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CIOs: Modernize Data Capabilities in Manufacturing, Supply Chains in 2014

Three Big Problems Big Data Will Create in 2014 Some of the best Big Data and sensor uses come from the manufacturing and logistics world. But while supply chains and manufacturing floors can generate plenty of important business data, those functions aren’t always the best equipped to use that data. Operations, supply chains and manufacturing […]

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Loraine Lawson
Loraine Lawson
Jan 2, 2014
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Three Big Problems Big Data Will Create in 2014

Some of the best Big Data and sensor uses come from the manufacturing and logistics world. But while supply chains and manufacturing floors can generate plenty of important business data, those functions aren’t always the best equipped to use that data.

Operations, supply chains and manufacturing are due for a technology overhaul, according to IDC Manufacturing Insights and other analysts who research these B2B functions.

The problem: Supply chain technologies and processes lag behind the highly digital world of the business side.

“The average manufacturing/retail company spent 1.7% of revenue on technology over the past decade: but nine out of ten are stuck,” Lora Cecere, an enterprise strategist and supply chain analyst, wrote in a Forbes column.

And what have they reaped with that investment? Cecere says these companies are stuck primarily because the technology is a holdover from three decades ago, built from the inside out, rather than outside-in.

“While the cost of computing has moved from $222 to $.06 per million transistors and the storage costs have shifted from $569 to $.03 per gigabyte of storage, we are implementing supply chain technologies that were designed in the 1990s when oil was 1/3 the price it is today, and corporate social responsibility initiatives were not on the agenda,” Cecere writes. “Today, we have real-time data, but we cannot use it in our processes to power factories, plan distribution, and/or sense customer demand.”

One of the ways she suggests companies become “unstuck” is by investing in new forms of analytics, including unstructured Big Data sets. Organizations should invest in unstructured text mining and sentiment analysis as well, she said.

This isn’t about technology for the sake of technology. Serious business drivers include:

  • The need for better visibility into the whole supply chain for compliance and risk management.
  • A push to make B2B relationships more collaborative.
  • The “3D value,” which requires engaging customers and being more responsive to emergencies and new demands.

In its Worldwide Manufacturing predictions for 2014, IDC pronounced this year will set the stage for a new manufacturing renaissance.

Traditionally, internal IT and manufacturing/B2B functions operated under different systems. That will need to change, since CFOs and others will expect IT to bring its data technologies and experience to bear on B2B operations.

The end result for CIOs and IT: You’ll need to become more involved with operations, manufacturing and other B2B functions. Several IDC predictions that support this view are:

  • Operational, information and consumer technology will converge to change technology management in manufacturing.
  • Supply chain technology investment will involve modernizing existing systems, while also trying new approaches. Other experts say that will include bringing in emerging technologies, such as mobile, cloud and Big Data.
  • IT will need to prioritize investing in the modernization of the underlying B2B commerce backbone.
  • Product lifecycle management (PLM) strategies will become more global, multidisciplinary, innovation-based and customer-focused.
  • Plant floor IT investments will continue to grab a higher share of the overall technology investment portfolio.

The first step, though, will be ensuring that solid data management practices are in place, suggests Capgemini Senior VP and business information management expert Scott Schlesinger.

“The true initiative and what they ultimately need to be concerned with is how they’re implementing better data management practices that account for the variety and complexity of the data being acquired for analysis,” Schlesinger told Information Week.

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