If you travel to first and second-tier cities of China, you will find on the street many delivery drivers (Chinese: 快递).
They take the parcels from small warehouses called customer service centres (Chinese:客户服务中心) located in each neighbourhood and deliver them to the final customers.
These centres are key elements of the Logistics Network of the major courier companies in China. They provide a large geographical coverage for last-mile delivery and a huge competitive advantage by offering the best service level and delivery lead time in the market.
Before arriving at your door, your parcel will be picked from the vendor’s…
With the recent surge in shipping price due to container shortage, the price of a container from Shanghai to North Europe went from $2,000 in November to a peak of $12,000, optimizing your container loading became a priority.
You are Logistics Manager in an International Fashion Apparel Retailer and you want to ship 200 containers from Yangshan Port (Shanghai, PRC) to Le Havre Port (Le Havre, France).
Most of the challenges faced by Distribution Centers (DC) handling Luxury Products, Garments or high-value goods are during Inbound Process.
We will take the example of a DC storing imported Luxury Bags, Garments and Shoes that need:
How many articles have you read stating that “Excel is Dead, long live Python”, “Python is the new Excel” or “Excel became obsolete”?
But when you look around you, in your team or other departments you can hardly find other colleagues using Python.
And when you mention it, it is seen as a black box that people can’t trust because “it’s too complicated”, “we can’t see formulas” or “I cannot run it on my computer”.
A first step to promote it would be to give them the possibility of running your scripts on their computers without prior knowledge of python.
As a Data Scientist, if you want to use Data to be impactful in your organization, contribute to large scale operations and see your models used to implement concrete solutions: Supply Chain is the best candidate to start your Data Science Journey.
I have been working in Supply Chain for more than 4 years with a great focus on Warehousing and Transportation Operations.
As a Supply Chain Solution Designer, my job was to translate our customer's requirements into actual operations (Retail, E-Commerce, Luxury, FMCG, Automotive), conduct re-engineering studies to improve warehouse operations and optimize transportation networks.
The common point of…
You are a proud Data Scientist presenting your new solution designed using optimized code, fancy libraries, advanced linear algebra and complex statistical concepts.
Your solution got less interest, recognition or enthusiasm from your management than a simple PowerBI dashboard presented by a new intern the day before.
Have you faced this frustration?
Simple fancy visualization can have more impact than a very complex model, especially for a non-technical audience.
If you‘ve read my previous articles, you will notice that my main focus is always to use Linear Programming, Machine Learning or Statistics to reduce operational costs.
As a Supply Chain Solution Designer for a 3PL, my mission was always to cut the costs to keep competitive pricing for our customers.
However, these tools and concepts can also be used to maximize the profit of your Supply Chain by focusing your production on high margin products.
In this article, I will show you how to help your local bakery to maximize its profit by producing the right items using Linear…
If you are Data Analyst and want to access a large amount of unstructured data, you have the desire to impact and you are obsessed with automating repetitive tasks: go to your Finance Department.
I will share in this article a solution based on my experience extracting data from very unstructured Excel Files to perform operational and financial audits.
You are working as Data Analyst for a major Logistics Company and your colleagues from the Finance Team request your support to build a model to predict the P&L of Warehouse Operations.
An Italian economist named Vilfredo Pareto developed in 1906 a mathematical formula to describe the distribution of wealth in Italy. He discovered that 80% of the wealth belonged to 20% of the population.
How many articles have you read explaining “How to teach Data Science to Non-Technical colleagues”, “How to explain Machine Learning to top management audience”?
The concept behind these articles is the idea of a dichotomy between the “Tech World” and the “Non-Tech World”. Data Science is a black box, a magical world where the Data Scientist must guide the uninitiated by explaining in simple words complex concepts.
Based on my experience working as a Solution Designer for large Logistics & Supply Chain Operations, the main concerns of your management is not to understand your model but more what impacts on…