I'm trying to help out a relative of mine who runs a small highly seasonal business in the clothing/textiles industry. 80% of sales are in Q1 of every year, and the purchasing and manufacturing decisions must usually be placed around May-July given lead time.
Their usual process is to assume a 5% yearly demand increase, so they calculate per-SKU inventory on April and ensure they're ordering enough to reach 105% inventory compared to previous year. Given the variability (and lot's of SKUs), it's not optimal, but simple enough and usually works well for them.
Given the high uncertainty of coronavirus, demand is highly uncertain as many of their customers are affected and would cut orders.
I'm trying to dust-off my Operations Research background from engineering undergrad to provide help that is slightly better than a hand-wavy guess. Right now the guess it to roughly cut all order by 20-40%. Which is obviously a very broad range.
This seems like a newsvendor-type problem, with uncertain demand. I do have access to 5yr historical sales & inventory levels, so can run to estimate the demand distribution. But I have no idea on how to go about estimating future demand, and obtaining and a better Q (order quantity). I also have access to the price & cost of SKUs. I assume a simple newsvendor isn't the right path forward, as D (demand) is highly uncertain (mean decreasing and std likely increasing).
Any recommendations of good pointers and/or practical examples to apply? I hope this is't too practical for this stackexchange.