A commercial bakery has recorded sales (in dozens) for three products, as shown below:        Day   Blueberry Muffins   Cinnamon Buns   Cupcakes 1 30 18 45 2 34 17 26 3 32 19 27 4 34 19 23 5 35 22 22 6 30 23 48 7 34 23 29 8 36 25 20 9 29 24 14 10 31 26 18 11 35 27 47 12 31 28 26 13 37 29 27 14 34 31 24 15 33 33 22       a

Plot of the Sales data:

Blueberry muffins sales data shows that the sale of muffins is stable, use simple naïve forecast method.

Forecast units of Blueberry muffins on 16^th day = units sold on 15^th day

Forecast units of Blueberry muffins on 16^th day = 33 dozens

Cinnamon Buns sales data shows that increasing trend in sales. Apply trend-adjusted naïve method. The difference of the last two daily sales data is obtained and then difference is added to previous sales data to forecast the next period demand.

Forecast units of Cinnamon Buns on 16^th day = Sales on 15^th day (sales on 15^th – sales on 14^th)

Forecast units of Cinnamon Buns on 16^th day = 33 + (33 – 31) = 35 dozens

Cupcakes shows seasonal pattern in sales data, the cycle repeats after five days, as shown below:

Apply seasonal-adjusted naïve forecasting method. The forecast value for period in next seasonal cycle is equal to the actual sales of corresponding period in recent seasonal cycle. Corresponding day 16 in cycle 4 is Day 11.

Forecast units of Cupcakes on 16^th day = sales in Day 11

Forecast units of Cupcakes on 16^th day = 47 dozens

b.

Option is "Sales adequetly reflects demand"

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