Artificial intelligence and machine learning capabilities are being claimed by just about everyone in the supply-chain market today. Many different people are writing about AI. Marketers, P.R. departments, bloggers, news media and analysts are cutting a wide swath to include everything in the AI category, rather than clarifying just what it is, and the unique value it may provide. In fact, AI and ML can already provide tremendous value in augmenting supply-chain applications. And we’re just getting started.
What exactly is artificial intelligence and machine learning for the supply chain? Firstly, definitions claiming that AI systems mimic human intelligence are inaccurate. AI systems, even connected with the internet of things, can’t absorb the environment around them and form new insights on their own. AI systems do recognize new patterns, but these are based on the limited data sets and auto-collection technologies that are put in place. Conversely, AI systems are able to codify their past experiences and improve systematically on past performance, whereas humans rely on their “organic” and often faulty memory.
Artificial intelligence is the umbrella term for many technologies, at the heart of which are an evolving set of algorithms and intelligent agents designed to discover patterns, learn, and create optimal insights, choices, and actions. AI today includes the broader set of data beyond traditional systems, often for inclusion in AI models. AI systems should maintain a history of their successes and failures to improve and self-correct for future recommendations and actions. Often, libraries of intelligent agents (that is, code) are available from the technology provider, or can be found in open-source libraries.
Machine learning is an application of AI that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to learn for themselves. There are supervised and unsupervised learning modes within ML. A lot of the news these days is focused on the unsupervised learning stage. This is extremely useful for processing big and unstructured data, which is just beginning to be leveraged by supply-chain users.
AI and ML will be great supporting players, but don’t worry. AI will not take over your job. The fact is that humans are needed to train these systems, evaluate their findings and modify the models over time. AI cannot act alone.
However, AI and ML will change things. New insights will open the door to great opportunities, based on the learning generated by AI. Hence, to avoid digital displacement, employees have to prepare themselves now to be the leaders of using these capabilities, and the advisers to their companies based on the new insights.
The market has already changed for technology companies and professional jobs due to AI. These changes will continue to gain traction within supply chains in:
- Jobs and professions:
- Data scientist. Supply-chain planning is an expanding area requiring greater expertise in discovering patterns and trends that could not be uncovered with past methods.Expertise is also needed in the many types of data sources and their meaning, and deriving practical value from them.
- AI consultant. The bulk of users will need advice and training on data and how best to leverage AI methods.
- New types of supply chain professional roles that we can only imagine.
- Growing device, software and platform services:
- IoT platforms, to connect to the sensory world in motion.
- Augmented reality, to provide the knowledge base along with guided task management.
- Data-as-a-service, for subscription-curated data sources.
- Database and data resource management tools, to interpret and formulate modern supply-chain data, both analog and unstructured, and not just digital in nature.
- New releases of software applications, to leverage AI and ML capabilities.
Continuing Growth and Integration of AI/ML into Supply Chain Suites
The acquisition of AI technology companies with a good bench strength of data scientists has occurred, and will continue throughout 2021 and beyond. AI and ML capabilities are being built into supply-chain software suites through organic development and partners’ source feeds. AI and ML-specific companies with practices in supply chain are growing in presence and traction in the market. Long term, though, AI and ML will be just another inclusion in the ever-growing smarts of our supply-chain technology application suites.
Just how should we think about jobs in Supply Chain and IT? Giving some thought upfront in your firm about roles and responsibilities will enable a smooth transition and on-going work.
Understanding AI/ML from a supply chain perspective is essential, rather than generic definitions. That will help in selection and adaption of the technology and methods.
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