Blayer Pharm sells two types of blood pressure cuffs at more than 50 locations i

Question

Blayer Pharm sells two types of blood pressure cuffs at more than 50 locations in the Midwest. The first style is a relatively expensive model, whereas the second is a standard, less expensive model. Although weekly demand for these two products is fairly stable from week to week, there is enough variation to concern management. There have been relatively unsophisticated attempts to forecast weekly demand but they haven't been very successful. Sometimes demand (and the corresponding sales) is lower than forecasts, so inventory costs are high. Other times, the forecasts are too low. When this happens and on-hand inventory is not sufficient to meet customer demand, Blayer requires expedited shipments to keep customers happy—and this nearly wipes out Blayer’s profit margin on the expedited units. Profits would almost certainly increase if demand could be forecast more accurately. Data on weekly sales of both products appear in the file for this week. A time series chart of the two sales variables indicates what Blayer management expected—namely, there is no evidence of any upward or downward trends or of any seasonality. In fact, it might appear that each series is an unpredictable sequence of random ups and downs

se the dataset to answer the following questions. Provide complete analysis and graphs, as appropriate.

Is it possible to forecast either series with some degree of accuracy or an extrapolation method (where only past values of that series are used to forecast current and future values)? Which method appears to be best? How accurate is it?

Is it possible, when trying to forecast sales of one product, to somehow incorporate current or past sales of the other product in the forecast model?

Are these products "substitute" products or are they "complementary" products? Conduct appropriate analyses to support your argument.

Excel table

Week BP Cuff Type 1 BP Cuff Type 2 1 455 832 2 490 798 3 425 890 4 466 855 5 456 848 6 454 871 7 476 838 8 465 826 9 481 853 10 463 870 11 483 849 12 440 888 13 445 859 14 452 894 15 447 873 16 440 888 17 459 863 18 460 885 19 424 912 20 447 913 21 397 878 22 386 916 23 366 888 24 357 931 25 367 888 26 372 883 27 336 871 28 327 873 29 328 891 30 354 826 31 352 830 32 394 822 33 378 820 34 410 850 35 441 854 36 422 835 37 436 835 38 424 883 39 461 826 40 474 842 41 484 856 42 498 842 43 526 808 44 517 875 45 504 901 46 503 915 47 483 858 48 497 899 49 475 921 50 493 849 51 506 869 52 513 902 53 506 908 54 486 872 55 498 930 56 487 844 57 453 899 58 503 880 59 491 908 60 455 898 61 501 893 62 501 863 63 450 907 64 455 877 65 495 847 66 452 901 67 493 858 68 494 860 69 477 823 70 510 795

Explanation / Answer

Time Series models could be used to use past data in order to forecast future trends. Exponential smoothing method would suit best here. It is relatively simple compared to other quantitative forecasting methods, and requires only a limited amount of data.

The forecasting formula is the basic equation

St+1=yt+(1)St, 0<1,t>0

or St+1=St+t, (New forecast is previous forecast plus an error adjustment)

where t is the forecast error (actual - forecast) for period t.

In other words, the new forecast is the old one plus an adjustment for the error that occurred in the last forecast.

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