Stage Detection Of A Product In Its Life

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In my presentation I have worked on how to detect the stage of a product in its life cycle. The ppt provides a brief description of the methodologies I have used and the importance of knowing the stage of a product in its life cycle. Any person who finds my analysis interesting and has any suggestions, please feel free to mail me.

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Stage Detection Of A Product In Its Life

  1. 1. UDAY THARAR   i2 Technologies, Bangalore [email_address] Stage Detection of a Product in its Life Cycle
  2. 2. Objectives <ul><li>The goal of this paper is to predict the stage a product is in, in its life cycle. </li></ul><ul><li>Analysis of time series data of the sales of a product over a period of time using Economic and Statistical tools has been done. Two tools used are-- </li></ul><ul><li>Method of Moving Averages </li></ul><ul><li>Price Elasticity of Demand </li></ul><ul><li>These tools have been applied to sample data on sales figures of different products and these products have a life cycle of less than a year. </li></ul>
  3. 3. Introduction <ul><li>The Product Life Cycle (PLC) is based upon the biological life cycle. For example- </li></ul><ul><li>a seed is planted (introduction); </li></ul><ul><li>it begins to sprout (growth); </li></ul><ul><li>it shoots out leaves and puts down roots as it becomes an adult (maturity); </li></ul><ul><li>after a long period as an adult the plant begins to shrink and die out (decline). </li></ul>
  4. 4. Introduction <ul><li>In theory it's the same for a product. </li></ul><ul><li>After a period of development it is introduced or launched into the market. </li></ul><ul><li>it gains more and more customers as it grows. </li></ul><ul><li>eventually the market stabilizes and the product becomes mature. </li></ul><ul><li>then after a period of time the product is overtaken by development and the introduction of superior competitors, it goes into decline and is eventually withdrawn. </li></ul>
  5. 5. Introduction
  6. 6. Method of Moving Averages <ul><li>Here the method similar to that of Moving Average Convergence/Divergence (MACD) developed by Gerald Appel is used with suitable adjustments. MACD is mainly used in stock markets to make forecasts about stock prices. </li></ul><ul><li>Here again two approaches have been used— </li></ul><ul><li>Graphical Analysis </li></ul><ul><li>Numerical Analysis </li></ul>
  7. 7. <ul><li>When the shorter moving average is rising and cuts the longer moving average from below and then diverges away then one can say that there is expected growth in sales in the near future. </li></ul><ul><li>On the contrary when the shorter moving average curve is declining and cuts the longer moving average from above and then diverges away then one can say that there is expected decline in sales in the near future. </li></ul>Graphical Analysis
  8. 8. Example-1
  9. 9. Example-2
  10. 10. Numerical Analysis
  11. 11. Numerical Analysis <ul><li>Initially till the point the difference figure is fluctuating around zero but neither increasing nor decreasing, the product is in the introduction phase. </li></ul><ul><li>When positive and increasing then growth phase starts and continues till the difference is positive. </li></ul><ul><li>Again when fluctuating around zero but neither increasing nor decreasing, the product is in the maturity phase. </li></ul><ul><li>When negative and decreasing then the product enters the decline phase. </li></ul>
  12. 12. Numerical Analysis Phase Range (%) Introduction -10 to 10 Growth above 10 Maturity -10 to 10 Decline below -10
  13. 13. Example-3 Time Sales 4 week Moving Average 12 week Moving Average Difference b/w 4week & 12week Moving Average Difference as % of 12 week 4/1/2006 696 4/8/2006 251 4/15/2006 562 4/22/2006 612 530.25 4/29/2006 685 527.5 5/6/2006 1072 732.75 5/13/2006 1131 875 5/20/2006 1102 997.5 5/27/2006 1174 1119.75 6/3/2006 1410 1204.25 6/10/2006 1010 1174 6/17/2006 1117 1177.75 901.8333333 275.9166667 30.59508409 6/24/2006 840 1094.25 913.8333333 180.4166667 19.74284151 7/1/2006 753 930 955.6666667 -25.66666667 -2.685734217 7/8/2006 1007 929.25 992.75 -63.5 -6.396373709 7/15/2006 620 805 993.4166667 -188.4166667 -18.96652965 7/22/2006 859 809.75 1007.916667 -198.1666667 -19.66101695 7/29/2006 778 816 983.4166667 -167.4166667 -17.02398102 8/5/2006 739 749 950.75 -201.75 -21.2200894 8/12/2006 762 784.5 922.4166667 -137.9166667 -14.95166682 8/19/2006 809 772 892 -120 -13.4529148 8/26/2006 1241 887.75 877.9166667 9.833333333 1.120075937 9/2/2006 1661 1118.25 932.1666667 186.0833333 19.96245307 9/9/2006 1612 1330.75 973.4166667 357.3333333 36.70918586 9/16/2006 1773 1571.75 1051.166667 520.5833333 49.52433804 9/23/2006 1249 1573.75 1092.5 481.25 44.05034325 9/30/2006 1189 1455.75 1107.666667 348.0833333 31.42491724 10/7/2006 1170 1345.25 1153.5 191.75 16.62332033 10/14/2006 1302 1227.5 1190.416667 37.08333333 3.115155758 10/21/2006 1018 1169.75 1210.416667 -40.66666667 -3.359724613 10/28/2006 1282 1193 1255.666667 -62.66666667 -4.990708787 11/4/2006 1146 1187 1287.666667 -100.6666667 -7.817758219 11/11/2006 1936 1345.5 1381.583333 -36.08333333 -2.611737741
  14. 14. Example-3 11/18/2006 2152 1629 1457.5 171.5 11.76672384 11/25/2006 2171 1851.25 1500 351.25 23.41666667 12/2/2006 1793 2013 1515.083333 497.9166667 32.86397888 12/9/2006 1458 1893.5 1488.833333 404.6666667 27.18011866 12/16/2006 1298 1680 1492.916667 187.0833333 12.53139827 12/23/2006 2441 1747.5 1597.25 150.25 9.406792925 12/30/2006 1665 1715.5 1638.5 77 4.699420201 1/6/2007 1929 1833.25 1690.75 142.5 8.428212332 1/13/2007 1451 1871.5 1726.833333 144.6666667 8.377569733 1/20/2007 1795 1710 1769.583333 -59.58333333 -3.367082647 1/27/2007 2403 1894.5 1874.333333 20.16666667 1.075938111 2/3/2007 1392 1760.25 1829 -68.75 -3.758884636 2/10/2007 935 1631.25 1727.583333 -96.33333333 -5.576190246 2/17/2007 1057 1446.75 1634.75 -188 -11.50022939 2/24/2007 979 1090.75 1566.916667 -476.1666667 -30.38876775 3/3/2007 1001 993 1528.833333 -535.8333333 -35.04851194 3/10/2007 789 956.5 1486.416667 -529.9166667 -35.65061389 3/17/2007 1528 1074.25 1410.333333 -336.0833333 -23.83006381 3/24/2007 884 1050.5 1345.25 -294.75 -21.91042557 3/31/2007 404 901.25 1218.166667 -316.9166667 -26.01587084
  15. 15. The difference of the two moving averages is expressed as a percentage of the 12 week moving average. The average of this percentage for each phase is calculated. The following table is obtained— Numerical Analysis Stage Average Introduction -6.402038097 Growth 17.43608426 Maturity 5.878892704 Decline -21.5199509
  16. 16. <ul><li>It can be seen from the table that average of the different stages lies within the range mentioned earlier. </li></ul><ul><li>In the example we also see a region shaded in orange. This false alarm is a decline in sales which occurs in the growth phase for a very short period but then sales again pick up. </li></ul>Numerical Analysis
  17. 17. Application of the method <ul><li>At a given point of time a company has the data of its past sales figures. These figures can be used to make the calculations mentioned and analyzed as shown to detect which stage the product is in. </li></ul><ul><li>It helps a company to find out which stage its product is going through at a particular point of time and then make adjustments accordingly. </li></ul><ul><li>This methodology can be extremely useful to companies in deciding their marketing and pricing policies for the future. </li></ul><ul><li>Using the numerical analysis one can find out about the past stages of the product by comparing the suitable values with the range mentioned and hence predict the current stage of the product. </li></ul>
  18. 18. Price Elasticity of Demand <ul><li>Law of Demand says that price and demand of a good are inversely related. That is when price rises (falls), quantity demanded falls (rises). </li></ul><ul><li>The Price Elasticity of Demand (commonly known as just price elasticity) measures the rate of response of quantity demanded due to a price change. </li></ul><ul><li>It is calculated by dividing the proportionate change in quantity demanded by the proportionate change in price. The formula for calculating price elasticity of demand is given by— </li></ul>
  19. 19. Methodology <ul><li>When a product is launched, firms keep the price of the product fixed at its highest levels & so price elasticity cannot be calculated for the introduction period. </li></ul><ul><li>With time when the product is known to the people and prices are generally reduced slightly by firms to capture the market and significant demand increase take place. The product is in the growth phase and high negative price elasticity is noticed. </li></ul><ul><li>In the maturity phase the difference is that here firms also increase prices and demand may fall. But demand falls are not of large magnitude (as compared with demand rise when price falls). It means that absolute value of price elasticity is larger when price falls as compared to a price rise. </li></ul>
  20. 20. Methodology <ul><li>Next it can be seen that price elasticity of the product is of very small magnitude (close to zero) or even positive at times. This means that demand of the product becomes less responsive to price changes. This happens because as the product becomes old, people do not demand it to the extent when it was new. So even when firms reduce prices, sales do not rise (also falls at times) .This happens in the stage when the product is in the declining phase. </li></ul>
  21. 21. Example-4
  22. 22. Conclusion <ul><li>This paper has mainly focused on the demand side of a product. </li></ul><ul><li>The first approach is only based on sales figures and their suitable averages, while the second takes into account the price of the product as well. The moving averages approach is easy to understand and follow. </li></ul><ul><li>The price elasticity approach on the other hand is a little more complicated and not easy to implement. It can only be used when price changes take place. </li></ul><ul><li>Both the approaches have been used to detect the stage of a product in its life cycle and the behavior of the product in the near future at any given point of time based on the appropriate calculated values from past sales figures. This way the firms for maximizing profits adjust their strategies accordingly and plan their future supplies and prices. </li></ul>
  23. 23. References <ul><li>J.Johnston and D.Dinardo: Econometric Methods. </li></ul><ul><li>T.C.Mills: Time Series and Forecasting. </li></ul><ul><li>Hal.R.Varian: Intermediate Economics </li></ul>
  24. 24. Acknowledgement <ul><li>I would sincerely thank Mr. Vinod Mathur, Solution Strategist in i2 Technologies, who has guided me throughout this project. </li></ul>
  25. 25. <ul><li>THANK </li></ul><ul><li>YOU </li></ul>

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