Inventory Management
Optimise your accounts payable process to improve on-time payment, reduce operating costs and streamline invoice processing.
Reducing Stockouts and Overstocks
Optimizing Lead Times
Enhancing Supplier Performance
Improving Forecast Accuracy
Challenges
Client case study
A leading manufacturer achieved significant operational improvements by optimising its inventory management process through process mining and automation. The organisation had been facing persistent challenges with slow inventory entry times, which caused inefficiencies and tied up valuable working capital. By gaining full visibility into their processes, they were able to uncover the root causes of these delays and identify where manual steps and bottlenecks were creating unnecessary friction.
Using data-driven insights and automated action flows, the company streamlined its workflows, reduced manual effort, and accelerated inventory updates across systems. This transformation not only improved process efficiency but also strengthened overall supply chain performance by ensuring faster, more reliable data availability.
As a direct result of these improvements, the company realised a €830K free cash flow impact, demonstrating the tangible financial benefits of process optimisation. The initiative’s success was further recognised when the project won the Celonis Use Case Contest 2024, showcasing its innovation and measurable business value.
Improvement Opportunities
Discovery what Apolix can help you with.
Reducing Stockouts and Overstocks
By analyzing process deviations, process mining helps identify where replenishment delays or over-ordering occur, balancing inventory levels efficiently.
Optimizing Lead Times
It reveals bottlenecks in procurement, production, or warehouse operations, enabling faster order processing and smoother supply chain coordination.
Enhancing Supplier Performance
Process mining tracks supplier-related processes, helping detect inconsistencies in delivery times or quality to support better supplier management.
Improving Forecast Accuracy
By linking process data with demand patterns, it provides insights that refine forecasting models and reduce excess inventory.