05 BBA 20 23 Sem IV POM Forecasting Methods.pptx

pom forecasting

1. FORECASTING TECHNIQUES Statement of Future Blending of Science & Intuition

2. Forecasting Practices

3. Forecasting Practices : Technology

4. Forecasting Method

5. Qualitative Techniques Delphi Method

6. Qualitative Techniques

7. Demand Behavior : Trend A gradual long-term up or down. Repetitive pattern with occasional random behavior.

8. Demand - Cyclical Up & down movement of demand that repeats over a longer span.

9. Demand - Seasonal Short-term regular variations related to calendar or time of day.

10. Quantitative Techniques

11. Time series method: moving average

12. Time Series Method: Weighted Moving Average

13. Quantitative Technique Time Series Method Moving Average 01. Simple arithmetic average of actual demand over a specified period. 02. Number of period fixed. 03. Each time one old period is dropped to accommodate new one A (t) = (D t + D (t-1) +D (t-2) +……+ D (t-n-1) ) /N N: Total number of periods: decision A: average demand. D: Demand. If N is more, random variation has less impact but agility is less.

14. Example Moving Average Last 5 years Sales of ELGI 56″ TV Year Nos 2017 3.00.000 2018 3,25,000 2019 3,60,000 2020 4,15,000 2021 5,00,000 2022 ???????? Simple Average: =Sum of last five year sales/5 =300000+325000+360000+415000+500000/5 =1900000/5 3,80,000 Year Nos 2017 3.00.000 2018 4,00,000 2019 3,60,000 2020 4,75,000 2021 5,00,000 2022 ???????? Simple Average: =Sum of last five year sales/5 =300000+400000+360000+475000+500000/5 =2035000/5 =4,07,000

15. Quantitative Technique Time Series Method Weighted Moving Average 01. Selecting weight for each data value & then estimating weighted mean of average values. 02. Each historical data is weighted differently. 03. Sum of all weights = 1. Forecast = F t+1 = W 1 D 1 + W 2 D t-1 +…..+ W N D t-N-1 Where N= total Number of Periods for averaging. W T= Weight Applied to demand in period t

16. Year Nos (A) Weightage (B) Nos x Weightage C=( AxB ) Weighted Average =C/5 2015 3.00.000 0.05 15000 2016 3,25,000 0.05 16250 2017 3,60,000 0.10 36000 2018 4,15,000 0.30 124500 2019 5,00,000 0.50 250000 2020 ???????? =441750/5= 88350

17. Quantitative Technique Mostly Software Programs Exponential Smoothing Special case of moving average method. Weight of most recent observation is stronger. Where recent data changes significant. Uses Minimal data: Forecast & actual demand for present period & weighing factor called Smoothing Constant required. Smoothing constant : Weightage of latest data and between 0.0 to 1.0. : α . Uses Hybrid Cloud for data and Big D ata Analytics & Artificial Intelligence for Forecasting Reports

18. Forecasting Ultimately Integrates : 1. Historical Sales Trends & Seasonal Patterns 2. Economic Data: Sales, GDP, International 3. Environmental Conditions 4. Qualitative Judgement Current Technique: Data from Cloud, Analysis Data Analytics and Report from Artificial Intelligence: COMBINE QUANTITATIVE WITH QUALITATIVE

19. Exponential Smoothing

20. Forecasting Packages

21. Key Points

22. THANK YOU