A presentation given in the CILT's 15th Student Conference on 20 February 2019 at the University of Greenwich.

1. www.cranfield.ac.uk/som
How can machine learning
help coordinate the
supply chain?
Professor Emel Aktas
20th February 2019

2. 2
o Supply Chain Coordination
o Artificial Intelligence and Machine Learning
o Achieving Coordination
Outline

3. 3
An integrated supply chain
Material
Control
Stage One: Baseline
Purchasing CustomersStorage
Stage Two: Process and systems Integration
Stage three: External Integration
DistributionMake
CustomersOperations
Materials
Management
ERP enabled
One plan
Suppliers Customers
Internal Supply
Chain

4. 4
The Bullwhip Effect
Chapter 10 • Coordination in a Supply Chain 251
1000
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WholesalerOrder
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ConsumerDemand
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ManufacturerOrder
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RetailerOrder
1 5 9 13 17 21 25 29 33 37 41
1 5 9 13 17 21 25 29 33 37 41 1 4 7 10131619222528 31343740
1 5 9 13 17 21 25 29 33 37 41
Time Time
Time Time
Wholesaler’s Orders to Manufacturer
Consumer Sales at Retailer
Manufacturer’s Orders with Supplier
Retailer’s Orders to Wholesaler
FIGURE 10-1 Demand Fluctuations at Different Stages of a Supply Chain
to coordinate information exchange with thousands of suppliers and dealers. The fundamental
challenge today is for supply chains to achieve coordination in spite of multiple ownership and
increased product variety.
One outcome of the lack of supply chain coordination is the bullwhip effect, in which
fluctuations in orders increase as they move up the supply chain from retailers to wholesalers
to manufacturers to suppliers, as shown in Figure 10-1. The bullwhip effect distorts demand
information within the supply chain, with each stage having a different estimate of what
demand looks like.
_CHOP3952_05_SE_C10.QXD 10/25/11 4:34 PM Page 251

5. 5
Coordination Typology
Arshinder, K., Kanda, A., & Deshmukh, S. G. (2011). A review on supply chain coordination: coordination mechanisms, managing
uncertainty and research directions. In Supply chain coordination under uncertainty (pp. 39-82). Springer, Berlin, Heidelberg.

6. 6
Supply Chain Analytics
agricultural production industrial refining packaging distribution consumption
How frequently should we transport to minimise costs?
What is the likely demand
for each product?
What was the yield of each field?Descriptive
Predictive
Prescriptive
CompetitiveValue
Analytics Spectrum

7. 7
oArtificial Intelligence
§ a sub-field of computer science and how machines
can imitate human intelligence (being human-like
rather than becoming human)
oMachine Learning
§ Ability to learn without being explicitly programmed
(Samuel, 1959)
§ Learn from experience E with respect to some
task T and some performance measure P
(Mitchell, 1997)
Artificial Intelligence – Machine Learning

8. 8
Typical Machine Learning Problems
https://becominghuman.ai/an-introduction-to-machine-learning-7db04da817c4

9. 9
AI and ML in Business
https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now

10. 10
AI Impact on Industries
https://www.raconteur.net/digital-transformation/artificial-intelligence-continues-progression-
mainstream?utm_source=email&utm_medium=Newsletter&utm_campaign=Weekly&utm_term=feb-14

11. 11
Economic Value of AI ($trillion)
McKinsey, 2018

12. 12
Transitioning into Digital Supply Chains

13. 13
oLogistics and Distribution
§ Route Selection
§ Autonomous ships, vehicles
oSupplier Selection and Monitoring
oAutonomous robots
§ Production
§ Warehouse
§ Delivery
oForecasting sales
oPredicting delivery times
oAutomated Decision Making
Achieving Coordination in the Supply Chain
KPMG / Harvey Nash, 2018

14. 14
oAutomated work environments
oRobot programming
oMaintenance of AI / bots
oCo-working with AI / robotics
oManaging the AI workforce
oBig data for planning
oIdentifying AI opportunities
Skills Gap and Job Prospects
Deloitte Insights (2018) The jobs are here, but where are the people?

15. 15
oExciting times for machine learning in supply chain management
§ Supervised: Regression, Classification
§ Unsupervised: Clustering
oAny task that requires less than 5 sec thinking time to be automated
oFuture Supply Chains need talent well-versed in robotics, automation
and data science.
Key Take-Aways

16. 16
Thank you and Questions?
@emelaktas
https://uk.linkedin.com/in/emelaktas
emel.aktas@cranfield.ac.uk
Dr Emel Aktas
Professor of Supply Chain Analytics
Logistics and Supply Chain Management
Cranfield School of Management
Cranfield University
College Road, Cranfield, MK43 0AL
+44 (0) 1234 75 11 22

17. 17
References
oDeloitte Insights (2018).Manufacturing Skills Gap Study
https://www2.deloitte.com/insights/us/en/industry/manufacturing/manufa
cturing-skills-gap-study.html
oKPMG / Harvey Nash (2018). The Transformational CIO
Transport/Logistics Industry Findings
https://assets.kpmg/content/dam/kpmg/be/pdf/2018/10/cio-survey-2018-
transport-corr.pdf
oMcKinsey (2018). Notes from the AI Frontier
https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-
from-the-ai-frontier-applications-and-value-of-deep-learning
oMitchell, T. (1997). Machine Learning, McGraw Hill.
oSamuel, A. L. (1959). Some studies in machine learning using the game
of checkers. IBM Journal of research and development, 3(3), 210-229.
oWIPO (2019) https://www.wipo.int/publications/en/details.jsp?id=4386