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 Chuck Schaeffer Big Data + The Internet of Things = Big Manufacturing Opportunity

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The Internet of Things describes a world where everyday objects are embedded with sensors and radio tags which give them network and Internet connectivity for capturing and transmitting data. RFID tags are the favored wireless transfer technique, but other tagging technologies include barcodes, QR codes, near field communication (NFC) and digital watermarking.

Despite being what is likely the broadest catch-all phrase ever coined in the technology industry, the Internet of Things offers real value to the manufacturing industry. Sensors can produce extremely high volumes of machine generated big data and manufacturers who trace their products sensor-based data can understand customer preferences, product utilization, wear and tear, operational problems and host of performance measures that facilitate product maintenance, replacement and innovation.

Let's get to some practical examples.

  • Product Innovation. I had a chance to meet a driver from the Lotus F1 team at the last Convergence conference. He shared with me that in Formula One racing there are actually two races going on – the race on the track and the race off the track. The off track race is one that lends many lessons in how manufacturers can apply machine generated big data to improve their products and services.

    These race cars are laden with sensors that detect flexing, vibration, load, wear, temperature and many other measures which impact machine performance. This data gives teams the opportunity to not just understand how various gear impact performance, but to use the data for modeling improved performance. In fact simulation testing using real data applied to various combinations of components has delivered the single biggest impact to improved race car performance and winning more races.

    Comparing on the track equipment performance measures with more stagnant product attributes such as component age, composite materials, manufacturing methods and so on enables detailed understanding of equipment deterioration pace or break point thresholds as well as optimization opportunities. This type of rich data analysis can help most manufacturers find ways to make better products at less cost, improve product reliability or become more environmentally friendly.

    These lessons have been extrapolated to many car manufacturers who now embed their vehicles with sensors and microprocessors which capture data for maintenance and repair purposes as well as R&D innovation.

    Other types of manufacturers can apply similar methods to detect usage patterns, variables which negatively impact performance and conditions which contribute to hazards and then model this data to experiment and determine materials and process techniques which increase performance or useful life. Adding to this big data equation, the analysis will be improved by including customer attributes such as region (i.e. cars driving in the snow or in the desert), road conditions (the smooth roads of one area compared with the potholes of another) or purpose (a pickup truck used for farm work compared to one in an urban setting). Customer use cases, environmental conditions and product patterns may materially affect modeling results.

    The ability to perform simulations using machine generated big data enables manufacturers of sophisticated equipment to model different product components, swap parts, change configurations or test other variables in order to predict consequences and outcomes. This learning not only accelerates innovation resulting in superior products, but lends itself to downstream processes such as warranty policies, field service operations or even product recalls.

  • Product Lifecycle Management. Machine based sensors give manufacturers real time visibility to recognize how a product is being consumed, when maintenance is needed, when the product is approaching the end of its useful life or when the useful life is over.

    Machine sensors populating data logs allow manufacturers or service providers to identify low oil levels, high temperature readings, excess waste, unusual vibrations, extra noise, declining production levels or runtime patterns which suggest performance degradation. With this type of information the regular dispatching of technicians for maintenance visits based on period intervals can be replaced with real-time maintenance delivered when it's actually needed.

    Real-time machine health information that shows how products are degrading also gives service providers a window to schedule maintenance during low impact hours or operations. This advanced planning represents a sea shift change from being notified only when a machine becomes inoperable and unplanned downtime along with a hit to production output are simply part of the scramble.

    Xerox is a good example of a manufacturer that monitors millions of copiers, printers and other devices located at customer sites. Rather than depend on customers to deliver accurate equipment feedback or incur the high costs to dispatch technicians for onsite assessments, the machines transmit log data to a central data warehouse at Xerox. Automated business rules identify equipment in need of maintenance or situations to escalate for human consideration and a predictive algorithm determines which equipment may stop operating in the future. Remote maintenance management has resulted in dramatically fewer trips, and ensuring the right people with right tools and parts are in-hand for each trip.

    Products ranging from race cars to printers, or consumables to industrial equipment, can deliver streaming data which enable manufacturers to manage their products health and utilization, increase customer value and append R&D for new product innovation.

Summary

Manufacturing business processes incur a profound change when product utilization is viewed in real-time. For example, product replacement becomes predictable, inventory control becomes continuous and product forecasting becomes more accurate.

Sensory-based data flags deviations, identifies patterns and permits simulations that facilitate timely and low cost experimentation to improve product performance, quality and longevity. However, this is only the tip of the ice berg as when this machine generated big data is thoughtfully linked with customer attributes and behaviors it becomes a valuable source to deliver real-time information about consumer preferences, customer intent and market demand.

According to Gartner, there will be nearly 26 billion devices on the Internet of Things by 2020. It's a relative certainly that all viable manufacturers will eventually leverage sensory-based and machine generated big data. However, that will take some time as we're still in early days. What's not so certain is which manufacturers will have the foresight to tap into this technology while it can still deliver advantage over their competitors who choose to ignore what may be the most valuable product and customer information source available. End

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Comments (11) — Comments for this page are closed —

Guest Sam Riedel
  I enjoyed this article and agree with the big data argument, but this seems really difficult. What are the challenges that are likely to hang up somebody doing it for the first time?
  Chuck Chuck Schaeffer
    The first challenge is the need for creative thinking. In my experience, you need to hypothesize how various new types of semi-structured and unstructured data can link with new and existing structured data to deliver new insights. This also includes getting creative in how to source new data over social networks, online channels, from aggregators and elsewhere. Referencing other company examples and working with consultants who have done this before will aid and accelerate this process.

The second challenge is the technology. The software and tools are powerful, but immature. I find most organizations don’t truly understand the need and value for extendable architectures, efficient (small footprint) backhauls (the connections between field sensors and company data centers), customer journey maps, the top key performance metrics and flexible business processes. I also advise companies not to create yet more disparate applications and siloed data repositories but instead make big data an integral component of your ERP, SCM or CRM business systems. We’re still in early days so there are few best practices available.

The third challenge is making information actionable. Many companies have tons of reports that never get used, and I’m seeing this problem continued with big data. Just capturing big data provides no competitive value. However, when big data becomes actionable it becomes very valuable. This is really a matter of mapping how to get the right information to the right people at the right time.
  Guest R. Toung
    The challenge is also getting your head around the many possibilities.

Guest Paul Haines
  Any technology that can improve product defects and boost quality will deliver any exponential effect to the bottom line. Nice article.
  Guest luvbigmachines
    Also identifying which products should be scrapped will help the bottom line.

Guest Zach Kirk
  Do you think manufacturers have more to gain that other industries?
  Chuck Chuck Schaeffer
    Every industry has a lot to gain but manufacturers are afforded some unique customer and product benefits. Manufacturing business models are more geared to increasing business with existing customers as opposed to the constant acquisition of new customers. They source more revenues and have longer relationships with customers. This offers an opportunity to apply big data to objectives such as product utilization, cross-sell, up-sell and customer share to boost top line revenues.

Guest David Ritter
  I’ve heard about sensors being used for field service and called virtual engineers. I think being able to detect degradation or predict when a component will fail and then be able to schedule service before it actually fails is the holy grail of field service.

Guest Howard Berns
  Key point you made, leading a big data project as a technology project by IT will challenge the projects success. It must be led by line of business leaders and business analysts, with IT playing a valuable technology support role.

Guest curiousken
  get big data not so sure about internet of things
  Guest Nathan McGee
    The two are linked and IoT is forecasted for big growth. Big data is actually growing the sensor market, which is forecast to grow 6.1% annually and reach $14.9 billion by 2016. The market for smart electromechanical devices that measure all types of environmental conditions is expected to grow 9.8% annually and reach $6.8 billion in 2018. These are big numbers that should not be ignored.
 

 

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Manufacturing business processes incur a profound change when product utilization is viewed in real-time. For example, product replacement becomes predictable, inventory control becomes continuous and product forecasting becomes more accurate.

 

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