Various qualitative and quntitative Demand Forecasting Models

Forecasting is a technique that uses historical data to make informed decisions about future events or conditions. It isn't simply guessing. A tool for businesses and investors alike, forecasting takes expert analysis and applies complex models to allocate portfolios and budgets. But just how reliable are these crystal ball-like predictions? After all, economists, investors, and financial planners frequently display a striking talent for mordant humor about the art of economic prediction. "The only function of economic forecasting," the famed 20th-century economist John Kenneth Galbraith said, "is to make astrology look respectable. Or, as an equally influential economist, Paul A. Samuelson, put it, Wall Street models "predicted nine of the last five recessions."
Key Takeaways
Forecasting involves making predictions.
In finance, companies use forecasting to estimate earnings or other data for later periods.
Traders and analysts use forecasts in valuation models, to time trades, and to identify trends.
Standard forecasting techniques include qualitative methods like expert opinion and quantitative methods like statistical models and trend analysis.
The limits of forecasting include the uncertainty of future events and the potential for errors in data or assumptions used in the forecasting process.
Yet, forecasting is central to modern investing and business practices. Businesses hire and expand based on predicting sales figures, market demand, or economic indicators. Investors trade stocks, invest in funds, or rashly exit the market based on predictions about stock prices, interest rates, or broader market moves. However, the work of forecasting applies far beyond boardrooms and trading floors. Consumer spending patterns, job market trends, and even geopolitical events all fall under the purview of forecasters.

1. By Dr. Peeyush Vats Demand Forecasting Models

2. Supply chain demand planners often ask themselves: What is the best model to forecast my demand? W hat are the best practices I should follow to improve my forecast? It is impossible to give a definitive, absolute answer to those questions. There no silver bullet model that would be the best for every single company for every single product.

3. Forecasting Models Qualitative Models Quantitative Models (Statistical Models)

4. Qualitative Models Delphi Technique Sales Force Opinion Market Research

5. The Delphi Technique The Delphi method is a process used to arrive at a group opinion or decision by surveying a panel of experts. Experts respond to several rounds of questionnaires, and the responses are aggregated and shared with the group after each round. The experts can adjust their answers each round, based on how they interpret the "group response" provided to them. The ultimate result is meant to be a true consensus of what the group thinks.

6. Sales Force Opinion The Sales Manager asks for inputs of predicted demand from every Salesperson in their team. Each Salesperson assesses their respective region and product categories and serves their individual customer demand. Eventually, the Sales Manager aggregates all the demands and sets up the final version of the Demand Forecast after management’s opinion.

7. Market Research This type of technique could be useful for products that have little to no demand history. In the market research method, customer-specific surveys are used to generate possible demand. Such surveys are generally question-based that directly seek personal, analytical, preference, and economic information from end customers. Since this type of research is on a random pattern basis, care needs to be involved in terms of the survey regions, locations, and polls of the end customer.

8. Quantitative (Statistical Models) Moving Average Weighted Moving Average Exponential Smoothing

9. Moving Average A n-month moving average is the sum of the observed values during the past n months divided by n.

10. Moving Average Method n =3 Month Sales Formula Forecast 1 100             2 80     3 90     4 110 =(100+80+90)/3 90.00 5 100 =(80+90+110)/3 93.33 6 110 =(90+110+100)/3 100.00 7 95 =(110+100+110)/3 106.67 8 115 =(100+110+95)/3 101.67 9 120 =(110+95+115)/3 106.67 10 90 =(95+115+120)/3 110.00 11 105 =(115+120+90)/3 108.33 12 110 =(120+90+105)/3 105.00

11. Weighted Moving Average The weighted moving average (WMA) makes forecasts more responsive to the most recent actual occurrences (e.g., demand). The most recent n periods are used in forecasting. Each period is assigned a weight between 0 and 1. The total of all weights adds up to one (1).

12. Weighted Moving Average (using monthly demands) Example: Forecast (4) = 0.2*(Demand 1) + 0.3*(Demand 2) + 0.5*(Demand 3)

13. Weighted Moving Average Method n=3 Weights ( wi ) Month Sales Calculation Forecast 1 100 = 0.2*100 + 0.3*80 + 0.5*90   0.2 2 80 = 0.2*80 + 0.3*90 + 0.5*110   0.3 3 90 = 0.2*90 + 0.3*110 + 0.5*100   0.5 4 110 = 0.2*110 + 0.3*100 + 0.5*110 89.00 5 100 = 0.2*100 + 0.3*110 + 0.5*95 98.00 6 110 = 0.2*110 + 0.3*95 + 0.5*115 101.00 7 95 = 0.2*95 + 0.3*115 + 0.5*120 107.00 8 115 = 0.2*115 + 0.3*120 + 0.5*90 100.50 9 120 = 0.2*120 + 0.3*90 + 0.5*105 108.00 10 90 = 0.2*100 + 0.3*80 + 0.5*90 113.50 11 105 = 0.2*80 + 0.3*90 + 0.5*110 104.00 12 110 = 0.2*90 + 0.3*110 + 0.5*100 103.50

14. Exponential smoothing The Exponential Smoothing (ES) method forecasts the demand for a given period t by combining the forecast of the previous period ( t-1 ) and the actual demand of the previous period ( t-1 ). The actual demand for the previous period is given a weight of α and the forecast of the prior period is given a weight of (1 - α ). α is a smoothing constant whose value lies between 0 and 1 (0 ≤ α ≤ 1). Those models forecast demand components (level, trend, and seasonality) by updating them slightly after each demand observation. Pro: easy to understand, implement, and interpret. Flexibility with additive and multiplicative seasonalities. Con: Difficult to add external features. Not able to forecast new products.

15. Exponential smoothing ( Contd ….) The equation for the forecast for period t is: F ( t ) = α *D ( t -1) + (1- α )*F ( t -1) The equation can also be written as: F ( t ) = F ( t -1) + α *{D ( t -1) –F ( t -1) } Where, F ( t ) = Forecast for current period F ( t-1 ) = Forecast for previous period D ( t-1 ) = Demand for previous period

16. Exponential Smoothing Method (sales are actual sales indicated by A in equation) α = 0.2 Month Sales Forecast Comment and Calculation 1 100 100 Forecast for period 1 should be available before starting the calculations. If it is not given then set it equal to the sales of period 1. 2 80 100.00 =(100 + 0.2(100 -100)) = 100 3 90 96.00 =(100 + 0.2(80 -100)) = 96 4 110 94.80 =(96 + 0.2(90 -96)) = 94.8 5 100 97.84 =(94.8 + 0.2(110 -94.8)) = 97.84 6 110 98.27 =(97.84 + 0.2(100 -97.84)) = 98.27 7 95 100.62 =(98.27 + 0.2(110 -98.27)) = 100.62 8 115 99.50 =(100.62 + 0.2(95 -100.62)) = 99.50 9 120 102.60 =(99.5 + 0.2(115 -99.5)) = 102.60 10 90 106.08 =(102.6 + 0.2(120 -102.6)) = 106.08 11 105 102.86 =(106.08 + 0.2(90 -106.08)) = 102.86 12 110 103.29 =(102.86 + 0.2(105 -102.86)) = 103.29

17. Seasonal Forecast Step 1 Quarterly demand for last four years is given in the table below. We use a 5-step process to forecast. Step 1: Find average quarterly demand for each quarter. 18 DEMAND Quarter Year 1 Year 2 Year 3 Year 4 Fall 2350 2690 2790 2860 Winter 2300 2420 2410 2600 Spring 1900 2000 2105 2175 Summer 1510 1775 1875 1945 Formula =(1510+1900+2300+2350)/4 =(1775+2000+2420+2690) =(1875+2105+2410+2790) =(1945+2175+2600+2860) Average Demand =2060 =2221 =2295 =2395

18. Seasonal Forecast Step 2 19 Step 2: Compute Seasonal Index (SI) for each quarter for each year. Step 2: Compute Seasonal Index (SI) for each quarter for each year Find Seasonal Index (SI) for each quarter Quarter Formula for SI Year 1 Year 1 Formula for SI Year 2 Year 2 Formula for SI Year 3 Year 3 Formula for SI Year 4 Year 3 Fall =2350/2060 1.228 =2690/2221 1.221 =2790/2295 1.216 =2850/2395 1.194 Winter =2300/2060 1.117 =2420/2221 1.090 =2410/2295 1.050 =2600/2395 1.086 Spring =1900/2060 0.992 =2000/2221 0.900 =2105/2295 0.917 =2175/2395 0.908 Summer =1510/2060 0.733 =1775/2221 0.799 =1775/2295 0.817 =1945/2293 0.812

19. Seasonal Forecast Step 3 Step 3: Calculate the average SI for each quarter. 20 Step 3: Calculate the average SI for each quarter Find average seasonal index for each quarter of each year Quarter Formula Average SI Fall =(1.228+1.221+1.216+1.194)/4 1.212 Winter =(1.117+1.09+1.05+1.086)/4 1.086 Spring =(0.922+0.9+0.917+0.908)/4 0.912 Summer =(0.733+0.799+0.817+0.812)/4 0.790

20. Seasonal Forecast Step 4 Step 4: Calculate the average quarterly demand for next year. First, the yearly demand has to be estimated or calculated for next year using one of the forecasting techniques. Suppose the estimated demand is 2,800. Therefore, the average quarterly demand = 2,800/4 = 700. The calculations are shown below. 21 Step 4: Enter Demand for next year and calculate Total Demand for next year 2800 Average Demand per quarter 700

21. Seasonal Forecast Step 5 Step 5: Forecast demand for the four quarters of next year.   Multiply the average demand by the SI for each quarter. For example, forecast for Spring quarter = 638 = 700*0.912. 22 Step 5: Forecast demand for quarters of next year by multiplying the average demand by SI for each quarter Forecast for next year Quarter Formula Year 5 Fall =(700*1.212) 848 Winter =(700*1.086) 760 Spring =(700*0.912) 638 Summer =(700*0.79) 533