demand-driven companies prefer to share information and proactively respond to shorter and unpredictable demand variation and lifecycles. Hence, the success of a collaborative supply chain depends on the mutual trust and willingness to share information between the partners and suppliers. In the information-sharing scenario, the exchange of demand information and action plans to align forecasts or long-term planning are usual; the stages of supply chain place the order independently. Agility, savviness and adaptability are the requirements for a supply chain to be highly competitive in the global environment. Communication between customers, suppliers and trading partners is required to ensure that the right products arrive at the right location at the right time. This can be best accomplished in the cloud; in place of a manually driven supply chain, cloud computing can transform the supply chain into an automated, demand-driven chain that offers visibility and control across all trading partners.
The objective of this paper is to introduce the concept of real time collaboration for inventory distribution management in supply chains using Google spreadsheet. The capability of Google spreadsheet to use for a complex structure of supply chain is demonstrated using simulation for inventory distribution management. To demonstrate the collaboration capabilities for inventory distribution management, a role play game is developed using the Google spreadsheet. The two periodic review policies considered for the simulation are Order Up-To (OUT) and (r, S) where, r is reorder point and S is maximum inventory level. Using these policies a two year simulation is carried which could demonstrate on-line inventory distribution management capability for 15-member 4-echelon divergent supply chain. The 4-member 4-echelon role play game could demonstrate collaborative capabilities of Google spreadsheet for inventory distribution in a supply chain.
Purpose
The aim of this article is to provide a concise methodology for the design of a widely used class of decision supply systems (DSS) which will enable precise control of bullwhip variance and inventory variance induced within a supply chain echelon.
Design/methodology/approach
The study exploits recent research that derived analytical formulae for calculating these performance metrics germane to the delivery process when the demand is randomly varying about a constant mean value. These formulae have been verified via extensive simulation‐based cross‐checks.
Findings
The design methodology focuses on the specification of bullwhip variance as an input. The output is to identify combinations of parameter settings to meet this target. Hence these parameters may be mapped to provide a visual display of competing designs with their associated inventory variance.
Research limitations/implications
Although the analytical solutions apply only to the case where the pipeline error and inventory error correction terms are equal, this is not a severe limitation. Both theoretical studies of dynamic response and industrial experience support this feedback gain equally as enabling good practice.
Practical implications
Design of this particular DSS to control bullwhip is now greatly simplified, and guaranteed via extensive verification tests. The formulae are equally sound as a means of establishing system robustness.
Originality/value
The methodology is unique in enabling transparency of both bullwhip variance and inventory variance computation. Not only are system design time saved and normal performance guaranteed, but considerable management insight is generated thereby.
Purpose
The aim of this article is to provide a concise methodology for the design of a widely used class of decision supply systems (DSS) which will enable precise control of bullwhip variance and inventory variance induced within a supply chain echelon.
Design/methodology/approach
The study exploits recent research that derived analytical formulae for calculating these performance metrics germane to the delivery process when the demand is randomly varying about a constant mean value. These formulae have been verified via extensive simulation‐based cross‐checks.
Findings
The design methodology focuses on the specification of bullwhip variance as an input. The output is to identify combinations of parameter settings to meet this target. Hence these parameters may be mapped to provide a visual display of competing designs with their associated inventory variance.
Research limitations/implications
Although the analytical solutions apply only to the case where the pipeline error and inventory error correction terms are equal, this is not a severe limitation. Both theoretical studies of dynamic response and industrial experience support this feedback gain equally as enabling good practice.
Practical implications
Design of this particular DSS to control bullwhip is now greatly simplified, and guaranteed via extensive verification tests. The formulae are equally sound as a means of establishing system robustness.
Originality/value
The methodology is unique in enabling transparency of both bullwhip variance and inventory variance computation. Not only are system design time saved and normal performance guaranteed, but considerable management insight is generated thereby.
We consider a two-echelon supply chain: a single retailer holds a finished goods inventory to meet an i.i.d. customer demand, and a single manufacturer produces the retailer’s replenishment orders on a make-to-order basis. In this setting the retailer’s order decision has a direct impact on the manufacturer’s production. It is a well known phenomenon that inventory control policies at the retailer level often propagate customer demand variability towards the manufacturer, sometimes even in an amplified form (known as the bullwhip effect). The manufacturer, however, prefers to smooth production, and thus he prefers a smooth order pattern from the retailer. At first sight a decrease in order variability comes at the cost of an increased variance of the retailer’s inventory levels, inflating the retailer’s safety stock requirements. However, integrating the impact of the retailer’s order decision on the manufacturer’s production leads to new insights. A smooth order pattern generates shorter and less variable (production/replenishment) lead times, introducing a compensating effect on the retailer’s safety stock. We show that by including the impact of the order decision on lead times, the order pattern can be smoothed to a considerable extent without increasing stock levels. This leads to a situation where both parties are better off.
An important contributory factor to the bullwhip effect (i.e. the variance amplification of order quantities observed in supply chains) is the replenishment rule used by supply chain members. First the bullwhip effect induced by the use of different forecasting methods in order-up-to replenishment policies is analysed. Variance amplification is quantified and we prove that the bullwhip effect is guaranteed in the order-up-to model irrespective of the forecasting method used. Thus, when production is inflexible and significant costs are incurred by frequently switching production quantities up and down, order-up-to policies may no longer be desirable or even achievable. In the second part of the paper a general decision rule is introduced that avoids variance amplification and succeeds in generating smooth ordering patterns, even when demand has to be forecasted. The methodology is based on control systems engineering and allows important insights to be gained about the dynamic behaviour of replenishment rules.
This paper examines the beneficial impact of information sharing in multi-echelon supply chains. We compare a traditional supply chain, in which only the first stage in the chain observes end consumer demand and upstream stages have to base their forecasts on incoming orders, with an information enriched supply chain where customer demand data (e.g. EPOS data) is shared throughout the chain. Two types of replenishment rules are analysed: order-up-to (OUT) policies and smoothing policies (policies used to reduce or dampen variability in the demand). For the class of OUT policies, we will show that information sharing helps to reduce the bullwhip effect (variance amplification of ordering quantities in supply chains) significantly, especially at higher levels in the chain. However, the bullwhip problem is not completely eliminated and it still increases as one moves up the chain. For the smoothing policies, we show that information sharing is necessary to reduce order variance at higher levels of the chain.The methodology is based on control systems engineering and allows us to gain valuable insights into the dynamic behaviour of supply chain replenishment rules. We also introduce a control engineering based measure to quantify the variance amplification (bullwhip) or variance reduction.
The well-known “Bullwhip Effect” concerns the increasing variance of orders as they proceed through the supply chain. In the continuous time representation we solve the delay-differential equation for the inventory balance, which is coupled to the ordering policy. The time domain evolution of the system emerges. We calculate the Bullwhip Effect and compare it to known results for the discrete time representation. The discrete and continuous Bullwhip Effect expressions have similar structures. We show that the two domains are managerially equivalent so that in practice either domain can be used to study a supply chain.
A discrete linear control theory model of a generic model of a replenishment rule is presented. The replenishment rule, which we term a “Deziel Eilon—automatic pipeline, inventory and order-based production control system”, is guaranteed to be stable. From a z-transform model of the policy, an analytical expression for bullwhip is derived that is directly equivalent to the common statistical measure often used in simulation, statistical and empirical studies to quantify the bullwhip effect. This analytical expression clearly shows that we can reduce bullwhip by taking a fraction of the error between the target and actual inventory and pipeline (or work in progress (WIP) or “orders placed but not yet received”) positions. This is in contrast to the common situation where ordering policies account for all of the error every time an order is placed. Furthermore, increasing the average age of the forecast reduces bullwhip, as does reducing the production/distribution lead-time. We then derive an analytical expression for inventory variance using the same procedure to identify the closed form bullwhip expression.We assume that a suitable objective function is linearly related to the bullwhip and inventory variance amplification ratios and then optimise the PIC system for different weightings of order rate and inventory level variance. We highlight two forms of the objective function, one where “the golden ratio” can be used to determine the optimal gain in the inventory and WIP feedback loop and another that allows the complete range of possible solutions to be visualised. It is interesting that the golden ratio, which commonly describes the optimum behaviour in the natural world, also describes the optimal feedback gain in a production and inventory control system.
Discrete Event System Simulation is ideal for junior- and senior-level simulation courses in engineering, business, or computer science. It is also a useful reference for professionals in operations research, management science, industrial engineering, and information science. While most books on simulation focus on particular software tools, Discrete Event System Simulation examines the principles of modeling and analysis that translate to all such tools. This language-independent text explains the basic aspects of the technology, including the proper collection and analysis of data, the use of analytic techniques, verification and validation of models, and designing simulation experiments. It offers an up-to-date treatment of simulation of manufacturing and material handling systems, computer systems, and computer networks. Students and instructors will find a variety of resources at the associated website, www.bcnn.net/, including simulation source code for download, additional exercises and solutions, web links and errata.
This article reviews lot-size models, which focus on coordinated inventory replenishment decisions between buyer and vendor and their impact on the performance of the supply chain. These so-called joint economic lot size (JELS) models determine order, production and shipment quantities from the perspective of the supply chain with the objective of minimizing total system costs. This paper first describes the problem studied, introduces the methodology of the review and presents a descriptive analysis of the selected papers. Subsequently, papers are categorized and analyzed with respect to their contribution to the coordination of different echelons in the supply chain. Finally, the review highlights gaps in the existing literature and suggests interesting areas for future research.
This paper presents a hierarchical model framework for the analysis of the stabilizing effect of inventories in multiechelon manufacturing/distribution supply chains. This framework is used to compare the variance of demand to the variance of replenishment orders at different echelons of the system (e.g., retailer, wholesaler). Empirical data and experience with management games suggest that, in most industries, inventory management policies can have a destabilizing effect by increasing the volatility of demand as it passes up through the chain (the "bullwhip" effect). Our model helps to explain these observations and indicates mechanisms that can promote stabilization. The analysis results also define sufficient conditions for the existence of stabilization and relate these conditions to the optimality of myopic control policies.
Based on APIOBPCS model, and treating forecast tache as an integral part of the supply chain, also transforming the traditional supply chain serial structure through information sharing, and adopting the methods of control-engineering based on z transformation and bullwhip effect measurement, the paper studied the impact of information sharing on the bullwhip effect of three-tier supply chain network which was comprised of manufacturer - distributor - retailor. The simulation results show that information sharing can reduce bullwhip effect, but it can not eliminate bullwhip effect. Finally, through discussing the impacts of logistics delay time parameters and the first-order exponential smoothing forecasting parameters on bullwhip effect, the measures to be used to weaken bullwhip effect is given.
Inventory control plays an important role in supply chain management. Properly controlled inventory can satisfy customers’ demands, smooth the production plans, and reduce the operation costs; yet failing to budget the inventory expenses may lead to serious consequences. The bullwhip effect, observed in many supply chain management cases, causes excessive inventory due to information distortion, i.e. the order amount is exaggerated while a minor demand variation occurs, and the information amplified dramatically as the supply chain moves to the upstream. In this paper, one of the main causes of bullwhip effect, order batching, is considered. A simplified two-echelon supply chain system, with one supplier and one retailer that can choose different replenishment policies, is used as a demonstration. Two types of inventory replenishment methods are considered: the traditional methods (the event-triggered and the time-triggered ordering policies), and the statistical process control (SPC) based replenishment method. The results show that the latter outperforms the traditional method in the categories of inventory variation, and in the number of backlog when the fill-rate of the prior model is set to be 99%. This research provides a different approach to inventory cost-down other than the common methods like: information sharing, order batch cutting, and lead time reduction. By choosing a suitable replenishment policy, the number of backorder and the cost of inventory can be reduced.
This paper attempts to study the impact of impulsive demand disturbances on the inventory-based performance of some inventory control policies. The supply chain is modelled as a network of autonomous supply chain nodes. The customer places a constant demand except for a brief period of sudden and steep change in demand (called demand impulse). Under this setting, the behaviour of each inventory policy is analysed for inventory performance of each node. It is found that the independent decision-making by each node leads to a bullwhip effect in the supply chain whereby demand information is amplified and distorted. However, under a scenario where the retailer places a constant order irrespective of the end customer demand, the inventory variance was actually found to decrease along the supply chain. The variance of the inventory remained constant along the chain when only the actual demands are transmitted by each node. The results also showed that the inventory policy which is best for one supply chain node is generally less efficient from a supply chain perspective. Moreover, the policy which performs poorly for one node can be most efficient for the supply chain. In a way, our results also provide a case for coordinated inventory management in the supply chain where all members prepare a joint inventory management policy that is beneficial for all the supply chain nodes. The results have significant industrial implications.
One of the important aspects of supply chain management is inventory management because the cost of inventories in a supply chain accounts for about 30% of the value of the product. The main focus of this work is to study the performance of a single-product serial supply chain operating with a base-stock policy and to optimize the inventory (i.e. base stock) levels in the supply chain so as to minimize the total supply chain cost (TSCC), comprising holding and shortage costs at all the installations in the supply chain. A genetic algorithm (GA) is proposed to optimize the base-stock levels with the objective of minimizing the sum of holding and shortage costs in the entire supply chain. Simulation is used to evaluate the base-stock levels generated by the GA. The proposed GA is evaluated with the consideration of a variety of supply chain settings in order to test for its robustness of performance across different supply chain scenarios. The effectiveness of the proposed GA (in terms of generating base-stock levels with minimum TSCC) is compared with that of a random search procedure. In addition, optimal base-stock levels are obtained through complete enumeration of the solution space and compared with those yielded by the GA. It is found that the solutions generated by the proposed GA do not significantly differ from the optimal solution obtained through complete enumeration for different supply chain settings, thereby showing the effectiveness of the proposed GA.
This paper deals with the problem of coordinating a vertically separated distribution system under vendor-managed inventory and consignment arrangements. We formulate the profit-maximization problem and carry out equilibrium analysis under cooperative and non-cooperative settings. In addition, a revenue-sharing scheme joint with a side-payment is proposed, which leads to Pareto improvements among channel participants. Our analysis reveals that the non-cooperative decentralization tends to price higher and stock less, which leads to a lower channel-wide profit. The consistent bias can be rectified by the dominant, cooperative wholesaler or by using the proposed two-part revenue-sharing mechanism.
This research evaluates how vendor managed inventory (VMI) affects a supply channel. Specifically, VMI always leads to a higher buyer's profit, but supplier's profit varies. In the short-term, VMI is found to reduce total costs of the channel system, but under certain cost conditions between buyer and supplier, it could decrease the purchasing price and supplier's profit. In the long-run, it could more likely increase supplier's profit than in the short-run. Finally, VMI is an effective supply chain strategy that can realize many of the benefits obtainable only in a fully integrated supply chain.
With the growing focus on supply chain management, firms realize that inventories across the entire supply chain can be more efficiently managed through greater cooperation and better coordination. This paper presents a comprehensive and up-to-date review of the joint economic lot sizing problem (JELP) and also provides some extensions of this important problem. In particular, a detailed mathematical description of, and a unified framework for, the main JELP models are given. Additionally, a comparative empirical study of the main policies proposed for JELP is conducted. The focus of this study is on assessing the deviation of these policies from the optimal solution. Studying the performance of different models provides additional insights that will help in justifying their use in more complex supply chain models that involve more stages or other practical considerations of interest.
This paper investigates the effects of information sharing and early order commitment on the performance of four inventory policies used by retailers in a supply chain of one capacitated supplier and four retailers. Model parameters and operating conditions are emulated from a local business supplying a standard product to its retailers. Through computer simulation and subsequent analyses, we found that the inventory policy used by the retailers, information sharing, and early order commitment can significantly influence the performance of the supply chain. Out of the four inventory policies examined, the economic order quantity rule is found to be the best for the retailers and the entire supply chain, but periodic order quantity and Silver–Meal provide the best performance for the supplier. The sharing of future order plans by the retailer and the supplier is also shown to be the most effective way for reducing the supplier’s cost and improving its service level; however, the magnitude of these benefits achieved is less for the retailers. In addition, early order commitment by the retailers is found to be beneficial to the supplier and retailers in reducing their total cost.
On the premise of discrete simulation technology, the study developed a simulation approach to quantify firms’ business operations and performances in a multi-tier supply chain. By careful simulation scenario design and statistical validation, the simulation model was applied to understand one practical business problem, i.e., how to evaluate the business model and its trade-off of implementing demand information sharing strategy. The results showed that with high demand variance, low demand correlation, and/or high demand covariance, the supply chain without the intermediate tier performed better than that with the intermediary. However, bypassing the intermediate tier in the chain might cause companies less responsive to demand variability. The simulation and analytical approaches presented in the paper can help firms make better decision on business model design and inter-organizational collaboration in supply chains.
This paper provides a quantitative assessment of the potential reduction in the bullwhip effect, and thus in safety stocks, in the supply chain, thanks to real-time visibility of product flows provided by the Radio Frequency Identification (RFID) technology and the EPC Network. The assessment is grounded on a “representative” Italian Fast Moving Consumer Goods (FMCG) supply chain; specifically, the “representative” supply chain is composed of three echelons, namely manufacturers, distributors and retailers of FMCG, whose main features, in terms of both quantitative and qualitative data, were derived through an appropriate survey phase. Reduction of safety stocks is determined based on quantitative methodologies available in the scientific literature. The results of the assessment show that real-time visibility of the supply chain, brought in by RFID and the EPC Network, can dramatically reduce the bullwhip effect, substantially affecting the economical profitability of the whole FMCG supply chain.
Consider multiple companies operating as a serial supply chain. Within this environment, end users form the demand for the last company in the supply chain, but the demand for upstream companies is formed by the companies in the immediate downstream supply chain link. It has been shown that demand seasonality and forecast error can increase as we proceed up the supply chain. These demand distortions, called the “bullwhip” effect, create inefficiencies for upstream firms. This work seeks to identify the magnitude of the problem by establishing an empirical lower bound on the profitability impact of the bullwhip effect. Results indicate that the importance of the bullwhip effect to a firm differs greatly depending on the specific business environment. Given appropriate conditions, however, eliminating the bullwhip effect can increase product profitability by 10–30%.
Consignment contract with revenue sharing has been widely applied in many industries and is especially popular in on-line marketplaces. In this paper we consider a supply chain with an upstream manufacturer and a downstream retailer where a single-period product is produced and sold. The manufacturer chooses the delivery quantity and the retail price, and the retailer sets the revenue shares. Utilizing Nash bargaining model, a cooperative game model is developed to implement profit sharing between the manufacturer and the retailer to achieve their cooperation. When the manufacturer and the retailer are assumed to be risk-neutral, under a very mild restriction on the demand distribution, the decentralized supply chain can be perfectly coordinated and both the manufacturer and the retailer can earn more in cooperation. In this paper, we also analyze how the supply chain system parameters impact the optimal supply chain decisions and the supply chain performance.
Most recent research on supply chain volatility has focused on one particular dimension of that volatility, namely the amplification of upstream order variability. While not ignoring this aspect of supply chain volatility, we focus on a different but equally critical aspect of volatility: the cyclical oscillation of on-hand and on-order inventories about their target values. We prove that such cyclicality does not require oscillatory or random retailer demand as a prerequisite; the resulting volatility is therefore endogenous rather than simply an amplification of exogenous demand inputs. We also measure the amount of amplification resulting from a step increase in demand. The order amplification is the product of two factors, each of which is clearly linked to either on-hand or on-order inventory. Our results attest that supply chain volatility can arise in the absence of exogenous oscillatory or random demand and suggest strategies for avoiding or minimizing such volatility.
Vendor-managed inventory (VMI) and consignment inventory (CI) are supply-chain sourcing practices between a vendor and customer. VMI allows the vendor to initiate orders on behalf of the customer. This presumably benefits the vendor who can then make replenishment decisions according to her own preferences. In CI, as in the usual independent-sourcing approach to doing business, the customer has authority over the timing and quantity of replenishments. CI seems to favor the customer because, in addition, he pays for the goods only upon use. Our main aim is to analyze CI in this supply chain under deterministic demand, and provide some general conditions under which CI creates benefits for the vendor, for the customer, and for the two parties together. We also consider similar issues for the combined use of CI and VMI.
he bullwhip effect is the phenomenon of increasing demand variability in the supply chain from down- stream echelons (retail) to upstream echelons (manufacturing). The objective of this study is to document the strength of the bullwhip effect in industry-level U.S. data. In particular, we say an industry exhibits the bullwhip effect if the variance of the inflow of material to the industry (what macroeconomists often refer to as thevarianceof an industry's "production") is gre ate r than thevarianceof theindustry's sale s. Wefind that wholesale industries exhibit a bullwhip effect, but retail industries generally do not exhibit the effect, nor do most manufacturing industries. Furthermore, we observe that manufacturing industries do not have substan- tially greater demand volatility than retail industries. Based on theoretical explanations for observing or not observing demand amplification, we are able to explain a substantial portion of the heterogeneity in the degree to which industries exhibit the bullwhip effect. In particular, the less seasonal an industry's demand, the more likely the industry amplifies volatility—highly seasonal industries tend to smooth demand volatility whereas nonseasonal industries tend to amplify.
Consignment contracts have been widely employed in many industries. Under such contracts, items are sold at a retailer’s but the supplier retains the full ownership of the inventory until purchased by consumers; the supplier collects payment from the retailer based on actual units sold. We investigate how competition among retailers influences the supply chain decisions and profits under different consignment arrangements, namely a consignment price contract and a consignment contract with revenue share. First, we investigate how these two consignment contracts and a price only contract compare from the perspective of each supply chain partner. We find that the retailers benefit more from a consignment price contract than from a consignment contract with revenue share or a price only contract, regardless of the level of retailer differentiation. The supplier’s most beneficial contact, however, critically depends upon the level of retailer differentiation: a consignment contract with revenue share is preferable for the supplier if retailer differentiation is strong; otherwise a consignment price contract is preferable. Second, we study how retailer differentiation affects the profits of all supply chain partners. We find that less retailer differentiation improves the supplier’s profit for both types of consignment contract. Moreover, less retailer differentiation improves profits of the retailers in a consignment price contract, but not necessarily in a consignment contract with revenue share.
This paper provides an assessment of the impact of collaboration and smoothing replenishment rules on supply chain operational performance and customer service level. Three supply chain configurations (i.e. Traditional, Information Exchange and Synchronised) in which orders are generated by smoothing (S, R) inventory control policies are studied for different proportional controllers. A supply chain stress test is performed through a sudden and intense change in demand. A structured and extended supply chain assessment framework is adopted. The main conclusions of this paper are the following. (i) The impact of Supply Chain Collaboration on overall supply chain performance is greater than that of order smoothing. Order smoothing mitigates the bullwhip effect, but it may have a negative impact on customer service. Supply Chain Collaboration mitigates the bullwhip effect, provides inventory stability, limits lumpy orders and enhances customer service level. (ii) The negative effect on c
In recent years, companies have strengthened their supply agreements, and even the management of their inventories. To this aim, vendor-managed inventory (VMI) represents an interesting approach to stock monitoring and control, and it has been progressively considered and introduced in several companies. The research proposed investigates the way how a particular VMI policy, known as Consignment Stock (CS), may represent a successful strategy for both the buyer and the supplier. The most radical application of CS may lead to the suppression of the vendor inventory, as this actor uses the buyer's warehouse to stock its finished products. As a counterpart, the vendor will guarantee that the quantity stored in the buyer's warehouse will be kept between a maximum level and a minimum one, also supporting the additional costs eventually induced by stock-out conditions. The buyer will pick up from its store the quantity of material needed to meet its production plans and the material itself will be paid to the buyer according to the agreement signed. In previous studies, Braglia and Zavanella [2003. Modelling an industrial strategy for inventory management in supply chains: The 'Consignment Stock' case. International Journal of Production Research 41, 3793-3808] developed an analytical model of the CS policy, referring to a single-vendor and single-buyer situation. The same authors presented a comparison with the optimal solution available in the literature (in particular, with reference to Hill's model [1997. The single-vendor single-buyer integrated production-inventory model with a generalised policy. European Journal of Operational Research 97, 493-499]). The analytical results obtained allow the identification of the benefits and profitability that the CS approach determines in environments affected by uncertain demand. In order to understand the potential benefits of the CS policy, an analytical model is offered with reference to the interesting industrial case of a single-vendor and multiple-buyer productive situation, thus obtaining the optimal replenishment decisions for both the vendor and buyers in such a situation. The results show how the CS policy works better than the uncoordinated optimisation.
The bullwhip effect refers to the phenomenon of amplification and distortion of demand in a supply chain. By eliminating or controlling this effect, it is possible to increase product profitability reducing useless costs such as stock-out and obsolescence costs. The main focus of this work is to study a single-product serial supply chain in which a control parameter can switch the chain from a series of filters to a series of amplifiers of the bullwhip effect and to analyse how the optimal values of the parameters change when discontinuities in order policy are considered. Furthermore, it is also shown that the bullwhip itself it is not a good index of the chain's performance, because it does not consider the oscillations that occur in the inventories, which also may affect the supply-chain performance.
Inter-organizational collaboration has been touted as the driving force behind successful supply chain management. Nonetheless, numerous examples indicate that before all parties in a supply chain can develop full mutual trust and establish an effective governance mechanism, deviations of collaborative agreements from supply chain members is inevitable. The primary objective of this paper is to shed light on the impact of replenishment policy deviations on supply chain performance. We first develop a benchmark case where all supply chain parties follow a base stock policy that maximizes overall supply chain performance. Subsequently, the performance (local as well as system-wide costs and service levels) of various supply chain scenarios that deviate from this benchmark case is examined using simulation methods. The simulation results show that the performance of a decentralized supply chain is contingent on the types of replenishment policies, source of policy deviations, and the interaction of these two factors. Finally, managerial implications for potential supply chain collaboration are discussed.
The "bullwhip" effect, in which order variability increases as one moves up the supply chain, has been observed in a range of industries, modeled by several authors and various remedies suggested. This paper provides a simulation of the effect of improved forecasting methods, and finds that Holt's and Brown's methods substantially mitigate the bullwhip effect across a range of performance metrics. The end result is to identify ordering policies that perform particularly well in combination with these forecasting methods and indicate how they can be implemented in practice.
An important observation in supply chain management, known as the bullwhip effect, suggests that demand variability increases as one moves up a supply chain. In this paper we quantify this effect for simple, two-stage, supply chains consisting of a single retailer and a single manufacturer. Our model includes two of the factors commonly assumed to cause the bullwhip effect: demand forecasting and order lead times. We extend these results to multiple stage supply chains with and without centralized customer demand information and demonstrate that the bullwhip effect can be reduced, but not completely eliminated, by centralizing demand information.
Consignment Stocks -An Opportunity
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