=Paper=
{{Paper
|id=Vol-1838/paper-02
|storemode=property
|title=Optimization of Logistics Processes by Mining Business Transactions and Determining
the Optimal Inventory Level
|pdfUrl=https://ceur-ws.org/Vol-1838/paper-02.pdf
|volume=Vol-1838
|authors=Linda Terlouw
}}
==Optimization of Logistics Processes by Mining Business Transactions and Determining
the Optimal Inventory Level==
https://ceur-ws.org/Vol-1838/paper-02.pdf
Optimization of Logistics Processes by Mining
Business Transactions and Determining the
Optimal Inventory Level
Linda Terlouw
Icris, Kokermolen 6, Houten,
linda.terlouw@icris.nl, http://www.icris.nl
Avans University of Applied Sciences, Heerbaan 14-40, Breda,
http://www.avansplus.nl
Nyenrode Business University, Straatweg 25, Breukelen,
http://www.nyenrode.nl/
Abstract. This paper presents a practical approach to process mining
of logistics processes that we applied for several organizations. We used
‘regular’ process mining for process discovery, giving us a first impres-
sion of the structure of the process (e.g. dependency between individual
events/activities, frequency of occurrence of activities, and timing as-
pects). Using the DEMO methodology (based on Ψ -theory) we manually
identified the main business transactions from this discovered process
and we annotated the different events/activities as either coordination
or products acts belonging to a certain business transaction. After this
step we were able to analyze the logistics process at a higher level of
abstraction; we could mine data about business transactions instead of
low level events and/or activities. The business transaction process vi-
sualizations were easier to understand for stakeholders than flowcharts.
Next to this, we determined the optimal inventory level for guaranteeing
a certain service level (using Lean Six Sigma), making it possible for
organizations to make a tradeoff between item price and service level.
Key words: process mining, logistics, enterprise ontology, DEMO, lean
six sigma, inventory control
1 Situation
Recently we have come across several organizations that face difficulties get-
ting the right material to the right place at the right moment for carrying out
preventive and corrective maintenance. Preventive maintenance deals with in-
specting the current state of a machine, detecting potential problems and clean-
ing/replacing items before defects occur. It is scheduled after a certain fixed
Copyright c by the papers authors. Copying permitted for private and academic
purposes. In: Aveiro et al. (Eds.): Proceedings of the EEWC Forum 2017, Antwerp,
Belgium, 09-May-2017 to 11-May-2017, published at http://ceur-ws.org
2 Linda Terlouw
period or after a certain amount of usage (e.g. working hours of a factory ma-
chine or vehicle mileage). Corrective maintenance deals with fixing the machine
after a defect occurred. The demand for items is less predictable for the second
type of maintenance (and quick delivery is even more important). Inventory can
be stored at different locations having different lead times for transportation of
the item to the machine or vice versa, e.g.:
– the project location (for instance a construction area or an offshore location),
– the mechanics workplace (for instance a garage or a hangar),
– a local warehouse,
– a central warehouse,
– warehouse of the supplier.
Organizations must find a balance between costs of inventory and provided ser-
vice level to the mechanic who requires the items. Too little inventory may lead
to unnecessary downtime of machines and mechanics waiting instead of working,
too much inventory leads to high storage costs, less money available for other
business activities, and higher risk of an item becoming obsolete, damaged or
stolen.
2 Task
The task we were faced with at several organizations was to find bottlenecks
in logistics processes that lead to unnecessary downtime of machines. This task
included determining which items should be kept in inventory to provide an
optimal service level to mechanics for preventive and corrective maintenance.
3 Approach
We extracted data from ERP systems (Infor, SAP, and tailor made systems),
combined this data with data from other enterprise applications and converted
them to a structure suitable for process mining [1]. We mined processes using
the inductive mining algorithm [2] to get a first insight into the process. This
enabled us to discover:
– the most frequent activities and process paths,
– the dependencies between different activities/events,
– time between activities/events.
This type of process mining, however, does not deal with the semantics of
the individual activities/events. We combined process mining with the DEMO
methodology (based on the ψ-theory [3]) to get a better understanding of the
semantics of the business process. We annotated activities/events as coordination
or production acts as defined in the complete transaction pattern. By doing this
we could get a better understanding of the mined business process; we could
Optimization of Logistics Processes 3
easily see which business transactions where executed as they should and which
business transactions failed somehow.
Though process mining can be used to analyze item movement between dif-
ferent locations, it does not deal with optimal inventory levels. To find such an
optimal inventory level, we had to make additions to our process mining factory.
We introduced ideas from Lean Six Sigma on this topic and used a continuous
review model (inventory can be ordered at any moment). We determined for each
item type when new items should be ordered and how many items should be
ordered by calculating the inventory reorder point and the optimum order quan-
tity. The inventory reorder point is the level of inventory at which the inventory
should be replenished to make sure a certain service level can be guaranteed. It
is calculated as follows:
d
n=L + σ [Φ(S)], where:
D
– L is the lead time in days,
– d is the annual demand for the item,
– D is the number of working days in a year,
– σ is the demand standard deviation (per lead time),
– S is the required service level,
– Φ is the inverse of the standard normal distribution.
As we can see a higher variation in demand leads to a higher inventory level.
In our cases we assumed we cannot influence the demand or the variation in
demand (though this might be possible by analyzing the maintenance process!).
The service level is a process requirement which is, in general, dependent on
the price of the item. In some situations (very expensive or rarely used items)
only one item is ordered each time. In other cases we want to know how many
items we should order. This optimum order quantity can be calculated as follows:
r
2od
Q= , where:
ui
– o is the order cost,
– d is the annual demand,
– u is the unit cost,
– i is the interest rate or carrying cost.
4 Result
We made custom visualizations (see Fig. 1) in our process mining factory for pre-
senting logistics processes. These comprised more coarse-grained business trans-
actions instead of fine-grained activities/events. This enabled us to show the
process to domain experts in a way that reflect their way of thinking (different
actors communicating about services they offer to each other).
4 Linda Terlouw
Fig. 1. This figure depicts the root business transaction and a business transaction for
local warehouse picking.
These visualizations show the following metrics per business transaction type:
– total number successfully executed (only coordination steps from basic trans-
action pattern),
– total number failed (includes decline, reject or cancellation acts),
– average duration of the transaction (when item is delivered from inventory or
backorder),
– median duration of the transaction (when item is delivered from inventory or
backorder),
– the survival curve (how many cases are still ‘in the transaction’ after a certain
period).
Because DEMO processes have a tree structure we can compare the metrics of
an individual business transaction to those of the complete process (which is the
root transaction!). The bars of the metrics in the different business transactions
are therefore made relative to the values of the metrics of the root transaction.
We can now easily see which business transactions take up most time and which
business transactions fail frequently. We can show this visualization for all types
of material, but of course we can also slice it for a specific type of material. When
we do this we can view an additional visualization that shows inventory related
information as depicted in Fig. 2.
5 Reflection
In this paper we presented a way to combine fully automated process discov-
ery with making manual annotations about the semantics of individual activi-
Optimization of Logistics Processes 5
Fig. 2. In this figure we see the monthly number of item requests from mechanics for
a certain material. Using the formula presented earlier the inventory reorder point for
service levels of 80%, 95%, and 99% are calculated automatically. The results can be
compared with the inventory level.
ties/events using the DEMO methodology. We did not only focus on analyzing
the logistics process itself, but also on determining the optimal inventory level
to guarantee a certain service level. We see some ways of further improving our
approach. First, we would like to make a distinction between the different rea-
sons why a business transaction may ‘fail’ (currently we only distinguish between
succeeded and failed). Is it because of a decline, reject, or cancellation? This can
give the organization a better understanding on why things go wrong and what
to do about it. A second improvement is to find a way of dealing with declined,
rejected and canceled orders in determining the inventory reorder point. At the
moment we exclude them from this calculation, but this is not in all cases the
best way to deal with them. A third improvement is to automatically mine re-
lations between business transactions and to see where the order does not go as
planned.
References
1. W.M.P. van der Aalst. Process Mining: Discovery, Conformance and Enhancement
of Business Processes. Springer-Verlag, Berlin, 2011.
Want to know more about data science or process mining with our process mining
factory (www.processminingfactory.com)? Email me or call us at +31 30 227 04 13.
Looking forward to hearing from you!
6 Linda Terlouw
2. S. J. J. Leemans, D. Fahland, and W. M. P. van der Aalst. Discovering Block-
Structured Process Models from Event Logs - A Constructive Approach. Springer-
Verlag, Berlin, 2013.
3. J. L. G. Dietz. Enterprise Ontology: Theory and Methodology. Springer-Verlag,
Berlin, 2006.