SAP : Using Intelligent Technology in Sourcing to Get Ahead of Rising U.S. Tariffs

Since the first list went into effect on July 6, 2018, tariffs on goods imported to the U.S. from China have been a significant source of pain, particularly for those in the high-tech industry.

As reported by SourceToday, the 818 categories of products listed are valued at $34 billion, with electronic components making up 58 of the categories, accounting for 15 percent of the value of targeted goods. A second list may soon be on the way and promises to hit electronic components even harder, with 27 percent of the value of goods, totaling $4.3 billion of the $16 billion, in this second wave of tariffs.

Bracing for the impact of existing and future tariffs, some distributors are trying to clear the way by rolling tariffs into their prices, while others are depending on manufacturers to account for the cost after the buy. These policies continue to be in flux as the industry comes to terms with the issue, making it almost impossible to determine total expenditures. So how can commodity managers making sourcing decisions begin to tackle the growing tariffs problem?

Complexity of Tariff Rates

Some suppliers require manufacturers to account for tariffs prior to purchase, so let's begin with the basics of determining the tariff rate. To calculate tariffs, manufacturers need to know the Harmonized Trade Schedule (HTS), which provides tariff rates and categories for imported goods, and the country of origin for each component.

Before looking through these long lists of HTS codes, let's step back and ask the seemingly simple question: What is the component's country of origin (COO)? It sounds simple, but think about applying that to tens of thousands of components that make up a single product, and things get complicated. In addition, shortages in the industry, contributing to lead times of 30 weeks or more, mean that buyers are scrambling to snatch up components wherever they can - and these may have different COOs. And it's not just a matter of scale; in many cases, the COO isn't readily available.

Moreover, manufacturers work with assembly subcontractors to complete the build of their components, and these subcontractors may or may not be based in China. The interchangeability of subcontractors produces a highly volatile COO, meaning that the buyer may not know the COO until the component arrives on the doorstep.

Even with such a complex buying landscape, commodity managers are still required to make the best supplier-award decisions. What we've learned from COO, varying tariff rates and policies, and the complexity of HTS codes is that a one-size-fits-all strategy will not work. But commodity managers that are supported by intelligent technologies can still make well-informed, cost-effective award decisions when negotiating with component suppliers.

Machine Learning

There are solutions available to help answer the two most pressing questions: What is the impact of tariffs on your products, and what can your company do about it? For example, Paradata leveraged its machine learning engine and extensive parts knowledge base to deliver COO, HTS codes, tariff exposure risk, and distributor policies to help commodity managers better understand the total tariffs' cost risk, which parts are impacted, and where shortage risks exist.

Predictive Analytics

With the financial impact of tariffs, the need for commodity managers to intelligently negotiate the best component price is greater than ever before. To further expand on the power of machine learning, commodity managers can use the relevant data to apply predictive analytics paired with simulation and optimization algorithms to set data-driven negotiation targets and evaluate the impact of potential award decisions before committing to a final decision.

The power of predictive analytics sets negotiation targets that incorporate historical data and market-benchmark data. Sourcing decisions are optimized across multiple dimensions, enabling overarching, top-down savings goals to be supported with a bottom-up analysis. The value of predictive analytics is further enhanced by simulation and what-if methods. Before making an award decision, commodity managers can evaluate multiple award scenarios and make any necessary adjustments from the optimization engine - all based on predefined business rules modeled by the manufacturer.

Planning for the Future

Looking into 2019, the rising rate of tariffs is introducing new challenges for high-tech manufacturers. Intelligent technologies can help streamline by saving time spent collecting data, improving supplier-award decisions, and ultimately reducing direct spend. Intelligent technologies can empower commodity managers and keep them well-informed in this continuously changing environment.

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