Rebuild parts management from the ground up and integrate it with order management and fulfilment
Our first step was to evaluate the strategic problems and possible solutions to make sure suggested changes delivered the right ROI.
We improved demand planning with a safety stock optimization solution to reduce parts forecast error and improve parts case fill rate to ensure that the right parts are available at the right location and time based on inventory demand trends.
Next, we began to digitize supply chain operations. We deployed our intelligent parts forecasting solution to analyze demand patterns, perform statistical forecasting, and manage exceptions from what was budgeted or expected. It determines whether parts and parts segments should be stored on-site or remotely. After this assessment, we created smart processes with built-in machine learning to learn from the results and forecast slow-moving parts.
To improve order fulfilment, we ran design-thinking sessions to select predictive analytics and machine learning solutions to make real-time parts management decisions. Our automated order fulfilment solution streamlines order-to-delivery workflows across systems to enable the efficient fulfilment of customer orders by removing manual processes. Now, artificial intelligence (AI) and mobile apps provide the firm's customers with real-time updates on order queries. The system also has a unique framework that uses AI to identify contract reconciliation issues involving chargebacks, claims, and deductions.
To improve predictive maintenance, we introduced an IoT solution that includes an IoT ROI modeler, a remote monitoring and diagnostic toolkit, warranty analysis, connected field services, and aftermarket services.
Armed with this data, a team of more than 30 engineers in our remote monitoring and diagnostics centers analyzes more than 190,000 operational alarms each year. This real-time data feeds into the parts-planning process and improves asset reliability and performance.