Challenges
Throughput.Inc put together 12 challenges of AI in SCM. There are a few important ones that should be considered before using AI.
Data Inaccessibility
AI requires data to analyze and produce results for SCM processes. Data retention has always been a high priority of managers, but there is always more to gather. The key is to improve tracking of metrics and communicate between sectors of the business. One example would be communicating and compiling data from marketing to better demand metrics. This also prevents functional silos from forming within a business.
Lack of IT Budget
AI isn't a cheap solution in the short-term. These infrastructures being implemented company-wide are expensive due to its complexity. Depending on the company-size, leadership needs to decide whether the risk is worth the future savings.
Lack of Quality Data
Not only is sharing data important, but ensuring the data is correct and relevant. AI can only analyze and provide answers to the data is being given. Companies with poor data collection could hurt their business by relying on decisions made from faulty data.
Other Downsides
As data becomes increasingly important, cyber-attacks will become more prevalent. Companies will need to spend additional resources on protecting their data and the privacy of their customers/suppliers.
In the future, most companies will use AI to reduce costs and develop better processes. As a result, stagnation of ideas can become a problem. This will be enhanced because everyone will have access to AI so no competitive advantage will exist. Companies can't result to complacency and rely too heavily on machines.
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