Machine Learning in Supply Chain (Logistic Analysis)
Machine Learning in Supply Chain
(Logistic Analysis)
Introduction
Supply-chain management
has been defined as the “design, planning, execution, control, and monitoring
of supply chain activities to create net value, building a competitive infrastructure,
leveraging logistics, synchronizing supply with demand, and measuring
performance”.
ML
Applications in SCM
ML applications that can
be explored and implemented in the SCM space have been discussed in this
section.
1.
ML-based Demand Planning:
Leading
SCM vendors do offer functionality for Regression modeling or causal analysis
for forecasting demand. The functionality is embedded in the DP module.
However, if a more rigorous and advanced approach is desired, then one can
forecast demand numbers outside of the SCM system using advanced modeling and
then upload them back to the SCM system.
Advanced
modeling may include using advanced linear regression (derived variables,
non-linear variables, ridge, lasso, etc.), decision trees, SVM, etc., or using
the ensemble method. These models perform better than those embedded in the SCM
solution due to the rigor involved in the process.
Further, in addition to
the above, one can implement a weighted average or ranking approach to
consolidate demand numbers captured or derived from different sources viz. modeling,
entered by the sales team, long-term planning, etc.
Whether deep learning
(neural network) will help in forecasting the demand in a better way is a topic
of research. Neural network methods shine when data inputs such as images,
audio, video, and text are available. However, in a typical traditional SCM
solution, these are not readily available or not used. However, maybe for a
very specific supply chain, which has been digitized, the use of deep learning
for demand planning can be explored.
2.
ML-based segmentation (clustering) of products:
Generally,
while implementing an SCM solution, ABC analysis of SKUs (classifying products
based on their importance i.e. on sales value or volume (quantity) or the
margin, etc.) is done. Such classification is used for configuring, applying,
and implementing a customized strategy for every class. Such analysis makes the
implementation more effective because A-class products need completely
different treatment as compared to the ‘C’ class. For example, for ‘A’ class
products, the organization may not allow any changes to the numbers as
predicted by the model.
A better approach will be
segmenting SKUs using clustering (e. g. K-Means) and then applying different strategies
to each segment. However, the interpretation of segments (clusters) has to be
done manually by business analysts/data scientists. And same segments may be
interpreted differently by different analysts. Maybe in the future, an AI-based
algorithm will be available which will provide a better and more interpretable
solution to the clustering problem.
3.
Reinforcement Learning (RL) as an advisor:
Reinforcement
Learning (RL) is the science of decision-making. It is the third paradigm in
Machine Learning (other than supervised and unsupervised learning) for learning
optimal behavior in an environment to obtain the maximum reward. The optimal
behavior is learned through interactions with the environment and observations
of how it responds like children exploring the world around them and
understanding the actions that help them achieve a goal. RL has been
extensively used for playing games like chess.
Similarly, in a Supply
Chain environment, the RL algorithm can observe planned & actual production
movements, and production declarations, and award them appropriately. Hence RL
algorithm can be used to fine-tune transactions in the supply chain. Unlike
games like Chess, RL can be used as an advisor in the supply chain. However
real-life applications of RL in business are still emerging hence this may
appear to be at a very conceptual level and will need detailing.
Normally supply &
production planning processes are run as batch jobs on a weekly, fortnightly,
and monthly basis as it is not feasible to run them daily and possibly
impossible to run them on a real-time basis. Rather it may not make sense to run
them in real-time as it will create more confusion However, a lot can change daily. So, if ML algorithms can amend, adjust, and refine plans daily without running all logic embedded in the SCM systems, then it will
be very useful to business users. Suggested approaches include a rule-based or
heuristics or some other ML algorithm, which will analyze the cumulative status
of the supply chain (e.g., to date in the month) and amend the supply or
production plan for the coming days/weeks.
5.
ML-based Production Planning and Scheduling:
Supply Planning or Supply
network planning optimizes production using a production capacity at a very
broad level. However, further optimization and scheduling are done using an
advanced optimizer, which may consider additional constraints such as
sequencing or constraints specific to a production process in the industry. If
it is not feasible to optimize using MILP or other optimization algorithms,
then specialized approaches like genetic programming are used.
6.
Digital twin for the Supply Chain:
A
modern Supply Chain is well connected by IoT devices, and all transactions are
updated in real-time, hence it is possible to compute the majority of KPIs in
real-time. The information on KPIs can be made available to management in
real-time using a suitable dashboard.
Further using AI-ML-OR,
if one can simulate the balance period and predict KPIs that will be achieved
at the end of a period, given the status, supply plans, and past trends, then
decision-makers will be able to take corrective action during the month itself
without waiting for month-end. In many scenarios, KPIs are reported at the
month-end or quarter-end, and sometimes, it becomes a ritual because, by that
time, SCM teams would have already initiated actions toward the next period.
7.
AI-based chatbots for the core SCM team
A chatbot can be very
useful to various user departments such as sales, purchase, production others,
which will access SCM databases and support queries using NLP modules. It is a
very effective approach to answering queries written in natural language.
A report showing very
‘odd’ product movements or production declarations will be very useful as it
will help management to focus on those specific movements. However, this will
obviously need labeling to be done for past periods i.e., classifying and
labeling movements as ‘odd’ or ‘ok’.
9.
Delivery / Truckload generation:
It
involves the generation of delivery/truckload considering the following:
·
Stock replenishment in the order of delivery priority (based on inventory
status, delivery time, and inventory norms).
·
As far as possible, generating a full truckload for a storage point makes sense
to simplify logistics, to avoid multi-drop scenarios or suboptimal
partial truckloads.
·
Avoid excess delivery pushed to storage points while forming a truckload.
·
And of course, depending on the inventory available at production centers.
Currently, various
strategies and optimizers are being used to generate a delivery schedule and
truckload. In the future, ML may be able to provide a more ‘perfect’ solution
to the above problem.
Python
libraries used
1. Scikit
learn
2. TensorFlow
3. Statsmodel
4. Prophet
5. Pytorch
6. Numpy
7.
Pandas
Conclusion
Today SCM solutions are
quite mature and offer a very good solution to streamline and improve the
supply chains. With the adoption of the Above-mentioned ML-based use cases, it
will progress toward an automated, intelligent, and self-healing Supply Chain.
Reference
1. https://www.analyticsvidhya.com/blog/2022/06/ai-ml-use-cases-for-supply-chain-management-scm/
Hitansh Lakkad
Business Analytics intern at
Hunnarvi Technologies Pvt Ltd in collaboration with nanobi analytics.
VIEWS ARE PERSONAL
#logistics#SCM#businessanalytics#business#analysis#hunnarvi
#nanobi#isme
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