How can AI be disruptive in Supply Chain Management?
Silicon Valley is known for bringing disruption to industries or segments by changing traditional business models. As supply chain management is no business model, I was curious if the Valley tackles this process-driven discipline of supply chain management. When reviewing blogs and articles of the past year I did not look like someone was out there for disruption of this discipline. As to my knowledge, the major fields of AI involvement have been:
| Process automation: application of working AI methods to in-process optimization |
| Forecasting, planning, and sensing: using the digitization of the supply chain in the IoT-sense to provide decision support based on AI/ML methods |
| The advent of digital twins in subareas of the supply chain |
This all is hardly disruptive. It leaves organizational structures where they are and provides support for these organizations. It does not introduce new ways of doing processes.
There are significant reasons why this is the case:
Supply chain managers are risk-averse: the old saying is “inventory is your friend.” And although there are myriads of initiatives, technologies, and discussions about how to run a supply chain with less inventory keeping the same service levels, inefficient inventory deployment in the right place will make the supply chain more robust. And supply chain disruption (not meant in the sense of the heading of this article) is very costly for an enterprise
Operations is not a top innovation field: I have seen many technology companies that had great products and systems on the market, but their operations were not close to their product’s technology standard. Again, this is all understandable: the talent and the investment go where money can be earned
However, if we believe in the raison d’être of supply chain management, it is meant to contribute to the competitive advantage of a company.
A company has, apart from launches and innovative products, easily 80% of products that are substitutable. If furthermore, you consider the supply chain process from S&OP to cash received for products, then you are in-sync with the current IBP thinking and you compete with your mid-life products against other supply chains.
In my view, the only way to be disruptive is to go one level of abstraction higher. At least to my knowledge all approaches up to now are a refinement of the old control tower idea:
| Better and more real-time data through newer network technologies and IoT to get supply chain events |
| In memory databases that make real-time analytics possible, and libraries of AI algorithms are used to analyze and predict in the supply chain |
The sacred cow of all these approaches is that the actual decisions being made in a supply chain are not touched. There are metrics that measure the success of the decision directly or indirectly, but the ultimate decision is with the human decision maker. As an example, consider value add forecasting: the value of the added forecasting steps in the S&OP process is measured and then corrective actions are taken. The underlying assumption is still decision support: the decisions will be improved by a critical review of forecast value add.
So, no disruption: IBP helps to make the process and decisions better. Let us sketch a potentially disruptive approach:
So, Mr. Silicon Valley buys a mid-sized company. The unusual goal is now not to grow the company and sell it at a higher price, but to bring the company’s supply chain to the next level. The company has characteristics as follows:
| It comes from a mature industry: relationships are long-standing, a lot of long-running products, few launches. In short, a sustainable business |
| The company is not overly innovative: there has been an innovative core some time ago and now this carries the company |
| It should have a lot of products: the higher the number of SKUs, the higher the chance that manual decisions, even with decision support, will lead to sub-optimal supply chain execution |
| The company has as standard supply chain execution system and is measuring the usual KPIs and service levels |
In summary, it should be a mid-life, mid-size company. Why is this? This company will compete with its supply chain against other companies. There will be no defining moment, like the launch of the super product, that will change the market.
Here is the sketch of the program that Mr. Silicon Valley communicates to the staff:
| We want to improve the competitiveness of the company by introducing an AI-driven supply chain. This means that AI algorithms will propose and often make the decisions after having learned sufficiently from the people |
| All this happens in interaction with the staff: the system’s decision can be overruled. This is just another learning input for the system |
| As before, everything is measured, and we want to see that the supply chain performance is improving. In addition, we want to see that the overall quality of decision making is improving |
The disruptive point is the quality of decision making: our AI agent will have all relevant decision options as choices. This will be the big work: finding where the most relevant decisions in the supply chain are taken, either by systems or the staff.
The transformation is from using AI to support decisions to let AI learn a supply chain decision model.
On a more technical note, the resembles actor-critic methods: the human is still the critic, and the AI agent tries to be the actor.
Such an approach would be a big cultural change for the company. But still humans can overrule. This would change organizations and bring operations to the center of innovations.
Happy to discuss.