5 Tips for a Seamless AI Implementation in Supply Chain

Harnessing the power of artificial intelligence (AI) has become a strategic imperative for companies seeking to stay at the forefront of innovation, increase their competitiveness, and maximize their operational efficiency. The realm of supply chain planning is no exception to this trend.

AI and machine learning (ML) are dominating supply chain digital transformation strategies, fast providing remarkable toolsets for improving the end-to-end multi-enterprise supply chain model. It comes as no surprise that the rate of adoption of AI/ML is rapidly growing, and it will continue to accelerate. Research firm IDC predicts that 40% of manufacturers will deploy AI-based tools by 2025 to support decision-making and maximize the value of data. This research also anticipates that the increased levels of AI deployment will result in up to 5% improvement in revenue for organizations.¹

AI has the potential to reshape the way businesses run their supply chains – and virtually every company, regardless of its size or industry, holds the potential to reap substantial benefits from the implementation of AI. As long as data exists in digital form, AI can be utilized to extract actionable insights, optimize processes, and enhance decision-making.

To reap the full benefits of this transformative technology, it’s important for businesses to adopt a strategic approach and address the right challenges.

The process of implementing AI needn’t be a daunting task. Here are five tips to ensure a smooth AI integration and elevate your supply chain performance and operations:

1. Define the Vision and Ownership

Before embarking on an AI-powered supply chain journey, it's crucial to articulate a clear vision and define the goals and objectives. What specific challenges are you aiming to address with AI? Is it about optimizing inventory management, predicting demand more accurately, or enhancing transportation logistics? Having a well-defined vision provides a clear direction, as well as aiding in allocating resources effectively.

Equally important is assigning ownership to these initiatives. Who in your organization understands the intricacies of your supply chain and will champion the AI implementation? Establishing ownership is essential for ensuring alignment, accountability, and successful outcomes. By defining the vision and ownership, you lay the foundation for a structured AI implementation strategy.

2. Embrace Human Oversight

While AI has the potential to make sense of vast amounts of data, it's imperative to note that AI models are not inherently aware of your business intricacies and are limited by boundaries of data and experiences you provide. A machine learning model, for instance, won't comprehend the concept of "seasonal data" unless it's provided with relevant data to make the association. This is where human oversight becomes crucial. AI models are self-learning, but they need to be nurtured and guided by domain experts who understand the intricacies of the supply chain and provide the additional data sets or experiences to explain gaps.

Having the proper oversight ensures that AI models are trained using accurate and relevant data to make correct assumptions and draw accurate conclusions and have the best possible impact on your supply chain operations. Human intuition, combined with AI's analytical prowess, creates a potent synergy that drives better decision-making.

3. Benchmark for Success

In order to empower AI models to help you make critical decisions, it's crucial to thoroughly test and benchmark their performance to see if the AI models can beat published results.

Machine learning involves training the models on historical data, evaluating their predictions against actual outcomes, and iteratively refining the models based on feedback.

Benchmarking involves comparing results against other state-of-the art models, regressions, heuristics or other simpler models.  The comparison can be focused on outperforming the benchmark in precision (accuracy), inference speed (responsiveness), training time, and dataset size.

Benchmarking helps establish the credibility and accuracy of AI models, enabling businesses to have confidence in the decisions driven by these models and the speed that models are run. The benchmarking process is key for building AI models that remain agile and responsive, so it shouldn't stop at the initial deployment. Make sure your technology partner provides your team with support for continuous monitoring and refinement, which is necessary to adapt to changing market dynamics and evolving business needs.

4. Align Your Team: Prepare for Transformation

The potential benefits of AI implementation are substantial. To maximize the results, it’s important to ensure alignment with the existing organizational infrastructure, willingness to support and provide model oversight and to assess the level of training and expertise available within the workforce.

To fully leverage AI's capabilities and embrace transformation, organizations should be prepared for change in some areas such data collection or management practices, which may be required to ensure a smooth transition and ensure the company is set up for success.

5. The Hype Factor: Balance Expectations

There’s no doubt the potential of AI is immense, and it continues to enable companies to achieve transformational outcomes. But supply chain leaders should understand the challenges and approach the technology with a balanced perspective. By setting clear objectives, providing diligent oversight, and benchmarking performance, you can mitigate the risk of AI projects falling short of expectations.

Defining a comprehensive strategy, guided by a well-defined vision, ensures that your organization is not merely caught up in the hype but is strategically leveraging AI to address real business challenges and drive tangible results.

Partner with a Technology Leader to Guide Your AI-Powered Transformation

In a landscape where data has become the new currency, AI has emerged as the tool that can decipher its hidden insights, transform information into actionable advice, and reshape the future of supply chain planning.

Integrating AI into your supply chain doesn’t have to be a complex or challenging process. We can show you just how simple it can be to seamlessly embed this transformative technology, to unlock unprecedented efficiencies, elevate decision-making, and enhance operational agility.

At John Galt Solutions, we help forward-thinking companies achieve remarkable outcomes with our AI-driven supply chain planning software solution that connects and orchestrates the entire supply chain ecosystem. We work closely with teams to help define their vision and guide them step by step to address specific challenges and implement robust practices to ensure a successful AI implementation aligned with the business’ needs.

Get in touch and we’ll show you how you can position your company to experience the full potential of AI and ML to bring more intelligence to your decisions and accelerate your digital transformation.

1 IDC FutureScape: Worldwide Manufacturing 2023 Predictions. Doc #US48630122. October 2022.