Lean Operations

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.

Against this backdrop, one very large manufacturer asked us to help bring advanced AI technologies to predict, as early as possible, when a supplier would miss a deadline.

The Problem

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:

    • One part could delay an entire product, regardless of the cost of the part.
    • The supply chain professionals couldn’t look across a complete product’s thousands of parts to accurately predict where to put their attention.
    • And the systems they use had unreliable datasets.

The Solution

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:

    • Intercession was a key component to a successful tool. Our aim should be to reduce the number of suppliers that a manager had to consider.
    • The system we built would need to use the ERP tools in ways that our client hadn’t considered.
    • Our system would need to provide tens of thousands, even 100,000 predictions in a day across all the parts in all the product lines.

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).

The Results

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.

For more information on what we did for this particular customer, and to find out how we could work with your company to solve complex problems using AI and ML capabilities, reach out today.