Smart logistics platform
This technology is about smart logistics platform in order to allow real-time process optimization.
Effective optimization in logistic is possible only by using CPOs (Cyber-Physical Objects) and CPSs (Cyber-Physical Systems) for building a Smart Logistics platform in order to allow real-time process optimization.
The design of so-called CPOs (Cyber-Physical Objects) and CPSs (Cyber-Physical Systems) generates a digital twin of the logistic process. This construction of CPOs and CPSs allows hierarchical correct input of data into optimization algorithms, machine learning and artificial intelligence. The data of the logistic process is so in the correct context, which is the basis for further mathematical analysis. CPS together with mathematical processing establishes smart logistics platform in order to allow the real-time process optimization, business prediction, maintenance prediction, simulation and business decision making. CPOs build larger CPSs and transform passive resources into a crowd of cells that form a network of intelligent objects and become active, allowing algorithms to process an unlimited quantity of parameters and data.
To date, many of these options have not been able to be implemented in existing systems or the implementation has been very demanding and expensive. Such a platform that creates CPS would translate processes into a math-speaking language without any changes in the information and production system. Users would still be able to perform their work in the same way as before the introduction of the platform.
Such a design enables the robustness of the platform and enables the real-time processing of all changes and real-time processes optimization, prediction and decision making.
Implementation and application are rather very simple and cost-effective, useful both for SMEs and for larger service providers.
The integration to the platform services is very simple, and it enables vertical and horizontal integration of service providers (sea transport, warehousing, road and rail transport).
The main platform advantage is that the platform can process large amounts of resources and optimize them within the business process in real-time, respectively its performance increases significantly when it is used in complex systems. The usage of machine learning and artificial intelligence recognize patterns in business and technology used. It allows the expansion of the user's network, thus creating new opportunities for the development of new business models with respect to trends in the industry and developing of partnerships among service providers.
Stage of development
The technology is in the TRL 3-experimental proof of concept.
In development, we relied on the Reference Architectural Model for I4.0 (RAMI), which defines the method of introducing the I4.0 paradigm in the production companies. On this basis, we started to develop a model that could describe any process in the company, and it also included all the resources involved in each process activity. On this basis, a model based on the so-called Actioms (the basic elements of the process), on the basis of which an arbitrary process can be assembled (as well as very similar cells of the human body forming functionally very different organs).
The model has already been used in the construction of a demanding ERP system in the pharmaceutical industry, where it has been proven in terms of the ability to describe very complex processes, while ensuring full traceability within the complex GMP standard. As part of the ERP system, the model has successfully passed the required GMP validation.
In the continuation, the model was completed and developed into a platform that, according to functionality, meets the requirements of i.e. CPS (Cyber Physical System), which is the basis for the digital transformation of the company and the introduction of smart logistics. By using such a design platform, any logistical (business) process is already structured in such a way that it enables the direct use of data for further mathematical processing: optimum algorithms, regression, machine learning, and the use of artificial intelligence.
Inventors are currently checking the possibilities of integration and the operation of such a platform in various logistic systems. It is precisely in the integration of the platform where we expect the most possible challenges.
Challenge and needs
Primary and secondary industry areas, together with the associated logistics, are under great pressure, as production is increasingly approaching the introduction of paradigm I4.0. This also requires the implementation of just-in-time production, which necessitates the introduction of a full optimization of all resources, especially at the level of logistics, which in some way represents an unnecessary time spend. That is why logistic processes must be optimized, which enables the production work to be performed in a quality manner.
From a technical perspective, traditional optimization and scheduling applications do not have access to all needed information. In such calculations, only part of the necessary resources is taken into account, which does not void into feasible and optimal processes. Thus, there are always discrepancies, for which, as a rule, we do not know the reason, as the reasons for them, can be outside the information system.
Therefore, it is absolutely necessary to present the processes so that all possible resources will be presented in a format that is mathematically legible and enables further mathematical processing: with optimization algorithms, simulations, machine learning, and artificial intelligence. To date, many of these options have not been able to be implemented in existing systems or the implementation has been very demanding and expensive. Such a platform that creates CPS would translate processes into a math-speaking language without any changes in the information and production system. This will enable new and already essential technologies to be enabled and accessible to all companies - including SMEs.
Industrial sector and application
Smart logistic principles can be implemented in all industry areas. CPOs, which are able to describe every basic process in logistics by taking into account all the resources and parameters, allow new technologies like process simulation, mathematical and bionic optimization, machine learning and artificial intelligence to help by optimal planning, maintenance and business process prediction and decision making.
There are only some areas where technology could be used:
- Port activity and shipping: optimization of the spatial distribution of cargo, optimization of logistics routes, optimization of the use of resources (human, spatial, ship), optimization of ship routes, minimization of transit costs;
- Transport: minimizing transit costs, optimizing route planning, optimizing the use of resources (human, space, freight vehicles);
- Logistic centers and warehouses: optimization of the spatial distribution of freight, optimization of logistics routes, optimization of the use of resources (human, spatial);
- Production companies: optimization of logistics routes, optimization of the use of resources (human, space, equipment, machinery), planning and optimization of production;
- Airports and air transport: optimization of the spatial distribution of freight, optimization of logistics and passenger routes, optimization of the utilization of resources (human, space, aircraft), optimization of airplane paths, minimization of transit costs;
- Railways: optimization of the spatial distribution of freight, optimization of logistics and passenger routes, optimization of the utilization of resources (human, space, trains), optimization of trains, minimization of transit costs;
- Energy: the installation of an optimum network of electric car chargers for cars, the design of smart grids;
The technology is still under development and it’s not yet protected by a patent.
Potential markets and targets
Potential markets are in the industrial sector and applications: port activity and shipping, transport, logistic centers and warehouses, production companies, airports and air transport, railways, energy.
Especially interesting for the direct implementation of the platform are companies that develop information and other logistics solutions for such sectors: ERP systems, quality control systems, business planning, business optimization.
The market of logistic service software is a mature market but still facing strong growth, there is predicted for the Global Logistics Services Software Market 2018-2022 to post a CAGR of 10% CAGR over the next five years. The logistics services software market size will grow by more than USD 14 billion by 2022. There are many Logistic software service providers some of them are global and a numerous of local providers.
There are identified different levels of logistic optimization, a progression from (1) basic execution, through (2) cost avoidance and partnership, and (3) productivity and process integration, up to the ultimate level of (4) demand-driven profitability.
For logistic this implies that the trend is moving from focusing on cost with sufficient capacity towards service in conditions of tight capacity. For the company, this means both embracing adaptive planning and taking a forward-thinking approach. To employ this level of optimization there are required powerful optimizers with more complex decision-making rules, which are still not available or only available to a limited extent.
There are many Logistic software providers (ERP and TMS) systems. As mentioned in this document there are no generally available service providers capable of cost-effective automatized optimisation since there are no models yet capable to design in a virtual environment a complete copy of all resources in the proper context of the real environment, which is the base for the cost-effective perfect deployment of opsonisation algorithms.
Potential partners are companies that develop information and other logistics solutions for the logistic sector: ERP systems, quality control systems, business planning, business optimization. In certain cases, this could also be the companies themselves - from targeting sectors like port activity and shipping, transport, logistic centers and warehouses, production companies, airports and air transport, railways, energy.