Smart Supply Chain

Machine learning is becoming an unavoidable component for companies to remain competitive in today’s economy. Defined as a data processing discipline in which algorithms can “learn” from great range of data, those algorithms continuously review and refine models to do predictions (Vidal, 2017)*. Within the context of Supply Chain Management, predictive analytics can be defined as using both quantitative and qualitative methods to improve supply chain design and competitiveness (Schoenherr & Speier-Pero, 2015).

Recent examples of applied machine learning corroborate this statement. Amazon now has algorithms to predict demand for hundreds of millions of products it sells, often as much as 18 months ahead (The Economist, 2018). However, it seems like this technology is not only reserved to giants such as Amazon or Google. In Germany, Otto, an e-commerce company, is using machine learning to predict what will be sold within 30 days with 90% accuracy which allows automatically replenishment of around 200’000 items a month with no human intervention (The Economist, 2017).

Goals:

By these examples, the aim of this project is to organize a conference and a workshop with private partners, both in Russia and in Switzerland, to further discuss and highlight the potential of applied machine learning and its impact on Supply Chain Management with the following goals:

  • To strengthen the collaboration between Russia and Switzerland
  • To further develop complementary skills in both academic institutions
  • To train young researchers in Smart Supply Chain Management
  • To reinforce interdisciplinary (engineering and management) projects
  • To develop an applied project with a private partner

Concept:

The conference in both countries will present the state of the art and results of the several applied researches conducted in this domain and by the partners. In addition, it will share the vision of the private partners about the benefits and constraints of this technology.

Then, the workshops will be organized and held in a company (could be the partner’s company or another one) where machine learning solutions are being developed or already being used. The purpose of the workshops is to better understand and discuss the impact on the day-to-day Supply Chain Management, how it can positively influence the key performance indicators and ultimately lead to operational excellence. Target audience will be companies interested in a joint collaboration with Russia and willing to implement innovative solutions.

We have already reached out to several companies that could potentially become partner of this project.

Farm SKD Ltd., a drug distributor based in the Samara region, already confirmed their willingness to participate in the project.  This company intends to use machine learning technology in order to optimize inventory planning and prevent counterfeit products. Working with the Swiss and Russian academic institutions will be an opportunity for this company to increase its knowledge in smart Supply Chain Management to increase efficiency.

It is expected that, once the project is started, additional Swiss and Russian business partners will collaborate in this project.

 

Participants 

Prof Karine Doan
Haute école Arc, HES-SO

Prof. Dr. Stefano Carrino PhD
Haute école Arc, HES-SO
HE-Arc Ingénierie

Nina Racine
Haute école Arc, HES-SO
Centre de recherche et de formation suisso-russe Runo

Dr. Tatiana Evtodieva Phd
Samara State University of Economics
Department of commerce, services and tourisme

Prof., Dr. Dana Chernova
Samara State University of Economics
Department of commerce, services and tourisme

Dr. Natalia Ivanova
Samara State University of Economics
Department of commerce, services and tourisme