Assessing the Difference Between Shock Sharing and Demand Sharing in Supply Chains

We consider the problem of assessing value of demand sharing in a multi-stage 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 under demand sharing by studying the properties of the moving average polynomials of adjacent supply chain players. 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. We then show that demand propagates through a supply chain where any player may share nothing, its demand, or its full-information shocks 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.NYU Stern School of Business; Syms School of Business, Yeshiva UniversityStatistics Working Papers Serie

Possible Sharing Arrangements in ARMA Supply Chains

We introduce a class of new sharing arrangements in a multi-stage 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 create a sequence of partial information shocks (PIS) from its full information shocks FIS and share these with an adjacent upstream player. We go on to show how such a sharing arrangement may be benecial to the upstream player by characterizing the player's FIS in such a case. Hence, we study how a player can determine its available information under PIS sharing, and use this information to forecast leadtime demand. We characterize the value of FIS sharing for a typical supply chain player. Furthermore, we show conditions under which a player is able to form and share valuable PIS without (i) revealing its historic demand sequence or (ii) revealing its FIS sequence. We also provide a way of comparing various PIS sharing arrangements with each other and with conventional sharing arrangements involving demand sharing or FIS sharing. We show that demand propagates through a supply chain where any player may share nothing or a sequence of PIS shocks with an adjacent upstream player as quasi-ARMA in - quasi-ARMA out.Statistics Working Papers Serie

Forecasting and Information Sharing in Supply Chains Under Quasi-ARMA Demand

In this paper, we revisit the problem of demand propagation in a multi-stage supply chain in which the retailer observes ARMA demand. In contrast to previous work, we show how each player constructs the order based upon its best linear forecast of leadtime demand given its available information. In order to characterize how demand propagates through the supply chain we construct a new process which we call quasi-ARMA or QUARMA. QUARMA is a generalization of the ARMA model. We show that the typical player observes QUARMA demand and places orders that are also QUARMA. Thus, the demand propagation model is QUARMA-in-QUARMA-out. We study the value of information sharing between adjacent players in the supply chain. We demonstrate that under certain conditions information sharing can have unbounded bene¯ts. Our analysis hence reverses and sharpens several previous results in the literature involving information sharing and also opens up many questions for future research.Sy Syms School of Business, Yeshiva University; Department of Information, Operations and Management Science, Stern School of Business, NYU; McCombs School of Business, University of Texas at AustinStatistics Working Papers Serie

Assessing the Difference Between Shock Sharing and Demand Sharing in Supply Chains

We consider the problem of assessing value of demand sharing in a multi-stage 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 under demand sharing by studying the properties of the moving average polynomials of adjacent supply chain players. 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. We then show that demand propagates through a supply chain where any player may share nothing, its demand, or its full-information shocks 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.NYU Stern School of Business; Syms School of Business, Yeshiva UniversityStatistics Working Papers Serie

Possible Sharing Arrangements in ARMA Supply Chains

We introduce a class of new sharing arrangements in a multi-stage 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 create a sequence of partial information shocks (PIS) from its full information shocks FIS and share these with an adjacent upstream player. We go on to show how such a sharing arrangement may be benecial to the upstream player by characterizing the player's FIS in such a case. Hence, we study how a player can determine its available information under PIS sharing, and use this information to forecast leadtime demand. We characterize the value of FIS sharing for a typical supply chain player. Furthermore, we show conditions under which a player is able to form and share valuable PIS without (i) revealing its historic demand sequence or (ii) revealing its FIS sequence. We also provide a way of comparing various PIS sharing arrangements with each other and with conventional sharing arrangements involving demand sharing or FIS sharing. We show that demand propagates through a supply chain where any player may share nothing or a sequence of PIS shocks with an adjacent upstream player as quasi-ARMA in - quasi-ARMA out.Statistics Working Papers Serie

Forecasting and Information Sharing in Supply Chains Under Quasi-ARMA Demand

In this paper, we revisit the problem of demand propagation in a multi-stage supply chain in which the retailer observes ARMA demand. In contrast to previous work, we show how each player constructs the order based upon its best linear forecast of leadtime demand given its available information. In order to characterize how demand propagates through the supply chain we construct a new process which we call quasi-ARMA or QUARMA. QUARMA is a generalization of the ARMA model. We show that the typical player observes QUARMA demand and places orders that are also QUARMA. Thus, the demand propagation model is QUARMA-in-QUARMA-out. We study the value of information sharing between adjacent players in the supply chain. We demonstrate that under certain conditions information sharing can have unbounded bene¯ts. Our analysis hence reverses and sharpens several previous results in the literature involving information sharing and also opens up many questions for future research.Sy Syms School of Business, Yeshiva University; Department of Information, Operations and Management Science, Stern School of Business, NYU; McCombs School of Business, University of Texas at AustinStatistics Working Papers Serie

Supply chain dynamics and forecasting

Nowadays, the global supply chain system needs to respond promptly to changes in customer demand and adapt quickly to advancements in technology. Supply chain management becomes an integral approach which links together producers, distributors and customers in collaborative management of the whole system. The variability in orders or inventories in supply chain systems is generally thought to be caused by exogenous random factors such as uncertainties in customer demand or lead time. Studies have shown, however, that orders or inventories may exhibit significant variability, even if customer demand and lead time are deterministic. Most researchers have concentrated on the effects of the ordering policy on supply chain behaviour, while not many have paid attention to the influences of applying different forecasting to supply chain planning. This thesis presents an analysis of the behaviour of a model of a centralised supply chain. The research was conducted within the manufacturing sector and involved the breathing equipment manufacturer Draeger Safety, UK. The modelling process was embedded in the organization and was focused on the client's needs. A simplified model of the Draeger Safety, UK centralised supply chain was developed and validated. The dynamics of the supply chain under the influence of various factors: demand pattern, ordering policy, demand-information sharing, and lead time were observed. Simulation and analysis were performed using system dynamics, non-linear dynamics and control theory. The findings suggest that destructive oscillations of inventory could be generated by internal decision making practices. To reduce the variation in the supply chain system, the adjustment parameters for both inventory and supply line discrepancies should be more comparable in magnitude. Counter- intuitively, in certain fields of decision, sharing demand information can do more harm than good. The linear forecasting ARMA (autoregression and moving average) model and the nonlinear forecasting model Wavelet Neural Network were applied as the supply chain forecasting methods. The performance was tested against supply chain costs. A management microworld was developed, allowing managers to experiment with different decision policies and learn how the supply chain performs

Aggregated Information in Supply Chains

We study a two-stage supply chain where the retailer observes two demand streams coming from two consumer populations. We further assume that each demand sequence is a station- ary Autoregressive Moving Average (ARMA) process with respect to a Gaussian white noise sequence (shocks). The shock sequences for the two populations could be contemporaneously correlated. We show that it is typically optimal for the retailer to construct its order to its supplier based on forecasts for each demand stream (as opposed to the sum of the streams) and that doing so is never sub-optimal. We demonstrate that the retailer’s order to its supplier is ARMA and yet can be constructed as the sum of two ARMA order processes based upon the two populations. When there is no information sharing, the supplier only observes the retailer’s order which is the aggregate of the two aforementioned processes. In this paper, we determine when there is value to sharing the retailer’s individual orders, and when there is additional value to sharing the retailer’s individual shock sequences. We also determine the supplier’s mean squared forecast error under no sharing, process sharing, and shock sharing.Operations Management Working Papers Serie

Smooth is smart: bullwhip, inventory, fill-rates and the golden ratio.

Inventory;