Enhancing supply chain resilience with AI

Constant change and disruptions are a norm in the supply chain industry. It’s how you’re able to adapt to this constant change, better manage risks and get ahead of disruption that can set you apart. Technologies, especially the new technologies that use AI and have real-time capabilities, are becoming true enablers of efficiency, smart(er) decisions and smart(er) forward planning.

Let’s take a peek under the hood of these technologies to understand how supply chain organizations can build resilience with the help of-AI driven insights and the latest tech. And we’ll dive deeper into fleet and transportation management, share practical applications and best practices for AI adoption.

Where’s my stuff?

In this chaotic and complex industry that depends on multiple stakeholders, and is constantly on the move, having visibility into your supply chain is critical. All stakeholders want to know where’s their “stuff” at any given point in time. They want real-time visibility. And today’s tech stack is better at enabling that. And by getting your arms around that data, both at the macro and all the way down to the micro level is what will drive supply chain excellence.

Armed with that kind of data, organizations can get to that elusive and super-important single source of truth. This holy grail can ensure that all stakeholders --- shippers, carriers, partners, customers --- have access to the same, accurate and up-to-date information. This eliminates discrepancies and confusion because knowing the exact location, condition and status of goods can help manage exceptions, delays and proactively address issues.

This single source of truth can be teased out by AI, but for that organizations must overcome the data hurdle

Overcoming the data challenge

The true foundation for successful AI lies in fundamental data models and data management. This foundational layer is the first step and is important not only within a single organization but needs to be established across organizations within the supply chain ecosystem. Next is data quality; the information must be accurate and reliable. The principle of "garbage in, garbage out" underscores the importance of data quality.

Achieving high data integrity is a significant challenge because companies often use three or four different solutions to manage their entire supply chain, and these solutions may not be well-integrated. Even if an organization has clean, complete, accurate and integrated data, the challenge of interoperability remains. Supply chain companies include suppliers, manufacturers, 3PLs, shippers, vendors and partners, each using different systems. Data must flow seamlessly across these various systems, which is particularly challenging due to the multiple silos within a business and the larger, more diverse ecosystem. To leverage and exploit the full range of AI technologies, organizations must relentlessly pursue:

1.  Data management
2.  Data quality
3.  Data interoperability

How NTT DATA and Penske are helping organizations succeed

Our supply chain strategy and operations improvement programs often begin with data and include data integration from various systems, such as Transport Management Systems (TMS), Warehouse Management Systems (WMS) and Enterprise Resource Planning (ERP). Over the years, we have developed our own methods for assessing data quality and completeness. These methods not only enhance efficiency but also provide clients with valuable insights for improvement. This approach helps us identify areas where we need to work with clients to augment their base data for more advanced analyses. It also highlights opportunities for clients to address data inconsistencies at the root cause.

The key to leveraging any technology is understanding how it can benefit both the individual and the organization. For example, an individual can look at a spreadsheet and make informed decisions based on the data. However, when dealing with gigabytes of data, this becomes challenging. This is where technology, particularly AI-enabled solutions, come into play. AI can analyze vast data sets that are too complex for human comprehension, helping to identify key trends and patterns. It provides optimal solutions based on data, enhancing your decision-making capabilities without replacing you.

Practical applications of AI in transportation and fleet management in Penske

Penske uses machine learning to analyze billions of data points, identifying patterns and trends to predict and prevent vehicle issues. This reduces service disruptions and improves response times. AI is also becoming more adept at turning data into actionable insights, particularly in managing fuel analytics, thus helping improve routes, fuel efficiency and overall fleet management. In one instance, Penske used machine learning to provide fleets with industry benchmarks by combining data from different fleets and incorporating key performance indicators (KPIs) such as miles per gallon (mpg) and utilization. These benchmarks are helping fleets make informed decisions.

At Penske, we have been using AI for some time now and continuously refine our models. We believe the best AI is invisible and subtle, enhancing human performance without being noticeable. Most of our AI applications focus on maintenance, delivering value to customers without them even realizing it. Three best practices for AI adoption

1. Start small: Start with specific, tangible problems to solve, instead of chasing the latest technology for its own sake; implement slowly, by enhancing existing processes without overwhelming users
2. Leverage large data sets: First make sure your data is accurate, of high-quality and interoperable; this will enable integration of large data sets, including those generated by transport, customers, suppliers; next, layer in external factors like weather or other known disruptors, to discover trends that can help you better manage your operations.
3. Focus on change management: AI adoption requires humans to change their ways of doing things. Make sure that the people involved are aligned and engaged with your AI initiative; position AI as an “augmenter of human capabilities” that can take care of routine tasks and enhance roles.

AI is here to stay and it’s going to get bigger and bigger. Listen to our in-depth conversation in this webinar to get ahead of the curve and overcome the current challenges to scaling AI solutions. We discuss how supply chain organizations can build resilience, using emerging technologies and AI-driven insights, share practical stories and provide best practices.