Machine Learning Support in Supply Chain Management- Potential PhD Topics
                     How Can I Apply To
Machine 
LToe Parerdincti Snupgply Chain 
Risks-  Potential PhD 
Topics
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TODAY'S 
DISCUSSION
In brief
The role of ML in predicting supply 
chain  risks
Importance of ML in supply chain risks  
Interpretation based on Machine 
Learning  Conclusion
References
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In Brief
In a world full of competition where every  
business is struggling to put itself ahead,  
Machine Learning (ML) can grant some 
exclusive  opportunities.
From increasing profit margins to reducing 
costs  and engaging customers, machine 
learning can  help you in many ways.
Contd...
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As the world is triggered by the COVID-
19  situation, managing and handling 
the supply  chain risk is what everyone 
is thinking about.
From lowering the risk and improving the  
forecast accuracy machine learning is the 
USB in  the supply chains.
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The role of ML in predicting 
supply  chain risks
This application is based on artificial  
intelligence that searches for trends, 
accuracy,  patterns and quality which 
makes your  experience better in the 
system.
Especially the ML algorithms which lead 
to the  platform of supply chain 
management helps to  predict various 
risks involved from unknown  factors this 
will help in keeping up the  constant 
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flow of all goods in the supply chain.
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Importance of ML in supply chain 
risks
Many renowned firms are now paying keen 
attention  to ML to improve their business 
effi ciency and  predict risk in supply chains.
So, let’s take some time to understand how 
AI  addresses the various problems involved 
in supply  chains. Moreover, we will also 
learn about the  advanced Technologies role 
in the Management of  the supply chain.
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1. Cost 
efficiency
ML can be great in waste reduction and 
improving the  quality. It can have an enormous 
impact on the supply  chains.
The power lies in its algorithms that detect the 
pattern  from the data and help in predicting the 
involved risks  in supply chains. ML can 
continuously integrate  information and 
emerging trends to meet the new  demands. 
Thus, it’s very useful for retailers and business  to 
deal with aggressive markdowns and helping 
them  in cost effi ciency.
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2. Enables product 
flow
With its set sequential operations it 
enables  smooth product flow. It 
monitors the product  line and ensures 
the targeted process of  production is 
achieved.
It offers an overview of the system thus 
it  minimizes risks involved in the 
supply chain.
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3. Transparent 
management
MI can communicate and explain the risk 
involved in  supply chains with transparency. It 
helps humans to  understand the procedure 
and take the right decision.
From e-commerce giants too small to medium-
sized  business MI helps to manage their sales 
and predict  future risks with transparency.
Moreover, it helps in relationship management 
because  of its faster, simpler and proven 
practices in  administrative work.
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4. Quick solution for 
problems
MI helps to resolve problems quickly with the 
help  of previous data. The MI prediction is 
based on  outcomes of the past results from 
data.
It is best to deal with unbiased analysis of  
quantifi ed factors to generate the best 
outcome.
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Interpretation based on 
Machine  Learning
ML is a way of Programming with Artificial  
Intelligence. It replaces set rules of 
calculations  with the program. With the 
given set of data,  algorithms statistics, it 
combines and represents  in a model  form.
These models will make predictions 
based on  the input data.
Co
nt
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.
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It involves computer-aided modell ing 
for  supply chains. It is a process to 
enhance  performance and limit risks 
with concrete  predictions. With the Help
 Coof lDleacttaion,
MI concludes with precise algorithms. 
MI is  perfect to manage the supply 
chain and deal  with all the risk 
involved in it.
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FUTURE RESEARCH 
TOPICS
S.No Type of Data Algorithm Purpose References
1 Patients data  Machine Learning To identify key biomarkers to 23predict the mortality of individual patient [1]
OntoLFR(Logistics Financial 
Ontology database(risk hidden To adapt to the variability, complexity and relevance of risk in early warning and 2 danger database)  Risk Ontology + Apriori  [2] pre-control.
algorithm
The blockchain data flow is designed to show the extension of ML at the level of 
Block chain machine learning-  
3 Cloud Database  food traceability.Moreover, the reliable and accurate data are used in a supply  based food traceability system [3]
chain to improve shelf life.
Statistical approach for power  
To evaluate idleness and create techniques to optimize the profitability of the  
control based upon multiple  
enterprise. The maximization of trade-off capacity against organizational  
4 Business Data costing frameworks using a  performance is demonstrated and it is seen to be organizational inefficiency by [4]
machine learning model 
 power optimization has been validated
(SCM–  MLM)
Datafrom physical Research and practice of SC risk management by enhancing predictive and  
sources (e.g.
Digital supply chain twin – reactive decisions to utilizethe advantages of SC visualization, historical  
ERP, RFID, sensors) and  
5  Industry 4.0 disruption data analysis, and real-time disruption dataand ensure end-to-end [5]
cybersources (e.g. blockchain, 
 visibility and business continuity in global companies.
 supplier collaboration portals, 
 andrisk data)
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Conclusio
n
The effi ciency level of the supply chain is crucial for businesses. 
Operating  businesses with tight profit margins and with certain 
improvements can impact  the overall profit line of the business.
MI Technologies make the job simple to deal with various 
challenges of  forecasting and volatility demand involved in 
supply chains.
Moreover, it ensures effi ciency, profitability and better 
management of the  supply chain.
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Referenc
es
Ivanov, D., & Dolgui, A. (2020). A  digital supply chain twin for managing the 
disruption  risks and resilience in the era of Industry 4.0. Production 
Planning & Control, 1-14.
Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting supply chain risks using 
machine  learning: The trade-off between performance and interpretability. 
Future Generation  Computer  Systems, 101, 993-1004.
Asrol, M., & Taira, E. (2021). Risk Management  for Improving Supply Chain 
Performance  of Sugarcane Agroindustry. Industrial Engineering & 
Management  Systems, 20(1), 9-26.
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Chowdhury, M. E., Rahman, T., Khandakar, A., Al-Madeed, S., Zughaier, S. M., Doi, 
S. A., … &  Islam, M. T. (2021). An early warning tool for predicting mortality risk 
of COVID-19  patients using machine learning. Cognitive Computation, 1-16.
Yang, B. (2020). Construction of logistics fi nancial security risk ontology model  
based on  risk association and machine learning. Safety Science, 123, 104437.
Shahbazi, Z., & Byun, Y. C. (2021). A  Procedure for Tracing Supply Chains for 
Perishable  Food Based on Blockchain, Machine Learning and Fuzzy Logic. 
Electronics, 10(1), 41.
Wang, D., & Zhang, Y. (2020). Implications for sustainability in supply chain  
management  and the circular economy using machine learning model. 
Information  Systems and e-Business Management, 1-13.
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