This is the same information for all six questions about this hypothetical situation.

Josh is a supply chain analyst for Wawa corporate headquarters. He is responsible for supporting Wawa$$s coffee selling team, and provide forecasts for the demand for coffee cups, in the different sizes $(12$oz$,16$oz$,24$oz$,$ etc.$).$ Josh realizes that he needs to improve on the forecasting for coffee cup demand. He is thinking to start at the top level first, and develop an overall monthly number of cups, and not yet be concerned with the different sizes, because he can help the Wawa purchasing department negotiate discounts with the cup suppliers based purely on total cup volume.

Josh has data for the past year, by month, of all coffee cup demands across all $1,067$ Wawa stores across the USA. In total, the register transactions for the past year show that Wawa has sold just under $200$ million cups of coffee in these $12$ months. Josh feels like this is a good representative number since post$-$COVID, the number has grown steadily proportionately with the number of new Wawa locations added.

Josh$$s data shows that the number of coffee cup sales for the past $12$ months, by month with oldest month first $($a summertime month$)$ has been $($expressed in millions$)$: $15\mathrm{.}13,15\mathrm{.}92,16\mathrm{.}01,16\mathrm{.}2,17\mathrm{.}4,18\mathrm{.}11,18\mathrm{.}2,17\mathrm{.}82,17\mathrm{.}14,16\mathrm{.}30,15\mathrm{.}24,15\mathrm{.}01.$

Josh will use the forecasting template file $($Forecasting template Wawa coffee$)$ provided in Canvas to answer the following questions.

$($c$)$ Determine what the forecast would be for the next month $($this would show up as month $13$ in your list$)$ if an exponential smoothing method is applied, with an initial estimate of $17\mathrm{.}15$ and a smoothing constant $($alpha$)$ of $0\mathrm{.}6.$ Express in millions.

Multiple Choice

$15\mathrm{.}92$

$17\mathrm{.}15$

$15\mathrm{.}40$

$15\mathrm{.}34$