Prepared by Prof. T. K. JANA. Mechanical Engineering Haldia Institute of Technology

1. Forecasting Prepared by Prof. T. K. JANA. Mechanical Engineering Haldia Institute of Technology

2. Forecasting Forecasting in the context of production management refers to future prediction about the sales of product(s) as evaluated by certain techniques. e.g. sales of (i) Maruti Swift Dezire during April – June, 17 (ii) Voltas A/C during Jan – March, 2017

3. Types of Automation Basis of all business decisions • Production • Inventory • Personnel • Facilities

4. Importance of Forecasting Various departments in the organization formulate and execute their plans based on forecast. Finance needs forecasts to project cash flows and capital requirements. HR needs forecasts to establish and recruit the man power requirements. Production unit or shop-floor requires forecasts to plan schedule, workforce, nos. of shifts, material requirements, inventories, lead time etc. Design department initiate new product design and / or modification/improvement of the existing product

5. Detailed use of system Types of Forecast Short-range forecast Usually < 3 months Scheduling, worker assignments Medium-range forecast 3 months to 2 years Sales/production planning Long-range forecast > 2 years New product development Design of system

6. Forecasting Techniques Qualitative or Subjective: • Executive Judgment:Opinion of a group of high level experts is aggregated • Sales Force Composite: Each regional salesperson provides his/her sales estimates. The forecasts are then reviewed to make sure that these are realistic. All regional forecasts are then pooled at the district and national levels to obtain an overall forecast.

7. Forecasting Techniques Market Research/Survey: Inputs from customers pertaining to their future purchasing plans forms the basis of forecasting. It involves the use of questionnaires, consumer panels and tests of new products and services.

8. Forecasting Techniques Delphi Method: This method relies on opinions of a group constituted by individuals from inside as well as outside the organization in such a way so that each member is unaware about the identity or credentials of other members.

9. Delphi Method The procedure consists of the following steps: Each expert in the group makes his/her own forecasts in the form of statements based on certain questionnaire. The questionnaire should be free from any ambiguity. The coordinator collects all group statements and summarizes them. The coordinator prepares a summary and gives another set of questions to each group member including feedback of other experts as input. The above steps are repeated until a consensus is reached.

10. Forecasting Techniques Quantitative Techniques Time Series modelRegression Naive Moving Average Exponential Smoothing (a) Simple(a) level (b) Weighted(b) trend (c) seasonality

11. Attempts to predict the future based on past data • The philosophy is that • “the factors influencing the past will continue to influence the future”. Forecasting Techniques

12. Trend Random Seasonal Composite Time Series models: Components

13. Trend component Seasonal peaks Product demand over time Demand for product or service Actual demand line Random variation 4th Year 3rd Year 2nd Year 1st Year

14. Naive approach • Demand in nextperiod is the same as demand in the most recentperiod. • Mathematically, • It is too simple and not very accurate Forecasting Techniques

15. Simple Moving Average: • It is considered that an average is a good estimator of future behavior. • While computing the average, the specified numbers of the most recent data are used. Forecasting Techniques Ft+1 = Forecast for the next period, t+1 n = Number of periods to be averaged D t = Actual demand in period t

16. Forecasting Techniques Weighted Moving Average: In this approach, more importance is given to the recent data. Such that

17. Forecasting Techniques • Ft+1 = Forecast value for time t+1 • Dt = Actual value at time t • Ft = Forecast value at time t • = Smoothing constant and varies as 0 < a < 1 and usually is small (around 0.1 to 0.2) for stability of forecasts. • N = No. of period Simple Exponential Smoothing: This is also a weighted moving average that automatically considers exponentially declining weights to the older data.

18. Forecasting Techniques w2 w3 w1

19. Forecasting Techniques

20. Forecasting Techniques Adjusted Exponential Smoothing (Corrections due to Trend) In reality, some form of trend exists that requires due considerations. Adjusted exponential smoothing forecast predicts the next period by adding a trend component to the current period smoothed forecast.

21. Forecasting Techniques Adjusted Exponential Smoothing: Where, where, and are smoothing constants. The trend adjustments utilizes a second co-efficient, .

22. Forecasting Techniques Linear Regression: The simplest type of the relationship is linear regression which is in the form

23. Accuracy of Forecasting The accuracy of forecast is determined by the attributes: Mean Absolute Deviation (MAD): It is the mean absolute deviation of the forecast value and the actual demand. 2. Mean Squared Error (MSE): It is the mean of the squares of the deviations of the forecast value and the actual demand.

24. Accuracy of Forecasting Mean Forecast Error (MFE): It is computed as the mean of the deviations of the forecast value and the actual demand. Mean Absolute Percentage Error (MAPE): It is the mean of the % deviations of the forecast value and the actual demand. 5. Root Mean Squared Error(RMSE): It is the square root of MSE.

25. Thank You