5 Reasons Why the Quality of Your Statistical Forecasts Can Deteriorate

  • by Gautam Narayan, Senior Consultant, Deloitte Consulting LLP
  • Jerry Hoberman, Director, Deloitte Consulting LLP (October 2009)
  • November 16, 2009
Learn why the quality of your statistical forecasts can deteriorate and ways to prevent the deterioration from occurring again.
Key Concept
Historical data is used in statistical forecasting to identify patterns, trends, and seasonality. Those factors are then used to effectively predict future demand. This can be achieved by using the appropriate forecast methods or models, and fine-tuning the trend and seasonal parameters so that the historical patterns of products are reflected in the future forecast. These forecasts can be further enhanced and refined through planner inputs to the forecast and effective maintenance of forecast models and parameters.
Statistical forecasting has long been recognized as a key element in making operational improvements and more informed decisions for the supply chain. Organizations often recognize that certain technologies, such as SAP Advanced Planning & Optimization (SAP APO), can help them achieve better improvements through forecast accuracy and supply chain performance. Unfortunately, many companies find that after implementing statistical forecasting technology, the quality of their statistical forecasts is not nearly as good as they had anticipated. Even worse, the quality of their less-than-ideal forecasts deteriorates even further over time.

Poor forecasting results are a risk that organizations face, but there are ways to mitigate that risk. You can address the poor results through multiple ways — through better implementation methods, by making sure end-user adoption of the system is better, or through an overall organizational model that supports continuous improvement. The root cause of many issues lies in the process design, technical configuration, and organizational accountabilities during the course of statistical forecasting technology implementations. While these causes are apparent, there are some less apparent causes of which many are not aware. Among the most common causes of low-quality statistical forecasts are:

  • Lack of understanding about statistical forecasting principles
  • Gaps in the composition of the demand planning/forecasting team
  • Deficiencies in data quality and maintenance processes
  • Ineffective technology configuration
  • Expecting immediate results

In the sections that follow, we take a look at each one of these issues and present important considerations for your efforts to avoid them.

Gautam Narayan

Gautam Narayan is a senior consultant with Deloitte Consulting LLP in the technology practice. His area of focus is supply chain planning, with specific experience, knowledge, and skills in demand planning and enterprise forecasting consulting services. He has consulting experience in demand management and process optimization, as well as broader supply chain strategy assessments and business transformation. Gautam is a specialist in advanced supply chain planning systems and has led the provision of consulting services in support of multiple, full life cycle implementation efforts for SAP Advanced Planning & Optimization (SAP APO) demand planning and Service Parts Planning (SPP) toolsets. He has experience working across a broad set of industries, including high technology manufacturing, healthcare/life sciences, consumer goods, and automotive.

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Jerry Hoberman

Jerry Hoberman is a director with Deloitte Consulting LLP and leads their Northeast SAP practice with responsibilities for serving clients, mentoring practitioners, and managing operations. His 15 years of SAP implementation-related consulting experience has focused on helping organizations in their efforts to achieve transformational value through finance, supply chain, and customer process and organizational changes. Jerry has extensive experience with manufacturing, distribution, aerospace and defense, media and high-tech organizations.

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