As risks pertaining to intellectual property security and manufacturing costs in China increase, companies will begin planning significant shifts in supply chain operations. Component suppliers in Vietnam and Thailand stand to win some of these orders, although there has always been concern around the strength and capacity of the supply chain infrastructure beyond China’s borders. These issues dovetail with additional sources of risk, including typhoons, hurricanes, factory fires, and labor disputes within Asia’s emerging economies. Despite China’s manufacturing dominance of certain technology components, Vietnam and Mexico might also look more attractive from a cost and security standpoint as the year unfolds. However, procurement managers must be alert to questions about quality and availability when switching to these new resources.
Cost inflation: An upward trend
With the U.S. Federal Reserve promising additional interest rates hikes in 2019, U.S. borrowing costs are expected to rise. An inflated dollar also diminishes its reach and value abroad. This trend line, coupled with wage growth, tariff threats, and high demand, will conspire to inflate costs further.
However, another factor is also in play. Over the last five to seven years, it has been relatively straightforward to drive cost reductions for components as overall prices in technology tended to fall. However, numerous technological components are increasingly being incorporated into “non-tech” products such as light bulbs, automobiles and trucks, and household appliances connected to the Internet of Things. This trend in product development is contributing to a measurable spike in overall demand for electronic components. Moving forward, sourcing in high-tech companies will need to get more creative to obtain cost reductions or limit the impact of cost increases.
AI adoption picking up pace
As McKinsey analysts recently documented, the pace of AI adoption is accelerating from a “generalized awareness” by C-suite executives to increasing investment and adoption across multiple vertical sectors. The pace is expected to pick up in 2019, with companies launching pilot programs and proof of concept initiatives to test the waters of AI efficiency and return on efficiency (ROI).
There is also a close connection between digital transformation initiatives and limited rollout of AI-enabled technology. Enterprises concerned with achieving best-in-class performance for competitive advantage are willing to conduct internal audits on maturity and performance, experiment and revise specific business processes, measure the impact, and conduct iterative changes.
Most companies are already on some path to digital transformation. Among the leaders surveyed in LevaData’s 2018 cognitive sourcing study, over 75% felt that a digital transformation initiative is underway:
- Launched in past year: 35%
- Initiative active for 1-2 years: 25%
- Initiative active for 2+ years: 21%
- Initiative in the planning stages: 9%
- No plans under consideration: 14%
Expect a hybrid halfway point
For many organizations, predictive technologies will serve as a halfway point between automated processes in siloed departments and an integrated AI-led business. Virtually every organization has automated some portion of what used to be manual processes. Most current applications of enterprise predictive technology digitally collects and evaluates significant data and metrics but only from in-house sources. And to be truly predictive, algorithms must be running on massive data sets that pull from outside and third-party sources.
Consider a supply chain executive’s negotiation on pricing for pre-commercial procurement. With siloed, Excel-based spreadsheet reporting, procurement professionals are limited to a trend analysis (on future pricing, for example) based only on its past order. But imagine the ability to drill down into specifics on subcomponents, such as copper or aluminum. Imagine real-time visibility into commodity pricing trends by region or over time with data pulled from Forex (foreign exchange market) or past earning reports from multiple third-party data aggregators. Large and varied data streams improve the predictive quality on supply chain issues ranging from price to availability. Applying predictive models around specific areas of spend is an appropriate bridge leading to a full AI-driven approach. But a business truly running on AI-enabled recommendations and pattern sensing is still some years away.
Finessing contingency management
To accommodate 2019’s risk landscape, procurement will have a greater need to conduct scenario planning. Teams that employ “what if” scenarios will run through different possibilities to determine the potential impact on supply and determine new options to mitigate this impact. For example, a procurement officer might identify new suppliers or change suppliers in different locations.