Process mining explained by an exampleThe logistics process at SmartCoat Inc.
SmartCoat
Episode 8 (out of 8): prediction and real-time
process mining explained by an example© 2016 horsum
Ep. 8: prediction and real-time2
What’s on this week?
Prediction Real-time
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What preceded…
Marie, CEO of SmartCoat Inc., asked us to analyze and make recommendations for the logistics process via process mining and dataanalytics techniques ... Just by looking at the data in SmartCoat’s ERP system!
In the previous episode, Cédric calculated and analyzed the costs related to SmartCoat’s logistics process. He proposed Marie to rearrange her logistics process, to renegotiate sales prices and to request improved quality checks performed by retailers.
Have you missed the seventh episode? Click on Marie … and you will be redirected to the seventh episode!
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Cédric, your horsum guide
Hi, nice to see you again! We arrived at the last episode!
In previous episodes, I told you how I discovered and analyzed the logistics process at SmartCoat Inc. I presented you some process
deviations and bottlenecks, performed some benchmarking analysis, calculated the process costs and identified the interactions between
people and locations.
In this episode, I will talk about predictive analysis and real-time process mining. Do you come along for the last time?
CédricConsultant
Process mining provides a picture of your current business processes. But you can also use it to predict future errors (predictive analysis). In the previous episodes, we saw that unnecessary process activities, bottlenecks and inefficiencies are identified fairly easily. We also found out why something goes wrong.
By using data mining techniques, we are also able to predict that something will go wrong. For instance, we can predict that a smartphone will be scrapped. If the probability of scrapping is high for a certain smartphone, we can maybe perform some other process activities to avoid scrapping.
In short: it’s a powerful tool to tackle the cause of process problems.
Prediction
So how could SmartCoat benefit from predictive analyses?
Well, I saw in Episode 3 that 5 smartphones are not checked upon arrival. I wonder if smartphones that are not checked upon arrival, have a higher chance to be scrapped? Let’s investigate.
In Episode 3, I discovered that 5 smartphones are approved for scrapping by Marie. I wonder how much non-checked smartphones are part of these scrapped smartphones.
Prediction
On the right, I filtered the process so I only see the paths of the scrapped smartphones.
I see that 2 out of the 5 scrapped smartphones did not undergo inbound controls. This means that 2 out of the 5 smartphones without inbound checks ended up as scrap (40%).
The other three scrapped smartphones are part of 30 smartphones who were subjected to inbound controls. Resultantly, this means that 10 % of the checked smartphones was scrapped.
As a result, I note that for the investigated smartphones, the probability on scrapping is higher if a smartphone is not subjected to inbound checks.
In a way, this is a first indication and step towards predictive analysis. I discovered in this (smaller) sample that the chance on scrapping increases if no inbound controls are performed.
Prediction
Real-time
In the previous episodes, we performed our process mining analysis based on an event log with 35 cases. We performed an ex-post analysis.
We can also monitor the logistics process at SmartCoat in real time by linking the process to the IT systems. As such, we can continuously follow up on certain core processes, and make quick adjustments when necessary. This enables us to intervenepromptly, and we can avoid in a timely manner that things go wrong, while at the same time preventing costs resulting from inefficiencies and dissatisfied customers.
Event log analysis
Number of cases 35
Number of activities 16
Number of events 393
Start 02.03.2016 08:10:00
End 31.03.2016 15:33:57
Real-time
So how would SmartCoat benefit from monitoring the logistics process in real time?
Marie told us that it is not allowed to have more than 4 smartphones at the same time in the testing room. In Episode 3, I discovered that this rule is often violated by having more smartphones in the testing room.
If SmartCoaters could follow up the process in real time, they could anticipate such situations and better manage the flow of smartphones. As such, they could improve the overall quality of coated smartphones and increase the customer satisfaction by lowering the chance on bottlenecks (and thus shorter throughput times).
Event log analysis
Number of cases 35
Number of activities 16
Number of events 393
Start 02.03.2016 08:10:00
End 31.03.2016 15:33:57
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Cédric, your horsum guide
Fellow Process Miners,
It has been a great pleasure to have your weekly attention. My goal for this series was to provide you with tangible, easy-to-understand and pragmatic
examples on the opportunities of process mining. I hope this series can serve you as an addition to the existing “academic”
literature.
I chose to write this series from a consultant’s perspective to give you insights in how process mining can help you in real-case business situations.
Therefore, please feel free to let me know how process mining helped you in similar cases.
Farewell, and keep on process mining!Cédric
Consultant
process mining explained by an example© 2016 horsum
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8 episodes
Episode 1: introduction
Episode 2: process discovery
Episode 3: process deviations
Episode 4: benchmarking
Episode 5: bottlenecks
Episode 6: interactions
Episode 7: process costs
Episode 8: prediction and real-time
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Contact us!Questions?
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Contact us!
Dennis Houthoofd Frederik Vervoort
T: +32 488 90 41 40E: [email protected]
T: +32 473 91 05 80E: [email protected]
www.horsum.be
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