Using data to improve WCR - iQo

Data has a major role to play in managing Working Capital Requirements (WCR). In a general context of tightening monetary policy, controlling working capital requirements is proving decisive for financial management. Companies must in particular manage their inventory levels – a crucial indicator not only of their operational performance but also of their financial health.

Managing cash flow in an uncertain environment

Access to inexpensive liquid assets and State-Guaranteed Loans (SGLs) enabled the vast majority of companies to survive the Covid-19 pandemic.

However, the tightening of monetary policy, rising interest rates, SGL repayment schedules and, more generally, the macroeconomic context have successively forced companies to improve their cash flow management.
Alarm bells are now ringing, as economist Marc Touati points out, noting that the number of business failures is at its highest level in France since 2009 (excluding micro businesses).

And growing businesses are not spared either: the sudden bankruptcy in November 2023 of Probikeshop, the former French leader in online bicycle sales, is a prime example. Buoyed by growth rates approaching 30% during the health crisis, the company had stepped up production to meet demand. However, the end of the pandemic and market saturation quickly led to a significant drop in sales, leaving the company with a massive inventory and a major cash shortfall, which hastened its demise.

Against this backdrop, controlling working capital requirements is a decisive factor in financial management. Companies need to specifically focus on managing their inventory levels – a crucial indicator not only of their operational performance, but also of their financial health.

Using data to optimize inventory levels

In our consulting activities, we frequently observe that sales forecasting and the resulting inventory management – however critical they may be – can suffer from an overly empirical approach. Another observation corroborated by our lessons learned is that the insights provided by data can deliver tangible added value across the entire supply chain.

Sales forecasts and replenishment decisions are often based on dated models, on the extrapolation of historical data, or on expert forecasts.

Other factors such as seasonality, changing consumer behavior, or the level of interaction between Sales, Marketing, and the Supply Chain can make it even harder to establish reliable sales forecasts and to make sound inventory management decisions.

A case in point: the fashion industry

To illustrate how data can contribute to the search for optimal inventory levels, let’s take the example of the fashion industry.

This sector has its own specific characteristics, notably linked to its seasonal collection approach, which leads to sales peaks, risks of shortages (missed sales) or, conversely, overstocks (which must then be liquidated at reduced margins through private sales, general sales, etc.) to avoid unsold inventory.

To find the optimal inventory level, brands need to offer the “right” number of product references, and to launch production batches in line with forecasts – while taking into account material lead times and production constraints (batch sizes, etc.).

Step 1 - Optimize product line depth

The first step is to identify the optimal number of product references required to satisfy customer demand. To achieve this, market saturation analysis is used to identify whether the depth of the proposed product line is too narrow or, on the contrary, too broad with respect to consumption profiles. Depending on the desired positioning, the brand can then enrich, maintain, or lighten its product lines, with full knowledge of the facts. Other parameters can also be taken into account to refine these analyses, particularly for products from new collections or for products with dedicated advertising campaigns.

Step 2 - Forecast production volumes

Once the product line depths have been established, the aim is to identify the optimal production volume per product, in order to minimize inventories without losing sales. This analysis can help companies to quantify the optimal inventory levels (and therefore WCR), on the one hand, and sales levels (and therefore margins), on the other. But remember that the balance sheet and income statement take different, and sometimes contradictory, approaches! This constrained optimization needs to be fed with data and Machine Learning techniques. Their use can improve Forecast Accuracy by around 20 to 30%, taking into account temporality (collection launch, mid-season, general sales, private sales, etc.) and restocking lead times. These forecasts can then be integrated by the Supply Chain teams to facilitate decision-making and to fine-tune the planning model.

Conclusion - using data to manage WCR: an accessible approach

The data-driven approach to inventory optimization described above has several advantages:

Firstly, no specific tooling is required (the necessary algorithms are accessible via cloud platforms), meaning that the approach can be implemented rapidly, possibly as a pilot on a limited panel of products;
In addition, it enables end-to-end supply chain transformation initiatives, promoting cooperation between teams around a common base of objective, cross-sectional data, with positive effects on logistics costs, service rates, delivery times, etc.

Would you like to discuss these topics with our teams?