All Ashore? Reshoring Highlights Value of Analytics to Pick Best Manufacturing Sites

The Boston Consulting Group published the results of an interesting survey in April that showed a third of manufacturers are considering "reshoring" back to the United States from China. The survey received a lot of attention, but not for the reasons I thought it would.

Initial articles resulting from the survey have focused on rising wages in China, and improved plant automation in the U.S. But buried several paragraphs down is what I consider the real story: manufacturers are beginning to understand the true cost of manufacturing overseas, and 70% of those responding agreed that "sourcing in China is more costly than it looks on paper."

It's not just about sourcing in China. Consumption is no longer a one-way street. Once you built a cell phone in China with the plan to ship it to the U.S. Today, some of those phones are being sold in China, too. Opening a tractor plant in India is less about seeking a low-wage workforce and more about serving the growing need for tractors in India. Manufacturers also are under pressure to shorten product cycles, offer a broader array of customization, and shoulder some of the risk once felt by distributors and retailers. In other words, where to manufacture a product is a more complex problem than simply calculating wages and shipping costs. Reshoring is merely a symptom of that. Manufacturers need to think about several factors as they choose where to build products and how to source materials for those plants.

Among them: Supply chain decisions, mass customization demands and shorter lead times.

Supply Chain Decisions

The Japanese tsunami of 2011 is a perfect illustration of supply chain volatility and its hidden costs. Japanese automakers supply their North American customers with products built mainly in North America. For example, the Toyota Camry is more "American" than many cars built by Detroit. But the tsunami exposed a weakness: several key parts for North American factories came from Japan, and only Japan. Weeks of backlogs in getting parts to the U.S. came just as auto sales were beginning to turnaround for all automakers, and the bottleneck cost the affected companies a reported $1.3 billion.

Manufacturers need the ability to model "what if" scenarios to anticipate possible supply chain disruptions and then choose the most cost-effective means of avoiding them. Modeling that uses causal data like weather events or war disruptions -- or less ominous events like promotions -- should be a regular part of a robust forecasting business process.

Mass Customization Demands

In my grandfather's day, Henry Ford promised you can have any color of Model T you want as long as it was black. Customization was non-existent. Forty years later my dad could scarcely believe that he only had to wait four weeks for his "specially" built 1968 Buick Electra 225. Today, my daughter can waltz into the Apple store where they have devices in a multitude of colors and, oh yes, she also can have her name imprinted on the device she buys. We are on the cusp of some manufacturers being able to predict each customers individual preferences before they come to shop, whether online or in a bricks-and-mortar store. For post-modern shoppers, it is all about us. We want unique products that express our personality. The manufacturers who thoroughly know their customers and have the data and advanced analytics to gain insight from that data in a timely way will win the day. There are 7 billion people on the planet. More than 2 billion of those people -- and the figure grows daily -- express their preferences through social media channels. Those expressed preferences represent a data goldmine that can lead to mass-produced yet customized products. Mass customization might sound like an oxymoron, but whoever figures out how to anticipate the individual consumers preferences and build it before they come, will own the market.

Shorter Lead Times

Electronics and soft goods manufacturers are increasingly being asked to shoulder the burden of getting the right amount of the right product to the right stores at the right time. Once upon a time, retail buyers would place an order for 100,000 pairs of a new style of athletic shoes and put the shoes that didnt sell by the end of the season on the clearance rack. Today's realities mean buyers now will place a smaller initial order with the contractual expectation that the shoe manufacturer will keep plenty of product in the pipeline. With that dynamic in place, suddenly an overseas plant is no bargain -- especially given the costs of expedited shipping to fulfill contract demands. But what if the local market has developed an appetite for some of the product being made there? In this case, analytical inputs to the sales operation and planning process can help manufacturers make cost-effective decisions regarding the balance of unconstrained demand and constrained supply, shipping costs and risks and time to market. Maybe the new model dictates that a Chinese plant is still a good bet for a large initial run -- along with the mass production of products for the Chinese market, while a smaller North American plant is tooled to handle restocks of hot products. Perhaps the Chinese plant should build the base product (that also can be sold as is in that market), with the North American plant customizing it for local tastes.

A similar optimization puzzle comes into play for the makers of heavy equipment. Shipping earth moving or agricultural equipment is costly, so it makes more sense to build that equipment near where the earth needs to be moved or where the corn needs to be harvested. Manufacturers need a keen understanding of not only where demand is today, but where it will be in 10 years as they plan plants and the supply chains that feed them.

Dow makes analytically driven decisions every day. It has a dedicated analytics excellence center that works with dozens of business units. Using this analytics know how, they determine whether and when to open plants in new markets, how much supply will be required, how much demand to expect by product line, how much raw material prices will fluctuate and other key factors that could sink them if they get it wrong. The profit on many of these products is narrow, so their analytics-driven decisions have an enormous impact. One interesting trend that has come out of their work is that decision-making executives now are schooled in analytics, and they make decisions based on the facts analytics deliver. It has become a critical part of their culture. They are not surprised to see a statistical analysis for any question because it is now a part of their corporate DNA.

When will analytics become a part of your company's DNA? Making manufacturing, supply chain or operational decisions without an analytics foundation is a sure path to lost opportunities and lost profits. Manufacturing products in the United States or China or Timbuktu isn't the issue. Making fact-based decisions about what's right for your company and your global customers is what matters most today.

Mike Newkirk is the Director of Manufacturing and Supply Chain Solutions at SAS.