Freeing 9.7m cash flow from slow moving inventory in 12 months – Actionable Insights Impact Case Study

Engagement Situation:

The client was a venture capital firm, which had acquired an automotive spare part distributor two years prior to the engagement.

The reason for the engagement was that the investor got concerned with the steady inventory build-up, coinciding with the sales growth but exceeding sales growth by 1.5 times. At the time of the engagement annual revenue was at 203m EUR, while inventory stood at 44m or roughly 21.5 % of revenue.

Management kept assuring the investor that the inventory levels were industry standard due to the availability requirements of the business as well as order lead times. Purchasing was conducted based on an Excel based “buyer tool” using inventory classification based on their expected availability and a forecast based on historical demand.

At the same time management claimed to frequently lose sales due to stock outs.

Actionable Insights:

The data analysis showed that almost 11.5m or 26.5% of inventory where slowing moving, meaning inventory on hand was covering > 1 year of forecasted sales.

The data further showed that 56% of the slow moving inventory had been bought in the last 6 months, 80.5% in the last 12 months. This proved that the core of slow moving inventory did not result from older parts the company needed to keep at hand to provide spare parts for older models as claimed by management.

The slow moving inventory proved to be especially high for “A” classified parts – parts with a supposedly high demand and fast turnover.

Instead the data analysis showed the following issues:

a) Supplier lead times were significantly exaggerated in the system – on average by 4.3 months. The lead times had been changed during the massive supply chain disruptions caused by COVID19 but not been monitored and adjusted since.

b) The rolling demand forecast, while accurate on an aggregated level and sufficient for sales projections, had not been conducted on an item level. Demand based item classification had not been updated in the last two to three years and diverged significantly from the current and last year demand.

c) In order to save on shipping costs per part, the buyers had filled up container on top of the wrongly calculated demand with additional “A” classified parts.

d) A detailed review of order cancellations and credit notes showed 4.4m in lost revenue due to stock outs:

• 2.9m in orders cancelled due to late arrival of parts and

• 2m in credit notes due to wrong parts delivered, of which 75% (or 1.5m) were due to the company trying to send alternative parts for stock out parts according to interviews conducted.

Modeling against a historic consumption based forecast with adjusted reorder points would have prevented 54% of the 4.4m in lost revenue.

Actionable Insights Impact:

With the detailed insights discovered during the analysis, the purchasing team set about implementing a rolling demand forecast on item level, adjusting supplier lead times and item classification in their buyer tool.

A comparison of projected purchases based on the revenue forecast for the next 12 months between using the pre-actionable insights purchase parameter and the adjust parameter predicted:

1) 9.7m in reduced inventory as excessive inventory was consumed and not repurchased

2) 2.3m to in prevented inventory build-up

3) 2.8m in prevented lost sales.

Overall the company is expected to improve their cash-flow in the next 12 month by 14.8m or roughly 6% of revenue. In addition the company is forecasting to increase profitability by 1.5m from reduction of sales lost and reduced interest payments.