In a 2016 survey of supply chain and manufacturing companies by Deloitte and supply chain association MHI, only 17% of companies were using predictive analytics. However, that number is anticipated to reach 79% by 2021.
What is to blame for the rather gradual adoption of AI in the supply chain and manufacturing sector? Despite its longtime reliance on predictive data, the industry has faced several challenges that have prevented a holistic integration of AI-backed systems, most of which stem from technology, processes, or people.
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Despite Data and Analytics, Challenges to Adoption Still Exist
Most of the factors preventing a higher level of AI adoption within the supply chain industry can be categorized into one of the below buckets:
1. Technology: Not having the proper tools to demystify AI and enable a communal understanding of the technology’s capabilities and benefits often leads to missed or wasted opportunities, data silos, and a lack of inclusivity, all of which can lead to miscommunication, grave project errors, and in serious cases, lost revenue.
2. Processes: Supply chain entities need to create a big-picture plan for making the development and use of data and models part of their overarching business objectives, not just something that is tackled piecemeal. Moreover, these companies need to leverage technology that can help enable clean, consistent, and usable data which is a cornerstone to any successful machine learning project.
3. People: While hiring the right data talent is a crucial piece of the puzzle, organizations need to remove any barriers that might be preventing inter- and cross-departmental collaboration. Teams need to be educated on the prolific gains that can be made from leveraging AI machine learning technology and, in turn, ensure any AI project is connected to the company’s central mission.