Recent trends in, among others, digitalization, consumer behavior, e-commerce, traffic congestion, urbanization, environmental concerns, affect many (international, national, and local) supply-chains.
Managing supply-chains is often not the sole responsibility of one organization, as supply chains consist of independent organizations linked by complex coordination mechanisms, including markets. The fashion supply chain is no exception to these challenging trends.
This case study is based on a project initiated in the Netherlands, which continues to attract follow-up studies and projects aimed at implementation.
The case is accompanied with real-life data sets (samples from the data collected in the project). While the actual data has been collected on an ad-hoc basis, the challenges to students are:
The organizational structure can be a consortium of organizations; a joint project run by the main players; a project initiated by one organization; or any combination thereof.
For this partly hypothetical case study, we assume that the year is 2021, and that the data refer to 2020.
Some of the names of organizations and companies, have been altered for reasons of confidentiality.
All relevant documentation and data are available on the Open Science Framework (OSF).
In the folder Case, you will find the Dutch version of the article and project/data description, and a (shortened) translation into English.
The folder Data contains the data for the project, in STATA format.
The data are split into the six regions of the Netherlands, as used in the project.
For the identification of region, the postal codes (at 2-digit level) of the retailers have been used. Since a substantial proportion of the goods flows are return flows, from retailers to wholesalers and distribution centers, the regions can act as locations of origin and destination of shipments.
Data in STATA-format can be read directly in R, with functions from the foreign package.
We will structure the assignment using the steps of the CRISP-model.
On May 11, Henny Jordaan, who has been heading the project, will introduce the case study and the assignment. In this session, you have the opportunity - preferably after reading the case study! - to ask questions relevant to your approach of the case!
In the same week, we will provide you with additional information (e.g., using R-scripts) to read, describe, explore and understand the data.
By the end of week 2, the groups working on the assignment will sit together with the supervisor(s) of the projects, in order to be perfectly clear about the objectives of the analysis and be able to formulate a plan of analysis for the next weeks.
By and large, as Henny Jordaan will explain in week 1, the challenge is to smartly bundle flows of goods, in order to prevent congestion in the inner cities; lower CO2-emissions; save costs (labor; fuel); and achieve any other benefits.
The main idea would be to bundle goods in hubs (new or existing warehouses) close to the cities. The general approach when analyzing the data would be to compute the benefits if we simulate such hubs, given the actual data.
You will work in two groups of maximum 4 students each.
There are two project folders, one for each of the two groups.
You can use these folders (or sub-folders) to share your work, and ask any questions and ideas that you have! We will regularly inspect the folders to see if any new ideas and questions have come up.
Alternatively, you can use (posts on) Teams, to communicate with fellow students and researchers in the project.
When uploading documents on OSF, of course see to it that you use the right (sub)folders, and, equally important, give informative names to your documents:
yyyymmdd_groupx_subject_version.ext
For example:
20210511_group1_questions_on_data_02.docx