Digitalize Your Manufacturing Operations

Challenge

The plant ran 10 progressive die stamping presses and 5 spot weld stations across three shifts with no real-time downtime capture. Shift leads logged stoppages on paper at the end of each shift — often incomplete and always too late to act on. OEE was calculated in Excel the following morning. Management had no visibility into which machines were the biggest contributors to lost production, and chronic delivery shortfalls to their OEM customer were becoming a contractual risk.

Challenge

The bakery produces over 40 SKUs across three tunnel oven lines. Oven temperature profiles — zone set points, actual zone temperatures, and belt speeds — were recorded manually by operators every 30 minutes on paper log sheets. When a customer returned a batch of bread rolls citing inconsistent texture, the quality team could not confirm whether the correct bake profile had been followed, as the paper logs for that shift were partially illegible. Separately, raw material traceability was limited to daily flour delivery records with no link to individual production batches, making a targeted recall practically impossible.

Challenge

The manufacturer operates an in-house finished goods and spare parts warehouse storing over 1,800 SKUs — extinguisher cylinders, valve assemblies, propellant cartridges, and service kits spanning multiple pressure ratings and certifications. Inventory was managed in Excel, updated manually after each despatch run. Stock counts regularly showed discrepancies of 3–5%, leading to either short shipments on urgent site orders or overstock write-offs on slow-moving variants. More critically, regulatory traceability requirements for fire suppression equipment meant that shipment records needed to show which certified batch of extinguisher bodies and propellant charges were sent to each installation site — a requirement that paper-based despatch notes could not reliably satisfy.

Challenge

The OEM builds semi-automatic and fully automatic liquid chemical filling lines for industrial and household chemical customers. Their machines were shipped with a conventional touchscreen HMI for operator control and a paper-based maintenance checklist. Customers had no visibility into machine performance between service visits, and the OEM had no data from the field to understand how machines were actually being used or where failures occurred most frequently. As customers grew more sophisticated, requests for OEE data, fill count logs, and detailed fault history were increasing — requirements the current HMI architecture could not satisfy without significant custom development on each project. When machines broke down, service engineers arrived on-site with no prior context — no data on what the machine was doing before the fault, making diagnosis slow and unpredictable.