8 Ways Data Analytics Can Transform Manufacturing

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Analytics in Manaufaturing

“In God we trust, everyone else bring data” — Edward Demming

Though the quote is a little old, the relevance is just increasing everyday especially with Industry 4.0, which revolves around data. Combine this with IoT, manufacturing industry is left with data that is waiting to be analysed.

Data Analytics in Manufacturing is neither something new nor something that seems revolutionary. But in reality, it can prove to have results that can transform the processes and bring in advantages for quality, quantity, and potential gains. Today in this article, let’s explore multiple ways in which data analytics can transform manufacturing.

We’ll also keep interchangeably using manufacturing analytics with data analytics or data science wherever needed.

What is Manufacturing Analytics?

Manufacturing Analytics is a broad term that refers to the use of algorithms, statistics, ML models, etc. on machine and operational data to improve and transform functions across all processes.

With the growth in real-time data, analysis can be helpful to identify parameters that directly affect production, analyze data from sensors to improve quality, improve and monitor KPIs, etc.

Though it sounds easy, in reality, it is more of a progressive effort to grow your analytics function starting from the most admirable to the most admirable.

One easy to break down the maturity level of data analytics in manufacturing is with the four basic types of analytics: Descriptive, Diagnostic, Predictive, and Prescriptive.

  • Descriptive Analytics: The foundation for analysis starts with this. It helps manufacturers understand trends, patterns, and key performance indicators (KPIs) to gain a comprehensive view of their operations.
  • Diagnostic Analysis: Goes beyond descriptive analysis to identify root causes of issues or inefficiencies in manufacturing processes.
  • Predictive Analytics: To analyse patterns and trends in historical data, this helps manufacturers anticipate potential problems or opportunities, enabling proactive decision-making and risk mitigation.
  • Prescriptive Analytics: Takes predictive analytics a step further by providing recommended actions or strategies to optimize manufacturing processes.

Normally data analytics with tools for business intelligence and reporting fall under the descriptive-diagnostic analysis whereas machine learning and advanced modelling comes under predictive and prescriptive analysis. This is just to give a general idea, the capabilities of BI can be integrated with ML and ML capabilities can be integrated into BI.

Also, instead of breaking down into types of analytics, you can also break it down by KPIs, to understand the levels of analysis needed based on where the company’s analytics maturity stands.

Generative AI is paving the path for AI in manufacturing for the future.

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Some of the KPIs that should be analysed are:

  • OEE
  • Production Yield
  • Downtime and Cycle time
  • Rework rates
  • Cost per unit

Some of the high value KPIs which are useful but can be tracked once the organization reached certain amount of maturity can be:

  • First time yield by product variants
  • Environmental sustainability metrics
  • Supplier Quality Index
  • Innovation Index
  • Analytics ROI

Now that we have a general idea about what we mean by manufacturing data analytics, let’s take a detailed look at this transformation.

8 Ways to Transform Manufacturing Solutions With Data Analytics

  • Predictive Maintenance & Fault Prediction: Manufacturers can implement predictive maintenance strategies that anticipate equipment failures and identify potential faults, enabling proactive maintenance and reducing costly downtime. This can be useful to maximize plant productivity and minimize interruptions.
  • OEE — Overall Equipment Effectiveness: The gold standard for measuring manufacturing productivity. By analysing this, manufacturers can assesses the efficiency and performance of equipment to identify bottlenecks and areas for improvement. Normally calculated by taking into consideration: Availability, Performance, and Quality.
  • Product Quality Analysis: This can be used to identifying patterns or anomalies that may affect quality. This enables timely interventions and process adjustments to maintain high product quality standards. Implementing quality improvement programs like six-sigma and lean manufacturing can be done easily by taking data from sensors or at regular intervals to streamline processes easily.
  • Productivity Analysis: By analyzing productivity data, manufacturers can identify inefficiencies, streamline workflows, and optimize resource utilization for improved productivity. This can help gain insights into production processes, labor efficiency, and resource allocation.
  • Throughput & Yield Optimization: Manufacturing throughput analyses the amount of WIP and/or finished goods that can be produced in a fixed amount of time whereas yield refers to the percentage of products that meet the required quality standards during the production process. Through data analytics, manufacturers can understand throughput rates and optimize yield, ensuring maximum output and minimizing waste or rework. This leads to improved efficiency and cost savings.
  • Production Scheduling: By considering factors such as machine availability, labor capacity, and demand fluctuations, manufacturers can create more accurate and efficient production schedules, reducing idle time and improving delivery timelines.
  • Warehouse Management: With the world embracing ‘just-enough’ and zero-inventory models — analysing inventory levels, demand patterns, and order fulfilment is needed to establish efficient warehouse arrangement. Inventory management can be optimized while minimizing stockouts and improving overall warehouse efficiency.
  • Supply Chain Risk Management: Analytics can help monitor supplier performance, identify potential disruptions, and enable proactive risk management strategies to ensure a smooth supply chain flow. It can help bring visibility into the cost and efficiency of every component in your production life cycle.

These eight ways highlight the transformative potential of data analytics in manufacturing, and how they enable manufacturers to drive operational efficiency, improve product quality, optimize resource utilization, and enhance overall supply chain management.

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