Data has a major role to play in managing working capital requirements (WCR). Against a backdrop of tightening monetary policy, controlling working capital requirements is a key factor in financial management. In particular, companies need to manage their inventory levels - a crucial indicator not only of their operational performance but also of their financial health.

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Managing cash flow in an uncertain environment
Access to low-cost liquidity, as well as State Guaranteed Loans (SGL) enabled the vast majority of companies to survive the Covid-19 pandemic.
However, the tightening of monetary policy, rising interest rates, EMP repayment schedules and, more generally, the macroeconomic context have successively presented companies with the challenge of better cash management.
The alarm bells are now ringing, as economist Marc Touati points out, noting that the number of business failures has returned to its highest level in France since 2009 (excluding micro-businesses).
Even growing companies are not spared
The sudden bankruptcy of Probikeshop in November 2023, 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 the saturation of the market quickly led to a significant drop in sales, leaving the company with a massive stockpile and a heavy cash-flow deficit, which precipitated its downfall.
In this context, control of working capital requirements is a key factor in financial management. In particular, companies need to manage their inventory levels - a crucial indicator not only of their operational performance, but also of their financial health.
Using data to optimize inventories
In our consulting activities, we frequently observe that sales forecasting and the resulting inventory management - critical though they are - often suffer from an empirical approach. Another observation corroborated by our feedback is that the contribution of insights provided by "data" makes it possible to achieve tangible added value across the entire sales process. supply chain.
Sales forecasts and replenishment decisions are based on models that are sometimes dated, on the extrapolation of historical data, or on declarative (expert) projections.
Other factors such as seasonality, changing consumer behavior, or the level of interaction between Sales, Marketing and Supply Chain can add to the difficulty of making reliable sales forecasts and stock management decisions.
A case in point: the fashion industry
To illustrate the contribution of data in the search for optimum stock levels, let's take the example of the fashion industry.
This sector has its own specific characteristics, notably linked to the logic of seasonal collections, leading to sales peaks, risks of shortages (missed sales) or, conversely, overstocks (which then have to be liquidated at reduced margins as part of private sales, sales...) to avoid unsold stock.
To find the optimum stock level, brands need to offer the "right" number of product references, and launch production batches in line with forecasts - while taking into account material lead times and production constraints (batch sizes, etc.).
Step 1 - Optimizing depth of range
The first step is to identify the optimum number of SKUs to meet customer demand. To achieve this, a sales saturation analysis is used to identify whether the depth of the proposed range is too narrow or, on the contrary, too broad in relation to consumer profiles.
Depending on the desired positioning, the brand can then decide to enrich, maintain or lighten its ranges, with full knowledge of the facts. Other parameters can also be taken into account to refine analyses, particularly for products from new collections or the subject of dedicated advertising campaigns.
Step 2 - Forecasting production volumes
Once the range depths have been established, the aim is to identify the optimum manufacturing volume per product, to minimize inventories without losing sales. This analysis makes it possible to quantify the optimum between inventory levels (and therefore working capital), on the one hand, and sales levels (and therefore margins), on the other. The balance sheet and income statement respond to different, and sometimes contradictory, logics!
This constrained optimization must be fed by the use of data and machine learning techniques (Machine Learning). Their use can improve Forecast Accuracy by around 20 to 30%, taking into account temporality (collection launch, mid-season, sales, private sales, etc.) and restocking lead times.
The forecasts thus produced can then be integrated by the Supply Chain teams, to facilitate decision-making and refine the planning model.
Using data to manage working capital: an accessible approach
The data-driven approach to inventory optimization described above offers a number of advantages.
- Firstly, it does not require any specific tooling (the necessary algorithms are accessible via cloud platforms) and can therefore be implemented rapidly, possibly as a pilot on a limited panel of products;
- In addition, it can be used to initiate end-to-end supply chain transformation initiatives, encouraging cooperation between teams around a common base of objective, cross-functional data, with positive results on logistics costs, service rates, delivery times, etc.

Franck CÉSAR
iQo Partner
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Marc-Antoine PAING
Senior Manager iQo
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