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Sustainable solutions in fulfillment

How does Process Mining achieve fast and efficient results?

What can we do to increase the number of "perfect processes"? From the ordering process to delivery and receipt of payment, things can go wrong. With sufficient available data, we make predictions, correct deviations or at least plan for them. How could this look like for the complex area of logistics?

Imagine you receive the following message as an employee of a logistics company in the dispatch of goods:

"Due to delays in the procurement of goods, the order 2020-123456 will probably arrive 3 days after the confirmed delivery date. Please check carefully if the order can be prioritized. If necessary, please notify the customer accordingly about the delay and ask for their understanding in order to reduce the probability of order rejection by 90%."

Wouldn't it be extremely efficient if data-based information could be generated so quickly that it would be an immediate decision-making aid for the employee?

The Fulfillment Process

Relationships in a commercial enterprise often develop in an organic way and tell a whole story in the process. Without the possibility of objective evaluation, one often clings to existing structures - as often happens to us in our everyday lives. I think the question of why some companies are more successful than others can also be traced back to the fact that successful retailers constantly re-evaluate and continuously optimize their existing relationships: The right supplier, the right carrier and the optimal transport route are subject to constant change.

Processes in logistics challenge us through their complexity: Some goods are only important seasonally, others are perishable, short-term trends increase the demands on flexibility and cost efficiency. On the one hand, overstocks must be avoided or reduced and on the other hand sell-outs must be prevented.

At the same time, the question "What does the customer want?" always comes first, and different sales channels must be used simultaneously. Often companies still discover too few commonalities in the channels, which leads to the individual channels being treated separately. This means additional effort.

I would like to take up an example from the FASHION trade here, as I have gained a lot of experience in this branch. We imagine that the following points are relevant for the fulfillment process:

  • the goods are procured directly from the manufacturer, who hands them over to the supplier after production is complete
  • the delivery is made to my central or regional distribution centre
  • my own production, if available, can lengthen or complicate some processes, but suppliers could pick up the goods directly at their own production site - which would shorten the process
  • the possibility of direct shipping would be another option I would have to consider in the fulfillment process

What difficulties in the process are we facing here?

  • Supply routes and process variants that have developed organically over time
  • Required manual intervention, even with actually automated processes
  • the desire "to be able to intervene after all" is what prevents efficient automation in the first place
  • Distribution channels exist in parallel rather than being interlinked
  • Special processes in the various independently operating departments - each department claims to have developed the best process for itself...

All problem areas can lead to deviating delivery dates, production delays, and generally higher costs. How can all processes be analysed in their entirety? And how can the processes be visualized in their individual steps in order to better understand them in the first place?

You remember: the traditional way is to ask process owners or employees of the departments, which always leads to a subjectively shaped illustration of the processes. Identifying problems is also subjectively influenced by this, and optimizations made do not necessarily lead to a leaner solution.

The new way: Process Mining

Let's imagine that a software makes the complete and objective analysis of all processes possible, and this is done by data extraction. Fast solutions and automation possibilities become "visible".

It does not matter whether a company works with many different databases and backends, and it does not have to be completely SAP-based, but all the databases involved have one thing in common: they set time stamps. Now all data points are recorded and, using Unique Identify, are combined and mapped to form an overall process. 

The goals of the optimization in our case would, of course, be: punctual, complete delivery, fast order processing and, in addition, as few manual interventions as possible. Desired time parameters can also be defined in the software.

Visualization from the Process Mining Software of Celonis

Now, using the example of process visualization, each process step is shown to me, and the straight process path becomes visible - the so-called "happy path". Here in the example, however, deviations are already shown: Changes in quantity and the change in the transport method, which lead to a change in the delivery date. It is immediately visible that the 5 days defined in the "Happy Path" from order confirmation to delivery document become long 14 days due to the interventions. Also a late receipt of payment can now be assumed.

This is not just a possible analysis - the goal is to better understand the processes and their current problems. This shows us the possibilities for optimisation. For example, a first-time-right rate shows how many runs of the processes correspond to the "happy path". If it is only 44%, for example, we have to ask ourselves whether and how this can be increased.

By filtering the material groups, we can see which material causes the most delays. The same material leads to different delays in different production plants. The deviations from the "perfect process" can therefore depend on the production site.

Another filter visualizes the delivery times: The frequency of changes regarding quantity or mode of transport can be compared. A direct comparison of two distribution centres with opposing results can make a solution visible: What happens in the successful distribution centre compared to the one with the greatest difficulties?

Helping employees in an intelligent way

If I can visualize the problems of a process, I can also deduce how to solve these problems and thus provide my employees with concrete and quick assistance at any time. This is especially important when interacting with my customers and in terms of a good, long-term relationship. Direct messages via bot to the employees, which transmit immediate information and contribute to quick decision making, optimize processes in a very concrete way, also with regard to the customer. Examples of this could be:

What can we do about changes in the mode of transport?

  • Calculation of the correct delivery time and/or the correct order time

What can I do against possible delivery blocks? 

  • Which customers pay on time? Can a block be reduced based on this information?

My conclusion

Together with Process Mining, the basis for successful digitization is created: Harmonious processes, AI, automation, robotics and a digital supply chain can be used optimally on this basis. And that sounds trend-setting.

 

 

Check out the german webinar recording on this topic:

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