As short-term consumer demand shifts from wants to needs, and some consumers hoard food and medical supplies, retailers are being faced with massive shifts in demand for their products, with some seeing demand fall to near zero.
The Problem: Dislocation of Inventory
Everything known by the businesses going into this crisis has been called into question. Inventory is the life blood of these businesses. Too much and the business struggles with cash flow and capital allocation. Too little and it faces shortages and customer satisfaction issues.
Short-term retailers who sell products that are not necessities are facing massive inventory backlogs and plummeting sales. Forecasts that existed to keep product in stock are now globally wrong. Businesses who counted on turning inventory over in order to drive revenue and make space for new products have closed locations or seen sales plummet while additional inventory is en route to them through vast supply chains.
This is known as dislocation, and it is sudden and drastic. Many companies have been caught unprepared, as the use of gut instinct and excel planning has exposed them to major losses and inventory overstocks of massive proportions.
As businesses close physical locations, many have begun to look to direct channels as a lifeline. Inventory placement and access becomes key. Can a retailer ship inventory? Is it all trapped in physical stores? Do they have the ability to close stores yet treat them as fulfillment nodes? Can those plans be activated in off-peak times? (A well-prepared business will already have made plans for peak season.)
A detailed analysis of on-hand and on-order inventory will yield an understanding of merchandise that can be sold through direct channels or liquidated upon reopening. How can these businesses understand current depressed demand and demand upon reopening for their inventory? AI and machine learning can play a role. But the retailer needs to be able to “teach” the machines that inventory models have changed; assumptions will need to be made.
What percentage of business will be in stores versus direct? At what prices points? For seasonal merchandise, what is the best exit strategy?
AI and machine learning are predictive tools that take data and trends and determine the right level and mix of product. The key there is data. The current business environment is a major anomaly and no data sets exist to model an inventory strategy.
The best way to understand the future forecast may be to ignore the past entirely or even hurricane or natural disaster recovery models may provide some insight.
However, restarting the business and right-sizing current inventories is only a small piece of the challenge.
As 2020 stabilizes, prepared retailers and CPG companies will bring supply chains and inventory back into balance. The economy may be slow and be difficult to navigate, but demand will eventually stabilize.
What will this new demand look like? It is probable that the environment for business will be one of stark contrast. Clear winners and losers. Those companies that right sized its inventory and reinitialized its forecasts will quickly have the greatest chance of success.
But the story won’t end with inventory; it will also be about forecasting demand in the right channel and understanding the customer environment.
Will it transition to a wants over needs paradigm? Or will business be like 2010 or 1933 — focused solely on needs and punishing to any company that moves slowly or lacks data and courage.
The new retail landscape will need to be more data driven, more dynamic and substantially leaner.
Gene Bornac is senior vice president of retail consulting at enVista.