China 
Africa 
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Proactive Yesterday, Responsive Today: Use of Information to Enhance Planning in Closed-Loop Supply Chains
Published in Surendra M. Gupta, A. J. D. (Fred) Lambert, Environment Conscious Manufacturing, 2007
Muhammad N. Jalil, Rob A. Zuidwijk, Harold Krikke
Forecasting methods are probably the most recognized information-enabling method within (closed-loop) supply chains and are used for all kinds of planning. Forecasting in itself does not provide a plan, but forecasting is essential for almost any kind of supply chain planning because proactive planning requires some form of forecast as an input. By definition, forecasts are with errors, and we usually observe that reduction of the forecast error is vital for efficient planning. The usual representation of uncertainty in forecast values is in probabilistic terms.
Forecasting in Global Supply Chain Engineering
Published in Erick C. Jones, Supply Chain Engineering and Logistics Handbook, 2020
A good forecast is more than a single number. A good forecast also includes some measure of the anticipated forecast error. This could be in the form of a range or an error measure such as the variance of the distribution of the forecast error.
Demand forecasts with judgement bias in a newsvendor problem
Published in International Journal of Production Research, 2023
Yini Zheng, Qi Fu, Juan Li, Lianmin Zhang
Proposition 2 can also be explained from the trade-off among bias and variance. As shown in Equation (4), there are two terms in the forecast error function: one is the bias term, , and the other is the variance term . For , as decreases, the bias term increases and the variance term decreases. When , optimistic bias in demand standard deviation forecast results in forecast accuracy increasing due to the dominant decrease of the variance term. However, when , optimistic bias in demand standard deviation forecast results in forecast accuracy decreasing due to the dominant increase of the bias term. Thus, only slight optimistic bias can help increase forecast accuracy.
Performance of judgmental–statistical forecast combination strategies under product-market configurations
Published in International Journal of Management Science and Engineering Management, 2023
Budhi S. Wibowo, Yoga J. Prakoso, Nur Aini Masruroh
The MSE is a common accuracy metric that measures the average of the squares of the forecast errors. The measure is often presented in a root square (RMSE) for interpretation purposes. Despite being the most common loss function used in statistics, the MSE is scale-dependent and sensitive to extreme values. Let us define the forecast error at period and SKU , as . The MSE is defined as follows:
Optimizing production-inventory replenishment and lead time decisions under a fill rate constraint in a two-echelon sustainable supply chain with quality issues
Published in International Journal of Systems Science: Operations & Logistics, 2023
Davide Castellano, Roberto Gabbrielli, Mosè Gallo, Bibhas C. Giri, Sumon Sarkar
Finally, additional beneficial effects can clearly be achieved by lowering the standard deviation of demand rate. Since this quantity can be put in relation to forecast errors, its reduction can be obtained through the adoption of an improved forecasting technique. The larger the standard deviation of demand rate, the higher the safety stock. Hence, implementing a more effective forecasting method can lead to less average inventory and, in turn, to a smaller expected cost rate.