Nada R. Sanders

Integrating judgmental and quantitative forecasts: methodologies for pooling marketing and operations information

Accurate forecasting has become a challenge for companies operating in todays business environment, characterized by high uncertainty and short response times. Rapid technological innovations and e‐commerce have created an environment where historical data are often of limited value in predicting the future. In business organizations, the marketing function typically generates sales forecasts based on judgmental methods that rely heavily on subjective assessments and “soft” information, while operations rely more on quantitative data. Forecast generation rarely involves the pooling of information from these two functions. Increasingly, successful forecasting warrants the use of composite methodologies that incorporate a range of information from traditional quantitative computations usually used by operations, to marketings judgmental assessments of markets. The purpose of this paper is to develop a framework for the integration of marketings judgmental forecasts with traditional quantitative forecasting methods. Four integration methodologies are presented and evaluated relative to their appropriateness in combining forecasts within an organizational context. Our assessment considers human factors such as ownership, and the location of final forecast generation within the organization. Although each methodology has its strengths and weaknesses, not every methodology is appropriate for every organizational context.

Judgmental Adjustment of Statistical Forecasts

Judgmental and statistical forecasts can each bring advantages to the forecasting process. One way forecasters can integrate these methods is to adjust statistical forecasts based on judgment. However, judgmental adjustments can bias forecasts and harm accuracy. Forecasters should consider six principles in deciding when and how to use judgment in adjusting statistical forecasts: (1) Adjust statistical forecasts if there is important domain knowledge; (2) adjust statistical forecasts in situations with a high degree of uncertainty; (3) adjust statistical forecasts when there are known changes in the environment; (4) structure the judgmental adjustment process; (5) document all judgmental adjustments made and periodically relate to forecast accuracy; (6) consider mechanically integrating judgmental and statistical forecasts over adjusting.

Bringing judgment into combination forecasts

Abstract This research investigates the benefits in forecast accuracy by combining judgmental forecasts with those generated by statistical models. Our study differs from prior research efforts in this area along two important dimensions. First, two different types of judgmental forecasts are evaluated for combination with statistical forecasts — one based on contextual knowledge and one based on technical knowledge. Contextual knowledge is information gained through experience on the job with the specific time series and products being forecasted. Technical knowledge is information gained from education on formal forecasting models and data analysis. Second, we investigate the conditions under which adding judgment to combination forecasts helps the most. Specifically, we test the improvement as a function of time series variability. Our results show that judgmental forecasts based on contextual knowledge, rather than technical knowledge, are the better input into combination forecasts. Bringing judgmental forecasts based on contextual knowledge into combination forecast improves forecast accuracy over the individual statistical and judgmental forecasts. However, the benefit attained from including contextual knowledge in the combination depends on the amount of inherent variability in the time series being forecast. More contextual knowledge is needed for combination forecasts if a time series has more data variability. If the amount of variability is low, less emphasis should be given to contextual knowledge when making combination forecasts. In general, our findings suggest a linear relationship between the amount of contextual knowledge needed and data variability.

Improving short-term forecasts

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.

The impact of task properties feedback on time series judgmental forecasting tasks

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.

A hierarchical production plan for a make-to-order steel fabrication plant

A three-tiered hierarchical production plan (HPP) for a strictly make-to-order steel fabrication plant with the objective of developing a production plan and master schedule for a set of product archetypes is implemented. Data are collected from an actual steel fabrication plant located in the Midwestern section of the US. An aggregate linear programming model, a non-linear disaggregate model and a master production schedule comprise the respective tiers. Appropriate models provide the forecasts needed in the first two tiers. A production plan and master schedule based on data collected at the plant, benefits expected for its implementation and practical limitations are reported.

On Knowing When to Switch from Quantitative to Judgemental Forecasts

The conditions under which forecasts from expert judgement outperform traditional quantitative methods are investigated. It is shown that judgement is better than quantitative techniques at estimating the magnitude, onset, and duration of temporary change. On the other hand, quantitative methods provide superior performance during periods of no change, or constancy, in the data pattern. These propositions were tested on a sample of real business time series. To demonstrate how these propositions might be implemented, and to assess the potential value of doing so, a simple rule is tested on when to switch from quantitative to judgemental forecasts. The results show that it significantly reduces forecast error. These findings provide operations managers with some guidelines as to when (and when not) they should intervene in the forecasting process.

Outsourcing of Core and Non-Core Competencies in U.S. Corporations

Outsourcing has been a significant industry trend over the past decade. However, some scholars have stressed that companies may have gone too far by outsourcing core, as well as non-core competencies. Using a survey of large U.S. corporations our study documents the current status of outsourcing, differentiating between core and non-core competencies. We identify functions being outsourcing, satisfaction with sourcing decisions, as well as links to organizational performance measures.

Management Forecasting: Survey Findings and Business Implications

Accurate forecasting is one of the most critical issues for business planning. Knowing how best to generate forecasts and how others are performing this function is important information for managers. This study reports on forecasting practices in US firms based on a survey of 500 companies. In addition to information on techniques and software used, this survey attempts to gain insights into managerial forecasting practices. These include identifying common causes of forecast errors, typical problem areas, and plans for the future to improve forecasting. Based on these findings specific implications for managers are developed.