Seven Skills For AI-ready Supply Chain Professionals


AI is making prominent changes in every industry today. With state-of-the-art technology and advancement in AI, business processes are becoming more automated and easy to operate.

In addition, with the increase in digitalisation, supply networks are also undergoing significant transformations. AI frees up time for supply chain specialists to do value-added operations and save countless productive hours. Tasks such as analysing massive data sets and identifying patterns in data are done through AI.

Relationship-building abilities such as listening to stakeholders, interacting effectively with business partners, inventing, and thinking strategically about difficulties can’t be replaced by the most advanced technology, even by AI.

Currently, at least six new supply chain skills need to be developed or enhanced as a consequence of AI adoption, according to APQC’s AI in supply chain study. These are mostly soft skills that assist supply chain workers in building better connections, interacting with partners more effectively, and solving difficult challenges.

Some of these abilities are technical. In order to make the most of the time saved by AI, hire dedicated development team wherein supply chain workers must work hard to develop and hone these abilities.

Analytical coding

Pre-processing and storing raw data produced by your systems is the initial stage in machine learning development. For the sake of illustration, consider an internet business that caters to consumers from all over the globe.

This online shop will generate a large amount of information on certain occasions. ETL (Extract, Transform, and Load) pipelines are needed to handle the data processing, cleaning, and storage that occurs when a consumer views or buys a product description or makes an online transaction. Analytical coding heavily entails critical and analytical thought, which encompasses the steps of recognising a problem, understanding it, formulating potential solutions, testing those solutions, and then putting those answers into practice,  at times making the solutions is a bit complicated and expensive in order to overcome this one can easily rent a developer as they are proficient in coding they can give to guide you to resolve the complex problems cost-effectively.


If you want to be a master in machine learning, you must have a thorough understanding of the algorithms used. That’s not all; you must also know how and when to use them.

You should use regression and classification algorithms, respectively, if you’re trying to learn the relationship between a set of data points and the outputs they produce, such as weight, age, or yes/no, and you want to find a model that explains this relationship.

Unsupervised learning algorithms are the way to go if you have a set of inputs and no outputs and wish to find patterns in the inputs and group them according to their similarities.

For more difficult tasks like picture categorisation and object identification (as well as for recognising faces), you’ll need more sophisticated algorithms based on artificial neural networks, which are used in deep learning.

Listening and speaking with intent

Communication and listening skills are the most important aspects of doing anything effectively. The business team must be able to understand and meet the demands of their partners, as well as communicate clearly with suppliers and other service providers along the supply chain.

Ingenuity and original thinking

Despite its dramatic ring, the business maxim “innovate or die” holds in today’s competitive environment. Those in charge of the supply chain must come up with innovative solutions to the myriad of unanticipated problems they confront.

Data science, machine learning, and modelling expertise

The need for new soft skills does not diminish the relevance of technical talents. In order to get the most out of their AI investments, supply chain workers need to know how to use the technology efficiently to get insights that may lead to better judgments.

The ability to think strategically

While artificial intelligence may utilise data to analyse patterns and anticipate the future, it is still up to people to understand what that data means in an organisation’s overall objectives and strategy.

Data-driven decision-making is essential for supply chain workers, particularly when there is no clear “correct” response to an emerging trend or difficulty.

Business savvy combined with analytical abilities

Supply chain workers must be able to communicate the results of AI in a manner that makes sense to their bosses and links to the issues they care most about. The gap between knowledge and action may be bridged by delivering a compelling narrative about data and how it relates to corporate strategy.

Analytical aptitude and commercial knowledge

Employees in the supply chain management must be able to communicate the results of AI research in a way that is understandable to corporate executives and relates to their top priorities. To close the gap between insight and action, data must be able to convey a clear, engaging tale that ties to company strategy.

Looking for a Needle in a Haystack of Supply Chains

A greater question has to be answered with this constantly growing mass of mashed-up data: Is there a way to identify business bottlenecks earlier, given this wise investment in technology like Artificial Intelligence (AI)?

Supply chain managers must be able to cut through the noise with a strong tool to exploit this enormous amount of data with focused operational analytics to identify, measure, and prioritise the bottleneck operations forming in business processes early on if they are to guarantee outcomes.


If you want your supply chain to prosper in the future, you need to ensure your staff has the correct combination of skills to support AI and use it effectively. Many companies have already committed to training supply chain personnel in these important skills.

In order to smoothen the transition to AI, for example, APQC found that firms spend a median of seven days in employee learning per employee. Investing in this training and choosing to hire a dedicated development team is a more cost-effective than attempting to acquire new employees with the required abilities.

If you liked reading this, you might like our other stories
Company Closeup: HPE’s AI & Analytics Play
Foundational Models – The Next Wave In AI