Lean operations have been the holy grail of manufacturing for decades with a variety of tools and processes applied over the years, such as Kanban, Poka Yoke, and Kaizen. Yet, often, the systems rely on many working behind the scenes racing to meet tight customer deadlines, ensuring supplier delivery, and keeping the warehouses as lean as possible. This results in a monthly sprint cajoling suppliers, juggling inventory, and managing clients.
At the root of our customer's problem was stranded inventory cost. Essentially, because our client built large, complex, and often custom-made products, which involve tens of thousands of components, one part missing or late could delay the rest of the product from being complete. This occurs regardless of the cost of the component, i.e. a $1,000 part could delay a several million dollar product.
Today, for our customer, this results in $150 million per year, nearly half a million dollars a day, in inventory costs.
Our customer needed to have the ability to intercede as early as possible. Today, the system functions on a very human basis. Supply chain managers often know who might need more time, which suppliers are habitually late, and which parts are irreplaceable.
In the end, the ML models reduce the error in classifying late orders by more than 60 percent.
Yet, even the best supply chain managers can't accurately map out tens of thousands of parts. And, worse yet, often the ERP systems they're working on don't have reliable nor accurate information.
So, in short, the problem for our customer came down to several dimensions:
We started with tying our customer's many databases together. These yielded several thousands of tables of data. From this, we interviewed the main supply chain managers who work on these problems daily.
From their notes, we generated hundreds of features along with additional tables through our analysis, ML deep learning and feature engineering work, which leveraged our extensive understanding of supply chain management processes and ERP systems.
We learned how they interact with the data, what types of signals they look for, and what kind of tools they needed to be more successful. In this effort, we discovered a few key insights:
The true difference and difficulty in our system comes in how we have adapted it to work on incomplete data. Using our customer's faulty ERP data, we could predict late deliveries at eight weeks out - the most important time to make a prediction about a supplier because it allowed buyers to react to a change in the expected behavior.
Furthermore, as the delivery date approaches system accuracy improves. Eight weeks was viewed as a key point in tension between the absolute certainty in a prediction and providing enough time to take mitigating actions. Our client also indicated that a supplier changing its delivery date eight days before delivery is equivalent to completely missing it, because it's too late for a change in behavior to react. Our system then classified all last-minute changes as being missed deliveries.
In the end, the ML models reduce the error in classifying late orders by more than 60 percent.
That translates into 60 percent reduction of effort for users (i.e., procurement managers) to contact suppliers. For orders expected to be late, the error in delivery date prediction is reduced by more than 50 percent by ML models as well. That is a great improvement, given most orders have long lead time (1+ year).
Our customer had a complete model that could accurately predict a late delivery or commit failure with enough time to allow supply chain managers to intercede before it endangered the shipment date of their product and caused stranded inventory issues.
We emplaced a robust ML system using the data they had on hand, deriving value that they had not realized existed. This system allowed for greater trust in the system, and greater transparency, and amplified the abilities of the supply chain managers to triage the most important and challenging problems.