1. Inability to generate insights from high volumes of data generated by IT and OT systems
Modern manufacturers leverage a variety of systems like product lifecycle management (PLM) and enterprise resource planning (ERP) in addition to SCADA systems, integrated manufacturing platforms, and control systems to drive processes in the back office and on the shop floor.
The client had already implemented these systems and was leveraging Apriso for manufacturing execution (MES). This was integrated with the ERP and the PLM to drive quality control, generate a bill of materials (BOM), plan processes, and orchestrate maintenance and repair operations (MRO). These systems were generating terabytes of valuable data that could provide valuable insights into production, maintenance, and quality assurance processes. However, our client was unable to build a solution that could turn this data into usable insights across the spectrum of its operations. Moreover, the existing MES was not being exploited to its full potential, where it could enable plant-level visibility and traceability into products, processes, and materials.
Our client saw a significant opportunity in this data and wanted to utilize it to drive improved decisioning and process orchestration across its manufacturing facilities.
2. Lack of real-time visibility resulting from difficulty in creating and tracking meaningful KPIs
Engine manufacturing is a highly complex process that entails multiple stages from concept to design, production, and sales. Therefore, production performance can be influenced by numerous factors, including, but not limited to asset reliability, quality control, production schedule, resource utilization, supplier relations, supply chain factors, and so on. Without complete visibility and control over these factors, it is not possible to ensure that each plant is running at maximal performance.
Operations spanning over multiple countries, our client was finding it challenging to achieve this requisite visibility into key performance indicators (KPIs) across its facilities. This was making it difficult to respond effectively to evolving conditions at the plant level. Moreover, without visibility into production performance, our client was unable to infer whether its plants were running at optimal levels. This made it impossible to improve the existing output and performance of its manufacturing facilities.
3. Unexplained increases in operational costs affecting profitability
Because engine manufacturing involves multiple stages involving complex processes, there are numerous factors that contribute to the operational costs of a manufacturing plant. In the absence of visibility at the process level, it is not possible to conduct detailed cost analyses. This is usually a consequence of the unavailability of operational data, or the inability to analyze and interpret it in time and at a massive scale.
In the case of our client, this lack of process-level visibility was making it difficult to pinpoint the root cause of rising operational costs. Without detailed insights into cost drivers, the right cost-saving measures cannot be implemented – and this ultimately affects the profitability and the bottom line of the business. To address this issue, our client wanted to implement advanced analytics which would enable them to track the factors influencing operational costs at a granular level.
4. Increased machine downtime across locations adding to lost revenues
Machine downtime is one of the leading causes of lost revenues for manufacturers. The cost of this downtime is increasing, and Fortune 500 companies lose nearly 11% of their yearly turnover
due to asset downtime. What’s more, the cost of unexpected downtime of critical machinery is even higher, and it can jeopardize production by necessitating plant-wide shutdowns for repair operations.
While our client was leveraging a global manufacturing platform to orchestrate maintenance and repair operations, they lacked plant-wide visibility into machine health. This led to the use of legacy maintenance practices like reactive and preemptive maintenance, which causes breakdowns or over-maintenance. Machine downtime was disrupting production schedules and increasing maintenance costs. In addition, suboptimal plant uptime was also adding to the company’s lost revenues.
5. Reduced machine productivity and throughput eroding competitiveness
As machines age, they may start functioning sub optimally and may warrant more proactive maintenance and repair. Moreover, frequent breakdowns ultimately subtract the remaining useful life (RUL) of the machine, while lowering the throughput of the plant. However, low throughput can also be attributed to inefficient manufacturing processes which can be difficult to spot in high-complexity procedures like engine manufacturing.
Our client was experiencing reduced throughput and suboptimal machine productivity across plants. This was impacting their competitiveness in global markets, negatively affecting the production volume, and contributing to lost revenues. Building systematic, real-time visibility into plant operations was the only way to pinpoint the root cause and make targeted interventions for maximizing throughput.