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Internship Proposal for Master's or Final Year Internship from an Engineering School (with the poss

Forum 'Stages' - Sujet créé le 22/12/2023 par ramdanec (330 vues)


Le 22/12/2023 par ramdanec :

Internship Proposal for Master's or Final Year Internship from an Engineering School (with the possibility of continuing into a CIFRE thesis)

Title: Revenue Management Applied to the Beauty Sector

 

Company:Kalendes

Partner Research Laboratory:Loria, Optimist Team, Nancy

Internship Duration: 6 months (start date: from March 2024)

Application Deadline:until March 2024

Internship Location:The offices of Kalendes company in Rueil-Malmaison (Paris region).

Expected Level:end of studies (Master's 2 and/or 3A of engineering cycle). The desire to pursue a thesis next year would be appreciated.

Skills:Algorithm and programming, Data science, Combinatorial optimization, Applied mathematics, Machine learning.

Application:

To apply, please send the following documents 

CV

Cover letter

M1 and current year's grades transcript

List of references

Any other document allowing the evaluation of the application

 

Contact:Wahiba Ramdane Cherif-Khettaf, Associate Professor, Mines Nancy (Univ. Lorraine) wahiba.ramdane-cherif@mines-nancy.univ-lorraine.fr or ramdanec@loria.fr

 

Selection Procedure:A first interview phase will be conducted by members of the Loria research laboratory (Optimist team), and the second phase by Kalendes executives.

 

Context Presentation:

Kalendes is a startup founded in December 2019, specializing in providing a SAAS solution for the complete management of beauty professionals' activities. To date, the company has 3000 clients, has made over 10 million online reservations, and is ranked No. 4 in the French market.

Unlike Doctolib, which operates in a market with excess demand compared to supply, the beauty market has an excess of supply compared to demand. The average occupancy rate is below 50%. It becomes necessary to stimulate demand through tarif modulation or revenue management to better utilize unoccupied slots.

Market trends are also favorable: Often a vital need for beauty professionals to have more clients: Occupancy rate below 50%, decrease in turnover, increase in fixed costs, PGE reimbursement, increase in business closures.

Consumers looking for good deals: Inflation, decrease in purchasing power, too many constrained expenses, reducing incidental expenses, more flexibility in hours, especially thanks to telecommuting.

 

 

Internship Project:

Research in the field of revenue management has mainly focused on the airline and hotel industries, but much remains to be done in other industries. The goal of this internship is to apply the principles of tarification and revenue management to the beauty sector. The key point is to stimulate demand through tarification or revenue management and propose an optimization method to maximize the salon's turnover by setting the right discounts at the right time, for the right time slots, the right service, and the right professional.

 

Missions:

Analyze company data (over 4 million end customers and 10 million appointments over 4 years) to:

a. Explain and forecast the occupancy rate: Are there seasonalities in online booking, by customer segment, by salon activity, by professional, by geography, depending on the weather...?

b. Explain and forecast booking behavior: How do professionals' schedules fill up, how much time before? Are there patterns in the appointment lead time before the service, professional activity, day of the week, customer segment, type of service?

Conduct a literature review on pricing and revenue management techniques used in the services sector.

Develop a method based on optimization and artificial intelligence techniques to set the right discounts at the right time, for the right time slots, the right service, the right professional to maximize a salon's turnover.

 

References:

Chung Kwan Shin & Sang Chan Park (2000). A machine learning approach to yield management in semiconductor manufacturing, International Journal of Production Research, 38:17, 4261-4271.

Y. Lin, M. B. Alawieh, W. Ye and D. Z. Pan (2018). Machine Learning for Yield Learning and Optimization, 2018 IEEE International Test Conference (ITC), Phoenix, AZ, USA, 2018, pp. 1-10, doi: 10.1109/TEST.2018.8624733.

Peter P. Belobaba (1987). Survey Paper—Airline Yield Management An Overview of Seat Inventory Control. Transportation Science 21(2).

Garrett van Ryzin and Jeff McGill (2000). Revenue Management Without Forecasting or Optimization: An Adaptive Algorithm for Determining Airline Seat Protection Levels, Management Science, Jun., 2000, Vol. 46, No. 6 (Jun., 2000), pp. 760-775.

Serguei Netessine, Robert Shumsky (2002). Introduction to the Theory and Practice of Yield Management. INFORMS Transactions on Education 3(1).







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