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Data Mining and Forecast Management Applied Management Science for Decision Making, 1e © 2012 Pearson Prentice-Hall, Inc. Philip A. Vaccaro, PhD Applied.

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Presentation on theme: "Data Mining and Forecast Management Applied Management Science for Decision Making, 1e © 2012 Pearson Prentice-Hall, Inc. Philip A. Vaccaro, PhD Applied."— Presentation transcript:

1 Data Mining and Forecast Management Applied Management Science for Decision Making, 1e © 2012 Pearson Prentice-Hall, Inc. Philip A. Vaccaro, PhD Applied Management Science for Decision Making, 2e © 2014 Pearson Learning Solutions Philip A. Vaccaro, PhD MGMT E-5070

2 What is Forecasting? Forecasting is the art and science of predicting future events. It may involve taking historical data and projecting it into the future by means of a mathematical model. It may also be an intuitive prediction. It may also be a mathematical model adjusted by good judgement.

3 Forecasting is Data Mining Too !  Data mining is the process of extracting patterns or correlations among dozens of fields in large relational data bases.  With the amount of data doubling every three years, it is becoming increasingly important for transforming data into in- formation, which in turn, can be used to increase revenues, cut costs, or both.  Data mining uses simple and multi- variate linear, and non-linear regression models as well as hypothesis testing.

4 Data Mining Example A grocery chain analyzed local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. one or more Simple Linear Regression models

5 Data Mining Example Further analysis showed that these men usually did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. A Multiple Regression Model

6   The retailer concluded that the men purchased beer to have it available for the upcoming weekend.  The grocery chain could use this newly discovered information in various ways to increase revenue.  For example, they could move the beer display closer to the diaper display. And, they could make sure that beer and diapers were sold at full price on Thursdays! Data Mining Example INFORMATION to KNOWLEDGE to DECISION !

7 There is seldom a single superior forecasting method. One firm may find exponential smoothing to be effective. Another firm may use several models, and a third firm may combine both quantitative and subjective methods. Whatever approach works best should be used. A Word of Advice…

8 Forecasting Time Horizons 1. Short-range forecast : Time span of up to 1 year but generally less than 3 months. It is used for planning purchasing, job scheduling, workforce levels, job assignments, and product- ion levels. 2. Medium-range forecast : Time span of 3 months generally to 3 years. It is useful in sales planning, production planning / budgeting, cash budgeting, and analysis of various operating plans. 3. Long-range forecast : Generally 3 years or more in time span. It is used in planning for new products, capital expenditures, facility location or expansion, and research and development.

9  Good forecasts are of critical importance in all aspects of a business.  The forecast is the only estimate of demand until actual demand becomes known.  Forecasts of demand therefore, drive the decisions in many areas. The Strategic Importance of Forecasting

10 Forecast Impacts Human Resources Hiring, training, and terminating workers all depend on anticipated demand. If the HR department must hire additional workers without warning, the amount of training declines and the quality of the workforce suffers.

11 Forecast Impacts Capacity When capacity is inadequate, the resulting shortages can mean undependable delivery, loss of customers, and loss of market share. When capacity is in excess, costs can skyrocket.

12 Forecast Impact Supply Chain Management In the global marketplace, where expensive parts for Boeing 787 jets are manufactured in dozens of countries, coordination driven by forecasts is critical. Scheduling transportation to Seattle for final assembly at the lowest possible cost means no last-minute surprises that can harm already low profit margins.

13 Product Life Cycle Influence Products and even services, do not sell at a constant level throughout their lives. Most successful products pass through four stages : introduction, growth, maturity, and decline.

14 Product Life Cycle Influence Products in the first two stages of the life cycle need longer forecasts than those in the maturity and decline stages. Forecasts that reflect life cycle are useful in projecting different staffing levels, inventory levels, and factory capacity as the product passes from the first to the last stage.

15 Forecasting Caveats  Forecasts are seldom perfect. Outside factors we cannot predict or control often impact the forecast.  Most forecasting techniques assume that there is some underlying stability in the system.  Product family and aggregated forecasts are more accurate than individual product forecasts. This approach helps balance the over and under predictions of each.

16 Service Sector Forecasting Barber Shops  Expect peak flows on Fridays and Saturdays.  Many call in extra help on the above days.  Most are closed on Sunday and Monday.

17 Service Sector Forecasting Flower Shops  When Valentine’s Day falls on a weekend, flowers cannot be delivered to offices, and customers are likely to celebrate with outings rather than flowers ( low sales ).  When Valentine’s Day falls on a Monday, some celebration will have taken place on the weekend ( reduced sales ).  When Valentine’s Day falls in midweek, busy midweek work schedules make flowers the optimal way to celebrate ( higher sales ).

18 Service Sector Forecasting Fast Food Restaurants  Use point-of-sale computers that track sales every 15 minutes.  May use the moving average technique to minimize the error of the 15-minute forecasts.  The forecasts are used to schedule staff, who begin in 15-minute increments, not the 1-hour blocks as in other industries.

19 Hourly Sales at a Fast-Food Restaurant Percent of Sales by Hour of Day 20% 15% 10% 5% 11-12 1-2 3-4 5-6 7-8 9-10 12-1 2-3 4-5 6-7 8-9 10-11 ( Lunchtime )( Dinnertime )

20 Monday Calls at a FedEx Call Center 12% 11% 10% 9% 8% 7% 6% 5% 4% 3% 2% 1% 0% 1 2 3 4 5 6 7 8 9 10 11 12 A.M. P.M.

21 Service Sector Forecasting Federal Express  makes 1-year and 5-year models to predict number of service calls, average handle time, and staffing needs.  breaks the forecasts into weekday, Saturday, and Sunday, and then uses the Delphi Method and time-series analysis.  tactical forecasts are monthly, and use 8 years of historical daily data. They predict caller volume by month, day of the week, and day of the month.  the operational forecast uses a weighted moving average and 6 weeks of data to project the number of calls on a 30-minute basis.

22 Service Sector Forecasting Federal Express  Fed Ex’s forecasts are consistently accurate to within 1% to 2% of actual call volumes.  This means that coverage needs are met, service levels are maintained, and costs are controlled.

23 Forecasting Fundamentals & Models TYPES  Time – Series Models  Causal Models  Qualitative Models

24 Time-Series Models  Predict the future by using historical data.  Assume that what happens in the future is a function of what has happened in the past. Moving Average, Weighted Moving Average, Weighted Moving Average, Exponential Smoothing, Trend Projection

25 Causal Models  Incorporate variables or factors that might influence the quantity being forecasted into the forecasting model.  The most common causal model is regression analysis. Ice cream sales, for example, might depend on the season, average temperature, day of the week, and so on.

26 Qualitative Models  Incorporate judgmental or subjective factors into the forecasting model.  Opinions by experts, individual experiences, and other factors are expected to be very important.  Used when accurate quantitative data are difficult to obtain. EXAMPLES ARE THE DELPHI METHOD, SALES FORCE COMPOSITE, AND CONSUMER MARKET SURVEY

27 1. The Delphi Method  Three types of participants: decision makers, staff personnel, and respondents.  The decision makers make the actual forecast.  The staff personnel prepare, distribute, collect, and summarize a series of questionnaires and survey results.  The respondents are those whose judgements and values are being sought. They provide in- put to the decision makers before the forecast is made.

28 The Delphi Method

29 2. Sales Force Composite  Each salesperson estimates what sales will be in his or her region.  These forecasts are reviewed to ensure that they are realistic.  These forecasts are combined at the district and national levels to reach an overall forecast.

30 3. Consumer Market Survey  This method solicits input from customers or potential customers regarding their future purchasing plans.  It can help not only in preparing a forecast but also in improving product design and planning for new products.

31 Consumer Market Survey

32 Consumer Market Survey

33 Types of Forecast Models  delphi method  jury of executive opinion  sales force composite  consumer market survey  naïve approach  arithmetic mean  moving average  weighted average  weighted - moving average  exponential smoothing  trend projections  decomposition  Regression Analysis  Multiple Regression Causal Methods Time – Series Methods Qualitative Models

34 Cost vs. Accuracy Tradeoff ECONOMETRIC MODELS CAUSAL MODELS SOPHISTICATED TIME SERIES SIMPLE TIME SERIES QUALITATIVE MODELS MODELCOSTS LOW HIGH DECLINING ACCURACY 100% 0% TOTAL of MODEL COST and FORECAST ERROR COST OPERATING COSTS DUE TO INACCURATE FORECASTS THE OPTIMAL REGION


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