ORL-33806 Data Driven Supply Chain Management


Credits 6.00

Teaching methodContact hours
Independent study0
Course coordinator(s)dr. R Haijema
Lecturer(s)dr. R Haijema
dr. J Valente
Examiner(s)dr. R Haijema

Language of instruction:


Assumed knowledge on:

Some prior experience in supply chain modelling, simulation and/or programming is beneficial. Students should have a basic understanding of statistics and probability distributions.

Continuation courses:

INF-31306 Engineering and Management of Information Systems
INF-33806 Big Data
ORL-30806 Operations Research and Logistics
YSS-32806 Advanced Supply Chain Management


UN-FAO reports show that about 30% of all food produced is not consumed in the end. To be able to feed future generations, innovations are needed to reduce this figure and to improve the efficiency of the food system. By nature of the product, Food Supply Chains (FSCs) need to be managed and controlled carefully. More and more data becomes available, which provide information that can be used to improve supply chain decisions. 
In this course we focus on the use of data science techniques in logistic decision making (e.g. forecasting to facilitate inventory decisions). Food waste at retail outlets can be improved by better demand forecasting, e.g. including weather data to better predict demand and to optimize order quantities accordingly. Next to traditional forecasting techniques, students learn to apply other modern data driven approaches (e.g. machine learning) during computer practical sessions. To compare the different methods and their impact on relevant performance measures such as profit, service level, and food waste, students learn to develop a simulation model of (part of) a food supply chain in Matlab/Python. The techniques you learn are transferable to other applications.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- explain the role of data in (food) supply chain management;
- apply data science techniques for forecasting to a provided data set;
- create a (computer) simulation model of a part of a (food) supply chain;
- quantitatively assess the impact of applying data and data science technique to a particular decision problem within the supply chain.


Lectures, tutorials, computer practicals, and assignments.


The examination will be based on practical attendance, tests and assignments during the course. Exact weights and minimum grades can be found in the Course Guide.


Handouts and references in Brightspace, e.g.:Hazen, B. T., Skipper, J. B., Boone, C. A., & Hill, R. R. (2018). Back in business: Operations research in support of big data analytics for operations and supply chain management. Annals of Operations Research, 270(1-2), 201-211. Li, D., & Wang, X. (2017). Dynamic supply chain decisions based on networked sensor data: an application in the chilled food retail chain. International Journal of Production Research, 55(17), 5127-5141.
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84.
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural Systems, 153, 69-80.
Zhong, D. R. Y., Tan, P. K., & Bhaskaran, P. G. (2017). Data-driven food supply chain management and systems. Industrial Management & Data Systems, 117(9), 1779-1781.

Restricted Optional for: MMEManagement, Economics and Consumer StudiesMScA: Business Studies3WD