How IT can fix 3 biggest manufacturing problems

Manufacturing is dealing with the problems of a global supply chain, rising costs, and finding skilled labor. But IT has a solution for each.

How big data can fix today's manufacturing challenges.'s

Big data can go a long way toward addressing the three major challenges facing manufacturing today.

Despite talk of a “Manufacturing Renaissance” in the U.S., there are major problems preventing manufacturing’s success here and abroad. Economic factors including increasingly stretched and risky supply chains, rising costs, and major skills gaps make it harder for global manufacturing companies to meet their demands. Fortunately, IT and big data can rise to the challenge.

Global supply chains are a necessity for manufacturing, but they also pose a major threat. Ever since IT first provided enterprise resource planning (ERP) to manufacturing, the ability to procure raw materials from ever-widening sources has allowed manufacturing to spread its reach and lower costs. However, political instability, climate change, currency fluctuations, and multiple other threats make long supply changes more difficult to manage. A breakdown anywhere in the supply chain, can leave factories idle for weeks and cost millions.

One major way to deal with that is through enterprise risk management (ERM) which has been around for a few years and takes a more complete view of “cost” of supplies when they are procured by calculating risk into cost. With traditional ERP, if a product could be sourced for 10 percent less from a supplier, the order was made regardless of their location. With ERM, if that same supplier is in a country known for its dock strikes, typhoon season, or terrorism threats, the potential for lost or delayed shipments would be taken into account and a different supplier might be selected. On the other hand, if the cost is cheap enough, those same risks may be worth the gamble.

Now that ERM is starting to mature, there are even great possibilities for the technology. Researchers are beginning to use big data to project social unrest in countries weeks in advance. They use social media and online publications to notice trends that lead to predicting a major social uprising in Egypt six weeks before last summer’s coup. A government group called the Intelligence Advanced Research Projects Activity (IARPA) is making similar efforts and expanding its predictions to include outbreaks of disease as well as mass violence. The IARPA initiative correctly predicted protests in Paraguay that lead to the impeachment of their president and an outbreak of Hantavirus in Argentina.

Fortunately, many of the same solutions can be applied to manufacturing’s growing cost problems. There are two major factors involved in these increasing costs—growing energy prices (especially in shipping fuel) and rising wages in countries developed nations like the U.S. have been offshoring to (including China). Between the growing wages and shipping costs, multiple reports conclude that by 2015, it will cost the same for a U.S. company to manufacture a product in the U.S. and in China.

Some responses to this change will include near shoring, returning the manufacturing closer to the home country, or finding newer, cheaper sources of manufacturing in a global market.

An increasing popular option is “fabless manufacturing.” Around since the late 1970’s, fabless is extremely common in the semi-conductor industry. Fabless is now increasingly possible in nearly every industry. Imagine ERP and ERM on a mega scale. If you can source raw materials and components from the best bid from a global economy on sp ec, why not finished products as well? If fashion houses can do it, why not car companies?

Going fabless is especially tantalizing given manufacturing’s last major problem—a shortage of skilled labor. Despite high unemployment in the U.S., manufacturers are facing a major skills shortage. It is estimated that five percent of all manufacturing jobs, over 600,000 jobs, are going unfilled because of a lack of easily accessed skilled labor. No factory means no worries about skilled line workers.

But that’s only part of the problem. There is also a shortage of so-called “middle skill” workers in aerospace, healthcare, manufacturing and elsewhere. These are jobs that require post-secondary technology education, and they represent nearly half of the jobs in the labor force. That shortage could account for another 4 million jobs going unfilled, and it is only going to get worse after baby boomers retire.

Ultimately, the problem is a local problem. Skilled workers in places where manufacturing and support for manufacturing has been hurt by globalization sit idle while boom towns look in vain for skilled labor that could transition to viable industries.

What’s needed to fix the problem (besides a massive overhaul of the education and training system) is a big data initiative that tracks local skills lacking in areas, and tracks the skills of the unemployed in others. Think of it as an ERP for people. It needs to work at a deeply local level, but also on an international level so that companies cannot only take advantage of labor in their own country but of programs such as the H1-B visa program. In other words, another Big Data to the rescue moment.

Of course, big data can’t solve the skills gap (or the supply chain or costs problem) alone. But it can give much needed transparency to the problem and allow for smart business decisions to be made in response to the problem. And that’s a lot more than manufacturers have had until now.

David Wagner
David has been writing on business and technology for seven years and was most recently an assistant editor at MIT Sloan Management Review, where he covered business topics including IT, innovation, and customer service. His work has also appeared in The New York Times and The Wall Street Journal.
David Wagner
Tags: Data Center,Manufacturing,Technology