$3.$ Consider a peak harvest day having $18,000$ barrels of berries unloaded, with $70\%$ being wet harvested. The $18,000$ bbls$.$ is based on the Sep. $23$ data in case Exhibit $1.$ The $70\%$ wet harvested relates to anticipated trends in wet vs dry harvesting, with wet harvesting becoming increasingly popular. For calculations, assume that trucks arrive uniformly over a period of $12$ hours, that is$,$ assume that the berry inflow process is continuous at a fixed rate $($not in truckload batches$)$; this uniform continuous arrival process is a best$-$case $/$ average scenario, intended to emphasize that NCC$$s key problem is not in the way berries arrive because the problems persist even with a smooth arrival pattern. You may want to see the Seasonal Inventory Beets Example under Process View $$ Inventory Classification video materials. Answering the International Cranberry Uncooperative practice problem in Module $2$ Process Capacity can help you understand the simplified continuous$-$flow$-$based analysis. FYI, this case does not focus on variability or use any material from the Module $3$ Waiting Lines submodule; yet you could consider the qualitative impact of variability.

i$.(*15$ points$)$ Wet$-$berry inventory: Draw $($or at least conceive $/$ describe$)$ a plot or provide a data table of total wet berry inventory over time in the system for a peak day. Briefly explain how this plot $($or table$)$ tells you the maximum amount of wet berries in trucks waiting to unload? Note: It suffices to use a time unit of an hour as all segments of the graphs are linear due to our uniform arrivals and continuous flow assumption. Notes: Before you do this plot, think about when you should start processing berries on a peak day and why. A legible scan of a quick hand$-$drawn graph is acceptable if it indicates slope and key inventory values. Excel is one way to draw this plot if you have tabulated data.