Throughout the last decade, the field of machine learning has witnessed achievements such as Watson, AlphaGo Zero and Dota 2, widely covered breakthroughs that raised both awareness and expectations for machine learning applications. As of today however, effectively all commercial machine learning algorithms are still performing very narrow tasks only, leveraging ever improving computer vision and voice capabilities to process large amounts of data and solve a specific set of problems including prediction, optimization and anomaly detection.
While all industries are likely to be impacted by machine learning, the manufacturing industry seems especially ripe for disruption. Machine learning’s current narrow abilities can already meaningfully improve manufacturing processes: Accurate prediction of customer demand drives down inventory holding costs; optimization of process flows improves cycle times and output rates; and early anomaly detection prevents costly factory shutdowns. For Foxconn, the largest contract manufacturer in the world, these potential process improvements could come at a critical time. As the main supplier of Apple iPhones, Foxconn’s recent profits have suffered as iPhone sales have plateaued. Additionally, production facilities in China increasingly face labor cost pressures as more young people shun factory work. With already thin margins at risk of eroding further, Foxconn Chairman Terry Gou announced in early 2018 that the Company’s goal is to become “a global innovative AI platform rather than just a manufacturing company”.
To turbo charge this pursuit in the short term, Foxconn partnered with Landing.AI, an organization led by AI pioneer Andrew Ng that aims to transform enterprises with machine learning solutions. One of the first manufacturing steps Landing.AI is looking to tackle is quality control. Instead of humans visually inspecting each manufactured component manually, Landing.AI has developed a classification algorithm that detects errors more quickly and accurately than humans. Foxconn is also looking to advance academic research focused specifically on the industrial space and has signed a cooperative research agreement with the University of Cincinnati for that purpose. In the medium to long term, the Company recognizes that external expertise alone will not suffice. Hence, it committed to invest ~$350m into AI R&D over the next five years and set up a new company called Industrial AI System in Silicon Valley, with the objective to recruit 100+ engineers and deep learning experts in Silicon Valley alone.
While these initial steps are promising, going forward, Foxconn needs to keep several challenges in mind as it looks to execute on its machine learning strategy. Firstly, Foxconn currently employs more than 800,000 people, the vast majority of which will not be familiar with machine learning. As a 2018 working paper by HBS professor Prithwiraj Choudhury highlights, a skilled operator is key to maximizing the impact of machine learning applications, which makes the immediate retraining of its current workforce a priority for Foxconn. That being said, a potentially large portion of Foxconn’s current workforce is at risk of becoming obsolete as machine learning takes over tasks previously performed by humans. With regards to these individuals, Foxconn has the duty to be transparent and proactive in its communication to make any impending transition as painless as possible, rather than reciting cryptic rhetoric that is often used nowadays when companies are confronted with the threat of machine induced job losses. Secondly, Foxconn will need to rethink its organizational structure. Should there be a separate machine learning vertical that works in collaboration with other divisions at the Company? Or should machine learning experts be integrated within the individual divisions? Lastly, looking at Foxconn’s long-term strategy, it needs to resist the temptation to view machine learning as the magic wand that fixes all challenges, and rather consider carefully which problems machine learning can address. For instance, while machine learning will likely increase manufacturing efficiency, it is unlikely to address the top line pressures from stagnating iPhone sales, a critical challenge that could require the Company to reassess its strategic priorities.
To conclude, while machine learning’s capabilities today certainly have the potential to markedly improve Foxconn’s manufacturing processes and bottom line, implementing machine learning goes far beyond simply applying a new technology solution, but rather entails a series of different challenges that will ultimately determine the success of Foxconn’s machine learning ambitions.
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