Are you frustrated with trying to use Process Mining to analyze your material flows through manufacturing and supply chain?

What makes LOP.ai better than process mining?

A new approach: we focus on the material, not on the order.

 

  1. Inventory: process mining tools see inventory as ‘anonymous‘. Not being able to analyze the time in inventory is a serious shortcoming.
  2. Material ID change: process mining tools don’t understand BOM, BOL. BOD, routings so cannot analyze supply chains, only individual steps/links
  3. Forecast: Without a make-to-stock forecast, you cannot understand what is driving recent material movements.

 

Current Process Mining tools are not able to create a Value Stream Map. They can analyze each link, but not the chain. This is where LOP.ai steps in.

 

More information:

What is a Digital Twin?

The term Digital Twin has become fairly common, though there is still a lot of confusion about its meaning. A digital twin is a virtual representation of a physical object or process that captures both physical and behavioral characteristics. The reality is that the definition has changed over time. Digital Twins are being developed for different purposes. While each purpose is valid and valuable in and of itself, there are common characteristics. More about the origins of digital twins: Read it here

What exactly is a Digital Supply Chain Twin?

According to Gartner, a digital supply chain twin is ‘’a digital representation of the End-to-End physical supply chain that can be used to drive understanding and make decisions’’. The goal is to have one End-to-End SC data model, from sourcing through to delivery. It is built from granular data to form a dynamic, synchronized, real-time and time-phased representation of the various associations between the data objects and entities that ultimately describe and make up how the physical supply chain operates.
More on the importance of DSCT: Read it here

Why you need a Dynamic Digital Supply Chain Twin?

By capturing your demonstrated performance and incorporating End-to-End inherent variability, not only can you drive understanding in order to facilitate design improvements, policy improvements, process improvements, and conformance and compliance, you can also enable better decision-making through early alerting when significant changes might be occurring and predicting the consequences of the changes, and by estimating the likelihood that a plan will produce the intended results. As Gartner states, a Digital Supply Chain Twin will allow you to “move from unknown uncertainty to known variability”, and to include that variability in your decision-making through Probabilistic Planning.
Learn more about Probabilistic Planning: Read it here