Analytics plays a crucial role in Manufacturing Execution Systems (MES) by helping organizations gain valuable insights from the vast amount of data generated on the shop floor. To leverage analytics effectively in MES, it’s essential to have a data strategy in place, including data collection, storage, and integration with other systems (e.g., ERP, CRM). Additionally, organizations should invest in the right analytics tools and talent to extract actionable insights from the data. Ultimately, the goal is to use analytics to drive data-driven decision-making, improve operational efficiency, and enhance overall manufacturing performance.

Here are some examples, demonstrating how analytics can be used effectively in MES.

KPI tracking, root cause analysis, and continuous improvement

Analytics in manufacturing execution can track Key Performance Indicators (KPIs) in real-time, allowing manufacturers to monitor production efficiency, quality, downtime, and other critical metrics. Historical trend analysis helps identify patterns and anomalies, enabling proactive decision-making and process improvements. When issues or defects arise, analytics can be used to perform root cause analysis by examining historical data and process variables. Identifying the root causes helps in implementing corrective actions to prevent similar issues in the future. Analytics support continuous improvement initiatives by providing data-driven insights into areas for process optimization. Manufacturers can use these insights to implement Lean Six Sigma and other improvement methodologies.

Predictive maintenance and quality control

Analytics can analyze machine data, including sensor readings and historical maintenance records, to predict when equipment is likely to fail. This enables scheduled maintenance, minimizing unplanned downtime and reducing repair costs. Analytics can continuously monitor quality data and detect deviations from established quality standards. Real-time alerts can be generated to prevent defects and ensure that products meet quality specifications.

Energy efficiency

Analytics can track energy consumption across manufacturing processes and identify opportunities to reduce energy usage. This can lead to cost savings and improved environmental sustainability.

Demand forecasting and supply chain optimization

MES analytics can incorporate demand forecasts into production planning, ensuring that production aligns with customer demand. This reduces excess inventory and stockouts. MES analytics can provide insights into supply chain performance, including supplier lead times, material availability, and inventory levels. This helps in optimizing procurement, inventory management, and supplier relationships.

One example of this is the supply chain wide simulation and control logic optimization that I developed in Python for a poultry meat supply chain. You can find that example here:

• Link: Poultry supply chain simulation in Python with SimPy

The analytics core of a MES should incorporate as many of the supply chain-wide optimization levers as possible, and take them into consideration for decision making.

Production scheduling

Advanced analytics can optimize production scheduling by considering various factors such as machine availability, labor resources, material availability, and order priorities. This ensures efficient resource allocation and on-time delivery of orders. Scheduling problems can be hard to solve, and heuristics might be applied when conventional solvers cannot solve problems within reasonable time. I have shared various smaller scheduling examples on this blog, and you read more about them here:

• Link: Job shop scheduling challenges
• Link: Optimized SCM capacity scheduling
• Link: Genetic job shop machine task scheduling
• Link: Production scheduling in SAP ERP system
• Link: Optimization via master production scheduling

Regulatory compliance

For industries with strict regulatory requirements, MES analytics can ensure compliance by tracking and reporting on data related to manufacturing processes, quality control, and product traceability.

Custom dashboards, reporting, and cost analysis

MES analytics often offer customizable dashboards and reporting tools that allow users to visualize data in a way that’s most relevant to their roles and objectives. Analytics can help analyze the cost of production, including labor, materials, and energy, enabling manufacturers to identify cost-saving opportunities.

Linnart Felkl

Data scientist focusing on simulation, optimization and modeling in R, SQL, VBA and Python