Coping with uncertainty - Demand planning from a distributed cognition perspective

Point of contact: Sebastian Achter
(The project on ResearchGate)


The ability to generate high-quality sales and demand forecasts is of profound importance for subsequent planning decisions like budgeting, purchasing, or financial planning.Thus, forecasting accuracy is of major concern in many organizations, but so far has only received limited attention in management accounting and control research regarding its consequences for decision-making during S&OP processes.

There is a wide body of knowledge in the management accounting and control community that can add value to the investigation of forecasting processes that accounting can profit from a comprehensive body of literature already established by the forecasting community. We see management accounting and control in the position to contribute rich theoretical principles that can offer new and relevant insights regarding the influence of contingency factors on the use and effect of forecasting information. Work conducted so far is characterized by its behavioral lens taking a closer look at self-interest and biases of agents who generate forecasts (Kremer, Siemsen, & Thomas, 2016; Sedatole, Brüggen, & Grabner, 2018). Another line of research explores the utilization of forecast accuracy measures as performance indicators and control tools (Chen, Rennekamp, & Zhou, 2015; Jordan & Messner, 2019). Both focus on the cognitive limitations of the human decision-maker on an individual leveland their behavioral consequences.

In our study, we aspire to investigate the demand planning routine in a S&OP process of a large semiconductor manufacturer characterized by high demand uncertainty and a modern advanced planning system (APS). We explore the role of the demand planner embedded in a socio-technical system capacitated to deal with high external uncertainty. We conduct in-depth interviews,use field observations to capture how the planner navigates through the complex planning environment, and a quantitative survey about perceived forecast and information quality. We thereby explore why the organization in our case does not employ forecast accuracy measures to control the performance of the demand planning process and how the forecast information is used to coordinate the process. We look at the role of the management control system and examine the information value of forecast data and their accuracy in the context of a modern production system.

Specifically, we give an in-depth look into an intriguing case, which shows that forecast quality despite high uncertainty and complexity is not only a matter of forecast accuracy. Analyzing the process from Hutchin's distributed cognition perspective demonstrates that forecast accuracy does not necessarily improve the quality of forecasting information. We find that the mere existence of a forecast with only a vague reflection of the real demand can already possess information value sufficient for substantial planning decisions. As such, the forecast quality depends on organizational circumstances,determining the type of information needed. Overall, we identify that the S&OP process is sensitive to the cognitive and behavioral aspects of the individual decision-makers, raising attention to the less addressed social perspective in an organizational forecasting setting.



Chen, C. X., Rennekamp, K. M., & Zhou, F. H. (2015). The effects of forecast type and performance-based incentives on the quality of management forecasts. Accounting, Organizations and Society, 46, 8-18. doi:

Jordan, S., & Messner, M. (2019). The Use of Forecast Accuracy Indicators to Improve Planning Quality: Insights from a Case Study. European Accounting Review, 1-23.

Sedatole, K. L., Brüggen, A., & Grabner, I. (2018). The folly of forecasting: The effects of a disaggregated sales forecasting system on sales forecast error, sales forecast positive bias, and inventory levels.


Related Publications

 Hauke, J., Lorscheid, I., Meyer, M., (2017). Individuals and their interactions in demand planning processes: An agent-based computational testbed. Paper submitted to International Journal of Production Research – Special Issue Modeling and Analysis of Semiconductor Supply Chains.

Achter, S., Meyer-Riehl, D., Lorscheid, I., Hauke, J., Sun, C., Ponsignon, T., Ehm, H., and Meyer, M. (2017). On Agent-based modeling in semiconductor supply chain planning. Proceedings of the 2017 Winter Simulation Conference: Las Vegas, Nevada.