Improving Judgmental Adjustments
Judgmental adjustments are commonly made in the forecasting process to incorporate business intelligence not captured by other methods. This session will review the rationale behind these adjustments and address the common traps faced by both forecasters and forecast users when applying judgment. Drawing from her experience with financial forecasters, Dr. Onkal will outline ways to enhance the quality of judgmental adjustments made in individual and group settings and review their managerial implications. At Wednesday’s session "Effectively Combining Judgment with Statistical Forecasts," Dr. Nada Sanders will expand on this theme, discussing how to best combine judgmental forecasts with statistically-generated forecasts.

Dr. Dilek Onkal
Professor of Decision Sciences
Faculty of Business Administration
Bilkent University

Inventory Forecasting and Planning
In an inventory context, demand forecasting is particularly challenging. The forecaster must contend with conflicting objectives, sparse data and sales patterns that become ever more erratic as demand moves up the supply chain. To help address these issues, this session will provide practical solutions to some frequently asked questions, such as:

 Should inventories be centralized or decentralized?
 At what level in the supply chain should demand be forecasted?
 How should Stock Keeping Units (SKUs) be classified?
 How should intermittent demand be forecasted?
 What measures should be used to assess forecasting performance?

Dr. John E. Boylan
Professor of Management Science
Buckinghamshire New University

Sales Forecasting Benchmarks: Real Help or Red Herrings?
Surveys of sales forecasting accuracy have been published for many industries—in fact, it is possible that your management has or will use such surveys to asses your forecasting performance. How useful are these surveys? In this session, Dr. Kolassa examines published surveys and finds many fundamental issues in applying their results to benchmark performance. Problems include differences among surveyed companies in: product, spatial and temporal granularity; measurement; and processes, making the survey results unsuitable for use as sales forecasting accuracy benchmarks. You will learn about other measures and targets for forecasting performance—such as adopting internal benchmarks—which often provide a better representation of the processes and targets your organization has in place.

Dr. Stephan Kolassa
Vice President, Corporate Research
SAF AG

How to Use Internet Resources in Your Forecasting
Many professionally prepared forecasts are available free (or nearly so) on the Internet, as well as the historical data on which they are based. Using these resources as drivers of your quantitative forecasts can present some frustrating challenges and pitfalls.

This session will focus on some keys in selecting these resources and on procedures for using them effectively. The two goals are 1) To briefly show as background a variety of forecast sources and the historical data related to them and 2) To show with specific examples how to use such sources in shaping and preparing business forecasts for the next year or two. The Internet sources are illustrated with on-line hits, screen shots and listings of the selected URLs. The applications include converting external forecasts to data that you can use to drive your organization’s forecasts.

Dr. Roy L. Pearson
Chancellor Professor Emeritus
College of William and Mary

Effectively Combining Judgment with Statistical Forecasts
Judgmental and statistical forecasts each have their own strengths and weaknesses and can bring different information to the forecasting process. This session will discuss different ways of combining judgment and statistical forecasts and the advantages and disadvantages of each approach, factoring in the inherent biases of each method. If you attended Tuesday’s session "Improving Judgmental Adjustments," with Dr. Dilek Onkal, you will find that this session expands on the theme of how to best apply business judgment to improve forecast accuracy. Drawing on extensive research and real-world experience in forecasting for supply chain management, Dr. Sanders will discuss principles that have been developed for deciding when and how to use judgment in adjusting statistical forecasts.

Dr. Nada R. Sanders and Raj Soin
Professor
Neeley School of Business, TCU

Panel of Experts: Meeting Forecasting Challenges
This session gives you the opportunity to ask direct questions of a panel of internationally-recognized experts in business forecasting. The questions may address any aspect of forecasting, including the management of the forecasting function, organizational impediments to good forecasting practice, establishment of performance targets and methods and software appropriate for the company’s products. Here is a sampling of questions raised at the last Forecasting Summit:

 How do you effectively set up a forecasting team? Where is the best place for the forecasting function to reside – marketing, finance, operations?
 Our company is trying to work from a single forecast for all groups – marketing, sales, finance and supply-chain. Is this realistic? What are the pitfalls?
 Upper management supplies a sales target. How do you forecast to avoid a “self-fulfilling prophesy”?
 How can a forecaster avoid being blamed for the failure of product sales to meet plan?
 How do you forecast sales in a company whose promotional campaigns do not match the work months?
 How can a company establish a target for forecast accuracy?
 How can a company steer between the pitfalls of (a) overly frequent forecast updates, thus introducing excessive variability and (b) failure to make timely updates, perhaps missing the market?

Moderator:
Dr. Len Tashman
Professor Emeritus
University of Vermont (UVM)

Recognizing the Biases in New Product Forecasting
A forecast for any new product should be developed with a keen eye towards realism and presented to management regardless of the outcome. Yet, this seldom happens. Inherent in most, if not all, organizations are multiple biases that hamper the new product forecasting endeavor and introduce unnecessary error. Only when systematic biases are identified and properly mitigated does the chance increase for the forecast to be more on target.

This session will review a sample of the more prevalent biases that affect new product forecasting in an effort to attune you to these predispositions. It is very unlikely that your new product forecasting will ever be free of all biases due to the need to rely on judgment. Nonetheless, by understanding these persistent biases, you can implement procedures that provide a transparent view of new product forecasting results and make sounder decisions.

Dr. Kenneth B. Kahn
Professor
Purdue University

Sales Forecasting — A New Approach
Forecasting the future is getting tougher as the future gets less and less predictable. Instead of just spending more time trying to do the same things better—using sophisticated mathematical models that don’t by themselves improve the forecasts—it’s time to change our approach. This session will present some “gripes & myths” that need to be overcome to develop a viable alternative to traditional methods. It will focus on:

 Forecasting less, not more, yielding higher customer service and lower inventory.
 Teamwork, good communication, and clear accountabilities which are often more important in improving the forecasting process than using complex statistical forecasting models.
 Understanding that forecasting is a process, and as such can be improved using standard techniques for process improvement such as Total Quality Management (TQM).
 Reaping greater benefits by pursuing process improvements rather than focusing narrowly on forecast accuracy.

Robert A. Stahl
President
R. A. Stahl Company

  Business Forecast Systems in cooperation with the International Institute of Forecasters