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Industrial Machinery Manufacturer sees a 10% reduction in order delays


$1bn Industrial Machinery Manufacturer



Use Case

Order management


10% reduction in order delays

The Challenge

Increased supply chain complexities and uncertainty led to significant order delays for our customer which impacted both the overall revenue due to numerous cancellations, as well as working capital caused by late payments and finally the customer satisfaction.

Given the different variation in causes for these delays, and the lack of historical data related to these supply chain issues, traditional machine learning techniques weren’t sufficient enough to tackle this challenge.

The client required a solution capable of identifying the true root causes of all order delays,  forecasting the likelihood of delays during the order lifecycle and recommending specific actions to reduce delays across individual and aggregated orders.

Order delays negatively impact revenue, customer satisfaction and brand

Using data & AI many of these delays can be prevented

Supply chain experts can't input their knowledge in traditional ML models

Current approaches don’t leverage the rich knowledge of supply chain professionals

Correlations-based Machine Learning can't help

Observing correlations and using black-box models to predict delays don’t make a real impact


causaLens quickly connected to client’s ERP system accessing rich data such as order details, plant availability, materials required etc.
Using decisionOS, the best of client’s domain expertise and data-driven causal discovery approaches (using our human guided causal discovery framework),
 a causal model was developed (using our structural causal model, causalNet).
The causal model empowered the client to:

  1. Trace back the most common root causes behind significant order delay
  2. Forecast the likelihood of delay from order creation and throughout the order lifecycle
  3. Receive optimal recommendations on how to allocate orders across manufacturing plants to reduce the likelihood of delays

Results and Benefits

The causaLens solution accurately forecasts 75% of order delays, including their unique root cause and recommends optimal actions to reduce this likelihood. This allows the client to reduce their overall delays by 10%, leading to a boost in client satisfaction and overall revenues.

Leveraging decisionOS’ Root Cause Analysis (RCA) engine, the client can identify common bottlenecks across their order lifecycle, such as material availability, change events or overlapping orders, allowing them to prioritize areas for investment in their supply chain and further reduce the likelihood of future delays.

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