Reduce Shop Floor Variability with Data & AI

Shop floor variability is one of manufacturing’s costliest challenges, leading to machine downtime, product defects, and missed deadlines. According to a study by the Manufacturing Technology Institute, shop floor variability can cost manufacturers up to 10% of their revenue each year.  

With the sharp rise of AI, some manufacturers believe a simple solution is finally possible, but that isn’t the case. AI isn’t a magic bullet that can solve all shop floor challenges. Manufacturers must instead build a custom data and AI strategy to effectively manage variability and cross-functional dependencies. Here’s how manufacturers can get past the hype and harness the power of data and AI. 

The cost of shop floor variability  

Shop floor variability is behind many manufacturers’ biggest operational pain points. Demand, supply, machines, or quality changes affect how leaders coordinate people, tools, and production on each shift. And while variability is a well-known problem, solving it is difficult because manufacturers must first understand the cause. Uncovering the root problem can be daunting when possibilities include everything from equipment issues and raw material inconsistencies to supply chain disruptions and changes in the production process.  

Why ERP systems fail to address variability

Most manufacturers use enterprise resource planning (ERP) systems to manage business processes and operations. However, these systems often fail to address variability on the shop floor for many reasons. For example, traditional ERP systems use batch processing, so they can’t show the live shop floor data necessary to fix problems quickly. The systems also aren’t flexible enough to handle production, equipment, or step changes. They also can’t find patterns to explain errors, suggest improvements, or prevent problems. Integration challenges and manual input errors only add to the problem.  

How data and AI help solve shop floor variability  

But there is hope! With a practical plan and actionable strategy, manufacturers can harness the power of data and AI to address shop floor variability, empowering them to: 

  • Collect and process real-time data: Data and AI can help manufacturers collect and process real-time data from various sources on the shop floor, such as sensors, cameras, RFID tags, and barcode scanners. Manufacturers then get a more accurate, timely view of production processes and output and the ability to make faster, smarter decisions. 
  • Adapt and optimize production processes: Data and AI may also learn from historical and current data and adjust to reduce variability. For example, data and AI can help optimize production schedules, inventory levels, energy consumption, and more based on demand, supply, capacity, and cost. 
  • Leverage advanced analytics: Data and AI can apply advanced descriptive, diagnostic, predictive, and prescriptive analytics techniques to identify patterns, trends, correlations, anomalies, and root causes in shop floor data. Then, companies can see what’s happening, why, what will happen next, and what actions they should take.  
  • Easily integrate with other systems: Modern data and AI platforms can help manufacturers improve adaptability by incorporating real-time data from multiple systems and even unstructured data in emails, PDFs, and spreadsheets. This integration creates a seamless and unified data flow and communication across the entire manufacturing value chain and unlocks the potential of automation and prediction.  
  • Empower and engage shop floor operators: Data and AI can help operators see the previous shift’s quality, maintenance, and output performance. They can also view trends and patterns affecting performance, enabling the next shift to meet or exceed efficiency levels.  

The four critical V’s of manufacturing data 

To realize the full value of data and AI, manufacturers must have a solid foundation for manufacturing intelligence. It’s not how much data they have but rather the 4 Vs: variety, volume, velocity, and veracity. 

  • Variety is the diversity and complexity of data types and formats manufacturers generate, collect, and analyze. And it’s critical. Every company wants a 360-degree view of their customers which comes from merging data from several sources. These sources may include customer relationship management (CRM) systems, website analytics, email engines, point of sales systems, and third parties.  
  • Volume is the amount of data manufacturers generate, gather, copy, and consume in a certain period. Volume can provide more information and insights to improve product quality, process efficiency, and customer satisfaction. 
  • Velocity is the speed at which manufacturers generate, collect, process, and deliver data. Speed is vital for real-time analytics, which requires fast data processing and delivery to support timely decision making and action. 
  • Veracity is the quality and accuracy of data. Many manufacturers overlook veracity because they assume the data they collect is reliable and consistent. However, this is usually not the case, as data can be incomplete, inconsistent, ambiguous, outdated, or deceptive. 

Varying conditions require custom strategies

Variability on the shop floor is unique to each manufacturing operation. To solve company challenges, manufacturers must take a custom approach.  

Trivium Packaging, a $3.3B leader in sustainable packaging solutions, struggled with scattered data and delayed reporting. With 60+ locations producing diverse packaging for global brands, Trivium needed timely, accurate data to improve efficiency, reduce waste, and enable faster, smarter decisions. However, the company required a custom solution because they produce packaging for various applications and industries. 

Here’s how Trivium consolidated data from various systems to maximize throughput and efficiency, reduce downtime, and improve on-time delivery to their customers with a custom approach. Learn more about their story here.  

Need help finding practical solutions? 

While data and AI generate a lot of hype, they also provide practical solutions for addressing shop floor variability. But they’re not like buying packaged software off-the-shelf. To gain the full benefits of these tools, manufacturers must assess their current situation, identify pain points and opportunities, and define desired outcomes. They should also prioritize use cases and potential data sources to combine for real-time intelligence, choose the proper data and AI solutions to achieve their business goals, and do it all faster than the competition. We can help. 

With over 20 years in the industry, MCA Connect® understands manufacturing’s unique culture and challenges. Through strategic solutions, innovation, and industry intelligence, we help manufacturers: 

Improve demand forecasting and production planning

  • Develop effective models to predict demand and improve forecast accuracy 
  • Smooth production plans to avoid excessive overtime 

Enhance operational efficiency and flexibility 

  • Extend and augment core systems to support proprietary processes 
  • Apply automation to repeatable processes and address labor constraints 
  • Eliminate non-value-add process steps and risk of errors 
  • Reduce unplanned downtime 

Optimize resource utilization and inventory management 

  • Gain global visibility to energy consumption and effective use of capacity 
  • Eliminate excessive inventory  
  • Mitigate and adapt to raw material shortages, supplier risks 

Boost customer satisfaction and profitability 

  • Meet fill rate targets. 
  • Reduce customer churn  
  • Improve cash flow and profitability 

Book your free 30-minute expert consultation to explore how to unlock the power of data and AI in your manufacturing processes. 

Doug Bulla

Senior Vice President – Manufacturing Strategy, Data, and AI Solutions, MCA Connect Co-Founder

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