With one disruptive global event occurring after another over the past two-plus years, companies are focusing on redesigning their supply chains 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 like 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 digitalization budgets, and where to scale their operations. We believe a general five-step roadmap can help make 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 or what 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 or what decides which route the self-driven truck should take to ensure that weather conditions do not affect the goods being transported? Digital tools like AI and ML serve as the brain for these types of automation.
An intelligent supply chain, supported by live data, enables real-time collaboration with multiple supplier partners and faster planning and execution. It also offers better accountability and a better 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. Some examples of supply chain intelligence include knowing that a shipment of temperature-sensitive vaccines is heating up or knowing when a transshipment at an airport is causing a temporary spike on the tarmac. 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 (including suppliers; manufacturers; providers of maintenance, repair and operations; and carriers).
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 what's occurring in 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 when a problem arises with their merchandise. For example, a contextual supply chain signal might be that a pharmaceutical consignment had failed its quality approval, which could trigger preventive business action on order fulfillment.
Step 3: Gather insights. At this step, data science analyzes one or more business signals to provide macro-level information and insights, such as whether a shipment is “on time in full” (OTIF) or 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 OTIF, cold chain compliance, or asset utilization. Without this kind of forecasting, problems with a KPI can often only be rectified in hindsight. An example of a foresight is a warning 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 use digital twins to digitally reproduce and visualize not only current network operations but also “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.
Sanjay Sharma is the chief executive officer of supply chain visibility company Roambee.