Artificial intelligence is becoming an increasingly popular solution in supply chain management. Often, supply chains can be restricted by cost, time or resources but machine learning provides a means of solving many of the key challenges faced in the supply chain management process.
However, there are still limitations to AI in its current form, and it’s important that we also understand these when considering implementing AI into supply chain management.
There are many simple administrative processes involved in the supply chain process, particularly with complex supply chains, and this often directs attention away from other aspects such as forecasting and strategic decision making. AI provides the opportunity to automate a number of these processes, freeing up time for supply chain managers to focus on planning for the future.
Some have noted how AI has the ability to coordinate planning and delivery between manufacturers and suppliers, allowing manufacturers to respond to suppliers’ changing needs without the inefficiency of human error.
Related article: Why Complex Supply Chains Can Be Dangerous
Ability to gather comprehensive data
The scale of AI is another factor that makes its implementation so efficient. Machine learning technology can be programmed to carry on operations at an extremely fast rate, as well as storing the data that they find. This ability to collect data is on a scale that humans simply couldn’t achieve and makes AI a valuable tool in supply chain management.
Automated predictive analytics
Due to the large amount of data that can be collected by AI, machine learning technology also has the ability to analyse the logistics, making forecasts, predictions and suggestions for improvement.
It has also been suggested that it will be possible to reduce forecasting errors by up to 50% by using machine learning-based techniques (Source: Digital/McKinsey).
It could pose some cyber security risks
51% of business leaders claim that cyber security is their number 1 reason for not adopting AI (Source: Deloitte). There is some hesitation to adopt the technology in some areas of supply chain management, due to the potential chaos a security breach could cause.
Therefore, any supplier who is considered adopting AI should also have an experienced cyber security team or advisor on board who fully understand the implications of the technology.
AI is not yet advanced enough for in-depth strategic planning
While AI can automate supply chain processes, act as the “go-between” for manufacturers and suppliers and even make accurate forecasts, you still need an experienced team to act on this data. Those who think they can simply replace a high-quality and strategic supplier because everyone will be using AI will soon realise their mistake. In other words, AI can make forecasts, but you will need your supplier to act on them.
The most effective supplier should be able to make individualised decisions, work with you to prevent problems in advance and put a strategy in place to avoid hold-ups and procurement issues.
It must be used by people who understand the capabilities/ limitations of the tech
At the moment, only a selection of individuals will understand the intricacies of AI and know how to operate it properly. It is extremely important that the technology is operated by somebody who understands what it can achieve, but also what it cannot be relied on for. Failure to use AI properly could result in systematic supply chain errors.
This is likely to change in the future, however, as project managers will be required to have a much deeper understanding of computers and computer programming. Training for these roles will likely involve data manipulation within the AI system and using these systems will be a central part of a manager’s role.