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Forecasts have traditionally served as the basis for planning and executing supply chain activities. Forecasts drive supply chain decisions, and they have become critically important due to increasing customer expectations, shortening lead times, and the need to manage scarce resources. Over the last ten years, advances in technology and data collection systems have resulted in the generation of huge volumes of data on a wide variety of topics and at great speed. This paper reviews the impact that this explosion of data is having on product forecasting and how it is improving it. While much of this review will focus on time series data, we will also explore how such data can be used to obtain insights into consumer behavior, and the impact of such data on organizational forecasting.

Buyer-supplier collaboration, an important part of operational performance, is predicated on the assumption that a firm's stated strategy on inter-firm collaboration is implemented in practice. We argue that a strategy is only as good as its execution and that micro-level factors—the employees' collaborative propensity and their internal collaborative behaviors—also play an essential role in the successful implementation of buyer-supplier collaboration. We test competing models based on secondary data collected from 330 manufacturing firms in 16 countries/regions. Our results show that both the macro-level firm strategy and micro-level employee factors exert substantial influence over buyer-supplier collaboration. In addition, when inter-firm collaborative strategy and micro-level factors are congruent, there is an enhancement effect. Our research explores the critical role of supply managers and surrounding employees in shaping buyer-supplier collaboration and actualizing operational strategies.

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.

This study evaluates the impact of task properties feedback on the time series forecast accuracy of four different judgmental forecasting processes. Specifically, we test the impact of providing information on time series data patterns amd degree of noise level to knowledgeable subjects to interpret this information. Ninety eight subjects were used as the source of the individual and three-person group forecasts for eight artificial time series with varying patterns and noise levels. Our findings show that such task properties feedback leads to improvements in forecast accuracy for all forecasting processes tested, particularly for high noise series. This is true for both individual and group judgmental forecasting processes, as well as combination forecasts. These findings have important implications for business practitioners who continue to rely on judgmental forecasting processes. The information provided to subjects in our study is such that it could readily be obtained as output from most statistical software packages. Our findings imply that all judgmental forecasting processes could benefit by relying on this type of cognitive aid as an input to their judgments.

This study compares the performance of judgmental and statistical forecasts, as well as the judgmental revision of statistically derived forecasts, for artificial time series with a number of controlled characteristics. Judgment is found to produce inferior and more biased forecasts overall. However, results suggest that judgmental revision may have a benefit for some low noise series, particularly when statistical techniques are applied “blindly” to the time series.

This empirical study compares the accuracy of combined forecasts, found by averaging individual forecasts from univariate time-series techniques, with judgmental forecasts actually made daily by experienced practitioners in real business settings. The value of judgment is assessed, used alone and in combination with quantitatively derived forecasts. The key finding is that the value of each forecasting approach depends on the characteristics of the time series, namely data variability. Automated quantitative forecasts are superior for time series that are relatively stable. Complete reliance on quantitative procedures is not only more efficient, but reduces forecast errors. However, as the volatility of the time series increases, a point is reached where judgmental inputs are desirable, either to supplement or even to replace the forecasts provided by quantitative techniques.