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 Chuck Schaeffer How to Use Big Data in Manufacturing

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A McKinsey Global Institute report advises: "Despite early advances, manufacturing, arguably more than most other sectors, faces the challenge of generating significant productivity improvement in industries that have already become relatively efficient."

Nobody disputes manufacturing's increasing competitive challenges and quest for improved productivity, however, these challenges are no small feat as most manufacturers have already adopted lean manufacturing, ridded themselves of non-value added activities, optimized the supply chain, streamlined and automated business processes, and squeezed costs to the point where there's just no more fat. So where exactly is the next productivity or business model break-through opportunity?

I firmly believe it's in the next information renaissance being brought by the combination of Big Data and the Internet of Things. These are two synergistic and disruptive technologies that can uncover and leverage data that is absent, hidden or unappreciated in order to deliver actionable information capable of changing production processes, customer engagement and manufacturing business models.

To share how, I'll avoid the technology esoterics and theories and get to some real world examples with tangible results that can be replicated by just about anybody willing to explore this opportunity further.

Big Data Examples in the Manufacturing Industry

Big data isn't a software application, technology tool or something you install. Sure there are big data tools but putting technology ahead of strategy is a recipe for disappointing results.

Instead begin with strategy, and to spur your strategy you'll need some creating thinking. Here some examples to stimulate that thinking in a way that can dovetail into designing a big data strategy.

  • Improved Demand Generation. Small improvements in demand gen and product forecasting models mean fewer stockouts, less idle inventory, decreased cash flow requirements and most importantly, satisfying more customers that want your product – when and where they want it.

    However, manufacturing demand gen models that only look at purchase history are of course not considering fluid market conditions and real-time customer demand. Big data offers a unique opportunity to look forward when estimating product demand.

    For example, record label EMI has turned their business around by shifting demand generation and product forecasting from tenured executives applying gut feel experience to big data analysts tapping into listening patterns on online music streaming services to understand which music is resonating by region and with which subcultures. They are also teaming with music web-based and mobile apps providers, online "second screen" collators and music aggregators to acquire the data and create models which show consumption trends and measure demand in real-time. Sometimes called mood mapping, manufacturers and retailers can buy or acquire consumer reactions which correlate to market demand.

    Manufacturers and retailers can also use social listening tools or acquire feeds from social networks to measure consumer feedback volume, reach and sentiment for their products. With live data in hand, they can then interrogate the data by geography, consumer profile and other characteristics that permit modeled interventions. The 24 by 7 constant streams of opinions, likes, dislikes, sharing and social propagation offer manufacturers and retailers a real-time audience to test variations in promotion, pricing elasticity, content, packaging and other variables in a way that correlates testing conditions with consumption and builds a predictable demand generation model.

  • More New Product Successes. Introducing new products is a high risk venture for manufacturers. Combining online ideation and big data reduces this risk, accelerates production innovation, lowers R&D expense and grows revenues by delivering products that customers most want.

    Using online ideation to engage consumers for their ideas and critique the ideas of others, manufacturers are able to test R&D concepts faster and with substantially larger samples of customer feedback. Real-time access to large volumes of customer opinions also allows one-off test conditions and accelerates successive iterations so that new product innovation cycles can be substantially reduced. When consumer feedback can be acquired in minutes instead of weeks, manufacturers are afforded more testing scenarios and valuable What-If analysis.

    Again referencing the music industry, music labels routinely arrange for leaked songs in order to test market acceptance and determine whether the launch of a new tune is ready for prime time or needs some more refinement. While this concept has always been replicable to other industries, the addition of big data allows manufacturers to tap into social media and other online channels for real-time analysis, increase the data population for improved confidence levels and integrate unstructured data in order to bring measurability to new types of information. These collective capabilities accelerate market analysis, enable measurement specificity by key variables (geography, demographics, consumer segment, etc.) and help manufacturers deliver new products which are more eagerly embraced by customers.

  • More Reliable Supply Chain. Supply chain interruptions are costly. It only takes one downstream supply chain partner to stumble and thereby impact revenues and damage the manufacturer's relationships with retailers and customers. To mitigate this complex threat, some innovative manufacturers are using big data to cull information that may suggest a supply chain partner is at risk.

    Manufacturers have long used structured data such as on-time deliveries, payment cycles or the use of factored receivables to gauge partner health. However, when also including semi-structured or unstructured data from external and online sources – such as published financial analyst recommendations, media reviews, filed litigation or various forms of insolvency protection to name a few – manufacturers can be afforded valuable time for advanced planning and contingency measures when a partner faces financial distress.

    This technique can also be used to avert public relations nightmares brought on by partners using illegal or culturally unacceptable business practices. For example, consider recent consumer backlashes directed toward garment makers who (allegedly) unknowingly had partners using child labor or dreadful wages in distant lands. In the tech industry, consider Apple's missteps by not identifying early or responding timely to the Foxconn fiasco born from subpar labor conditions in China.

    Big data offers a new opportunity to keep close tabs on your suppliers and creditors.

    Page 2: How the Internet of Things Fuels Big Data for Manufacturers

 

 

 

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Small improvements in product forecasting models mean fewer stockouts, less idle inventory, decreased cash flow requirements and most importantly, satisfying more customers that want your product – when and where they want it.

 

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