Manufacturing Analytics Services: Reduce Scrap and Improve Throughput

Services that provide analytics in manufacturing offer factories the ability to use many types of data to help identify hidden bottlenecks. This data analytics service can help factories improve Overall Equipment Effectiveness (OEE), throughput, and scrap metrics. Traditionally, factories relied on shift-end reports to gather information to help identify and eliminate defects in the production process. However, with the help of this analytics service, factories now have the ability to utilise dashboards that provide real-time data to help identify and minimise downtime, defects, and production speed losses. 

Quick Answer 

Through the use of various analytics processes, manufacturing analytics services provide factories the opportunity to decrease scrap and improve throughput. These processes help capture floor-level data and analyse manufacturing execution systems (MES), enterprise resource planning (ERP), programmable logic controllers (PLC), Supervisory Control and Data Acquisition systems (SCADA), quality data, maintenance data, and operator data and present it in a format that can guide process improvements. 

Manufacturing analytics provide the ability to create actionable decisions based on manufacturing data. Factories can use this data to answer many operational questions. Some of these questions can include why there is an increase in defects, where and what the bottleneck in the production process is, which machine is affecting production the most, what is causing scrap, and how it is possible to improve throughput without increasing capital expenditures. 

Key Takeaways 

• 1. Manufacturing analytics services improve visibility across machines, lines, shifts, and plants. 

• 2. Scrap reduction depends on finding defect patterns early, not after final inspection. 

• 3. Throughput improves when bottlenecks, downtime, changeovers, and micro-stops are measured correctly. 

• 4. OEE analytics is useful because it connects Availability, Performance, and Quality. 

• 5. Predictive maintenance and quality analytics can reduce unplanned downtime and rework. 

What Are Manufacturing Analytics Services? 

Manufacturing analytics services allow factories to gather and process data, which then helps business managers enhance the performance of the factory in terms of quality, cost, and efficiency. 

According to NIST, smart manufacturing analytics is the capability to derive actionable insights for decision-making from data of manufacturing processes.  

Definition Box: A factory's performance can be enhanced via analytics when production data is integrated with machine, quality, maintenance, and business data. 

Some common sources of production data are: 

• 1. PLC and SCADA systems 

• 2. MES and ERP systems 

• 3. Quality inspection systems 

• 4. IoT sensors 

• 5. Machine logs 

• 6. Maintenance records 

• 7. Operator inputs 

• 8. Energy meters 

• 9. Inventory and batch data 

How Manufacturing Analytics Reduces Scrap 

Manufacturing analytics minimises scrap by determining defect causes, the timing of defects, the machines and shifts responsible, and which process variables contribute to quality issues. 

Scrap analytics examines the: 

• 1. Type of defect 

• 2. Where the defect occurs 

• 3. Condition of the machine 

• 4. Material being used 

• 5. The shift of the operator 

• 6. Temperature and pressure 

• 7. Tool wear 

• 8. Frequency of rework 

• 9. Rate of inspection failures 

Scrap Reduction Analytics Table 

Key Manufacturing Analytics Use Cases 

Manufacturing analytics services usually concentrate on quality, throughput, maintenance, energy, inventory, and production planning. 

Key use cases: 

• 1. OEE analytics 

• 2. Analytics for scrap and rework 

• 3. Predictive maintenance 

• 4. Bottleneck analysis 

• 5. Production planning analytics 

• 6. Yield improvement 

• 7. Defect prediction 

• 8. Energy analytics 

• 9. Analytics of downtime 

• 10. Root-cause analysis 

• 11. Digital twin and simulation 

• 12. Real-time production dashboards 

According to McKinsey, the potential benefits of predictive maintenance are reduced machine downtime of 30-50% and machine life extension of 20-40%. These numbers depend on the implementation and the use case.  

Step-by-Step Implementation 

A concrete problem at the plant is a better starting point than an imprecise dashboard for building an industrial analytics solution. 

• 1. Choose one specific business objective: Reduce scrap, improve throughput, increase OEE, reduce downtime, etc. 

• 2. Identify and map data sources: MES, ERP, PLCs, SCADA, along with quality and maintenance data. 

• 3. Prepare the data: For issues such as missing timestamps and counts, duplicated data, and manual data entry errors. 

• 4. Specify and define the KPIs: OEE, scrap, yield, cycle time, downtime, throughput, and rework. 

• 5. Develop dashboards: Design dashboards that track performance in real-time and compare it historically at the line, shift, product, and machine level. 

• 6. Analyse and explore the data to determine the root cause: Utilise correlation, Pareto, control and anomaly charts. 

• 7. Execute the findings: Alter parameters, the maintenance schedule, SOPs, and material checks. 

• 8. Quantify the results: Assess and compare the pre/post metrics for scrap, throughput, downtime, and costs. 

Comparison Table: Traditional Reporting vs Manufacturing Analytics 

Benefits 

Instead of having to buy new equipment, manufacturing analytics can help factories improve the output and quality of their services while also reducing costs. 

The major advantages include: 

• 1. Decreased scrap 

• 2. Increased throughput 

• 3. Increased overall equipment effectiveness (OEE) 

• 4. Decreased planned downtimes 

• 5. Rapid root cause determination 

• 6. Increased yields 

• 7. Improved shift performance 

• 8. Decreased rework 

• 9. Improved maintenance 

• 10. Increased production visibility 

According to Deloitte's 2025 smart manufacturing survey, up to 20% of the factories surveyed improved production output by as much as 20% and were able to use smart manufacturing investments to improve production capacity by as much as 15%.  

Limitations 

Manufacturing analytics is only applicable when data is trustworthy, process owners are engaged, and derived insights are actionable. 

Some obstacles: 

• 1. Inadequate connectivity to machines 

• 2. Unspecified reasons for downtime 

• 3. Errors from data entry 

• 4. Ongoing usage of machines lacking metrics 

• 5. MES, ERP, and quality systems remain separate 

• 6. Absence of acceptance of the processes on the shop floor 

• 7.Visualisation systems lacking owners of the actions 

• 8. No points of reference for evaluation or metrics 

Common Mistakes 

One of the most critical errors in dashboard development is the failure to define the operational problem driving the need for the dashboard. 

Dashboard developers need to avoid the following errors: 

• 1. Dashboard contains too many KPIs 

• 2. Lack of operator feedback 

• 3. Usage of inaccurate data 

• 4. Inconsistent calculations for OEE 

• 5. OEE not separating scrap, rework, yield 

• 6. Viewing analytics as a purely IT function 

• 7. Pilots that do not scale 

• 8. No connection of insights to daily management 

Best Practices 

Highly effective manufacturing analytics projects have three key characteristics: simplicity, measurability, and applicability to everyday decisions around production. 

Having the following practices in place is beneficial: 

• 1. Focus on only one production line or product family. 

• 2. Clearly define KPIs for scrap and throughput. 

• 3. Use Pareto to select defect focus. 

• 4. Monitor OEE as a function of Availability, Performance, and Quality. 

• 5. Integrate machine and quality. 

• 6. Go to the data in production meetings. 

• 7. Enhancing data analytics for manufacturing optimisation is the responsibility of the assigned owner. 

• 8. Only expand efforts after validating ROI. 

Expert Tips 

A good manufacturing analytics service provider needs to know manufacturing as well as data analytics. 

Ask them: 

• 1. What experience do you have with MES, ERP, PLC, SCADA data? 

• 2. Can you show me a correct OEE calculation? 

• 3. Can you manage shop-floor data that could be incomplete and/or disorganised? 

• 4. Will dashboards display root causes and not just present data visually? 

• 5. Will you be able to implement predictive quality or predictive maintenance? 

• 6. What is your plan for showing measurable return on investment? 

• 7. Can you prove that the system will be usable for shop-floor employees and managers? 

Real-World Examples 

Example 1: Auto Parts Manufacturing Facility 

An auto parts manufacturer applies analytics to correlate rejection data with temperature data of the machine, tool life, and material batch. The facility observes that a particular pattern of wear of a specific tool causes dimensional defects after a certain number of cycles. 

External Authority Suggestions 

Useful references: 

• NIST Smart Manufacturing Analytics 

• NIST Smart Manufacturing 

• OEE Calculation Guide 

• IBM OEE Overview 

• Deloitte Smart Manufacturing Survey 

• World Economic Forum Global Lighthouse Network 

Conclusion 

Analytics solutions for manufacturing facilitate a positive change at plants, moving the plant from reactive reporting to proactive improvements. By assembling data on production, quality, machines, and maintenance, manufacturers are empowered to make choices on the shop floor that lead to a reduction of waste, an increase in throughput and efficiency, and an increase in OEE. 

The most significant improvements are seen when analytics are implemented with a definable goal, with incremental improvements based on honest and transparent data. 

When implementing a manufacturing analytics system, begin with a single focused constraint. Develop the solution on that constraint, validate the solution, and only then implement the solution across the organisation.