Five steps to an intelligent supply chain

With one disruptive global event occurring after another over the past two-plus years, companies are focusing on redesigning their supply chain to be more resilient, transparent, and agile. We believe that the common thread among all companies that are resilient and thriving in the current environment is their ability to extract and use the “intelligence” that is embedded in their supply chain. Intelligent supply chain management is possible when businesses take full advantage of the latest digital transformation technologies, artificial intelligence (AI), machine learning (ML), predictive analytics, unified commerce, and big data.

However, with a plethora of AI and technology solutions available in the market, the modern supply chain professional is spoiled for choice. It can be hard for them to know which technologies to choose, where to invest their digitization budgets, or where to scale their operations. We believe a general five-step roadmap can help guide that decision-making process easier.

What is supply chain intelligence?

Clearly, today's supply chain operations are becoming highly automated with the advent of warehouse automation systems, self-driving trucks, and more. There is no doubt that the future of the supply chain is automation. But who or what is the “brain” behind these highly automated operations?

For example, a self-driven truck knows exactly how to get to its destination on its own. But who decides what that destination should be, and whether it is the most optimal one for balancing stock and demand across the supply chain on any particular day? Who decides which route the self-driven truck should take to ensure that weather conditions do not affect the goods being transported? The intelligent supply chain is like the brain behind automation.

An intelligent supply chain, supported by live data, enables real-time collaboration with multiple supplier partners, faster planning and execution while offering better accountability and customer experience. Plus, it allows supply chain professionals to make the right decisions to increase efficiency through automation.

With internet of things (IoT)-enabled sensors placed throughout the intelligent supply chain, companies can now collect thousands of data points that can be utilized to improve each step of the supply chain process. Predictive analytics and big data will empower companies with insights to reduce downtime, optimize workflows, and keep operations running at their maximum efficiency. This reduces costs, improves profitability, enables greater competitive advantage, and improves planning and execution for all supply network participants (suppliers, manufacturers, providers of maintenance, repair and operations, and carriers).

For example, knowing that a shipment of temperature vaccines is heating up, or when a transshipment at an airport is causing a temporary spike on the tarmac, are some examples of supply chain intelligence.

5 key steps to building an intelligent supply chain

To build a good intelligent supply chain strategy, you need to take five key steps:

Step 1: Create on-demand monitoring of your supply chain. This refers to the tracking and tracing of your supply chain without relying on various supply chain actors for data. To do this, companies need to gain reliable visibility of their products at every stage of the supply chain including location, condition, and security using IoT and AI. If businesses achieve this kind of visibility, they can reduce response time, even for the day-to-day mini disruptions that reduce efficiencies, customer satisfaction, and predictability in the supply chain.

Step 2: Leverage business signals. It is important to have an intelligence platform that can curate visibility data and turn them into business signals. These signals can be used to trigger live/predictive business actions. Frontline teams can use the contextual business signals to mitigate business risks. The signals can trigger alerts to the customer contextually when a problem arises with their merchandise. For example, a contextual supply chain signal might be that a pharmaceutical consignment fails its quality approval days before it actually arrives, triggering preventive business action on order fulfillment.

Step 3: Gather insights. This step provides macro-level information created by analyzing one or more business signals using data science to offer insights, such as: on time in full (OTIF) and cold chain compliance by region, by transporter, and more. This macro-level information actually helps companies drill down to a micro level into the causes of issues within a lane, facility, or region, but only in the area that is creating business risk. Insights work better as companies gather more data over time.

Step 4: Combine insights and signals to create “foresights.” These foresights are early warnings that can be used by AI or ML to predict a business key performance indicator (KPI) such as “on time in full” (OTIF), cold chain compliance, or asset utilization. Without this kind of forecasting, problems with a KPI would often be rectified in hindsight. An example of such forecasting is the idle warning of that an expensive reusable railcar had arrived at a particular customer's location, based on live signals and historical insights. This then leads to predicting a KPI, such as asset utilization.

Step 5: Implement a digital twin. A digital twin creates digital simulations built on relevant, reliable, and real-time data. It allows the creation of digital replicas of the past, present, and future of companies' logistics operations and supply chain. Companies can not only reproduce and visualize network operations digitally, they can also reproduce “what-if simulations” across lanes, facilities, transportation partners, regions, or the entire network.

It's important for companies to have end-to-end control of all their merchandise, and with supply chain intelligence, it's possible. You just need to get the right visibility and the right intelligence to transform your supply chain, make more informed decisions, and implement a successful future growth plan.