Data Science and Supply Chain – Connecting People and Algorithms

Data Science and Supply Chain – Connecting People and Algorithms In its constant pursuit of efficiency, the Supply Chain sector can now rely on new Big Data-driven technologies to improve the performance of its activities. Because of the abundance and diversity of data generated every day by its various actors, a plethora of very appealing applications has emerged. However, when it comes to artificial intelligence (AI), the key is human-machine collaboration. How is this connection between human intelligence and algorithms made? What role does humanity play in the development of a connected supply chain?

Supply Chain Management is entering a new era!

The logistics industry underwent its first major transformation in the 1990s, fueled by academic research and large corporations such as Walmart. While some players are still working on best practices, Big Data is once again revolutionizing the supply chain.

These promising advances, dubbed "Supply Chain 4.0" or "Connected Supply Chain," are the result of teams of Data Scientists utilizing artificial intelligence, blockchain, or even robotics. These technologies aim to make organizations' supply chains more agile, predictable, and profitable. How are they able to do this? By reducing lead times, fully automating demand forecasting, and improving production and delivery on time.

Data Science's Contributions to the Supply Chain Sector ● Improve demand forecasting Data Science and Machine Learning are particularly interesting for identifying trends in large amounts of data because they can exploit very large and diverse sources of information.

Data Science is used specifically in the Supply Chain sector to: -

Identify weak signals that must be actively monitored to develop prospective options; Combine data from various sources (web.); Categorize products based on different consumption habits; Highlighting action plans tailored to each situation

● Improve logistics flow management.

In terms of warehouse management, data analysis can be correlated with certain external factors (raw material supply issues, goods traffic, weather conditions, and so on) to assist businesses in reducing the risk of disruption.

Many factors can be considered to facilitate carrier selection and optimize round delivery organization: costs, product type to be handled, specific transport standards and conditions, packaging, and road traffic. AI algorithms contribute to better resource allocation and, thus, greater efficiency by optimally distributing tasks based on the warehouse's own data. Refer to a machine learning course in Mumbai to gain profound understanding of the ML algorithms.

● Enhance customer relations With Data Science, the relationship with customers is becoming increasingly personalized.

Unsupervised Machine Learning algorithms enable us to segment our customers to target promotional offers and services to each profile.

When combined with the analysis of customer feedback, this segmentation data provides valuable information on the steps to be taken to improve customer satisfaction, which remains a primary concern for any supply chain.

Collaboration between humans and machines is a critical issue in data science.

From information to action The collaboration between humans and machines then occurs in four stages: 1. The machine's analysis of data (Analytics); 2. The amount of human intervention required to interpret the data (Human input); 3. The final decision (Decision); 4. The conversion into concrete action (Action).

As time passes, we gradually give the machine more autonomy until we have complete confidence in the system. However, in order for the machine to decide as well as a human, a phase of collaboration is required during the various stages of algorithm development. It varies in length and complexity depending on the level of autonomy desired.

The various types of algorithms There are three types of machine learning algorithms, depending on the nature and intensity of the collaboration between humans and machines: supervised, unsupervised, and reinforcement learning.

● Learning Under Supervision In supervised mode, the algorithms operate on data selected by humans for their characteristics and known impact on the outcome. For example, the outdoor temperature

curve influences beverage sales, and the number of orders to be shipped influences the warehouse picking load. This type of algorithm is commonly used in sales forecasting models.

Intelligence, in this case, is primarily provided by humans. Based on several data series, the machine is primarily used for calculation capabilities.

● Learning Without Supervision The goal here is to achieve two specific goals: -to form clusters, or groups of individuals with similar behaviors, to define refined and thus particularly efficient management rules; -to discover, using machine learning, which data impacts supply chain performance: the theoretical approach acquired as a professional is not always sufficient to detect and explain certain phenomena affecting warehouse efficiency. Capable of detecting even weak signals in real-time and continuously, the machine becomes a powerful vector for analyzing operations and thus improving processes.

In both cases, the machine is used to diagnose, while the human is involved in data analysis and definition.

● Learning Through Reinforcement These algorithms, which are primarily used by voice or banking assistants and robotics, operate on experience cycles and improve their performance with each iteration. This is the most advanced mode of human-machine collaboration. The human gradually teaches the system to make the best decisions using a scoring principle. It imparts its knowledge to the system and teaches it to adapt to a wide range of situations. Check out the data science course in Mumbai which is accredited by IBM for industry professionals.