Forecasting and information sharing in supply chains under ARMA demand

Giloni, Avi; Hurvich, Clifford M.; Seshadri, Sridhar

doi:10.1080/0740817x.2012.689122

Cited by 28 publications

(23 citation statements)

References 16 publications

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“…While our empirical observations show that there is value of information sharing, the theory (that follows the same spirit as Gaur et al 2005 andGiloni et al 2014) would suggest zero value of information sharing for 10 out of 14 studied products. These different results suggest that we need a better theoretical understanding of the missing component in the theoretical literature, which makes the results in the literature no longer apply.…”

Section: Introductionmentioning

confidence: 50%

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Information Sharing in Supply Chains: An Empirical and Theoretical Valuation

et al. 2015

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We provide an empirical and theoretical assessment of the value of information sharing in a two-stage supply chain. The value of downstream sales information to the upstream firm stems from improving upstream order fulfillment forecast accuracy. Such an improvement can lead to lower safety stock and better service. Based on the data collected from a CPG company, we empirically show that, if the company includes the downstream sales data to forecast orders, the improvement in the mean squared forecast error ranges from 7.1% to 81.1% across all studied products. Theoretical models in the literature, however, suggest that the value of information sharing should be zero for over half of our studied products. To reconcile the gap between the literature and the empirical observations, we develop a new theoretical model. While the literature assumes that the decision maker strictly adheres to a given inventory policy, our model allows him to deviate, accounting for private information held by the decision maker, yet unobservable to the econometrician. This turns out to reconcile our empirical findings with the literature. These "decision deviations" lead to information losses in the order process, resulting in a strictly positive value of downstream information sharing. Furthermore, we empirically quantify and show the significance of the value of operations knowledge-the value of knowing the downstream replenishment policy.

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Section: Introductionmentioning

confidence: 50%

“…The benefits of information sharing have been theoretically quantified in the literature. The works of Gaur et al (2005) and Giloni et al (2014) are important antecedents of our paper. The authors find that the value of sharing downstream sales to improve upstream forecasting is limited.…”

Section: Introductionmentioning

confidence: 99%

Information Sharing in Supply Chains: An Empirical and Theoretical Valuation

et al. 2015

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“…Chen and Disney (2007) and Gaalman and Disney (2009) analyze the impact of ARMA demand on inventory policies with arbitrary lead times. Giloni et al (2014) investigate the effect ARMA demand in multistage supply chain in-depth and study whether information sharing is valuable for the supply chain partner based on the parameters applied, thus extending the analysis by Gaur et al (2005). Finally, Wharburton and apply differential equations to model continuous inventory and ordering policies rather than the usual discrete case.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing Variability, Cost, and Responsiveness of Base-Stock Inventory Policies with Linear Control Theory

Hoberg¹,

Thonemann

2015

IIE Transactions

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The effect of inventory policies on order variability has been extensively analyzed. Two popular means of reducing order variability are demand smoothing and order smoothing. If the objective is minimizing demand variability, demands and orders can be heavily smoothed, resulting in an inventory policy that orders equal amounts in each time period. Such a policy obviously minimizes order variability, but it leads to high cost and low responsiveness of the inventory system. To optimize the overall performance of an inventory system, the effect of the inventory policy on all relevant dimensions of operational performance must be analyzed. We address this issue and analyze the effect of the parameter values of an inventory policy on three main dimensions of operational performance: order variability, expected cost, and responsiveness. The inventory policy we use is the partial correction policy, a policy that can be used to smooth demand and to smooth orders. To analyze this policy, we use linear control theory. We derive the transfer function of the policy and prove the stability of the inventory system under this policy. Then, we determine the effect of the policy parameters on order variability, cost, and responsiveness and discuss how good parameter values can be chosen.

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“…We also adopt several important concepts from Giloni et al (hereafter Giloni, Hurvich, and Seshadri (GHS)). A supply chain player would use the model above and all its available information at time t (using its historic demand and potentially incorporating any additional information shared by an adjacent downstream player) to create a one (or multi)‐step ahead forecast of demand.…”

Section: Introductionmentioning

confidence: 99%

Assessing the value of demand sharing in supply chains

Kovtun

Giloni

Hurvich

2014

Naval Research Logistics

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We consider the problem of assessing the value of demand sharing in a multistage supply chain in which the retailer observes stationary autoregressive moving average demand with Gaussian white noise (shocks). Similar to previous research, we assume each supply chain player constructs its best linear forecast of the leadtime demand and uses it to determine the order quantity via a periodic review myopic order‐up‐to policy. We demonstrate how a typical supply chain player can determine the extent of its available information in the presence of demand sharing by studying the properties of the moving average polynomials of adjacent supply chain players. The retailer's demand is driven by the random shocks appearing in the autoregressive moving average representation for its demand. Under the assumptions we will make in this article, to the retailer, knowing the shock information is equivalent to knowing the demand process (assuming that the model parameters are also known). Thus (in the event of sharing) the retailer's demand sequence and shock sequence would contain the same information to the retailer's supplier. We will show that, once we consider the dynamics of demand propagation further up the chain, it may be that a player's demand and shock sequences will contain different levels of information for an upstream player. Hence, we study how a player can determine its available information under demand sharing, and use this information to forecast leadtime demand. We characterize the value of demand sharing for a typical supply chain player. Furthermore, we show conditions under which (i) it is equivalent to no sharing, (ii) it is equivalent to full information shock sharing, and (iii) it is intermediate in value to the two previously described arrangements. Although it follows from existing literature that demand sharing is equivalent to full information shock sharing between a retailer and supplier, we demonstrate and characterize when this result does not generalize to upstream supply chain players. We then show that demand propagates through a supply chain where any player may share nothing, its demand, or its full information shocks (FIS) with an adjacent upstream player as quasi‐ARMA in—quasi‐ARMA out. We also provide a convenient form for the propagation of demand in a supply chain that will lend itself to future research applications. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 515–531, 2014

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Forecasting and information sharing in supply chains under ARMA demand

Cited by 28 publications

References 16 publications

Information Sharing in Supply Chains: An Empirical and Theoretical Valuation

Information Sharing in Supply Chains: An Empirical and Theoretical Valuation

Analyzing Variability, Cost, and Responsiveness of Base-Stock Inventory Policies with Linear Control Theory

Assessing the value of demand sharing in supply chains

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