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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 phenomen...
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... acknowledge that this last demand pattern is somewhat artificial and rarely observed in reality, but it provides a good illustration of a wildly fluctuating customer demand. The (discrete) probability distributions of the three demand patterns are shown in figure 4. The retailer has to determine the parameter β to control his inventory. ...
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... They focus their analysis on two common subclasses of policies: moving weighted average (with a finite smoothing time window) and exponential smoothing (a similar approach is taken in Miyaoka and Hausman 2004). Boute et al. (2007) study the impact of order smoothing on the supplier's replenishment lead times and show that smoother orders create shorter and less volatile leadtimes reducing the retailer's inventory costs. Hoberg et al. (2007) study the stability of three classes of inventory replenishment policies which differ on the information they use on inventory positions and orders. ...
We study the interplay between inventory replenishment policies and information sharing in the context of a two-tier supply chain with a single supplier and a single retailer serving an independent and identically distributed Gaussian market demand. We investigate how the retailer’s inventory policy impacts the supply chain’s cumulative expected long-term average inventory costs [Formula: see text] in two extreme information-sharing cases: (a) full information sharing and (b) no information sharing. To find the retailer’s inventory policy that minimizes [Formula: see text], we formulate an infinite-dimensional optimization problem whose decision variables are the MA([Formula: see text]) coefficients that characterize a stationary ordering policy. Under full information sharing, the optimization problem admits a simple solution and the optimal policy is given by an MA(1) process. On the other hand, to solve the optimization problem under no information sharing, we reformulate the optimization from its time domain formulation to an equivalent z-transform formulation in which the decision variables correspond to elements of the Hardy space H ² . This alternative representation allows us to use a number of results from H ² theory to compute the optimal value of [Formula: see text] and characterize a sequence of ϵ-optimal inventory policies under some mild technical conditions. By comparing the optimal solution under full information sharing and no information sharing, we derive a number of important practical takeaways. For instance, we show that there is value in information sharing if and only if the retailer’s optimal policy under full information sharing is not invertible with respect to the sequence of demand shocks. Furthermore, we derive a fundamental mathematical identity that reveals the value of information sharing by exploiting the canonical Smirnov–Beurling inner–outer factorization of the retailer’s orders when viewed as an element of H ² . We also show that the value of information sharing can grow unboundedly when the cumulative supply chain costs are dominated by the supplier’s inventory costs.
Funding: R. Caldentey acknowledges the University of Chicago Booth School of Business for financial support.
Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2023.008 .
... Fluctuations in demand can affect an SC's performance, particularly in inventory management and production scheduling, transportation costs, and customer service levels (Boute et al. 2007;Cachon 1999;Fang et al. 2013). Stress testing can help simulate different demand scenarios and evaluate the impact on the SC's reliability and responsiveness (Scholten, Stevenson, and van Donk 2020). ...
Supply chain (SC) resiliency and risk management have garnered increasing attention recently. While several studies have explored the use of scale-free network models to design and optimise SC networks, there remains a lack of a generalised stress-testing method that can be applied to various types and sizes of SCs. To address this, we propose a novel approach to assess the resiliency of scale-free SC networks based on entropy measurements. By considering various sources of disruptions, including demand fluctuations, capacity constraints, and transportation disruptions, we create a stress test that measures the network's resiliency. Our method categorises the SC into plants, ports, and customers and analyses the impact of disruptions on individual nodes within similar categories. We utilise real-world SC logistics data and map it into a scale-free network representation. By calculating the global vulnerability of the entire SC network and the localised impact of node vulnerability, we gain insights into how disruptions propagate and affect interconnected nodes. We use value-at-risk (VaR) and conditional-VaR (CVaR) to identify high-risk nodes, highlighting nodes at risk of extreme performance fluctuations or failures. The analytical results have led to developing six propositions, which serve as references for practitioners and help generalise the findings.
... Ref. [27] stated that smooth ordering patterns can dampen upstream demand variability. Demand variation is known to be one of the causes of the bullwhip effect [28,29], causing excessive inventory. ...
Spoilage reduction in fresh product supply chains is an important challenge and represents great opportunities for cost savings and reduced environmental and social footprints. The purpose of this paper is to identify the drivers of spoilage and to discuss how these insights can be used to reduce spoilage.We use panel data techniques to quantify the drivers of spoilage in the days-fresh category using daily spoilage and supply chain data (457,539 store-SKU level observations) for fresh fruits and vegetables at Switzerland’s largest retailer. We quantify to what extent inventory, promotions, delivery type, commitment changes, order variations, order cycle, and quality issues influence spoilage. We discuss the mechanisms through which inventory age and product standards impact spoilage of days-fresh products. Our novel findings underline the necessity for specialized supply chain processes, tracking inventory age and damage, and collaboration with supply chain partners in the management of this fundamental product category.
... Koukoumialos and Liberopoulos (2005) as well as Rodrigo and Wein (1996), analysed pure production systems with stochastic demand and processing times, using kanban methodology. Boute et al. (2007) studied a two stage periodic review system, with backorders and phase type distributed lead times. Katehakis et al. (2015) as well as Shi et al. (2014) examined a system with the assumption of lost sales with stochastic demand that followed the Poisson distribution. ...
... One possible explanation for this may be the fact that sales forecasts are often prone to errors primarily given their dependence on exogenous and nonlinear variables (e.g., retail price and advertising) that, all together, potentially hamper the development of effective forecasting models. While it is well-known that demand variability may lead to large cost implications to upstream SC echelons (even when it is not amplified but simply transmitted) [9], understanding of the processes that lead to improvements of demand forecasting at the manufacturer stage is limited. It is interesting to note here that demand forecasting at the manufacturer level is quite challenging, mainly for two reasons: first, it is in practice very difficult to accurately predict end-customer demand based on market information, due to its erratic behavior. ...
Demand forecasting works as a basis for operating, business and production planning decisions in many supply chain contexts. Yet, how to accurately predict the manufacturer's demand for components in the presence of end-customer demand uncertainty remains poorly understood. Assigning the proper order quantities of components to suppliers thus becomes a nontrivial task, with a significant impact on planning, capacity and inventory-related costs. This paper introduces a multivariate approach to predict manufacturer's demand for components throughout multiple forecast horizons using different leading indicators of demand shifts. We compare the autoregressive integrated moving average model with exogenous inputs (ARIMAX) with Machine Learning (ML) models. Using a real case study, we empirically evaluate the forecasting and supply chain performance of the multivariate regression models over the component's life-cycle. The experiments show that the proposed multivariate approach provides superior forecasting and inventory performance compared with traditional univariate benchmarks. Moreover, it reveals applicable throughout the component's life-cycle, not just to a single stage. Particularly, we found that demand signals at the beginning of the life-cycle are predicted better by the ARIMAX model, but it is outperformed by ML-based models in later life-cycle stages.
... Conventional models for inventory management with uncertain demand, such as variations of Harris formulation [11][12][13], Markov equation-based ones [14,15], and Wilson's formulation [16][17][18][19] are designed to minimize the expected costs of replenishment and stock-outs. They assume that complete satisfaction of uncertain and hardly predictable demand is too expensive or even deemed impossible. ...
The present study deals with the modification of Wilson's formulation by taking into account changes in the supply chain represented by the parameters of the model, namely varying delivery costs and price of goods stored. The four different models are presented. The proposed models avoid the main drawbacks of Wilson's formulation-the constant price and reordering time-and discuss the case where varying parameters are used alongside discounting. The proposed models render lower costs under particular settings.
... When studying the productivity of manufacturing systems, van Ooijen (2003) recognizes that changes in the order arrival rate generally lead to a change in throughput where increased arrival rates equate to increased throughput. In their study of the impact of order decisions in a two-echelon supply chain, Boute et al. (2007) concluded that a smooth order pattern leads to shorter and less variable lead times. ...
This effort extends our previous research into the elements of the design of manufacturing systems and seeks to separate the effects for the internal elements (those due to system design) from the basic elements of external complexity – order variation (frequency and size). Simulation experiments were conducted that included two levels of the external complexity to determine the significance and effect of ten elements of complexity resulting from managerial decisions relative to the design of the manufacturing system. The results show that the external complexity elements dominate the cause of system unpredictability. We also confirm the findings from our previous research while discovering the difference in effects of the internal complexity factors based upon the amount of variation in the order arrival rate.
... The conventional models for inventory management with uncertain demand, such as variations of Harris (1913) formulation (Cárdenas-Barrón, Chung, & Treviño-Garza, 2014;Nobil & Taleizadeh, 2016;Budd & Taylor, 2019), Markov equation based ones (Boute, Disney, Lambrecht, & Van Houdt, 2007;Broyles, Cochran, & Montgomery, 2010;Liu, Feng, & Wong, 2014), Wilson's formulation (Wilson, 1934;Schwartz, Wang, & Rivera, 2006;Sarkar, 2013;Manna, Dey, & Mondal, 2017) are designed to minimize the expected costs of replenishment and stockouts, assuming, that complete satisfaction of uncertain and hardly predictable demand is too expensive or even deemed impossible. All these models are design under the constant order quantity principle, where the size of following order is based on the objective to minimize the whole costs of company's inventory management. ...
A timely response to a fluctuating and ever-changing consumer demand is an important decision for a company, as it may impact its position in the market. Thus, proper inventory management becomes a focal point in retail business process management and can provide a substantial competitive advantage. In this paper, we introduce a modified version of Wilson’s model, which takes into account trends in consumer demand and offer flexibility in reordering time. The illustration of the proposed model is presented, showing the significant economic benefit under particular conditions.
... When customer demand is wildly fluctuating, the member in an SC sends a highly variable order pattern to the associated member in the upper stage, which may cause amplification of order variance (which is termed as bullwhip effect) through the SC. Customer demand volatility also results in high capacity and inventory costs on the manufacturer [37]. Different demand patterns such as constant, seasonal, seasonal with increasing trend, seasonal with decreasing trend are generated from a common demand generator for different values of the various parameters such as base, slope, season and noise influences the service level and total cost of SC [7], [36]. ...
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.
... Order rate variance ratio [43,48,61,64,[69][70][71][72][73][74][75][76][77][78][79] Amplitude rate cost ratio [80] Amplification ratio [81] Ratio inventory [71,75] Ratio backlog inventory [64] Variance ratio fill rate [75,77] Ratio inventory integrated squared error [69] Ratio root mean square costs [61] Fill rate [82] Costs order rate variance ratio [43,73,83] Costs [84] Ratio inventory stock [70,74] Out size stock out number [70] As Figure 1 illustrates, the most common performance metric suggested in the literature is the order rate variance ratio [85], which was proposed by Chen et al. [48]. This indicator is given by the following Equation (1) [29]: ...
Several suppliers of oil and gas (O & G) equipment and services have reported the necessity of making frequent resources planning adjustments due to the variability of demand, which originates in unbalanced production levels. The occurrence of these specific problems for the suppliers and operators is often related to the bullwhip effect. For studying such a problem, a research proposal is herein presented. Studying the bullwhip effect in the O & G industry requires collecting data from different levels of the supply chain, namely: services, upstream and midstream suppliers, and downstream clients. The first phase of the proposed research consists of gathering the available production and financial data. A second phase will be the statistical treatment of the data in order to evaluate the importance of the bullwhip effect in the oil and gas industry. The third phase of the program involves applying artificial neural networks (ANN) to forecast the demand. At this stage, ANN based on different training methods will be used. Further on, the attained mathematical model will be used to simulate the effects of demand fluctuations and assess the bullwhip effect in an oil and gas supply chain.
