Why Forecasting Does Not Need To Be Hostile to an Agile Forecasting and Planning Process

Demand planners and forecast practitioners in supply chain organizations today can expect data-driven predictive analytics to support them in forward-looking decision making. Regrettably, many top-level managers have a tendency to avoid dealing with forecasting issues because these decisions have to be made for uncertain outcomes. By mastering agility and navigating uncertainty management has greater flexibility dealing with the business issues. Continuously improving the demand forecasting and planning process enhances accuracy and agility when the organization can embrace both change and chance in the forecasting process.

Here is how:

1.?????Get Top Management Involvement. A champion of the demand forecasting process needs to be identified at a high enough level in the company to focus attention and resources on improved forecasting performance. This individual needs to have a stake in the outcome. Operations executives whose organizations depend on credible and accurate forecasts could be excellent champions. The champion does not need to manage the demand forecasting function, but does need to have a strong interest in sustaining an Agile Forecasting process. When top management recognizes the importance of demand forecasting, both to the business and operational planning, it will increase its level of support.

2.?????Select Overall Goals. The first step towards implementation deals with selecting overall goals for quantitative modeling. Clearly, it is difficult to be successful in any area of the business without having decided what it is that needs to be done. Because both quantitative and qualitative approaches can be used for many areas, the specific requirements of a company will determine which techniques are most useful.?

3.?????Use Predictive Modeling with the goal of reducing uncertainty thereby improving forecast accuracy. Because quantitative (statistical) models can give predictions with measured uncertainty, they are potentially more accurate than corporate forecasts based primarily on judgmental perceptions. Planners should be prepared to work with new models and approaches.

4.?????Demand Planners and Managers Need to Look for More Quality Data than just a set of forecast numbers. They need to understand the relationships that exist among the various measures of interest and between (internal) corporate?data corporate data and the (external) factors affecting demand, what the relevant relationships are, and how they have been changing over time.

5.??Creating Predictive Models can also provide supply chain professionals and management with the ability to explore alternative scenarios with associated risks. Most likely, optimistic, and pessimistic scenarios for economic or market forecasts can be used to assess the demand for a firm's products and services. This helps management generate necessary continency plans before they are needed.

6.?????Demand Forecasters Face the Need to Provide More Credible Substantiation for the forecasts presented to management or regulatory bodies. Good documentation is often required to satisfy reviewers who question the demand forecasting job that has been done. Forecast accuracy measures, stability tests, and forecast simulations are a valuable part of the documentation package. In this regard, the demand forecaster can also use the forecast test as a criterion for model selection. If a given model's forecast test results in errors that are above the objective set by the demand forecaster, the model can be rejected and re-examined..

?7.?????Statistical and Econometric Models Can Also Provide Prescriptive Estimates of advertising effectiveness and price elasticities that can be used to assess the impact that promotion and pricing strategies may have on revenues. There are numerous business situations in which extremely large numbers of forecasts have to be generated and seasonally adjusted. Typically, tens of thousands of forecasts are required to determine the customer-specific requirements for products (SKUs) in inventory and production-planning systems on a periodic basis.

For example, electricity demand forecasts are now being created in 30-minute intervals, where a forecasting work cycle is only 24 hours. To attempt to provide all of these forecasts in a spreadsheet environment or a manual, one-at-a-time basis is not practical. It requires a forecast relational database decision support system with automated statistical forecasting and computing algorithms that can quickly and easily provide credible, reliable forecasts for the great majority of cases. The exceptions that warrant additional time and money can then be given the individual attention they deserve.

Demand forecasters also need to develop documentation of useful forecasting techniques that work well in specified contexts. When a request for a one-time forecast is received, the forecaster can review the documentation to determine the models that will most likely provide the best results. Unsuccessful attempts should also be noted to avoid the repetition of false starts.

Predictive modeling can also be a way of increasing the productivity of a demand forecasting organization while reducing overall supply chain costs.

If you want to learn more about achieving agility with smarter forecasting for a data-driven supply chain, I refer you to my book Change&Chance Embraced: Achieving Agility with Smarter Forecasting in the Supply Chain and Kindle e-book entitled Demand Forecasting: Practice, Process & Data Analytics for Smarter Forecasting in the Supply Chain.. It is available on Amazon websites worldwide along with the five-star?reviews like these:

BETTER INSIGHTS Gather DATA, make the right sorts of simple calculations and end-up literally SEEING how well you are forecasting changes in both demand and its uncertainty. This new book by Dr. Hans Levenbach makes the advantages of tried-and-true "technical" methods more accessible to everybody involved in the forecasting process. Loved the pragmatic "Takeaways" at the end of each chapter! Bob Obenchain, PhD, Fellow of the American Statistical Association

?An outstanding book on how to build agile demand planning processes. I've worked as a demand planner for several years, and this is the first book I have seen that looks at the complete process of building and maintaining agile forecasts. It has helped fill in many gaps in the training I have received.

Hans Levenbach, PhD is Owner/CEO of Delphus, Inc and Executive Director,?CPDF Professional Development Training and Certification Programs.

Dr. Levenbach is the author of a forecasting book (Change&Chance Embraced) recently updated with the new LZI method for intermittent demand forecasting in the Supply Chain.

With endorsement from the International Institute of Forecasters (IIF), Dr. Hans created CPDF, the first IIF certification curriculum for the professional development of demand forecasters. and has conducted numerous, hands-on?Professional Development Workshops?for Demand Planners and Operations Managers in multi-national supply chain companies worldwide.

The 2021 CPDF Workshop manual is available for self-study, online workshops, or in-house professional development courses.

Hans is a Fellow, Past President and former Treasurer, and member of the Board of Directors of the?International Institute of Forecasters.

He is Owner/Manager of these LinkedIn groups: (1)?Demand Forecaster Training and Certification, Blended Learning, Predictive Visualization, and (2)?New Product Forecasting and Innovation Planning, Cognitive Modeling, Predictive Visualization.

I invite you to join these groups and share your thoughts and practical experiences with demand data quality and demand forecasting performance in the supply chain. Feel free to send me the details of your findings, including the underlying data without identifying proprietary descriptions. If possible, I will attempt an independent analysis and see if we can collaborate on something that will be beneficial to everyone.