From Production to Delivery: Leveraging AI to Optimize the Entire Value Chain

The past few years have been challenging, frustrating and exciting times as the supply chain world struggled to navigate shortages, demand uncertainty, logistical constraints and the addition of new tech all at the same time. Supply chain resilience is the phrase of the day— companies, whether large or small,  know they need to improve their ability to withstand disruptions.

This realization is stretching supply chains to broaden the traditional understanding of supply chain to include the entire value chain— a scope that expands beyond a company’s four walls to include suppliers, transporters and customers.

The Value Chain Digital Thread

Supply chains are no longer linear product roadways, but a web of intersecting processes and data points. When you build a value chain digital thread that’s woven through the entire enterprise, data moves through all the different supply chain functions providing context and insights along the way. Think about how all these areas can be linked: procurement, contract management, inbound warehousing, production, logistics, outbound warehousing, distribution, fulfillment and more.

Creating this digital value chain thread is core to transforming a company’s operations, gathering data from many important stages including design, analysis, production, testing, validation, and delivery. With this approach, critical data— formerly siloed in different functions — is now interconnected and can create insights to improve products going forward.

Innovation potential increases exponentially because learnings gathered at any point within the digital thread can be used to optimize and improve processes and outcomes at another point. For example, data related to product sentiment and sales can be fed directly into the development of another product. Not only are tangible assets accounted for in the digital thread, but intangible assets such as knowledge and industry experience can also be used for improvements and optimization. To create this value chain, we need AI and we all know AI is growing at a steroid-induced rate.

IDC is forecasting that global AI spending including software hardware and services will reach $154 billion this year, an increase of 26.9 percent over 2022. With a compounded annual growth rate of 27 percent for the next three years, spending is expected to exceed $300 billion by 2026.

AI Applications in the Value Chain

As it matures, AI is being increasingly used in the value chain because it can take much of the guesswork out of weighing the costs and benefits of the variables; it can identify patterns, and call out changes in the value chain that might escape a human’s attention, such as a subtle variation that could turn out to be a highly profitable piece of information.

AI can analyze data from dozens of sources, including production information, IoT sensors, transportation routes, and even social media. It builds on prediction models to better understand causes and effects in the value chain. It helps identify potential risks and disruptions, so companies can make better decisions to mitigate risks, develop contingency plans, and improve overall resilience.

Gartner calls it actionable AI, and describes it as learning patterns based off past decisions and experiences to adapt to changing, real world circumstances. AI can continuously retrain models and learn within the software environment based on the new data that is fed to it.

Successfully implementing AI-enabled supply-chain management has enabled early adopters to improve logistics costs by 15 percent, inventory levels by 35 percent, and service levels by 65 percent, compared with slower-moving competitors.                                                                                                                      Gartner

Intelligent Planning

Intelligent planning with AI opens a world of possibility, and activities like predictive analytics and scenario-based modeling take on a whole new meaning. Before the proliferation of advanced technologies, planning was reactive and ad hoc, and for many it still is. According to McKinsey, only 37 percent of companies have implemented any sort of scenario planning leveraging technology. In another study by Gartner, just 60 percent said  they have a suitable strategy in place to adapt to unplanned downtime or material shortages.

Leveraging AI and machine learning can help identify meaningful correlations and connections around demand forecasting, planning, and logistics by leveraging historical data, economic outlook, geopolitical scenarios, weather conditions and a host of other external affecting elements. Bringing together data from multiple sources, both external and internal, not only improves the forecast itself but also enables validation of plans and identification of demand drivers that can support better business decisions.

Unifying Your Data

Companies can’t accomplish any of the above without first focusing on their data. It’s critical to have a single source of truth based on unified data, meaning everyone is pulling from the same data sources and those sources are contextualized, normalized, and properly indexed. Companies need to be confident that whether a data point is in production or in the inventory warehouse, everyone is looking at the same thing.

With a unified data source, supply chain, procurement, and finance teams can work together to quickly devise the best approach by weighing factors such as scalability, security, data integrity, real-time responsiveness, and processing speeds.

There’s still a long way to go. It’s surprising that 80 percent of the data surrounding the supply chain is not used. Many companies operate under the false premise that ERP data is what’s needed to drive the supply chain, when actually, what drives a value chain is when it’s coupled with industry data and AI.

What Does the Future Hold?

As AI technology continues to evolve, its applications in supply chain management are expected to expand and become more sophisticated. We don’t have a crystal ball, but here’s our take what we’ll see in the upcoming months and years.

Upstream and Downstream Partnerships

Introducing AI into the value chain means that all the partners in it will have to coordinate a lot more, to absorb the uncertain ripple effects throughout the ecosystem, much like a stone thrown in a pond.

It’s not always easy to get everybody on board to share insights, data, and strategies, especially in supply chain management, where the focus tends to be within the four walls. In the future, companies that base part of their forecasting and demand planning on data that other companies are actually sharing with them, not just their own projections. That’s a big change in the way that all these companies can work together to advance the industry as a whole.

Cyber Resilient Supply Chains

As supply chain partners, vendors and service providers are added to the digital value chain, each addition represents a security vulnerability or risk that needs to be mitigated. Supply chains often involve multiple third-party vendors, each with their own cybersecurity practices. If a vendor has weak security measures, it can expose the entire supply chain to cyber threats.

By 2025, 60% of supply chain organizations will use cybersecurity risk as a significant determinant in conducting third-party transactions and business engagements.

To mitigate these risks, we’ll see an increasing focus on cybersecurity measures, like conducting regular risk assessments, establishing strong security policies, monitoring third-party vendors, and providing employee training on cybersecurity best practices.

Responsible AI

Governance, security, and compliance have always been top of mind. With emerging technologies like AI, the concern only grows, and companies are spending more and more time talking about the importance of responsible AI. But what is it? Responsible AI is looking at AI though the lens of these tenants: accountability, transparency, fairness, reliability, inclusivity, privacy, and security. Most simply, it’s about taking advantage of AI in a way that people can trust. AI doesn’t replace human interaction; it facilitates and augments it.

During Microsoft’s annual Build Conference this year, CEO Satya Nadella emphasized that people can’t abdicate responsibility for AI and emphasized the need to keep humans in the loop and put guardrails in place. You can read more about how Hitachi Solutions is working with Microsoft on establishing responsible AI programs in our AI and the way we work blog by Hitachi Solution’s CTO Luke McGrath.

There is still more to research and more to learn that will undoubtedly lead to new policies and engineering practices that advance the trustworthiness of organizations’ AI engagements. And most importantly, it’s a matter of humans staying fully in control and confident in and accountable for AI systems.

Sustainability

Sustainability is top of mind for all companies and AI has a role to play. This includes finding innovative ways to manufacture, package, ship, use, and dispose of products in a more sustainable manner. Projects focused on efforts like decarbonization, and zero net impact are becoming common for process manufacturers. When AI enters into the sustainability equation, it can do things like analyze how recycled material interacts with raw materials and adjust related processes and specifications in real time.

Don’t forget the customer is king. Consumers are becoming more conscious of the environmental and social impacts of products that they purchase, and supply chains will become more and more focused on adopting sustainable practices to meet these expectations. Doing so can provide a two-fold benefit: companies reduce their environmental footprint and at the same time improve their reputation with customers.

Customer Experience

The burgeoning focus on customer experience will only increase, as companies attempt to fine-tune experiences to meet continually rising consumer expectations. AI is already a fundamental piece of how big companies like Amazon are creating personalization campaigns, delivery options and better products based on targeted consumer needs, wants and expectations, and those predictions will only get better and more refined as time goes on.

Bots will continue to learn and become better communicants through the advent of generative AI, and be able to handle even more complex transactions. Still, there are situations where generative AI and bots will fall short—and this underscores the continued need for human involvement in customer service if the problems are complex and customers have a difficult time articulating the issue and their needs effectively.

Getting Started

Professionals who have grown up in the supply chain arena have been dreaming about such innovations and technologies for a long time. The solutions are finally here, and we should all feel positive about the future of supply chains as we understand and experience what AI can do.

But where do you start? As a first step, companies should ask themselves:

• Where do we need to basically drive more insight?
• What data do we have that could be valuable that we aren’t using?
• How do we really use different data to answer our questions and the issues of the day?

Think about the signals available today that have never been used before, whether it’s transportation, Internet of Things, GPS data, map data, or weather data. How can your company use this data to better solve problems?

Hitachi Solutions Can Help

These are all questions that Hitachi Solutions has heard before, and continues to answer every day.

There can be decision paralysis for some supply chain leaders due to the overwhelming speed of innovation. That’s where Hitachi Solutions has been digging in with our customers through our business advisory practice. We can help you not only improve your digital supply chain, but also to step back and plan, prioritize, and proof your solutions.

We’ve been expertly guiding customers through impactful business change for years, with a people-first approach to technology that maximizes the impact and outcomes of your supply chain investment.  And it’s a method that’s borne success and reward. Contact us to hear more from our experts to learn how you can leverage AI to make your organization’s entire value chain more resilient. Our team of experts will work with you to assess and build confidence in your ability to safely adopt AI technology to gain the most ROI and value. 

These concepts and additional insights were discussed as part of a Top Trends in Technology Shaping Manufacturing video series with Hitachi Solution’s Jeff Winter and Lora Cecere, industry expert and founder of Supply Chain Insights. You can listen to that discussion here.