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The contemporary business world -- The u.s. business environment -- Understanding business ethics and social responsibility -- Entrepreneurship, new ventures, and business ownership -- Understanding the global context of business -- Managing the business -- Managing the business -- Organizing the business -- Operations management and quality -- People in organizations -- Employee behavior and motivation -- Leadership and decision making -- Human resource management and labor relations -- Principles of marketing: building relationships with customers for competitive -- Advantage -- Marketing processes and consumer behavior -- Developing and pricing products -- Distributing and promoting products -- Managing information for better business decisions -- Information technology (it) for business -- The role of accountants and accounting information -- The financial system and issues in financial management -- Understanding monday and the role of banking -- Managing business finances

ABSTRACTTypical 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.