Quantifying costs of forecast errors: A case study of the warehouse environment

Our study evaluates the impact of forecast errors on organizational cost by simulating a labor-intensive warehouse environment using realistic cost data from a case study. Unlike past studies that measure forecast error in terms of forecast standard deviation, our study also considers the impact of forecast bias, and the complex interaction between these variables. Two cases of organizational cost curves are considered, with differing and asymmetric structures. Results find forecast bias to have a considerably greater impact on organizational cost than forecast standard deviation. Particularly damaging is a high bias in the presence of high forecast standard deviation. Although biasing the forecast in the least costly direction is shown to yield lower costs, sensitivity analysis shows that increasing bias beyond the optimum point rapidly increases costs. ‘Overshooting’ the optimal amount of bias appears to be more damaging than not biasing the forecast at all. Given that managers often deliberately bias their forecasts, this finding underscores the importance of having a good understanding of organizational cost structures before arbitrarily introducing bias. This finding also suggests that managers should exercise caution when introducing bias, particularly for forecasts that inherently have large errors. These findings have important implications for organizational decision making beyond the simulated warehouse, as high forecast errors are endemic to many labor-intensive organizations.     

AN EVALUATION OF FORECAST ERROR IN MASTER PRODUCTION SCHEDULING FOR MATERIAL REQUIREMENTS PLANNING SYSTEMS.

Typical forecast-error measures such as mean squared error, mean absolute deviation and bias generally are accepted indicators of forecasting performance. However, the eventual cost impact of forecast errors on system performance and the degree to which cost consequences are explained by typical error measures have not been studied thoroughly. The present paper demonstrates that these typical error measures often are not good predictors of cost consequences in material requirements planning (MRP) settings. MRP systems rely directly on the master production schedule (MPS) to specify gross requirements. These MRP environments receive forecast errors indirectly when the errors create inaccuracies in the MPS. Our study results suggest that within MRP environments the predictive capabilities of forecast-error measures are contingent on the lot-sizing rule and the product components structure When forecast errors and MRP system costs are coanalyzed, bias emerges as having reasonable predictive ability. In further investigations of bias, loss functions are evaluated to explain the MRP cost consequences of forecast errors. Estimating the loss functions of forecast errors through regression analysis demonstrates the superiority of loss functions as measures over typical forecast error measures in the MPS.     

Improving the accuracy and operational predictability of project cost forecasts: an adaptive combination approach

Conducting an early warning forecast to detect potential cost overrun is essential for timely and effective decision-making in project control. This paper presents a forecast combination model that adaptively identifies the best forecast and optimises various combinations of commonly used project cost forecasting models. To do so, a forecast error simulator is formulated to visualise and quantify likely error profiles of forecast models and their combinations. The adaptive cost combination (ACC) model was applied to a pilot project for numerical illustration as well as to real world projects for practical implementation. The results provide three valuable insights into more effective project control and forecasting. First, the best forecasting model may change in individual projects according to the project progress and the management priority (i.e. accuracy, outperformance or large errors). Second, adaptive combination of simple, index-based forecasts tends to improve forecast accuracy, while mitigating the risk of large errors. Third, a post-mortem analysis of seven real projects indicated that the simple average of two most commonly used cost forecasts can be 31.2% more accurate, on average, than the most accurate alternative forecasts.     

The Impact of Forecast Errors on Early Order Commitment in a Supply Chain*

Supply chain partnership involves mutual commitments among participating firms. One example is early order commitment, wherein a retailer commits to purchase a fixed‐order quantity and delivery time from a supplier before the real need takes place. This paper explores the value of practicing early order commitment in the supply chain. We investigate the complex interactions between early order commitment and forecast errors by simulating a supply chain with one capacitated supplier and multiple retailers under demand uncertainty. We found that practicing early order commitment can generate significant savings in the supply chain, but the benefits are only valid within a range of order commitment periods. Different components of forecast errors have different cost implications to the supplier and the retailers. The presence of trend in the demand increases the total supply chain cost, but makes early order commitment more appealing. The more retailers sharing the same supplier, the more valuable for the supply chain to practice early order commitment. Except in cases where little capacity cushion is available, our findings are relatively consistent in the environments where cost structure, number of retailers, capacity utilization, and capacity policy are varied.     

政府奖惩下闭环供应链中需求预测信息分享研究

考虑政府奖惩机制,在制造商直接回收的闭环供应链中,当市场不确定且制造商和零售商都能预测需求时,为研究零售商的需求预测信息分享问题,本文构建一个Stackelberg博弈模型,分别求得零售商信息分享和不分享情形下的均衡结果,探讨了需求预测信息精度对供应链成员利润及信息分享价值的影响,并研究了供应链均衡的信息分享策略。研究发现,需求预测精度的提高在大多数情形下会使供应链成员都受益。当制造商回收效率较高时,自愿分享需求信息是一个均衡;当制造商回收效率较低时,信息不分享是一个均衡;当制造商回收效率处于中等水平时,通过设计一个讨价还价机制,可以促使信息分享是一个均衡。此外,政府奖惩力度也会影响均衡的信息分享策略。     

Forecast Updating and Supplier Coordination for Complementary Component Purchases

We study a supply chain where an original equipment manufacturer (OEM) buys subassemblies, comprised of two complementary sets of components, from a contract manufacturer (CM). The OEM provides a demand forecast at the time when the CM must order the long lead‐time set of components, but must decide whether or not to provide updated forecasts as a matter of practice. Forecast updates affect the CM's short lead‐time purchase decision, and the anticipation of updates may also affect the long lead‐time purchase decision. While the OEM and CM both incur lost sales costs, the OEM can decide whether or not to share the overage costs otherwise fully borne by the CM. We investigate when the OEM is better served by committing to provide updated forecasts and/or committing to share overage costs. For a distribution‐free, two‐stage forecast‐update model, we show that (1) the practice of providing forecast updates may be harmful to the OEM and (2) at the OEM's optimal levels of overage risk sharing, the CM undersupplies relative to the supply chain optimal quantity. For a specific forecast‐update model, we computationally investigate conditions under which forecast updating and risk sharing are in the best interest of the OEM.     

THE APPLICATION OF A DYNAMIC FORECASTING MODEL WITH INVENTORY CONTROL PROPERTIES

This article summarizes the application of a forecasting model. Forecasts are made of monthly sales of products which do not change in style on an annual basis. The model is an exponential smoothing model. Adjustments of the parameters of the model are made whenever the average forecast error over the previous four periods is too large to be explained solely by unassignable causes. The efficiency gained in using the model is measured by the ratio of the standard deviation of the forecast errors to the standard deviation of sales. If this ratio is less than one, then the safety stock level that is carried for a given product can be reduced if sales are forecasted with the model and the standard deviation of the forecast errors is used to determine the safety stock level. The net effect is the reduction in the cost of carrying safety stocks. The results of the proposed model are also compared to a similar set of results generated from a basic, exponential model.     

基于信任的供应预测信息共享策略

现实中供应不确定是比较普遍的现象，供应预测信息共享对改善供应链绩效起着重要作用。针对由一个制造商和一个零售商组成的单周期供应链，利用斯坦克尔伯格博弈方法研究信任对供应预测信息共享的影响以及回购契约对该供应链的协调作用。研究表明：零售商对制造商的信任影响供应预测信息共享效果，进而影响供应链绩效；制造商说谎的心理负效用系数越大，越倾向于说真话；回购契约可以促进制造商共享真实供应预测信息；当制造商说谎的心理负效用系数较大时，存在一个最佳的回购价格，使供应链实现完美协调。     

Interorganizational Collaborative Forecasting and Replenishment Systems and Supply Chain Implications

Collaborative Forecasting and Replenishment (CFAR) is a new interorganizational system that enables retailers and manufacturers to forecast demand and schedule production jointly. The capabilities of CFAR exceed those of the traditional EDI because CFAR allows exchange of complex decision support models and manufacturer/retailer strategies. The proponents of CFAR claim that the fastest way for retailers and manufacturers to slash inventory costs and product shortages is by coordinating their decisions. The impact of CFAR on the supply chain using a model consisting of a manufacturer selling its product through two independent identical retailers is analyzed. The model assumes that the manufacturer doesn't face capacity constraints, the demand is stationary, and retailer prices are constant over time; the shortages are backordered at the manufacturer and retailers to isolate the effects of information sharing in CFAR. The most significant findings relate to the increase in the cost incurred by the nonparticipant in CFAR, the increase in the manufacturer's cost of serving the nonparticipant if the participant can obtain delivery guarantees in return for demand information sharing, and the incentives of the players to move towards universal retailer participation in CFAR. The findings are explained using the risk structure faced by the players in the supply chain. The managerial implications of the impact of CFAR on the supply chain structure are also discussed.     

供应链环境下合作预测效果的分析

本文定量分析了供应链中合作预测对需求长鞭效应的减小作用,在多节点企业所组成的供应链中,合作预测对订单量的波动和需求预测误差的波动起到积极的抑制作用。本文通过理论研究和数值分析的结果表明,供应链信息共享和合作预测能够控制需求流动现象。     

制造商成本削减策略对风险规避型零售商信息共享策略的影响

考虑一个风险中性制造商和一个风险规避零售商构成的供应链，需求随机且受销售价格的影响。在销售季节之前，零售商对需求进行预测，获取需求信号；制造商对生产进行投资降低生产成本。基于零售商的不同信息共享策略及制造商的投资策略，考虑四种不同策略模型，分别得到最优零售价、批发价（及投资水平），并分析需求预测精确度对供应链成员决策和效用的影响。通过四种模型效用的对比分析，探讨制造商的投资策略以及零售商的风险规避态度对零售商信息共享策略的影响。研究发现，零售商共享需求信息对于制造商总是有益的，且制造商总是愿意采取成本削减策略；只有当制造商采取成本削减策略，且其投资成本系数较低时，共享需求信息对零售商才有益。最后，得到了制造商和零售商的均衡策略。     

绿色供应链中不对称需求预测下的信息共享研究

研究了由一个制造商和一个零售商组成的绿色供应链和他们的需求预测信息共享问题。考虑了两种方案：订货型生产方案和库存型生产方案，两种方案下分别考虑了无信息共享和信息共享两种情况。分析了预测信息对两种方案下制造商利润、零售商利润和信息共享价值的影响，同时研究了绿色成本系数对信息共享的影响。研究表明，两种方案中制造商始终能从信息共享中获利，而零售商只有在制造商绿色成本系数较低时才会自愿共享预测信息。当绿色成本系数较高时，制造商可以通过一个讨价还价合同，促进零售商进行信息共享。当绿色成本系数很高时，供应链成员之间不存在信息共享。此外，库存型生产方案中信息共享带来的总收益增量要高于订货型生产方案，因此，库存型生产方案中参与者信息共享的可能性要高于订货型生产方案。     

Decision knowledge sharing: flexible supply chains in KM context

Supply chain excellence has a real impact on business strategy. Building supply chains as flexible systems represents one of the most exciting opportunities to create value and one of the most challenging tasks for the policy makers. It requires integrated decision making amongst autonomous chain partners with effective decision knowledge sharing between them. The key to success lies in knowing which decision has more impact on the overall performance and this can be achieved by appropriate knowledge sharing. In this context, knowledge management (KM) can be used as an effective approach to achieve knowledge sharing and decision synchronisation in supply chains. Flexible supply chains (FSC_s) are more complex and involve multiple autonomous players with varying technical cultures (affects knowledge mindsets), managerial background (affects decision knowledge) and supply chain management (SCM) exposures (affects knowledge sharing attitudes). Thus there is a need to develop demo models that can encourage chain managers towards collaborative knowledge sharing in the supply chains. This paper presents the application of one such model based on decision knowledge sharing (DKS) for improved supply chain management. A simulation model of a flexible supply chain based on DKS framework is developed for demo purposes. The key results are highlighted along with industry implications. The cost based performance of DKS at different levels of flexibility is studied. Thus a careful analysis of the chain with a focus on collaborative decisions is useful to ensure success. This paper addresses this interesting and challenging domain.     

With or Without Forecast Sharing: Competition and Credibility under Information Asymmetry

Forecast sharing among trading partners lies at the heart of many collaborative and contractual supply chain management efforts. Even though it has been praised in both academic and practitioner circles for its critical role in increasing demand visibility, some concerns remain: The first one is related to the credibility of forecast sharing, and the second is the fear that it may turn into a competitive disadvantage and induce suppliers to increase their price offerings. In this study, we explore the validity of these concerns under a supply chain with a competitive upstream structure, focusing specifically on (i) when and how a credible forecast sharing can be sustainable, and (ii) how it impacts on the intensity of price competition. To address these issues, we develop a supply chain model with a buyer facing a demand risk and two heterogeneous suppliers competing for order allocation from the buyer. The extent of demand is known only to the buyer. The buyer submits a buying request to the suppliers via a commonly used procurement mechanism called request for quotation (RFQ). We consider two variants of RFQ. In the first type, the buyer simply shares the estimated order quantity with no further specifications. In the second one, in addition to this, the buyer also specifies minimum and/or maximum order quantities. We fully characterize equilibrium decisions and profits associated with them under symmetric and asymmetric information scenarios. Our main findings are that the buyer can use a RFQ with quantity restrictions as a credible signal for forecast sharing as long as the degree of demand information asymmetry is not too high, and that, contrary to above concerns, the equilibrium prices that emerge between competing suppliers under asymmetric information may indeed increase if the buyer cannot share forecast information credibly with its upstream partners.     

THE USE OF FORECAST ERROR MEASURES AS SURROGATES FOR AN ERROR COST CRITERION IN THE PRODUCTION SMOOTHING PROBLEM

This paper presents research on the problem of selecting a proper surrogate for a forecast error cost criterion in the production smoothing problem. Various forecast models estimated future selected demand process values. Resultant error costs were computed and the coincidence of the selection of a forecast model on the basis of least error cost and the various error measures was noted. The error measures used were the mean absolute deviation, average algebraic error(bias), and the mean squared error. Computations necessary to develop the mathematical form of the error cost criterion are presented in an Appendix. Also presented are the penalty costs of using an error measure as a surrogate for an error cost criterion.     

Supplier Behavior in Capacity Investment Competition: An Experimental Study

Many manufacturers ensure supply capacity by using more than one supplier and sharing their capacity investment costs via supplier development programs. Their suppliers face competitive pressure from peers despite the reduced capacity investment cost. Although standard game theory makes clear prediction that cost sharing increases the suppliers' capacity choice and supply chain profit, the complex decision environment of capacity competition makes it interesting to test whether the theory predictions are robust and, if not, whether systematic deviations occur. We present a laboratory experiment study. The experiment data show that supplier subjects invested in higher capacities than what our theoretical analysis predicted, resulting in profit loss for the supply chain. Our econometric analysis indicates that the subjects are bounded rational and their concern for relative standing may be the potential driver of capacity over‐investment. Based on the experimental findings, we study a modified cost‐sharing mechanism that adapts to the behavioral biases. Its performance is validated in a second experiment.     

双边道德风险下供应链质量协调契约研究

契约的公平性是契约有效性的前提,也是提高供应链协调效率的基本保证。本文基于外部市场变化对契约公平性影响,运用委托代理理论,同时考虑供应商和制造商质量失误的基础上,构建双边道德风险下二级供应链质量控制博弈模型,分析外部损失分担与内部惩罚质量契约协调下双方产品质量控制水平,研究外部市场及质量成本变化对两种契约的公平性和有效性的影响,并对模型分析结果进行仿真验证。结果显示：外部认证系统直接影响外部损失分担契约的公平性与有效性,而对内部惩罚契约无效;当供应商不必为制造商质量失误负责时,外部损失契约将实现对供应链的质量协调;随着供应商质量控制成本的提高,内部惩罚契约质量控制效果将高于外部损失分担契约,而当制造商质量控制成本下降时,外部损失分担契约较内部损失分担契更有效。因此,保证契约的公平性是实现供应链产品质量协调的根本前提。     

Role of the Standard Deviation in the Estimation of Benchmark Doses with Continuous Data

For continuous data, risk is defined here as the proportion of animals with values above a large percentile, e.g., the 99th percentile or below the 1st percentile, for the distribution of values among control animals. It is known that reducing the standard deviation of measurements through improved experimental techniques will result in less stringent (higher) doses for the lower confidence limit on the benchmark dose that is estimated to produce a specified risk of animals with abnormal levels for a biological effect. Thus, a somewhat larger (less stringent) lower confidence limit is obtained that may be used as a point of departure for low-dose risk assessment. It is shown in this article that it is important for the benchmark dose to be based primarily on the standard deviation among animals, s(a), apart from the standard deviation of measurement errors, s(m), within animals. If the benchmark dose is incorrectly based on the overall standard deviation among average values for animals, which includes measurement error variation, the benchmark dose will be overestimated and the risk will be underestimated. The bias increases as s(m) increases relative to s(a). The bias is relatively small if s(m) is less than one-third of s(a), a condition achieved in most experimental designs.     

Value of and Interaction between Production Postponement and Information Sharing Strategies for Supply Chain Firms

We analyze the value of and interaction between production postponement and information sharing, which are two distinct strategies to reduce manufacturers’ uncertainty about demand. In both single‐level and two‐level supply chains, from the manufacturer's perspective, while information sharing is always valuable, production postponement can sometimes be detrimental. Furthermore, the value of production postponement is not merely driven by savings in inventory holding cost as postponement enables the manufacturer to avoid both excess and shortfall in production. We find that production postponement and information sharing strategies may substitute, complement, or conflict with each other, depending on the extent of the increase in the unit production cost when production is postponed. In a two‐level supply chain, from the retailer's perspective, information sharing and production postponement can be beneficial or detrimental. When information sharing is beneficial to the retailer, the retailer always shares her demand information with the manufacturer voluntarily. In addition, this voluntary information sharing is truthful because inflated or deflated demand information hurts the retailer through a higher wholesale price or a stock‐out. However, the retailer never shares her demand information voluntarily if the manufacturer has already adopted production postponement because production postponement and information sharing strategies always conflict with each other. Even when the retailer does not benefit from information sharing, we show that the manufacturer can always design an incentive mechanism to induce the retailer to share the demand information, irrespective of whether the manufacturer has already implemented production postponement or not. The above findings underscore the need for a careful assessment of demand uncertainty‐reduction strategies before the supply chain players embark upon them.     

The Impact of Information Sharing on Supply Chain Performance under Asymmetric Information

The use of screening contracts is a common approach to solve supply chain coordination problems under asymmetric information. One main assumption in this context is that managers without specific incentives would rather use their private information strategically than reveal it truthfully. This harms supply chain performance. This study investigates the impact of information sharing in a principal‐agent setting that is typical for many supply chain transactions. We conduct a laboratory experiment to test whether information sharing has an influence on supply chain coordination. We find that information sharing within the supply chain has two positive effects. First, information sharing reduces the inefficiencies resulting from information deficits if there is a certain amount of trust in the supply chain. Second, communication can limit out‐of‐equilibrium behavior with a small impact on the firm's own payoff, but a large impact on the supply chain partner. Furthermore, we find that both effects are amplified when communication takes place in an environment that allows the less informed supply chain party to punish or to reward the better informed party. Although our extended mechanisms substantially enhance the poor performance of the theoretically optimal coordination contract menu, we find no mechanism that implements supply chain performance superior to the theoretically predicted second‐best level.