Designing Custom Logistics Intelligence Layers with Appian
Introduction:
Modern Logistics is more interconnected and more complex now. From the rapid shifts in consumer demand and increasing transportation costs to global disruption and changing sustainability goals, logistic leaders face constant pressure to adapt. Legacy systems may operate in silos, limited visibility which in return slows down decision making.
This is when the concept of Logistic Intelligence Layer comes into place.
Logistic intelligence layers work as a brain of the whole logistic ecosystem, a unified layer which combines the data from the different sources like ERP, TMS, WMS, and helps in transforming the data into real insights. It helps organizations to anticipate disruption, improve service level, optimize routes, and make faster decisions.
Challenges that logistics face today:
- Conflicts and geopolitical shifts are currently at the top of the list. Due to the disruption of important shipping channels caused by the conflict in Ukraine and the decline in Chinese industrial production, businesses must reevaluate their global supply chain decisions. Political unrest may lead to worldwide energy shortages, which will probably affect production capacities and force certain industries to run at lower capacity.
- International supply lines were disrupted by the COVID-19 pandemic, and businesses have been attempting to recover ever since. Shutdowns were brought on by zero-COVID policies in nations that produce and distribute goods to global markets. As teams work to recover, supply constraints persist to this day. Demand for particular raw resources also rose as some countries looked to ESG (environmental, social, and governance) initiatives, such as green infrastructure, to boost their economies. ESG must now be included in supply chain scenario planning.
- Logistics are still impacted by labor shortages, which lead to issues including a shortage of shipping crews, long-haul truckers, and last-mile delivery workers.
- Transforming the existing IT ecosystem is complex, significant changes to the ecosystem may be required as some legacy systems may not be equipped to handle that. Risk can be reduced when a step by step approach is implemented for modernization, it will lead to a smooth transition.
Importance of logistics Intelligence Layer
Logistics intelligence uses AI prediction technology to learn from data gathered at every stage of the supply chain. Machine Learning (ML) techniques are employed to continuously examine this data in order to find patterns.
Businesses can strengthen demand forecasting, asset and facility management, and delivery route optimization with the help of AI logistics systems. The productivity of the workforce is increased by using robots to carry out manual duties including tracking, identifying, sorting, and transferring cargo.
Reasons why business use Logistic Intelligence Layer:
- Demand Forecasting: By using logistics intelligence, businesses may anticipate future demands and reduce the probability of an inventory shortfall during periods of high demand. Managers may allocate resources more effectively and create additional opportunities for growth by using LI software to make effective warehouse management decisions.
- Automation of Warehouse Operations: From procurement to last-mile delivery, logistics intelligence helps in the automation of warehouse operations. AI robots, for instance, load and unload pallets and select and transfer cargo across the warehouse. In the same way, machine learning makes superior warehouse decisions on inventory replenishment through continuously learning from data through algorithms.
- ERP (Enterprise Resource Planning): Companies use AI ERP software to plan logistics for the supply chain and distribution, as well as to find the most effective routes and warehouses. It assists managers in managing labor performance, workflows, and human resources independently.
- Route Optimization: To identify the optimal routes for drivers, businesses employ AI logistics software to gather and evaluate huge volumes of data in real-time. Additionally, logistics intelligence makes use of historical data to continuously optimize the drive’s path and even pinpoint the closest location for parking, food, relaxation, and refueling.
Providing an integrated service and good customer experience is important for logistics companies. Managing interactions, customer requests, and work tasks through siloed systems and paper-based processes is no longer sustainable. To achieve a more effective process and transparency organizations must look into more innovation which strengthens the end-user communication and also provides a platform for shippers and carriers to make faster decisions. Appian helps in low-code applications management, 20X faster than traditional methods.
How can Appian help you?
Using Appian you can quickly build, or deploy an application including:
- Cross-Docking management
- Digital Driver Dispatch
- Freight tracking
- Fleet Management
- Product and Lot release
- Scheduling and route Planning
- Load Management
- Track and trace
- Transportation Management
- Shipping Compliance
- Data Integration: Data integration is the base of the Logistics Intelligence layer. It combines information from various sources and ensures accuracy, real-time visibility, and allows for faster response to any disruption or demand change.
- Process Modelling: It provides visuals and functional templates of logistic workflows. It is used for identifications of inefficiencies, standardizing procedures, and simulating different scenarios. In logistics, they may mean mapping everything from warehouse pickups to delivery routes.
- Decision Layers: The decision layer relies on rules-based reasoning, AI, and predictive models to enable or make logistics decisions. Whether rerouting a truck because of traffic, redistributing inventory, or shifting delivery times, this layer facilitates quick, uniform, and data-driven action.
- User Interfaces: User interfaces connect individuals with the system. They are built to display logistics information in a simple and actionable format, perhaps by using dashboards, mobile applications, or notifications. Using an effective interface makes it possible for cross-functional teams to track operations and respond promptly when necessary.
- Analytics: Analytics shape raw logistics data into actionable insights. By examining trends, performance metrics, and outliers, logistics teams can make intelligent choices, maximize routes, minimize costs, and foresee upcoming issues. Analytics also helps in continuous improvement by identifying regions of strategic change.
Client: A well-established food distribution company that began over a century ago as a local butter and egg delivery service is now the largest family-operated food distribution company in North America, serving approximately 100,000 business customers.
JRD Context: Implement a Material Management System (MMS) within Appian to streamline the material request handling process and ensure seamless data integration with SAP.
Results:
- 30% increase in processing efficiency through automated workflows.
- 25% decrease in data entry errors, improving accuracy.
- 20% reduction in operational costs due to streamlined processes.
- 15% improvement in collaboration efficiency among teams.
Conclusion:
When systems are networked, decisions are quicker, and teams are able to count on real-time insight instead of speculation, logistics moves more smoothly. Data aggregation, workflow efficiency, and greater clarity and control for every element of the supply chain are enabled through a layer of logistics intelligence.
You don’t have to rebuild your entire technology foundation using a platform such as Appian. With what you already possess, you can create a dynamic, intelligent layer that causes things to happen more quickly and more intelligently.
The real value? Better coordination, fewer delays, and a supply chain that gets the results you want.