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Big Data and What it Means for Manufacturers

Big Data

Big Data is coming, but what will that mean for manufacturing?

Big Data—you hear about it everywhere these days. What makes it different from ordinary data? How big is big? And what does it mean for manufacturers, large and small?

How big is big?

The scale of the entire Internet is unquestionably big. But Google delivers ever faster and more relevant searches with its continuously evolving technology. PepsiCo collects some big data when it monitoring channels like Facebook, Twitter, and the news for any mention of Pepsi products with new social media monitoring systems. Walmart’s real time checkout sales data is big, and Walmart has the technology to immediately trigger both marketing and supply chain actions from it.

(20 Must Read Infographics on Big Data)

What’s changed?

Advances in data acquisition and storage mean that immense masses of rich data can now be continuously gathered, and large datasets can be easily combined and connected. Processing power continues to increase at an unimaginable speed. Leveraging that processing power is made possible by the ability to network thousands of cloud-based servers. As the data is integrated and analyzed, advanced graphics make it as visual as your car’s speedometer.

What’s in it for manufacturers?

Manufacturing supply chain managers dream of a quick-response, demand-based, integrated, multi-level supply network. That would entail tracking every SKU’s status and pathways from raw material through production, distribution, transportation, and delivery of the final product. That dream is becoming a reality. Now it’s possible to constantly monitor a flow of data, making exception-handling faster and more effective. In addition, monitoring factors like defect ratios and on-time delivery can help with supplier selection and performance assessment.

In manufacturing operations, synchronizing demand forecasting with production planning continues to be a struggle. Since actual orders will always differ from predicted demand, it’s a matter of having time to respond. With transparent multi-level integrated supply chain data, there’s better advance notice for adjusting plans and material supply.

Inside the factory, having the ability to utilize the mass of both order and machine status data allows production managers better operations optimization, factory scheduling, routings, production leveling, maintenance planning, and workforce scheduling and deployment.

What are vendors offering?

With innovation in exploiting massive data streams, big data vendors are making new claims about the software they offer. They say their systems feature:

  • Predictive analytics that use data to build models and simulations that help decision makers.
  • Operational analytics that optimize deployment of people, processes and assets for improved productivity and profitability.
  • Data customized in real time to fit the needs of decision-makers in different roles and functions the enterprise.
  • Monitoring abilities that identify and measure quality processes over time to help eliminate defects.

Problems with big data

Let’s not be dazzled. Manufacturers need to remember the lesson of enterprise resources management (ERP). Some vendors claimed their ERP systems would handle the IT aspect of everything that could happen to a product moving from order to delivery. Their brand of ERP would solve the disconnect between forecasting and production to actual orders. But ERP implementation often proved to cause headaches, unplanned cost, overtime, and late launches. Some companies bought more complex systems than they really needed. Some systems that promised integration from one end of the company to the other were simply dressed up MRP. Big data claims could be the same—repackaged business systems with no fundamental difference from current products.

Big data management capabilities won’t fit all shapes and sizes. Some companies won’t have the resources to get the advanced tools, but they may not have unmanageable amounts of data to deal with either. They are the ones that should proceed cautiously.

However capable and complex, no IT system or analyst can prevent the unpredictable. And unpredictability is a certainty. IBM’s Watson, with all the information and all the processing power it had, stumbled a few times playing Jeopardy with human opponents.

Big data is coming

Whatever the stumbling blocks, producing and capturing the information about everything that happens in an organization is an exciting vision. It’s a good time to consider what you would do if you could know everything. What if all your decisions could be fact-based?

Read more on big data and manufacturing.

Karen Wilhelm has worked in the manufacturing industry for 25 years, and blogs at Lean Reflections, which has been named as one of the top ten lean blogs on the web.


    April 01, 2013

    Some opinions expressed in this article may be those of a contributing author and not necessarily Gray.

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