As supply chains in the US and around the world remain tumultuous, facing pandemic closures and labor shortages, supply chain professionals are turning to data to stay ahead. An intelligent supply chain, supported by live data, enables real-time collaboration, faster execution speeds, and improved accountability.
Inventory shortages have become an issue that supply chain professionals encounter daily, and major roadblocks such as the Suez Canal blockage or port closures and/or backlogs have become more common since the beginning of the COVID-19 pandemic. However, with real-time data intelligence, availability can be ensured across all critical demand centers to improve financial business, overall customer experience, optimize company objectives, and achieve rapid problem detection.
Supply chain intelligence data drives business efficiencies
As analytics platforms and tools continue to evolve, businesses can increasingly use data to gain end-to-end supply chain visibility that is reliable, actionable, and covers items shipped from origin to delivery destination. However, reliable and actionable visibility can only be achieved through a combination of firsthand sensor-driven signals and smart intervention. A visibility and analytics platform filters our real-events from noise by marrying first-hand location and condition sensor signals with Electronic Logging Device (ELD) to detect verifiable anomalies in supply chain operations. For example, you can verifiably detect if an item has been stolen from a truck by combining ELD data with item-level sensor intelligence.
Notably, IoT (internet of things) enabled sensors plugging into well-designed visibility and analytics platforms allow companies to collect thousands of data points that can be utilized to improve every step of the supply chain process and mitigate any potential roadblocks along the way. Predictive analytics and big data then empower companies with insights to reduce downtime, streamline workflows, and keep operations running at their maximum efficiency. Other benefits include reduced costs, improved profitability, greater competitive advantage, and improved planning and execution for all supply network participants (suppliers, manufacturers, providers of maintenance, repair and operations, and carriers).
But the big question remains on how to build the right data analytics into your supply chain operations to bring actionable intelligence and drive efficiencies.
How to build an intelligent supply chain for operations efficiency
To build an intelligent supply chain, a business must continually follow several steps to achieve and maintain successful operations.
The first of these steps is On Demand Monitoring, which refers to the tracking and tracing of your supply chain. With increased visibility, businesses can leverage their operations, reduce response time, and mitigate disruptions that can set back efficiencies, customer satisfaction, and predictability in the supply chain.
The second step is leveraging Business Signals, which will allow companies to trigger live predictive actions, as frontline teams can use contextual business signals to mitigate any potential risks. It is important to have a system that can curate data and alert the customer when a problem arises with their merchandise, whether it is due to a rise in temperature or because the package cannot connect to a flight.
The third step is Insights, which provides macro-level information including: on time in full (OTIF) and cold chain compliance by region, by transporter, and more. This macro-level information also allows companies to dive deeper to a micro-level and examine the causes of issues within a lane or facility.
The fourth step is Foresights, which refers to insights and signals combining to predict a business Key Performance Indicator (KPI). Business KPIs include: on time in full (OTIF), cold chain compliance, or asset utilization forecasting that are often rectified in hindsight.
A final step is to create a Digital Twin, which creates digital simulation built on relevant, reliable, and real-time data. Companies can create digital replicas of past, present, and future logistics and supply chain models, allowing them to reproduce and visualize network operations digitally, and also simulate different scenarios across regions, transportation partners, facilities, lanes, or the entire network. For example, you estimate the transit time, condition risks, and other roadblocks in shipping via Atlanta versus Chicago for a San Francisco to New York shipment. It is important to note that every data point has an expiration date. For example, data that warns you that a shipment’s temperature has increased two degrees in transit is only useful for the next two hours. A shift in temperature may cause products to spoil in that time frame, and this would affect supply chain quality standards. By comparison, the scheduled estimated time arrival (ETA) data point of a cargo carrier’s impact doesn’t change as dynamically as a shipment’s temperature. Therefore, the nature of decision making within the supply chain is often time-sensitive, to varying degrees depending on the specific scenario and data points involved.
Another challenge is that all data cannot be stored forever. Deciding what data to keep for the future and what to leave behind is often a science unto itself. Real-time data visualization is essential to making these decisions. Many times, some data is not in a position to be processed and displayed instantly. In cases like these, creating data lakes, past identifications, reusing what is known, and extrapolating data without amplifying errors becomes critical. Sensor-enabled intelligence provides hyper-accurate data on deliveries, helping companies improve the quality of their deliveries and build trust between parties in business.
Building a data-driven culture is a phased approach. A company must empower teams to use data to light up various business processes, help them identify glitches in their operations and measure the outcomes of newly applied methods.
Using data can also improve teamwork and make organizations more employee centric. While areas within a company may have different priorities, they have similar goals. Data shared between these groups can reinforce company-wide relationships and underscore common ground between each department’s objectives, leading to increased productivity and morale at a time when labor shortages are on the rise globally.
By demonstrating transparency and business process improvements with data, supply chains and manufacturing companies are lowering their insurance premiums and achieving better regulatory compliance. End-to-end control and visibility of merchandise better equips companies to make informed decisions in the face of new supply chain challenges.