Supply chains are one of the main areas in which the use of these technologies can cause a revolution in the optimization and automation of processes.
The main problems that supply chains face currently are lack of transparency along the chain and the difficulty in tracing the goods that go through it. The cost of managing the shipping of a container is currently higher than the cost of the physical transport of the container itself, due to the authorizations and procedures that must be carried out in the countries involved and the pertinent authorities. In short, because of the lack of transparency of information and the traceability of assets, which slows down the entire process and raises dramatically costs.
That is the reason why the application and integration of the Internet of Things, Blockchain and Big data technologies, referring to the 4th industrial revolution will mark a turning point in the production processes, and this is what we will explore throughout the article. In particular, IoT technologies are of special interest in this revolution, to the point that a new specific term was coined to refer to the application of these technologies in the 4.
The devices that are part of these scalable and integrable ecosystems must be subject to an extremely effective management, since authentication and communications between the different cyberphysical systems are key in these technologies, failures in the collection, processing or storage of the generated data may have catastrophic consequences throughout the production chain and even impact on human losses.
6 Ways AI is Impacting the Supply Chain
That is why the decentralized, immutable and integrated management of data is crucial and it is in this context that Blockchain technologies can provide a differential value. Thanks to the application of this technology, the activity and identity of each integrated device can be recorded in the production system, without risk of manipulation of the data and its consequences. In addition, the Blockchain can be integrated through communication protocols between machines, allowing the creation of a new economy between the devices themselves in which they can reach agreements on supplies of raw materials, energy, parts, maintenance, and even logistics, via Smart Contracts whose payment will be executed automatically once the previously established conditions are met.
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There are already examples of micropayments through the Blockchain or the Tangle with sensors that sell their data, and electric cars that trade electric power between themselves and recharging points. This integration involves the streamlining and automation of hundreds of processes that currently require a large number of intermediate steps that hinder and increase the current production processes.
This added with the dramatic decrease in the need for intervention by regulatory and human third parties, will greatly reduce the consequent expense involved. In this way, the reduction in marginal costs necessary to meet the needs of personalized and unitary production can be achieved. The key is the disintermediation of the production process, so that companies can receive requests for a decentralized portal, incorruptible and easily accessible by all parties involved.
Once these data stored in secure and transparent networks is available, the technologies referring to the Data Science context such as Data Analytics, Machine Learning and Big Data allow the treatment of data.
10 Ways Machine Learning Is Revolutionizing Supply Chain Management
This enables us to extract significant information and to perform an accurate and efficient predictive analysis of demand, part prices and maintenance to ensure the proper functioning of supply chains and production systems. From the point of view of a supply chain system based on products and customers, which requires collaboration between different agents such as buyers, suppliers, distributors … there are a number of objectives in which it is essential to emphasize:.
The management of assets like inventory or transport of resources requires efficiency through collaborative efforts.
It is therefore extremely important how information is shared and processed throughout operations. Each agent involved in transporting, ordering and shipping goods relies on the optimization of activities that avoid high costs and poor synchronization.
Trends Accelerating the Use of AI in Supply Chain and Logistics
Automatic transactions are very useful in this context, but especial care must be taken and a person has to periodically check the proper functioning of the system. Knowing where problems or deficiencies of the system are located will allow agents to focus on trustworthy information that points out vulnerabilities or mistakes made along the way.
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Building a relationship of trust in the supply chain ecosystem can create stability in operations and strengthen collaborative plans, coordination and distribution of common business initiatives, resulting in the raising of the harmonize exchange of goods and whit it, a better customer-manufacturer relationship. IoT, is understood as the network of devices, vehicles and home applications integrated with electronics, software, sensors and actuators that are interconnected with the objective of collecting, storing and sharing information, with the possibility of performing certain actions with respect to it.
The notorious decrease in the price of microprocessors, controllers and sensors has allowed the proliferation of IoT systems that allow the collection, transmission and storage of a huge amount of data. Currently, the concept goes far beyond Machine to Machine M2M communication and describes an advanced connection network for devices, systems and services that complies with a wide variety of protocols, domains and applications. IoT, and more specifically its industrial version, IIoT, are called to revolutionize supply chains in regards to operational efficacy and business opportunities and revenue for manufacturers.
Analysis and Algorithms for Service Parts Supply Chains | SpringerLink
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The focus in this work is on the management of high cost, low demand rate service parts found in multi-echelon settings. This unique book, with its breadth of topics and mathematical treatment, begins by first demonstrating the optimality of an order-up-to policy [or s-1,s ] in certain environments. This policy is used in the real world and studied throughout the text. The fundamental mathematical building blocks for modeling and solving applications of stochastic process and optimization techniques to service parts management problems are summarized extensively.