A Process Carol
“What’s Christmas time to you but a time for paying bills without money; a time for finding yourself a year older, but not an hour richer; a time for balancing your books and having every item in ’em through a round dozen of months presented dead against you?” – Ebenezer Scrooch, ‘A Christmas Carol’ by Charles Dickens
Processes are an inherent part of any business even if we are not always aware of them. As such, they are a large factor in the success of a business. So it is not surprising that there is a whole industry devoted to improving processes.
However, the way we look at processes and understand them has changed tremendously over the last decades and will continue to change in the decade to come. Basically we turned from looking at the past to understand the present to looking at the present to predict and change the future.
And we have turned to data to provide us with information. In contrast to Scrooch’s pessimistic view, data is not dead but can tell us a very lively tale about the ongoing processes in any company or office.
The ghost of process past
“These are but shadows of the things that have been,” said the Ghost. “That they are what they are, do not blame me!” – The Ghost of Christmas Past, ‘A Christmas Carol’ by Charles Dickens
In the past, we used interviews and workshops to create a model of an as-is process. Or at least that’s what we attempted. In reality, the process models created that way were not based on reality but on an imagined process. In a way they were already a should-be model as they modeled what people considered the right way to do things.
They were also often outdated once the actual modelling and following redesigning was done, as it simply took a lot of time to build the models, determine an improvement strategy and design a new process.
They were mere shadows of the real process.
That changed with the introduction of data driven Process Mining. In contrast to interviews and workshops Process Mining actually reveals the as-is Process with all variants, deviations, and outliers. With Process Mining it became possible to actually reimagine a process, as for the first time it was possible to know what actually happened.
However, Process Mining is not limited to historical data of processes past. We can use it to understand and shape the present.
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The ghost of process present
“You have never seen the like of me before!” The Ghost of Christmas Present, ‘A Christmas Carol’ by Charles Dickens
Looking and comparing past process data is a great tool to find out why certain things happened and if they can happen again. For example, we could compare the processes of different plants, or for different months, or different vendors. Discovering patterns and root causes for process problems is one of the greatest features of Process Mining as it saves us from guessing.
Learning from the past is one way to help us in the present but it is not the only way. With technology moving forward, we can look at running processes as they happen. We don’t have to rely on the past alone as we can see what is happening right now. A process doesn’t have to be finished for Process Mining, we can watch it progress and intervene when something happens.
Monitoring functions such as built-in alerts notify us when things don’t go as planned. We don’t have to wait to discover a potential issue, we know as soon as it happens and can act immediately.
With the combination of knowledge from the past and knowledge of the present that is updated constantly we get a look on our processes that we have never seen before. We get smarter every day and we can use our knowledge to constantly get better as well.
But wouldn’t it be great to predict the future and prevent problems from happening; to act instead of reacting?
The ghost of process future
“Men’s courses will foreshadow certain ends, to which, if persevered in, they must lead. But if the courses be departed from, the ends will change.” – Ebenezer Scrooch, ‘A Christmas Carol’ by Charles Dickens
Open processes are subject to changes and outside influences which are out of the control of a business. For example, if a vendor runs into delivery problems the production process can be thrown into jeopardy and cause a domino effect of delivery issues.
Of course, we could pay the invoice after we received the first reminder. Of course, we could notify a customer of delayed deliveries. But in the end, this would mean financial losses for a business, needless stress for the people involved in the process, and if it is a frequent issue, cause the loss of customer trust and business in the long run.
But many of these problems may be preventable if they were recognized early on. We could order from another vendor, we could pay the invoice, we could be able to deliver on time.
Predicting potential issues such as these with Process Mining, can work in two ways, either by learning from historical data (for example if one vendor has reoccurring delivery issues with a specific material) or by looking at open processes.
In the case of historical data, we identify patterns of problematic factors so that we can eliminate them. This is a useful tool, but it reaches its limits facing surprising events. This is where the second method shows the full potential of Process Mining.
There are several ways to look at running processes to predict and prevent problems.
For example, we can look at the lead times of running processes and specifically focus on those with unusually long times. Looking at the data we can find out why processes take a long time to finish, prioritize tasks, reassign resources, and trigger actions.
Another way to look at open processes is by looking at the sequence of activities and compare it to the should-be model. An open process with repetitions or weird sequences most likely needs intervention. As we know from our data what activity usually should happen next in an open process, we can also check the progress on that activity and set it in motion.
Those are just a few examples of how we can use Process Mining to prevent business problems from happening. There are many more ways to look at processes, compare, monitor, and analyze them and spot rogue behavior. But we are no longer passively waiting for things to happen, so that we can do damage control. We’re able to spot suspicious activity, so we can stop it from becoming a problem.
“The shadows of the things that would have been, may be dispelled. They will be. I know they will!” – Ebenezer Scrooch, ‘A Christmas Carol’ by Charles Dickens