Question $2$

Demand can never be static and easily predicted. At times, demand can be cyclical or seasonal. It's important for operations managers to at least understand the seasonal pattern and plan their supply and demand fulfillment strategy better. Seasonal adjustment plays a crucial role in official statistics, allowing for timely interpretation of time series data. Its primary purpose is to eliminate systematic calendar$-$related fluctuations linked to specific times of the year$-$what we commonly refer to as seasonal effects.

A fashion retailer has noticed there is a seasonal pattern in the fashion and implications to their sales and operations. The marketing department has collected monthly sales data over $5$ years as shown in table $1$ below. The marketing department manager wanted to understand the seasonal index and to predict the average monthly sales for $2024.$

$\backslash$table$[[,$Actual Demand$],[,2019,2020,2021,2022,2023],[$Jan$,23,26,32,38,45],[$Feb$,43,46,52,55,57],[$Mar$,19,23,28,30,33],[$Apr$,18,19,20,24,26],[$May$,23,27,29,29,35],[$Jun$,30,32,33,36,38],[$Jul$,20,20,23,24,25],[$Aug$,14,16,19,21,23],[$Sep$,18,19,19,23,28],[$Oct$,20,23,25,28,32],[$Nov$,27,29,32,35,42],[$Dec$,46,48,52,63,78]]$

a$)$ Construct a table that shows the total yearly demand over $5$ years.

$\backslash$table$[[$Year$,1,2,3,4,5$