In recent years, companies have invested in technologies that help them integrate analytics across the entire enterprise, with a goal of capturing the more than $9.5 trillion in business value that the McKinsey Global Institute estimates could be unlocked with full integration of advanced analytic.
Yet even with these capabilities, supply chain planners still spend a great deal of time collecting and working with data, then making decisions using essentially the same processes they relied on before. Today, the volume of decisions that must be made each day, and the amount of data and variables that must be analyzed to make them, have grown past the ability of even the best planners to keep up.
This planning-to-execution gap is growing, consuming the time that leaders could use to address strategic problems and planning. Meanwhile, data quality continues to suffer as only 3% of companies’ data meets minimum standards, according to an HBR study.
The lack of visibility that results from these factors has created a hidden, yet costly, problem: critical business decision-making opportunities are going unaddressed across supply chain planning, procurement, logistics, finance, marketing and other high-value areas.
The good news is that predictive analytics and enterprise AI offer a solution that is already making better supply chain decisions at the point of greatest impact.
A brief overview: Enterprise AI vs. other technologies
Before we continue, it’s important to clarify which type of AI has the greatest potential to address these challenges.
A great deal of attention has been focused on generative AI — tools such as ChatGPT that, as defined by IDC, apply unsupervised and semi-supervised algorithms to create new content utilizing previously created text, audio, video, images or code. Generative AI tools show promise in specific business applications, particularly marketing and customer engagement.
Enterprise AI combines artificial intelligence, machine learning and data science in software solutions designed to address business needs. These technologies are critical to decision intelligence — a growing field that enables the digitization, augmentation and automation of business decision making. Traditional decision-making processes are often siloed and disconnected, requiring people to rely on spreadsheets and other tools to analyze data and come to a decision. Decision intelligence digitizes this process, allowing people to address more decisions, more quickly, with a full view of a decision’s impact on the end-to-end value chain.
Decision intelligence platforms are purpose-built with additional functionality and frameworks designed to operationalize AI in a business decision-making context. They orchestrate the data, intelligence, automation and user engagement capabilities required to help companies make faster and more accurate decisions at scale, in real time.
Data-driven decisions, at enterprise scale
These technologies give supply chains the ability to make decisions they cannot address today due to a lack of time, visibility, or capacity to analyze data quickly and accurately across business functions. These are decisions that can help companies cut costs by right-sizing inventory or help them adapt to changing demand patterns or respond more effectively to line-down situations.
For instance, how can manufacturers adjust in real time when there’s a spike in demand for a particular product in a specific region – or even align marketing promotional plans to sense demand more effectively? Or, how can they respond more quickly and efficiently when raw materials become constrained? And, with environmental concerns and regulations becoming a driving factor in supply-chain decisions, how can companies balance carbon emissions with service levels in logistics and shipping?
These are just a few of the common decision types that enterprise AI is addressing, every day. By considering data from across the entire value chain, not just portions of it, an enterprise AI system can apply predictive analytics and machine learning models to evaluate scenarios and choose the best course of action.
One global company I’ve worked with deployed a decision intelligence platform over five years ago to address these challenges. Today, the team has accepted millions of AI-generated recommendations for stock rebalancing, demand forecasting, production planning and procurement decisions – with total revenue impacts and cost savings measured in tens of millions of dollars.
Indeed, some of the most challenging decisions supply chains are facing today are those that sit at the intersection of functions — supply chain with marketing and trade promotions, procurement with manufacturing, sustainability with logistics and so on. Decision intelligence technology empowers teams to overcome data and process silos, leveraging data from all their point solutions and data sources. The result: enterprise-wide visibility that enables a true digital transformation of decision making and all the benefits that come with it.
Today, enterprise AI is making possible the data-driven, collaborative environment that McKinsey & Co. predict will be commonplace by 2025 — where machines harmonize data across the enterprise and recommendations generated by AI and predictive analytics are part of nearly every employee’s daily work. Not only does data quality improve, but decision-making challenges that formerly took days or weeks to address can be resolved in minutes or hours.
How to start your journey
Accomplishing the type of digital transformation I’ve described here would have been thought impossible just a few years ago. Yet today, businesses can fully augment or even automate decision making — from collecting and analyzing data, to deploying decision logic, to engaging with users and finally writing back to transactional systems and data sources.
For supply chains that are currently struggling with the challenges we’ve described, the path to digitizing decision making can seem frustrating. The good news is that today’s AI solutions can deploy across nearly any existing system, without the need to “rip and replace” the current technology stack.
With a data model designed for decision making, busy planners can hand over data wrangling tasks to machines that can analyze information more quickly and accurately. These systems then begin delivering data-driven recommendations while also recording and learning from the outcome and context of each decision made.
This learning capability is key to gaining the full benefits of advanced analytics across the end-to-end value chain. AI unlocks the ability to set business rules and automate routine or time-sensitive decisions – and to capture a digital memory of those decisions, their contexts and their outcomes for the future.
From selecting a new supplier based on OTIF performance thresholds, to adjusting manufacturing capacity utilization based on demand signals, to rerouting inventory around delays, just to name a few, today’s AI systems of intelligence can take the burden of analysis off of busy planners. Enterprise AI also reveals new insights and opportunities to mitigate risk, increase revenue and operate more efficiently in an increasingly digital world.
