Adopting it Advances: Artificial Intelligence and Real Challenges
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Adopting it Advances: Artificial Intelligence and Real Challenges

Scott A. Roberts, Vice President, Logistics, CHEP U.S.A.
Scott A. Roberts, Vice President, Logistics, CHEP U.S.A.

Scott A. Roberts, Vice President, Logistics, CHEP U.S.A.

Recent advancements in IT create transformative opportunities for companies both in the supply chain and in all the industries we serve. Those technological possibilities, however, come with a number of very real challenges – and those challenges are having an impact.

According to PwC’s third annual AI Predictions Report, only 4% of executives plan to deploy AI enterprise-wide in 2020; last year, nearly 20% hoped to do the same. And according to the PwC survey, the primary reason is the need to focus on fundamentals before enlarging AI projects.

For many companies, a key fundamental is the talent gap, specifically when it comes to artificial intelligence (AI) and machine learning (ML). Do you hire out or train within?

The answer is both. It’s critical businesses build their teams with professionals who bring much needed skills in AI and ML – mainly data scientists with deep learning experience. It’s also important to provide internal analysts with the training needed to create tools specific your global supply chain needs. This step in the process means developing less advanced, valued added applications to enhance a legacy business process while cultivating a learning environment. These novice applications will become more advanced as your team becomes more astute in AI and ML capabilities.

Think local, act global

Among the biggest challenges our interconnected economy faces today: globalizing applications while keeping processes nimble and localized.

At CHEP, we’ve seen success with an internal program called Shaping Our Future. This global initiative unites our IT and technology experts with business practitioners who have synchronized objectives to deliver a superior customer experience and improved supply chain performance. By coming together, we are able to select and strengthen a business process supported by advanced analytics, which local teams can embrace and deploy across their business units.

In addition to the benefits of forming a cross functional, multi-national team, it’s been exciting to watch the collaborative process evolve as Baby Boomers, Gen X, Gen Y and Gen Z colleagues work to solve business critical challenges. We’ve found that by bringing these generations together, we can leverage the necessary experiences and skillsets to create a balanced vision that forms the strategy as the work streams begin to develop their actions. Pairing the multi-generational workforce with our focus on inclusion and diversity also fosters internal ownership. This participation yield steam unity and pride through clearly understood program goals, objectives and--ultimately--improved adoption deep across all business regions.

Build confidence

Even with a global, inter-generational team building advanced applications, there’s still a question of confidence in the information delivered through AI and ML techniques. Can the information being provided actually be used to create a better, more reliable experience for our customers?

A recent article by Towards Data Science, an online organization for data scientists and ML engineers, put it best: At the end of the day, one of the most important jobs any data scientist has is to help people trust an algorithm that they most likely don’t completely understand.

  ​By putting the right processes in place now, forward-thinking
businesses are better prepared for a quicker response when tackling IT
challenges and on the path to finding very real solutions   


To build that trust, the heavy lifting done early in the process must contain algorithms and mathematical calculations that deliver correct information while being agile enough to also capture the changes experienced on a very dynamic basis in our business. This step begins further upstream in the process by first establishing a cross-functional group that owns, validates and organizes the data sets needed for accurate outputs. This team also holds the responsibility for all modifications made post-implementation as continuous improvement steps are added into the data driven process. While deploying this step may delay time to market delivery, the benefits gained by providing a dependable output decreases the need for rework and increases user reliability.

Time matters

How flexible is your business? It takes time and dedication to successfully incorporate AI and ML into an organization since it requires the ability to respond quickly.

Business complexity has evolved over the years along customers’ increasing expectations for excellence. Our organization continues reaching new heights by deploying AI and ML techniques that include an integration that:

 Creates a diverse pool of talented external candidates

 Leads to stronger training and

development processes and programs for our employees

 Localizes a global application

 Bridges technological enhancements with business processes

 Drives business value from delivering reliable information

By putting the right processes in place now, forward-thinking businesses are better prepared for a quicker response when tackling IT challenges and on the path to finding very real solutions.

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