Business Forecasts with Artificial Intelligence

Today's business world is driven by customer demand. Unfortunately, the patterns of demand vary considerably from period to period. This is why it can be so challenging to develop accurate forecasts. Forecasting is the process of estimating future events, and it is fundamental to all aspects of management. The goals of forecasting are to reduce uncertainty and to provide benchmarks for monitoring actual performance. Emerging information technologies and artificial intelligence (AI) techniques are being used to improve the accuracy of forecasts and thus making a positive contribution to enhancing the bottom line.

A new generation of artificial intelligence technologies have emerged that hold considerable promise in helping improve the forecasting process including such applications as product demand, employee turnover, cash flow, distribution requirements, manpower forecasting, and inventory. These AI based systems are designed to bridge the gap between the two traditional forecasting approaches: managerial and quantitative.

Forecasting Approaches

Generally speaking, forecasts are based on quantitative analysis, qualitative analysis or a combination of both. Often quantitative forecasting is referred to as objective analysis while qualitative forecasting is called managerial or judgmental analysis. Typically, there is tension between these two approaches. Quantitative forecasts, which are often favored by operations, tend to be developed using a bottom up approach while managerial-based forecasts, usually preferred by the marketing group, are approached from a top down perspective. For example, a primary marketing goal is to insure adequate supply while operation's focus is on minimizing inventory. The resolution of these two approaches is how forecasting errors occur and presents an opportunity for using artificial intelligence methods. Quantitative forecasting can be characterized by one of the two basic techniques:

• Time Series - The future will tend to look and behave like the past. For example, gasoline prices for the next six months will continue along the same lines as they have over the past six months.
• Relational - The future is dependent on the direction of a variety of factors. For example, new housing starts might be a function of interest rates and local weather conditions.

A time series is a set of data points recorded over successive time periods. Examples include monthly billables, weekly unit product demand and quarterly inventory levels and stock prices. A relational database consists of the recording of several variables for a number of observations. For example, a financial relational database could consist of revenues, earnings and assets for the Fortune 500.

The following graphic highlights the typical forecasting process. The resultant forecasts are evaluated by comparing predictions with actual results. This assessment is accomplished by examining the error terms. An error term is the difference between the prediction and the actual outcome. Based on an error assessment, the forecasting process is continually updated through the adjustment of model inputs.

Forecasting Approaches

Generally speaking, forecasts are based on quantitative analysis, qualitative analysis or a combination of both. Often quantitative forecasting is referred to as objective analysis while qualitative forecasting is called managerial or judgmental analysis. Typically, there is tension between these two approaches. Quantitative forecasts, which are often favored by operations, tend to be developed using a bottom up approach while managerial-based forecasts, usually preferred by the marketing group, are approached from a top down perspective. For example, a primary marketing goal is to insure adequate supply while operation's focus is on minimizing inventory. The resolution of these two approaches is how forecasting errors occur and presents an opportunity for using artificial intelligence methods. Quantitative forecasting can be characterized by one of the two basic techniques:

• Time Series - The future will tend to look and behave like the past. For example, gasoline prices for the next six months will continue along the same lines as they have over the past six months.
• Relational - The future is dependent on the direction of a variety of factors. For example, new housing starts might be a function of interest rates and local weather conditions.
• The following process outlines a plan for improving forecast accuracy using artificial intelligence support systems:

  1. Evaluate and characterize the current forecasting system.

  2. Measure the current level of error.

  3. Compare error levels with industry norms.

  4. Specify new requirements.

  5. Characterize the economic impact of improved forecasts.

  6. Identify alternative AI forecasting options.

  7. Select best approach(s).

  8. Develop implementation schedule.

  9. Identify potential bottlenecks and problem areas.

  10. Implement new system and monitor performance.