Use of Industry 4.0 in Supply Chain Management Projects

Demand in modern society is becoming more and more complex and personalized. In industry, this means that in order to meet the needs of people and companies, it is essential to drive a more efficient and smarter way of production, maximizing the profitability of all relevant processes, radically reducing costs and marginal production times. In short, it is optimizing production. Currently, supply chains are no longer just systems for tracking products along the chain, but a way to gain competitive advantage and even build your own brand.

Thus, the fourth industrial revolution is characterized by the creation of smart factories that implement and integrate cutting-edge technologies such as cyber-physical systems, IIoT, data analytics, additive manufacturing, 3D printing and artificial intelligence. The application of these technologies makes it possible to achieve the necessary optimization and automation to reduce costs and production times. These technologies will allow us to produce thousands of different product configurations and produce very small batches of goods at very low cost.
Supply chains are one of the main areas where the use of these technologies can revolutionize the optimization and automation of processes. The main problems supply chains are currently facing are the lack of transparency throughout the chain and the difficulty of keeping track of the goods passing through it.

The cost of managing the transport of a container is currently higher than the cost of physical transport of the container due to the authorizations and procedures required in the respective countries and authorities. In short, due to the lack of transparency of information and traceability of assets, which slows down the whole process and increases costs significantly.

Therefore, with reference to the 4th industrial revolution, the application and integration of Internet of Things, Blockchain and Big data technologies will mark a turning point in the production processes and we will focus on this throughout this article.
In particular, IoT technologies are of particular interest in this revolution, to the point where a new special term has been coined to refer to the application of these technologies in the 4.0 industry context: the Industrial Internet of Things (or IIoT).

Devices that are part of these scalable and integratable ecosystems must be subject to highly effective management, as authentication and communication between different cyber-physical systems play a key role in these technologies, failures in the collection, processing or storage of the data produced can have catastrophic and even disastrous consequences throughout the production chain. can affect human losses. Therefore, decentralized, immutable and integrated management of data is very important, and in this context, Blockchain technologies can provide a different value. Thanks to the application of this technology, the activity and identity of each integrated device can be recorded in the production system without the risk of manipulation of the data and its results.

Blockchain can also be integrated through communication protocols between machines, allowing for the creation of a new economy where devices can negotiate the supply of raw materials, energy, parts, maintenance and even logistics among themselves (via Smart Contracts, which will be paid automatically when the previously set conditions are met). . There are already examples of micropayments via Blockchain or Tangle, with sensors selling their data and electric cars trading electricity among themselves and between charging points.

This integration includes orchestrating and automating hundreds of processes that require a large number of intermediate steps that hinder and augment already existing production processes. In this case, together with the dramatic reduction in the need for intervention, the associated cost will be greatly reduced. In this way, the marginal costs required to meet individual and unitary production needs can be reduced. The key issue is to make the production process agentless so that companies can receive requests for a decentralized, non-disruptive and easily accessible portal to all interested parties.

Technologies that refer to the Data Science context (such as Data Analytics, Machine Learning, and Big Data) allow the processing of data when this data is available, stored in secure and transparent networks. This enables us to extract important information and perform an accurate and efficient predictive analysis of demand, parts prices and maintenance to ensure the smooth functioning of supply chains and production systems.

Supply Chain Goals and Current Inefficiencies
In terms 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 goals that need to be highlighted:

  • Optimized shipping and Logistics
    Each agency involved in the shipping, ordering and shipping of goods relies on the optimization of activities, which avoids high costs and poor synchronization. Automated processes are very useful in this regard, but special care should be taken and a person should periodically check that the system is working properly.
  • Feedback for Quality Improvement
    Knowing where the system’s problems or shortcomings lie will allow agents to focus on reliable information that indicates vulnerabilities or errors made along the way. This is based on the principle of effective management, “What cannot be measured cannot be improved”.
  • Building Long-Term Stability
    Building a relationship of trust in the supply chain ecosystem can create stability in operations and strengthen their collaborative plans, coordination and distribution of joint business initiatives, resulting in increased harmonious exchanges of goods and with it, a better customer-manufacturer relationship.

Supply Chain Related IoT Features:
IoT is understood as a network of integrated devices, tools and home applications with interconnected electronics, software, sensors and actuators for the purpose of collecting, storing and sharing information. The fall in the price of microprocessors, controllers, and sensors has allowed the proliferation of IoT systems that allow large amounts of data to be collected, transmitted, and stored. Currently, the concept goes far beyond Machine-to-Machine (M2M) communication and defines an advanced network of connections for compatible devices, systems and services with a wide variety of protocols, domains and applications.

The IoT, and more specifically its industrial version, IIoT, is called upon to revolutionize supply chains in terms of operational efficiency and business opportunities and revenue for manufacturers. It will achieve this in the following ways:

  • Asset traceability
    In the past, numbers and barcodes were tracked to manage goods throughout the supply chain. Currently, RFID methods and GPS sensors can monitor the status and location of products from the moment they are produced until they reach the end customer. Having timely control over the management and quality of deliveries and anticipating the demand this brings will provide a huge competitive advantage.
  • Relations with Suppliers
    According to IBM, up to 65% of the value of the company’s products comes from its suppliers. Data from asset tracking allows manufacturers to optimize production planning and pattern recognition in relationships with suppliers and uncover key business opportunities. It is therefore very important to pay special attention to the whole process associated with them, because a higher service and product quality leads to a better relationship with the customer.
  • Stock and Forecasts
    IoT sensors can track stocks and stock supplies for future production with a single click, and also store information in shared spaces in the cloud that are easily accessible to all interested parties. All this allows to create even more efficient production schedules.
  • Linked Shipments
    Supply chains do not stop both vertical and cross growth and it is increasingly important to ensure that all containers and fleets are interconnected so that there is an integrated and effective transfer of information throughout the entire supply chain.

Data Science Features Related to Supply Chain
Predictive analytics is positioned as the most powerful tool that can revolutionize supply chains. Data science and the ability to not only extract relevant information from it, but also to make accurate predictions enable the capture of real-time decisions that will significantly improve strategies and performance actions in this industry.

Due to the massive drop in technology and sensor prices, we can now create, store and send more data than ever before in history. Up to ninety percent of the world’s data today has been created in the last two years alone. At our current rate, 2.5 quintillion bytes of data are created every day, and this rate is only expected to increase. This data feeds machine learning models and is also the main driver of the boom this science has experienced in recent years.
Concretely, the application of data analytics and machine learning models will impact three main areas of supply chains:

1) Demand Forecast
Effective forecasting of future demand for products and goods based on past events and trends is a key component for improving after-sales service without increasing costs.
The application of these technologies eliminates excess inventory and allows warehouses to run between them, increasing the integrity and fluidity of the supply chain, ensuring product uptime and better customer service with minimal risk.

2) Pricing Estimate
The main event of this area is spreadsheets based on historical prices to evaluate the prices of service parts. The problem is that parts and products are sold at different prices in different locations, resulting in poor customer experience and lost opportunities for manufacturers to reap benefits.
By using predictive algorithms to estimate the price of parts, manufacturers must take into account different factors that affect sales, such as parts location, seasonality, weather and demand. Machine Learning models allow for all of these factors to be taken into account and therefore to set prices much more precisely.

3) Predictive Maintenance
Troubleshooting service is reactive and inefficient. Up to 55% of services fail because the service part is not available when needed, resulting in increased product downtime, inventory failures, lost revenue for the product owner, and customer dissatisfaction.
With the integration of IoT and Predictive maintenance, manufacturers will be able to identify and communicate when service parts will fail, and therefore the manufacturer will be able to determine when new parts will be needed.
All this allows to reduce excess inventory and the associated overhead, in addition to improving parts fill rates, avoiding the cost and disruption of unplanned downtime, and ultimately improving the customer experience.




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