Use Case: Shared Economy
The sharing economy is especially relevant to core transportation companies as well as to heavy users of transportation services.
In a world of shared assets, changing economics and customer preferences are increasingly driving transportation players not to go it alone.
The sharing economy can be defined as the preference to pay for assets or services by consumption or on-demand, rather than owning assets permanently or signing long-term contracts for services
Use Case: Asset Right: “Manage and monetize excess capacity through the shared platform”
Heavy users of transportation services could become “asset right” by focusing on the core business while effectively using the excess capacity in the broader transportation system. At present, retailers and other heavy users of transportation services typically invest in transportation assets (e.g., trucks or rail cars) or hire a third-party logistics (3PL) provider to fulfill key needs.
To shift to an “asset-right” model whereby asset ownership is balanced with excess capacity in the broader transportation system, heavy users would at least need to develop the capabilities to:
- Forecast the supply and price of excess assets in the marketplace
- Integrate logistics, warehouse management, and telematics systems
- Manage an increasing number of transportation service providers across the network
- Enable dynamic, real-time decision-making in the supply chain to move products efficiently through the shared network.
Use Case: Waste HunterThe Challenge: While collection of regular household waste follows optimized routes, pick-up of bulky waste & electronics follows an on-demand scheme. The city is divided into six collection districts for bulky waste & electronics. Every day of the week collection within a specific district is performed. Citizens request an appointment a few days ahead, which is confirmed by the waste service. Route optimization is manual performed by the drivers on a day-by-day basis. It is expected, that an automatic optimization considering additional factors like vehicle capacity, time of day, construction sites and others can improve routes and would allow to optimize size and location of the current collection districts.The collection of bulky waste & electronics are performed by different vehicles. The districts are the same, but the routes differ because some collection appointments contain bulky waste and electronics, others only one of a kind.
digiBlitz’s understanding of the Challenge:
Bulk Waste is Spatial entities that do not follow the traditional pattern of everyday waste from households. The volume, size, and number of such wastes are unpredictable, so there cannot be a traditional fixed optimization of vehicle routes and vehicles. Besides common constraints like a number of collection vehicles and their capacity, costs, additional information of weather, season, time of day, complaints by citizens, changes to population and social environment may be considered when calculation optimized routes and districts for bulky waste & electronics collection services. So, there is a need for a solution that could create route optimization, Influencing factors capturing and making indicators out if them and create a qualification matrix for cost.
Use Case: STREET LIGHTING AND E-MOBILITY
The Challenge: Intelligent grids, street lighting, and e-mobility-solutions are important steps to fulfill the promise of a “smart city”. Smart public street lighting adapts to movement by vehicles and pedestrians, dims when no activity is detected and is capable of integrating further smart city-related functionalities. Public street lights can become charging points for electric cars for instance. But which locations promise the most impact? Traffic numbers, electric vehicle (EV) numbers, weather season, population development, traffic law, demographic factors, power grid capacity and other factors have to be considered. Your simulation of the grid might help to introduce “smart” to a smart city! The target of this use case is to create a flexible simulation, which allows identifying optimal locations of public street lights depending on multiple layers of data, e.g. traffic density and grid-infrastructure.
digiBlitz’s understanding of the Challenge:
The city of Herne wants to switch to intelligent lighting and e-mobility-infrastructure in a reasonable way, step-by-step. The major challenge is to identify the locations with the most potential for power savings without loss of inconvenience. Additionally, if the city had to start offering EV charging from electric posts, there has to be a tremendous effort taken to analyze the grid potential. Choosing the right location would also mean to identify the hotspots for e-mobility within the city.