The team would have started by conducting a thorough inventory analysis to understand the current inventory levels and composition across all categories, product types, and warehouses. They would have pulled inventory data for the past 12-24 months to analyze trends in inventory balances as well as inventory turnover rates. This historical analysis would have provided important context on normal inventory levels needed to support sales as well as identify areas of excess or obsolete inventory that need to be reduced.
With the inventory analysis complete, the next step would be to forecast future sales by category. The team likely pulled historical sales data by month for the previous 2-3 years to analyze trends and seasonality. They may have also obtained the latest sales projections from the sales and marketing teams. Forecasting future demand is critical to determine the optimal inventory levels needed to support sales without excessive overstock.
To develop a financial projection, the team would have estimated the financial impact of reducing inventory levels to the forecasted amounts. They first identified inventory dollar amounts in each category or product that exceeded the forecasted demand levels. Multiplying this excess inventory by the respective purchase costs would give them the total inventory investment tied up in overstock.
The team then projected the timeline to sell-through this excess inventory, taking into account expected monthly sales volumes as well as planned promotions and markdowns. This allowed them to estimate the “carrying costs” of holding onto the excess stock for the projected period until it could be sold. Typical carrying costs included storage and warehousing fees, opportunity costs of capital tied up in inventory, potential obsolescence costs if items don’t sell, etc.
By summing the total overstock inventory levels and estimated carrying costs, the team developed a baseline projection for the total financial costs of maintaining excess inventory levels. They likely also incorporated some contingency amounts since forecasting sales and sell-through timelines carries uncertainty. Some excess inventory may ultimately require deeper price markdowns or be written off/disposed.
To estimate the financial benefits, the team then forecasted the expected proceeds from liquidating the excess inventory through channels like clearance sales, wholesale, auction, etc. They would have analyzed historical sell-through and price realization data for similar past inventory reduction initiatives to determine reasonable recovery rates. Liquidation timelines were also factored in to estimate when the cash proceeds would be realized.
The projected recovery amounts were subtracted from the carrying cost projections to quantify net savings from optimizing inventory to the new, lower levels. These net savings were input into financial models across various future time periods to estimate the positive impact on financial metrics like operating margins, cash flows, returns. Sensitivity analyses using different recovery rate and timing assumptions helped identify a reasonable range for potential benefits.
Of course, reducing inventory also carries costs such as promotional markdowns, liquidation fees, employee hours spent with the initiative, etc. Careful tracking during past reductions helped estimate these liquidation costs. The team ensured their projections accounted for both the positive savings quantified earlier, as well as the actual costs to achieve the targeted inventory reductions.
The financial projections would have been presented to management along with qualitative considerations like reductions in risks from obsolescence or being stuck with excess stock. Alternative scenarios with different liquidation timelines, recovery rates, and excess inventory levels were also modeled to help executives evaluate various options for optimizing inventory investments across the company.
This systematic process involving detailed inventory and sales analyses, financial modeling techniques as well as incorporating learnings from previous experience would have enabled the team to develop a robust, data-backed set of projections quantifying the potential benefits of reducing inventory levels to better match forecasted demand levels. Regular monitoring and reporting against projections during execution would then help ensure results met or exceeded expectations.