AI in Supply Chains: Which Data is ‘Just Right’ for Your Business? 7 Steps to Your Goldilocks Moment

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Data is the lifeblood of AI in the supply chain. Without sufficient data, AI models can't uncover meaningful patterns, make accurate predictions, or provide valuable insights for informed decision-making in complex and dynamic environments.

At the same time, feeding your AI models too much data can also be a problem. Including excessive or irrelevant data can introduce noise and complexity, potentially overwhelming the algorithms, producing skewed predictions, and gobbling up unnecessary computational power. 

Like Goldilocks’ quest for the perfect bowl of porridge, to leverage AI in your supply chain you need to identify which data is ‘just right’ for your business.

Not all data is created equal

Just as a gourmet meal requires the perfect quantity and quality of ingredients, your AI-powered supply chain needs the right mix of data to work its magic.

While it might be theoretically possible to monitor weather patterns on Mars, unless your company has interplanetary operations or dependencies, that data’s not going to be relevant for managing your supply chain! Back on earth, although the weather absolutely has an impact on demand patterns, if you don’t have the location data to go with it, it’s not going to help you.

And, just like adding too many ingredients to a dish, adding too many data signals adds noise, making it harder for the signals that matter to stand out and provide their predictive effect. So just like it’s best not to load down a dish with too many ingredients, to maximize their impact, choose your signals wisely. 

But, with the proliferation of available data from within and outside your organization – from sales and promotions to economic indicators and weather patterns, knowing what to include and what to leave out isn’t straightforward. So, how do you achieve that Goldilocks moment? 

Striking the perfect balance

Identifying the data sets that matter the most to your organization depends on many factors, including the complexity of your supply chain, the specific AI applications being used, and the quality of your data.

Although there’s no one-size-fits-all solution, the following steps provide a useful rule of thumb to help you determine what data is relevant when using AI to help manage your supply chain.

Define business objectives: Clearly define the business objectives and key performance indicators (KPIs) that your AI-driven supply chain management system aims to optimize. Understanding the specific goals and priorities will help identify which data points are most relevant for achieving those objectives.

Collaborate across departments: Engage stakeholders from different departments within the organization – not just supply chain management including logistics and procurement but sales, marketing, and finance. Collaborative discussions can help identify relevant data sources and metrics that capture the end-to-end supply chain process and align with overall business goals. 

Lean on the experts: Your planners know your business well from immersing themselves in it day to day, so leverage that knowledge to help them turn data in stories that provide context. And, alongside your internal experts, collaborating with an established supply chain technology vendor offers invaluable expertise in identifying the precise data sets necessary to unlock the potential of AI in your supply chain. Leveraging their industry knowledge and experience will streamline the process of identifying and collecting the data needed to fuel AI-driven insights and decision-making.

Map supply chain processes: Map out the various processes within the supply chain, from sourcing and procurement to production, inventory management, distribution, and customer fulfillment. Identify key touchpoints and decision points where data-driven insights can drive improvements and inform decision-making.

Data inventory and assessment: Conduct a comprehensive inventory of available data sources within the organization, including internal systems (e.g., ERP, CRM, SCM), external sources (e.g., market data, weather forecasts, geopolitical factors, events), and IoT devices (e.g., sensors, RFID tags). Assess the quality, granularity, timeliness, and relevance of each data source to determine its suitability for AI-driven analysis.

Cleanse your data: Identifying the data is one thing, but ensuring it’s accurate is just as important – as the saying goes, garbage in, garbage out.  Fortunately, there are AI-based tools that can automatically identify data outliers, reducing the significant amount of work required to do it manually.

Prioritize data needs: Prioritize data needs based on their relevance to the defined business objectives and their potential impact on supply chain performance. Focus on collecting and integrating data that directly contribute to improving decision-making, enhancing operational efficiency, reducing costs, and meeting customer expectations.

Fine-tuning your data strategy

Now you have all the ingredients for that perfect bowl of porridge. But you’re not done yet! 

Business environments are constantly changing, and what works today may not work tomorrow. Adopting an iterative approach ensures your data strategies remain agile and responsive to evolving needs and challenges. By continuously refining and updating your data sources and models based on feedback and performance metrics, you can stay ahead of the curve and maintain a competitive edge.

Now is also the time to mix in predictive analytics to get ahead of future supply chain events and trends. Identifying historical data patterns and correlations can inform predictive models and enable proactive decision-making in areas such as demand forecasting, inventory optimization, and risk management.

And, to make sure your porridge really is as tasty as you think it is, you’ll want to put it to the test. People may have firm beliefs that particular data signals are key contributors to the forecast, but with robust hypothesis testing you can properly determine the correlation between different signals to truly understand the impact that each has on the output.

Finding your fairytale ending

Just as Goldilocks sought the perfect porridge, when it comes to AI in your supply chain, it’s critical to strike just the right balance in your data selection—enough to inform AI algorithms effectively, but with a clear focus on the signals that matter. Achieving this equilibrium requires a strategic approach, considering business objectives, cross-departmental collaboration, and expert guidance from seasoned technology vendors. And, as you refine your data strategy, combining an iterative mindset with predictive analytics lets you adapt and anticipate evolving market dynamics. 

Armed with these strategies, businesses can unlock the transformative potential of AI in their supply chains and enjoy the sweet taste of success.


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