Savoye introduces a Labor Management software module based on machine learning

As an extension of the existing ODATiO WMS/TMS software package, Savoye presents an innovative Labor Management module. The software uses machine learning technology to learn from the collected data.

Labor Management, as part of WMS, has as its main focus the management and coordination of personnel and equipment. Labor Management makes it possible to plan the work of employees in the warehouse based on the expected workload from the received customer orders, pending orders and products yet to be delivered by the suppliers, although this functionality is often present on the wish list of the larger retailers and e- trading companies, it is rarely used. Savoye has thoroughly researched how customers can actually best implement Labor Management.

The failures of existing labor management systems

Savoye developed a new benchmark in Labor Management solutions. During the development process, it became clear that most existing solutions have five major flaws: short-term, vague, limited in scope, annoying, and lacking ROI.

After all, their one-sided nature makes it difficult to allocate operational resources. Without an effective forecasting tool, the WMS saves its user at most a few hours when planning the workload: it becomes quite short-term. This is accompanied by a lack of precision in activity monitoring and an inability to integrate and measure the productivity of the processes not controlled by the WMS. In addition, Labor Management solutions typically include name-based scheduling; which is particularly complicated to handle.

All these shortcomings result in insufficient return on investment.

Practical features and user expectations

Savoye’s new Labor Management module makes it possible to define the necessary KPIs for proper management of the warehouse: productivity per “sector”, “cell” and also per “delivery destination” or “sales channel”. With the ODATiO solution, Savoye can deliver fully tailored solutions for each installation.

To simplify the rollout, and especially the daily use, with often quick decisions, Savoye has chosen to be able to allocate resources, based on an FTE (full time equivalent) instead of a name.

A significant feature of this module is real-time productivity reporting. This measures employee productivity, including those tasks not covered by the WMS.

Better HR management through machine learning

To optimize the Labor Management module and provide all the necessary information about the expected workload in the warehouse, Savoye uses artificial intelligence and machine learning. “ERP systems mainly use statistical techniques for their forecasts. The advent of machine learning is gradually making this approach obsolete. Therefore, our software uses the stored data from the warehouse so that it can make its own forecasts using real-life business cases. explains Marwane Bouznif, machine learning and optimization engineer at Savoye.

To demonstrate the effectiveness of the new solution, Savoye has already realized three POCs (Proof of Concept) in the retail sector: “In almost five years, we have been able to achieve a discrepancy of only 5 to 10% between our calculations and the actual applications. This excellent result enables our customers to better anticipate their operational workload, especially during peak periods or one-off campaigns, and to increase profitability,” concludes Grégory Lecaignard, Software Product Manager at Savoye.

The new Labor Management module will be integrated into the latest version of ODATiO WMS/TMS.

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