Highlights:
- When depending on a static Estimated Time of Arrival also known as ETA, it is necessary to take real-time traffic, vehicle profile, truck qualities, port congestion, vehicle breakdown and other such factors into account.
- Now shipments can be tracked and monitored, on-time and in-full delivery (OTIF) performance can be improved, costs can be cut and less time is wasted estimating ETAs and searching for goods in warehouses.
Predictive supply chains seemed unattainable a few years ago because the data wasn’t readily available. There was no digital connectivity! Therefore, tasks were frequently completed manually, using the traditional pen and paper.
But there was a catch! Warehouse workers were often unaware of when the cargo arrived and often ended up losing a valuable forklift or a roll container!
While some businesses still continue to function in this manner, for the most part, the times and operations have now changed.
Companies today are creating warehouse data lakes and have easy access to technology. They are developing partnerships to manage this data and are consolidating it in one location for use (e.g., they are forging connections with carriers for enhanced end-to-end shipment visibility).
With all this readily available information at their fingertips, supply chain leaders are now in an ideal position to create more anticipatory supply networks.
Location’s function in supply chains with predictive capability
Supply chains have already benefited from the power of location and the most resilient ones incorporate various location technologies.
Location technology is an essential component of predictive supply chains because it provides a unifying link between various types of information.
It lets businesses build digital networks with end-to-end visibility, collaboration, responsiveness and risk mitigation, agility and optimization in place of functional silos.
Terabytes of data, such as those related to warehouses, cars, fleets, ships, trains, air cargo, shipments and the environment are gathered, enhanced, and analyzed to create a predictive supply chain.
Most data produced by each link in the supply chain is also fragmented and confined to silos, limiting the ability of your cloud computing firm to develop new goods and services’ logistical intelligence and analytics.
For this reason, your Platform as a Service (PaaS) provider must consider the factor that unites all the data sources: ‘The Location’. Each data item now has a location attribute, which can link different data kinds together.
The use of location can connect diverse and dynamic data sources, delivering rich context and improved relevance.
What are the advantages of predictive supply chains based on location:
- Precision – Transportation optimization entails forecasting delays in the arrival of multimodal shipments and doing post-trip evaluations.
- Quality Data/science – Using machine learning (ML) in location-based analytics, PaaS provides you with the most accurate data possible.
- Resilience and Sustainability – Makes it possible to maintain productive workflow and decision support despite unexpected events.
- Data integration and Orchestration – Data on shipments, carriers, telematics, and logistics are brought together with precise contextual information.
The three phases of supply chain evolution – static, dynamic, and foreseeing
Real-time visibility is essential in the face of ongoing disruptions in the world’s supply chain and logistics arena.
Businesses are attempting to include visibility into their supply chain, logistics and transportation planning processes.
The supply chain evolution of a corporation is currently branched into three stages: static, dynamic, and predictive. Let’s examine each of these to learn how it functions.
The static phase
A static supply chain is used in the initial phase. Although this type is the simplest to construct because it relies on static maps and simple information, it provides the most negligible value to an organization.
Let’s examine it from the perspective of ETAs. An estimation of distance and speed based on the route would be a static ETA.
However, as any motorist is aware, unforeseen situations frequently arise on the road, making static ETAs uncertain.
When depending on a static ETA, it is necessary to take real-time traffic, vehicle profile, truck qualities, port congestion, vehicle breakdown and other such factors into account.
The estimate will be quite accurate if the intended route and the actual route traveled coincide.
Any hope of getting an accurate ETA is lost, though, if the real route deviates from the planned one or the projection is based on false traffic reports or different driving habits.
The dynamic phase
Incorporating traceability and variable ETAs is the next stage in developing a predictive supply chain. This stage provides additional opportunities for optimization by making use of real-time ETAs to trigger alarms, controlling and analyzing dwell periods and developing driver-facing applications. The dynamic phase is more difficult because of the inclusion of external factors like weather, traffic, legislation, etc., but it ultimately leads to improved productivity and value for the business.
From the perspective of service providers, a successful dynamic ETA would include reliable estimations based on optimal route selection and real-world traffic conditions. You can now use real-time location information to analyze your trip’s results.
The predictive phase
A predictive supply chain is the third and last stage. It uses a predictive algorithm powered by deep learning and supports decision-making through the automated supply chain process management.
In this phase, users can replace time-consuming, manual operations like load and capacity, optimization and risk analytics by creating accurate multimodal ETAs.
For enterprises to make informed judgments on predicted ETAs, the following factors are considered: historical traffic patterns, driving behavior, seasonality patterns, derived features, routing history, break data and KPIs that can be directly modified to customer requirements.
The predictive phase undoubtedly has many benefits. Because it requires fewer updates and it is simpler to add more data sources, many known features are used to provide predictive estimates (e.g., specific waiting times at warehouses, port delays.)
The drawbacks are minimal given the benefits it offers (those would mostly be efficiency gains and expansion potential).
Predictive ETAs need data for testing and training, including a large amount of historical data that includes seasonal patterns.
Additionally, the quality of the data has a direct impact on the performance of the model; poor data will yield poor performance.
Bringing your predictive supply chain to life
Predictive supply chains can benefit several areas, such as yard management, traffic management (both inbound and outbound), first-, middle-, and last-mile deliveries, and connected warehouses. Let’s examine a couple of instances in more detail.
End-to-end shipment tracking: Maintaining delivery SLAs between you and your customers depends on end-to-end shipment visibility.
When shipments are tracked and monitored, on-time and in-full delivery (OTIF) performance can be improved, costs can be cut and less time is wasted estimating ETAs and searching for goods at warehouses.
Automation of the warehouse: The labor, material handling equipment, and the inventory visibility is limited when using standard warehouse management systems.
Additional operational data can be produced in real-time by adding a layer of location intelligence over traditional yard or warehouse management systems (WMS).
This lets operators make proactive decisions, removes blind spots and inefficiencies, saves money, and improves service levels.
It also gives operators uninterrupted access to the location and operating status of their staff, machinery, and inventories.
Middle- and last-mile operations optimization: Managing fleet operations is challenging. It takes visibility into your fleet’s operations, the present situation – and the future considerations to respond in real-time to changing conditions.
With smart routing based on truck features and predicted traffic instead of best-guess estimates, location intelligence can help you streamline your business and optimize middle- and last-mile journeys.
Tour planning algorithms that consider current conditions, time slots, and task limits reduce human error and improve the efficiency of your dispatchers.
You can provide precise ETAs while lowering operating costs by closing the communication loop between the dispatcher, the driver, and the customer.
Final thoughts
Organizations should implement a more predictive supply chain to stay competitive as the industry changes.
PaaS offers products integrated into an existing client system that could be implemented soon. Solutions based on the cloud computing concept lower service costs while enhancing process automation and supply chain visibility.
Customers can choose end-to-end solutions or integrate PaaS solutions into their own systems and applications.
The widely available solutions and services offered by PaaS providers are created with prediction models and correlation analysis in mind and use location as a unifying layer.
You can streamline your operations throughout the whole supply chain with quick access to information, freeing up your resources so you can concentrate on what matters most!
