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Supply Chain Analytics: Myth or Reality?

As tech clichés go, “big data” falls somewhere between “cloud” and “social” on the list of terms you're probably tired of hearing — ideas so broad they mean hardly anything at all. Nevertheless, since we first looked at the growth of predictive analytics last year, spending in that area has only increased.

The redundantly named but allegedly reliable investment research firm Markets and Markets predicted this year that corporate spending on logistics analytics — big data for supply chains — should grow from slightly more than $7 billion this year to more than $29 billion in 2019. That's an overall corporate figure, but electronics appears likely to play a central role to the boom.

Just last week, a Royal Bank of Scotland analyst told Financial Times (subscription required) that interest in predictive analysis spiked after the infamous electronics supply chain break that followed the 2011 earthquake and tsunami in Japan. The disaster disrupted the world's supply of a broad range of electronic components. One particular cause of panic was the gargantuan chip orders pending at the time for the auto industry. Autos are seen as a vanguard product in integrating electronics, and the disaster in Japan served as a warning for other industries. As home appliances, houses, and even bathroom fixtures become increasingly wired (the vaunted Internet of Things), supply chain risk in electronics becomes supply chain risk for everything.

Could big-data analytics really prevent logistical breakdowns from a devastating, random event like the disaster in Japan — or even a lesser threat, like a sudden shift in currency exchange rates or fuel prices? In theory, yes. Where old-school modeling might take into account the likely sources of occasional interruptions (Japan's seismic risk isn't new, for example), the only real remedy for a supply chain manager is to calculate the possible costs and warehouse accordingly. That is a hit-or-miss process, and it's expensive if your ballpark figures are wrong.

In just the past 18 months, a number of analytics startups have shifted the emphasis toward using big data to source new supplies in real-time as interruptions are happening. (One of the shops we highlighted last year, Precogs, has gone on to prominence.) Looking at production and consumption in more than simple supply/demand terms, and correctly predicting where production is most likely to create overruns and shortfalls — even before the factories themselves know — significantly improves the odds of successfully responding to surprises.

By how much? Even the analytics firms can't predict that yet. But the spending increases on real-time data suggest a gelling consensus that the costs, which are considerable, are worth it. And with electronics supply chains likely to become more intertwined with non-electronics industries, those costs are spreading wider. As the scale grows, the cost of collecting and analyzing all that data will explode. But so will the cost of not having it. Soon, it will stop being a choice at all.

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8 comments on “Supply Chain Analytics: Myth or Reality?

  1. t.alex
    April 17, 2014

    How would a company generate and collect lots and lots of data so that it can be automatically analyzed at the backend?

  2. Hailey Lynne McKeefry
    April 18, 2014

    Today, the average sourcing software, ERP system, corporate database, etc. is all fileld with useful and usable data–that when combined in different ways provides even more intelligence. We are already creating tons of data with existing systems. Most of it, though, isn't used to its full potential.

  3. t.alex
    April 19, 2014

    Hailey, for companies to make best use of the current data, do they really need to hire so-called big data scientists/engineers or they'd better go for outsourcing service?

  4. Hailey Lynne McKeefry
    April 21, 2014

    It depends on the size of the company and the size of the big data project, t.alex. I think that right now there's more need for expertise than expertise exists though.

  5. lamarwilliams
    May 12, 2014

    It is great to see such a healthy discussion on the role that analytics is playing in the supply chain today and in the future. I would just add that the increasing need for solutions that address the unpredictability in the electronic supply chain is growing. And, similar to the use of data analytics in the consumer business to predict purchasing behaviors, manufacturing can now have the same power, but it is directed at component supply risk & pricing visibility. Don't you feel that being the first to know about the risk of supply shortages is necessary in such a volatile industry?

  6. Hailey Lynne McKeefry
    May 13, 2014

    @lamarwilliams, thanks for chiming in on the discussion! I agree with all that you say…and i would say being first to know is even more than necessary…it's vital and may be the difference between success and failure of a a business. In not too distant future, we'll get to a point of predictive anaytics being table stakes and a bit further down the road, automation of business dealings is likely to happen. In five years, it will be a whole new world.

    What's the biggest challenge ahead of us do you think?

  7. lamarwilliams
    May 14, 2014

    The next step in this situation is education, as is with everything that is new. It is all based on getting the appropriate stakeholders behind predictive solutions, and then working with them to instill those ideals into their teams. What are your thoughts? 

  8. Hailey Lynne McKeefry
    May 14, 2014

    I think getting all the stakeholders at the table is important–adn building a business case that proves that analytics move the organizatoin forward in terms of key supply chain  objectives (shorter lead times, lower prices, more compliance to contracts, etc.) that can be traced to measureable business goals (in terms of products, supplier relationships, etc.). I think that one of hte biggest challenges; useful data is spread across the enterprise in a variety of systems and would be made even more useful if brought together and analyzed–but folks just don't keep track of where everything is. As we get better at it, too, good data security practices (both for data in motion and at rest) are going to be critical. Two sets of harmless information when brought together can potentially create information that would create a rich target for hackers.

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