The effect of information sharing on supply chain stability and the bullwhip effect

This paper analyzes the bullwhip effect in multi-stage supply chains operated with linear and time-invariant inventory management policies and shared supply chain information. Such information includes past order sequences and inventory records at all supplier stages. The paper characterizes the stream of orders placed at any stage of the chain when the customer demand process is known and ergodic, and gives an exact formula for the variance of the orders placed. The paper also derives robust analytical conditions, based only on inventory management policies, to predict the presence of the bullwhip effect and bound its magnitude. These results hold independently of the customer demand. The general framework proposed in this paper allows for any inventory replenishment policies, any ways of sharing and utilizing information, and any customer demand processes. It is also shown as a special case that sharing customer demand information across the chain significantly reduces, but does not completely eliminate, the bullwhip effect.     

Robust tests for the bullwhip effect in supply chains with stochastic dynamics

This paper analyzes the bullwhip effect in single-echelon supply chains driven by arbitrary customer demands and operated nondeterministically. The supply chain, with stochastic system parameters, is modeled as a Markovian jump linear system. The paper presents robust analytical conditions to diagnose the bullwhip effect and bound its magnitude. The tests are independent of the customer demand. Examples are given. Ordering policies that pass these tests, and thus avoid the bullwhip effect in random environments for arbitrary customer demands, are shown to exist. The paper also presents possible extensions to multi-echelon chains.     

A robust optimization approach to reduce the bullwhip effect of supply chains with vendor order placement lead time delays in an uncertain environment

Supply chain management is important for companies and organizations to improve their business and enhance competitiveness in the global marketplace. The bullwhip effect problem of supply chain systems with vendor order placement lead time delays in an uncertain environment is addressed in this paper. Among the numerous causes of bullwhip effect, we focus on uncertainties with respect to demand, production process, supply chain structure, inventory policy implementation and especially vendor order placement lead time delays. Minimizing the negative effect of these uncertainties in inducing bullwhip effect creates a need for developing dynamical inventory policy that increases responsiveness to demand and decreases volatility in inventory replenishment. First, a dynamic model of supply chain with above uncertainties is developed. Then, a novel uncertainty-dependent robust inventory control method using inventory position information is proposed. Additionally, the maximum allowable vendor order placement lead time delay that ensures the stability of supply chains and the suppression of bullwhip effect under the proposed inventory control policy is explored and measured. We find that vendor order placement lead time delays do play important role in supply chain dynamics and contribute to its turbulence and volatility. The effectiveness and flexibility of proposed method is verified through simulation study.     

The bullwhip effect in supply chain networks

This paper analyzes the propagation and amplification of order fluctuations (i.e., the bullwhip effect) in supply chain networks operated with linear and time-invariant inventory management policies. The supply chain network is allowed to include multiple customers (e.g., markets), any network structure, with or without sharing information. The paper characterizes the stream of orders placed by any supplier for any stationary customer demand processes, and gives exact formulas for the variance of the orders placed and the amplification of order fluctuations. The paper also derives robust analytical conditions, based only on inventory management policies, to predict the presence of the bullwhip effect for any network structure, any inventory replenishment policies, and arbitrary customer demand processes. Numerical examples show that the analytical results accurately quantify the bullwhip effect; managerial insights are drawn from the analysis. The methodology presented in this paper generalizes those in previous studies for serial supply chains.     

Optimizing of bullwhip effect and net stock amplification in three-echelon supply chains using evolutionary multi-objective metaheuristics

In this article, we intend to model and optimize the bullwhip effect (BWE) and net stock amplification (NSA) in a three-stage supply chain consisting of a retailer, a wholesaler, and a manufacturer under both centralized and decentralized scenarios. In this regard, firstly, the causes of BWE and NSA are mathematically formulated using response surface methodology (RSM) as a multi-objective optimization model that aims to minimize the BWE and NSA on both chains. The simultaneous analysis of the BWE and NSA is considered as the main novelty of this paper. To tackle the addressed problem, we propose a novel multi-objective hybrid evolutionary approach called MOHES; MOHES is a hybrid of two known multi-objective algorithms i.e. multi-objective electro magnetism mechanism algorithm (MOEMA) and population-based multi-objective simulated annealing (PBMOSA). We applied a co-evolutionary strategy for this purpose with eligibility of both algorithms. Proposed MOHES is compared with three common and popular algorithms (i.e. NRGA, NSGAII, and MOPSO). Since the utilized algorithms are very sensitive to parameter values, RSM with the multi-objective decision making (MODM) approach is employed to tune the parameters. Finally, the hybrid algorithm and the singular approaches are compared together in terms of some performance measures. The results indicate that the hybrid approach achieves better solutions when compared with the others, and also the results show that in a decentralized chain, the order batching factor and the demand signal processing in wholesaler are the most important factors on BWE. Conversely, in a centralized chain, factors such as rationing, shortage gaming, and lead time are the most effective at reducing the BWE.     

A measure of bullwhip effect in supply chains with a mixed autoregressive-moving average demand process

In this paper, we quantify the impact of the bullwhip effect – the phenomenon in which information on demand is distorted as moving up a supply chain – for a simple two-stage supply chain with one supplier and one retailer. Assuming that the retailer employs a base stock inventory policy, and that the demand forecast is performed via a mixed autoregressive-moving average model, ARMA(1, 1), we investigate the effects of the autoregressive coefficient, the moving average parameter, and the lead time on the bullwhip effect.     

Measure of bullwhip effect in supply chains: The case of high order autoregressive demand process

Bullwhip effect – the phenomenon in which variance of demand is amplified when moving upstream – has attracted the attention of many researchers for the last few decades. Although the main sources that cause bullwhip effect have been identified, quantifying the bullwhip effect still remains a challenge. In the past, measuring the bullwhip effect for supply chains with autoregressive demand process has been conducted by some researchers. However, most past researches focused mainly on the simple AR(1) model. In many practical situations, autoregressive models with higher order should be employed because they might better represent the demand process. Up to now, very little effort has been spent on this matter. Therefore, this research is conducted to fill this gap by first dealing with AR(2) demand process and investigating the behavior of the developed measure with respect to autoregressive coefficients and order lead-time. Extension to the general AR(p) demand process is then considered.     

Inventory control of supply chains: Mitigating the bullwhip effect by centralized and decentralized Internal Model Control approaches

In this paper, a two-degrees-of-freedom Internal Model Control structure is incorporated in production inventory control for a supply chain system. This scheme presents an intuitive and simple parametrization of controllers, where inventory target tracking and disturbance (demand) rejection in the inventory level problems are treated separately. Moreover, considering that the lead times are known, this scheme presents a perfect compensation of the delay making the stabilization problem easier to handle. This control structure is formulated for a serial supply chain in two ways (by using a centralized and a decentralized control approach). The behavior of these inventory control strategies is analyzed in the entire supply chain. Analytical tuning rules for bullwhip effect avoidance are developed for both strategies. The results of controller evaluations demonstrate that centralized control approach enhances the behavior with respect to the inventory target tracking, demand rejection and bullwhip effect in the supply chain systems.     

Benefits of working capital sharing in supply chains

Supply chain finance and working capital management are important avenues to reduce supply chain costs. Small suppliers may not have sufficient working capital to finance their operations and efficiently supply their customers. We develop a model that captures the fundamental aspects of financial and operational planning in a two-stage supply chain, with both strong and weak members. A strong member can negotiate for more favorable financing rates, more advantageous payment terms, and shorter lead times than a weaker member. We investigate two working capital allocation scenarios. In the dedicated working capital allocation scenario, the members of the supply chain each have their own working capital. In the joint working capital allocation scenario, the members of the supply chain have a joint pool of working capital. Our results demonstrate significant benefits when the members of the supply chain share the working capital. We also show that extending payment delays to a supplier upstream results in higher overall supply chain costs.     

Measuring endogenous supply chain volatility: Beyond the bullwhip effect

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.     

Impact of information sharing and lead time on bullwhip effect and on-hand inventory

This work analyzes a two echelon (warehouse–retailer) serial supply chain to study the impact of information sharing (IS) and lead time on bullwhip effect and on-hand inventory. The customer demand at the retailer is assumed to be an autoregressive (AR(1)) process. Both the echelons use a minimum mean squared error (MMSE) model for forecasting lead time demand (LTD), and follow an adaptive base-stock inventory policy to determine their respective order quantities. For the cases of without IS and inter as well as intra echelon IS, expressions for the bullwhip effect and on-hand inventory for the warehouse are obtained, considering deterministic lead-time. The results are compared with the previous research work and an easy analysis of the various bullwhip effect expressions under different scenarios, is done to understand the impact of IS on the bullwhip effect phenomenon. It is shown that some part of bullwhip effect will always remain even after sharing both inter as well as intra echelon information. Further, with the help of a numerical example it is shown that the lead time reduction is more beneficial in comparison to the sharing of information in terms of reduction in the bullwhip effect phenomenon.     

Optimal production and rationing decisions in supply chains with information sharing

This paper considers a two-echelon capacitated supply chain with two non-identical retailers and information sharing. We characterize the optimal inventory policies. We also study the benefits of the optimal stock rationing policy over the first come first served (FCFS) and the modified echelon-stock rationing (MESR) policies.     

Feasibility principles for Downstream Demand Inference in supply chains

Many companies are adopting strategies that enable Demand Information Sharing (DIS) between the supply chain links. Recently, a steady stream of research has identified mathematical relationships between demands and orders at any link in the supply chain. Based on these relationships and strict model assumptions, it has been suggested that the upstream member can infer the demand at the downstream member from their orders. If this is so, DIS will be of no value. In this paper, we argue that real-world modelling requires less restrictive assumptions. We present Feasibility Principles to show that it is not possible for an upstream member to accurately infer consumer demand under more realistic model assumptions. Thus, we conclude that DIS has value in supply chains. We then move our focus to the supply chain model assumptions in the papers arguing that there is value in sharing demand information. Using a simulation experiment, we show that the value of sharing demand information in terms of inventory reductions will increase under more realistic supply chain model assumptions.     

Controlling the bullwhip effect in a supply chain system with constrained information flows

The bullwhip effect problem is one of the most important issues in supply chain management. Limited information sharing increases the difficulty of reducing the bullwhip effect and leads to inefficient supply chain management. The purpose of this paper is to explore new ways to reduce the bullwhip effect in supply chain systems that face uncertainties with respect to information sharing. We first present a supply chain state transition model, based on which we explore the endogenous mechanism of bullwhip effect, especially those related to impacts from limited information sharing. Then we propose a novel inventory control method and study the corresponding control optimization problem, with the aim of reducing inventory volatility in supply chains. Both quantitative analysis and simulation study are conducted. Simulation results show the effectiveness and flexibility of our proposed method in reducing bullwhip effect and in improving supply chain performance, even under conditions of limited information sharing.     

Logistics cost sharing in supply chains involving a third-party logistics provider

In many cases, end customers are sensitive to a product’s logistics service level which is provided by a third-party logistics (3PL) provider, therefore, the continuous improvement of the logistics service is imperative and valuable. However, the problem is that improving the logistics service benefits all of them, but is costly to only the 3PL provider. The 3PL provider is not willing to do this. Sharing the logistics cost is one solution to this problem. This study investigates cost sharing in two kinds of supply chains, i.e., one manufacturer-one 3PL provider-one retailer supply chain and two manufacturers-one 3PL provider-one retailer supply chain. Two types of cost sharing mechanisms, i.e., decentralized cost sharing mechanisms and centralized cost sharing mechanisms, are explored. Decentralized cost sharing mechanisms are proposed as contracts that chain members separately decide their cost sharing portions to optimize their own profits, ignoring the collective impacts of their decisions on the channel as a whole. Centralized cost sharing mechanisms are in the situation that chain members negotiate their cost sharing portions so that their profits are the shares of the entire supply chain’s profit, implying that the supply chain is coordinated perfectly. This study aims to analyse how cost sharing mechanisms affect supply chain performance and under what conditions chain members are willing to engage in cost sharing mechanisms. Conditions necessary for cost sharing mechanisms to achieve win-win outcomes are identified.     

Taming the bullwhip effect whilst watching customer service in a single supply chain echelon

We study a generalised order-up-to policy that has highly desirable properties in terms of order and inventory variance and customer service levels it generates. We quantify exactly the variance amplification in replenishment orders, i.e. the bullwhip effect, and the variance of inventory levels over time, for i.i.d. and the weakly stationary auto regressive (AR), moving average (MA) and auto regressive moving average (ARMA) demand processes. We demonstrate that high customer service as measured by fill-rate, and smooth replenishments need not increase inventory cost substantially. We observe that in some instances of the ARMA demand pattern this comes at the expense of a relatively small increase in safety stock, whilst in other instances inventory levels can actually be reduced.     

Nonlinear goal programming models quantifying the bullwhip effect in supply chain based on ARIMA parameters

In less than half a century, the supply chain management (SCM) imposed itself as a strategic expertise. Today, it lands a new era, more complex, and must be the synonymous of competitive advantage. The supply chain has essentially served as a link between customers and products, producers and suppliers. The generation of the new supply chain (SC) should be evolutionary, and should be adjusted quickly to the rise or the decrease of the various customers’ demands. Several problems of the supply chain are superimposed such as the amplification of the demand, also called the bullwhip effect (BWE). This latter is a distortion in the market demand when this demand propagates from enterprise to enterprise. Finally, at the end of the chain, the supplier of raw materials receives completely uncertain commands. Our research aims to reduce, or even eliminate, the bullwhip effect in two respects-namely increase of the stock level and reduction of the service given back to customers. The solution that we propose to the bullwhip rests on, firstly, the use of the preference functions based on a statistical chronological series analysis (Box and Jenkins method) in order to construct the different models such as demand, stock level, and the order quantity. Secondly, the integration of the decision maker preference in the demand forecast and inventory management processes.     

Demand uncertainty in construction supply chains: a discrete event simulation study

The delivery of construction projects is typically an assembly operation involving a high number of subassemblies and materials brought on site by the supply chain. However, although supply chain management in construction has attracted significant attention, paradoxically little focus has been placed on construction supply networks and operations. This paper places emphasis on supply chain operations by looking at the logistics function of construction material suppliers. Specifically, the paper examines the impact of demand uncertainty on supply chain performance in order to assess the capacity of material distribution companies to provide a timely and cost-efficient service to the construction industry. The study adopts a discrete event simulation approach to assess the impact of demand fluctuations on two crucial logistics performance measures; lead time and cost efficiency. The results show that lead times are particularly sensitive to fluctuations under conditions of low demand. The findings also reveal that, although transport is a significant cost element for lower demand levels, higher inventory costs result in a negative exponential relationship between increasing demand and cost efficiency.     

The effect of forecasting and information sharing in SCM for multi-generation products

Proper selection of information sharing policy and forecasting method has a significant impact on supply chain performance, especially in the high-tech industry where the product life cycle is short and multiple generations of products coexist. This paper evaluates the value of information sharing with various forecasting methods where two generations of high-tech products compete with each other in the same market. We consider two market environmental factors and two supply chain factors for the Monte Carlo Simulation and find out the most ideal combination of information sharing policy and forecasting method producing the maximum profits and service level.     

Modeling and analysis of the causes of bullwhip effect in centralized and decentralized supply chain using response surface method

The “bullwhip” effect is a major cause of supply chain deficiencies. This phenomenon refers to grow the amplification of demand or inventory variability as it moves up the supply chain. Supply chain managers experience this variance amplification in both inventory levels and orders. Other side, dampening variance in orders may have a negative impact on customer service due to the increase in the inventory variance. This paper with simulating a three stage supply chains consisting of a single retailer, single wholesaler and single manufacturer under both centralized and decentralized chains. In this paper, it is intended to analysis the causes of bullwhip effect from two dimensions of order and inventory variance using the response surface methodology. The results show that in both supply chains, rationing factor is considered as the least important cause of bullwhip effect. While the wholesaler’s order batching and the chain’s order batching are considered as the main causes for the bullwhip effect in the decentralized and centralized chains, respectively.