What if you could automatically detect supply chain anomalies as they happen, or even predict them in advance? You'd be able to take timely corrective action and help maximize revenue, margins, customer satisfaction and shareholder value.

There's no question: Supply chain planning and execution is complex. From design and sourcing, to manufacturing and testing, to distribution and warehousing, to customer fulfillment and satisfaction, through warranty and aftermarket service -- variability and uncertainty make planning and execution difficult. But when managed well, an organization’s supply chain can become a differentiator. It can become a competitive edge.

For supply chain professionals, solving complex problems is nothing new, and analytics have played an important role since the terms “advanced planning and scheduling” and “supply chain” were coined. What has changed is the sophistication of analytics and the institutionalizing of analytic platforms. You now have an unprecedented arsenal of integrated capabilities available to you.

Today, it's not uncommon to see large corporations investing in integrated analytics platforms while at the same time embarking on large ERP deployments. Earlier in my career, ERP deployments often meant an organization was consumed and unable to consider additional decision support solutions. These new analytic platforms can augment and extend your existing investments in supply chain capabilities and enhance your flexibility and ability to grow.

Simultaneously, the digital transformation of manufacturing and supply chain is resulting in more IoT data across the supply chain. Analytics is the discovery and insights engine that creates value from this data. Together, this sets the foundation for a paradigm shift to a more connected and data-driven supply chain. The proliferation of sensor data across the end-to-end supply chain greatly improves supply chain visibility and opens possibilities for improved planning, execution and response management to anomalies. Streaming data, “edge analytics,” artificial intelligence, machine learning and predictive modeling will allow the detection of anomalies, prediction of pending supply chain disruptions, and automatically route alerts and trigger response management in a timely fashion.

An effective Industrial Internet of Things (IIoT) strategy is crucial to success in digitally transforming your supply chain. Here are just a few ways IoT, analytics and AI enable innovation and profitability in supply chain:

1. Supply chain visibility. Edge analytics and sensor data can feed KPI dashboards and control towers to show near real-time status of assets, orders and shipments, and trigger alerts for review, contingency analysis and subsequent re-planning/optimization.
2. Demand management. Next-generation capabilities in statistical forecasting are helping drive forecast accuracy improvements all the way down to the sku/location level. Machine learning is being applied in new product forecasting to automatically determine surrogates for analogous forecasting, improving the accuracy of demand planning for new product launches. AI is being applied to the collaborative demand override process to guide planners toward overrides that minimize their bias and improve their forecast value add. Analytics are helping organizations improve the effectiveness of promotional campaigns.
3. Demand-supply balancing. Sales and operations planning and integrated business planning processes can use optimization in their demand/supply balancing tradeoff decisions to facilitate profit-optimized, integrated plans.
4. Production quality and predictive asset maintenance. Equipment sensor data can be used to monitor assets to predict/detect impending yield excursions or equipment failures, thereby triggering proactive actions to avoid costly production defects, conduct preventive maintenance and maximize uptime of critical assets.
5. Perceptive quality. IoT data can be monitored and analytics applied to proactively evaluate customer reactions and the effectiveness of new products, facilitating timely product launch corrections to maximize profitability and market share.
6. Procurement and warranty fraud. IoT data can be used to detect patterns of fraud in both procurement spend and warranty claims, reducing the “time to detect” fraud, and reducing warranty and procurement expense.
7. Aftermarket and service parts. The combination of improved forecast accuracy and use of multi-echelon inventory optimization can be used to right size service parts inventory to support desired service levels or fulfillment goals. Advances in analytics allow taking this a step further, incorporating engineering reliability data, including Weibull curves, age of deployment, hours of use, or environmental factors (wind, sand, temperature, humidity etc.) as a casual influence of service parts demand.
8. Logistics optimization. Telematics and streaming IoT data can be used to optimize logistics networks and react to unexpected events in real-time, and forecast demand based on changing customer preferences in a short-term cycle. Sensor data can be analyzed to understand network circumstances accurately, in real-time, and machine learning can be applied for daily decisions on new data.

The digital age of manufacturing is already bringing disruptive technologies that are forever changing the face of manufacturing and supply chain. The IIoT will only serve to make the benefits of analytics in supply chain significantly more dramatic and impactful. IoT will help increase the velocity of supply chains and our ability to rapidly detect, predict and respond to supply chain anomalies to drive greater productivity, improved decision making, profitability and shareholder value.

Analytic platforms offer integrated capabilities for organizations to augment and extend their legacy investments, and increase both their flexibility and ability to grow. These platforms can bring unique combinations of data management (integration, data quality), workflow, IoT streaming, data mining, visualization, text analytics and sentiment analysis, forecasting, price and inventory optimization, discrete event simulation, machine learning, predictive modeling, steaming data, and more. This will allow organizations to creatively and proactively attack supply chain problems in new ways to drive revenue, profits and shareholder value. Learn more about getting started with the IoT.