Functions and system image to realize the next generation factory “smart factory”

2023年11月2日 10:11

A smart factory is an advanced factory that utilizes the latest digital technology and automation systems to achieve efficient and flexible production. Compared to traditional factories, they integrate various digital technologies, Internet of Things (IoT) devices, robotics, artificial intelligence (AI), big data, and other technologies, and focus on automation and data utilization. I'm leaving it there.

What kind of functions and equipment will be included in a smart factory? I added the image and content of AI to the expected functional group and summarized it in a concrete form.

production control schedule

It is expected that smart factories will be equipped with production management schedule AI assist functions. This feature will be introduced to improve the efficiency of the entire production line by automating the creation of production plans and adjustment of schedules.

The sophistication of manufacturing processes in smart factories is achieved by leveraging various technologies and methods. ●Simulation and virtualization: Smart factories leverage simulation and virtualization of manufacturing processes. This allows you to design products and optimize processes in advance. Using 3D modeling and virtual models called digital twins, you can simulate manufacturing processes, identify problems and bottlenecks, and derive improvements. ●Process automation: In smart factories, manufacturing processes will be automated. We will introduce automation equipment such as robots and autonomous vehicles (AGVs) to improve work efficiency and quality. Automation increases the accuracy and consistency of work and reduces the risk of human error and production stoppages. ● Utilization of IoT and sensor technology: Smart factories incorporate sensors into machines and equipment used in the manufacturing process and utilize IoT technology. Sensors monitor and collect data on equipment condition and performance in real time. This makes it possible to predict machine failures, optimize maintenance, and improve production line efficiency. ● Utilization of data analysis and AI: Smart factories analyze collected data to improve and optimize production processes. By leveraging big data analysis, machine learning, and AI technology, you can identify trends and issues related to production line status, production efficiency, and quality, and take appropriate countermeasures in real time. ●Collaborative robotics: Collaborative work between humans and robots plays an important role in smart factories. Robots take advantage of human skills and judgment while complementing dangerous tasks and heavy labor. Collaborative robotics can make production processes more productive, safer, and less stressful for workers. ● Supply chain integration: Smart factories provide a high degree of integration across the supply chain. Information such as production planning, inventory management, and logistics is shared in real time to coordinate production processes and appropriately procure materials. This ensures production efficiency and on-time delivery. ●Cloud computing and networking: Smart factories leverage cloud computing and networking. Production data and equipment status data are accumulated on the cloud and shared in real time with multiple locations and stakeholders. This enables remote monitoring and control, and enables efficient operation and maintenance. ●Modularity and flexibility: Modularity and flexibility are important factors in smart factories. Production lines and equipment are modularized and have the flexibility to handle the production of different products. It has the advantage of being able to quickly respond to market fluctuations because it is easy to change or add products and expand the production line.

Real-time data collection:
Collect production data in real-time through sensors and monitoring systems on the production line. This includes production quantities, working hours, error rates, equipment utilization, etc. Data is automatically collected through sensors and IoT devices and accumulated in a central database.

Optimize production planning:
Production Management Assist analyzes collected data and provides decision support to optimize production planning. Based on demand forecasts and inventory level monitoring, you can create appropriate production schedules and material procurement plans. This improves production line utilization and production efficiency, and prevents wasted inventory and production delays.

Real-time monitoring and alerts:
Production Control Assist monitors your production line in real-time and provides appropriate alerts and notifications when anomalies or problems occur. When problems such as machine breakdowns, production line stoppages, or quality deterioration are detected, relevant parties are notified and can respond quickly.

Quality control and statistical analysis:
Production Control Assist is also used for quality control. Analyze quality indicators and abnormal patterns from production data and provide information useful for improving product quality. Furthermore, it is also possible to improve production processes and investigate the causes of defective products based on statistical analysis.

Worker support and training:
Production Control Assist also has functions to support workers. This includes displaying work steps and work instructions, measuring work time, and providing guidelines for quality checks. As part of training, the assist system also provides real-time feedback and advice to workers, helping them learn efficient work methods and quality standards.

Machine learning and predictive analytics:
Production Management Assist leverages machine learning and predictive analytics techniques to improve production processes and predict problems. By learning past production data and predicting future production performance, it is possible to optimize resources and prevent problems in advance. Examples include predicting machine failure risk and optimizing maintenance schedules.

Real-time visualization and reporting:
Production Management Assist visualizes the status of production processes and production lines in real-time and provides them as dashboards and reports. Important performance indicators such as production quantities, production efficiency, and quality indicators are visually displayed, allowing managers and stakeholders to understand production status and immediately determine necessary countermeasures and improvements.

The above are the main functions provided by Production Management Assist and their supplements. These features streamline production processes, improve quality, and provide real-time monitoring and response. In addition, by utilizing AI and data analysis technologies, more advanced prediction and optimization will become possible, improving the competitiveness and efficiency of production.

Order specification confirmation Raw material calculation

It is expected that smart factories will be equipped with AI-based order requirement confirmation and raw material calculation functions. This function was introduced to streamline requirements confirmation and raw material calculation work required when ordering parts and materials, which is one of the challenges in the manufacturing industry.

When the specifications of a product are detailed and ambiguous, and there are specifications for each part and material, automatic calculation functions using AI are important. Since AI can process large amounts of data efficiently and accurately, it can produce far better results than manual processing when handling large amounts of product data.

Specifically, by inputting information on the parts and materials to be ordered using AI, it is possible to automatically perform necessary requirement checks and raw material calculations. For example, by inputting information such as the quantity, size, weight, and characteristics of parts and materials to be ordered, it is possible to automatically calculate the amount of raw materials needed and the cost required for ordering.

This function allows you to reduce the time and effort required for conventional requirements confirmation and raw material calculation work. In addition, AI can perform accurate calculations to quickly and accurately provide the information needed when ordering parts and materials. This not only ensures product quality and on-time delivery, but also leads to more efficient production processes.

exchange survey

In future smart factories, it is also possible that AI-powered currency exchange research functions will be related to parts and materials used within the factory. This function is introduced to deal with exchange rate fluctuations when importing raw materials and parts from overseas.

The exchange research function collects information on various currencies other than the dollar and allows you to understand exchange rate fluctuations. For example, you can target currencies from all over the world, such as the euro, yen, pound, yuan, rupee, dong, etc. Because exchange rates fluctuate every moment, the exchange research function requires real-time information collection. Additionally, because countries around the world have different time differences, it is necessary to collect information around the clock to constantly monitor exchange rate fluctuations. Regarding these points, the currency research function automatically collects information and provides the latest currency information, helping businesses make quick and accurate decisions.

Specifically, AI collects information such as exchange rates and market conditions from external sources and uses that information to predict price fluctuations for materials and parts. Additionally, based on the information investigated by AI, it is possible to select suppliers for raw materials and parts at the most appropriate timing.

This feature allows manufacturers to reduce risks such as increased raw material costs and overstock due to exchange rate fluctuations. AI also provides accurate market analysis, allowing manufacturers to optimize the sourcing of parts and materials. This is expected to lead to greater efficiency in the entire manufacturing process, including reductions in raw material costs and appropriate inventory management on production lines.

Quotation request assist

It is expected that smart factories will be equipped with an AI quotation request assist function for suppliers. This function is being introduced to eliminate the complexity and time wastage of estimation work, which is one of the issues in the manufacturing industry.

Specifically, by inputting information about the parts and materials to be ordered into AI, AI can automatically send a request for quotation. AI also helps in selecting suppliers. Based on past transaction results and reviews, it is possible to select the most suitable supplier by comprehensively evaluating conditions such as quality and delivery time.

This feature reduces the time and effort required for traditional estimation work. In addition, by using AI to select the most suitable supplier, risks such as the quality of parts and materials and delivery dates can be reduced. This not only ensures product quality and on-time delivery, but also leads to more efficient production processes.

Supplier AI selection

It is expected that AI-based supplier selection functions will be used in future smart factories. This function is introduced to select the most suitable supplier by considering various conditions related to the products and services to be ordered.

The AI supplier selection function allows you to search for new suppliers in addition to traditional suppliers. This is because AI can analyze past data, market information, etc., and propose new suppliers based on new information not found in conventional suppliers. For example, if we discover that a small manufacturer in a region that has traditionally not received much attention has superior production capabilities for a particular product, we can select that manufacturer as a new supplier. . Additionally, based on market information analyzed by AI, we can suggest suppliers that are cheaper than traditional suppliers. In this way, the AI supplier selection function allows you to find new suppliers that are not bound by traditional frameworks, leading to efficient procurement. The AI supplier selection function uses machine learning algorithms to analyze past data and select suppliers, making it possible to select suppliers more efficiently than conventional manual selection. However, supplier selection using AI relies on the quality of past data used for selection, so if the data is biased or has abnormal values, accurate supplier selection may not be possible. Therefore, when humans intervene, checking the AI's judgment may lead to more accurate selections. In addition, when humans are involved, human factors that AI cannot take into consideration can be taken into account when selecting suppliers, such as procurement sources with special conditions that cannot be selected by AI, or an emphasis on partnerships. . Therefore, when selecting suppliers, AI and humans will work in a complementary manner to make selections more efficient and accurate.

Specifically, by inputting the terms and conditions of the product or service to be ordered by AI, it is possible to automatically select the supplier. Conditions include quotation details, delivery date, quality, price, past transaction results, etc. Based on this information, AI selects the most suitable supplier.

This feature allows manufacturers to streamline the process of selecting suppliers. In addition, by selecting appropriate suppliers using AI, we can ensure product quality and delivery times. Furthermore, AI can be used to appropriately select suppliers, which is expected to lead to reductions in ordering costs and other improvements in the efficiency of the entire manufacturing process.

Smart truck inter-base transportation

It is expected that future smart factories will utilize self-driving smart trucks for transportation between locations. This feature aims to automate factory logistics processes using trucks equipped with self-driving technology.

A smart truck is a next-generation truck that utilizes autonomous driving technology and IoT technology. Compared to conventional trucks, they have been improved in many ways, including vehicle automation, more efficient operation management, and improved safety. The features of SmartTrack include the following: ●Autonomous driving function: By utilizing autonomous driving technology, it is possible to reduce the burden on the driver and reduce accidents while driving. ●IoT technology: By equipping vehicles with sensors and communication functions, it is possible to understand the location and condition of the vehicle, the status of cargo, etc. in real time. ●Big data analysis: By aggregating vehicle travel data and cargo information, we can create efficient transportation plans through big data analysis. ●Improving fuel efficiency: We are working to improve fuel efficiency by optimizing engines, reducing vehicle weight, and introducing energy-saving technologies. By utilizing these functions, smart trucks can achieve more efficient transportation, leading to reduced transportation costs and environmental impact. We are also working on human-centered issues, such as reducing the burden on drivers and improving safety.

Specifically, smart trucks use self-driving technology to autonomously navigate roads within and between factories. In addition, AI monitors the truck's driving status and automatically controls it as necessary, ensuring a high level of safety.

This feature allows manufacturers to reduce the time and cost of inter-site transportation. Additionally, automated truck driving is expected to reduce the burden on drivers and reduce the risk of driving errors and traffic accidents. In addition, AI monitors truck driving conditions, allowing for more efficient routes and appropriate speed management, reducing fuel consumption and truck wear and tear.

This feature allows manufacturers to automate the entire logistics process, increasing productivity and efficiency.

Automatic receiving check

It is expected that future smart factories will use an automatic receiving check function for incoming goods. This feature is aimed at increasing the automation of logistics and increasing the efficiency of the entire manufacturing process.

This function is used to accurately identify parts and materials necessary for a product, as well as defective products and waste from among the wide variety of cargo delivered to a factory. Specifically, identifiers such as RFID and QR codes are often attached to packages and scanned to obtain information, and based on that information it is automatically determined whether the package will be used in a product. To do. Types of cargo include parts, materials, products, materials, tools, waste, etc. For example, each part or material has its own part number or lot number, and by attaching these as identifiers, you can accurately determine the contents of a package. Additionally, in the case of products, identifiers are often attached to the product itself. Luggage comes in a variety of shapes and sizes, including boxes, pallets, containers, and bags. Equipment and systems are prepared according to the size and weight of the baggage, so that the baggage can be handled automatically.

Specifically, cameras and sensors installed in smart factories can automatically check the packaging, quantity, quality, etc. of incoming items. This allows the traditional manual inspection work to be omitted, reducing work time, reducing errors, and eliminating human error.

In addition, by analyzing images and sensor information using AI, it is possible to accurately understand the condition, quality, weight, size, etc. of the cargo. This makes it possible to automatically send alerts in the event of an abnormality, such as if the packaging of the incoming items is different or if defective items are mixed in.

This feature allows manufacturers to perform quality control more accurately and quickly. In addition, the entire logistics process of a smart factory can be automated, leading to shorter work times, lower costs, and the elimination of human errors. Additionally, an automated warehouse management system combined with an automatic receiving check function can also automate inventory management.

Movement within the factory

Future smart factories are expected to use AI-equipped robots to move around the factory. Since this system can automatically transport various equipment, materials, and components, it is expected to improve work efficiency, reduce human error, and improve safety.

Specific examples related to the movement of goods and people in the factory movement function include the following. ●Movement of materials and parts: This includes the movement of materials and parts necessary for product production from the appropriate location to the production site. For example, this includes movement from the raw material storage area to the loading/unloading area for each process, and movement to the inspection area before product shipment. ●Product movement: This includes the movement of manufactured products to warehouses and shipping locations. For example, moving from an assembly line to a shipping location, or from a warehouse to a delivery vehicle. ●Moving machines: There are times when it is necessary to move machines within the production site. For example, this may involve recombining production lines or moving machinery due to improvements. ● Movement of workers: Workers may need to move closer to the product or machine. For example, this includes product assembly work, inspection work, and machine maintenance work. For these movements, the intra-factory movement function can plan and manage the movement schedule, confirm the destination, etc., and realize a smooth flow within the factory. It also leads to improved productivity by reducing the burden on workers.

Specifically, it can be used in various locations within the factory, and can be used for a variety of purposes, such as transporting imported items, transporting equipment, and transporting products. The robot can operate autonomously using AI, read map information and obstacle information within the factory, select the optimal route, and move automatically. It is also possible for robots to communicate with each other and work collaboratively.

In-factory transportation robots are equipped with attached devices such as arms and hooks for carrying loads, and can carry loads of various shapes and weights. Furthermore, through image recognition using AI and analysis of sensor information, it is possible to accurately determine the condition, quality, weight, size, etc. of the baggage. This prevents accidents such as falling cargo or collisions.

Mobile robots in factories can automate traditionally manual tasks, reducing work time and eliminating human error. In addition, robot automation can replace heavy and dangerous work that was traditionally performed by humans, leading to improvements in the working environment. In-factory mobile robots will become one of the important functions in smart factories to improve work efficiency, improve productivity, and reduce costs.

Raw material check measurement

Smart factories have high demands on the quality of raw materials. In the future, it is expected that AI-powered raw material check metrology will be introduced to automatically check and measure the quality of raw materials. This function prevents low-quality raw materials from entering the production line, leading to improved production quality.In a smart factory, the materials used in the products made are important, and must be of high quality. In the future, material checking machines equipped with artificial intelligence will be used to automatically measure the quality of materials.

This helps improve product quality by preventing products from being made with low-quality materials. We can expect that artificial intelligence will analyze the data and automatically determine the method of manufacturing that best suits the material, leading to improved efficiency in manufacturing methods. In addition, data analysis using AI makes it possible to automatically set optimal production conditions according to the characteristics of raw materials, which is expected to improve the efficiency of the production process.

Operation schedule management

In next-generation smart factories, it is expected that operation schedule management for the entire production line will become more sophisticated. Compared to current factories, next-generation smart factories will be able to monitor the status of equipment and systems in the factory in real time and automate schedule management for the entire production line.

These capabilities include AI capabilities that automatically optimize production schedules, taking into account information such as production line conditions, production plans, and scheduled maintenance. Additionally, by detecting abnormalities on the production line, it is possible to automatically adjust production schedules and maintain optimal production efficiency.

In addition, in smart factories, real-time data analysis allows you to monitor the entire production process and provide information for increasing efficiency and making improvements. These functions are expected to improve operation schedule management for the entire production line and improve production efficiency and quality.

Utility bill real-time monitoring

Next-generation smart factories will introduce automation technologies such as AI and robots, and will require energy-saving and ecological factory operations. Therefore, it is expected that real-time monitoring of utility bills will be included to reduce utility costs.

Real-time monitoring of utility costs is an important function from a management perspective. This is because utility costs can account for a certain percentage of a company's costs, such as issues related to power consumption in the manufacturing industry, so efficient monitoring and management is necessary. This function is also important as a steady means to maximize profits. Utility costs are costs that companies cannot ignore, but at the same time, it can be difficult to reduce them beyond a certain level. However, by monitoring in real time and reducing wasteful consumption, it is possible to reduce costs. Therefore, this function is a simple but important means of maximizing profits.

Specifically, by collecting data using sensors and analyzing using AI, we will identify the usage status of utility costs and areas where reductions can be made, improve operations, introduce automation technology, etc., and aim to optimize utility costs. I can. It is also possible to reduce utility costs by adopting equipment and systems that specialize in energy conservation. Real-time monitoring of utility costs is expected to contribute to cost reduction and environmental conservation.

Real-time inventory management

Smart factories are expected to improve productivity and reduce costs by automating inventory management. Therefore, real-time inventory management functions that utilize AI and robots are expected to become widespread in the future.

Specifically, sensors installed on production lines and warehouses monitor the amount and status of inventory in real time, and by collecting and analyzing that data, it is possible to accurately understand the status of inventory. In addition, by using AI to predict inventory demand and calculate the optimal order amount, it is possible to prevent overstocks and shortages and optimize production plans.

This real-time inventory management function is expected to reduce production line stoppages and worker rework, leading to improved production efficiency and cost reductions.

ERP-linked hourly settlement

In a smart factory, various data such as production, inventory, and ordering are generated, and it is important to manage them efficiently. For this reason, a company integrated system called ERP (Enterprise Resource Planning) is generally introduced. ERP can centrally manage data generated in all departments of a company, making operations such as production management and financial management more efficient.

The "Hourly settlement function" is a function that allows companies to understand the status of income and expenditure and cash flow on a daily or hourly basis and utilize it for management decisions. This allows you to understand business conditions in real time and make quick and accurate business decisions. With conventional monthly settlement, there is a delay in updating information, making it difficult to make quick management decisions, but by introducing the hourly settlement function, this issue can be resolved. Furthermore, it is linked to production management indicators (indicators used to visualize the status of productivity and quality in the manufacturing process and identify areas for improvement, production volume, production time, yield rate, defective product rate, equipment utilization rate). With the hourly settlement function, these production management indicators can be collected and analyzed in real time. For example, you can calculate the production volume per hour by totaling the production volume for each hour and dividing it by the production time. In addition, the timely settlement function allows you to grasp information such as cost and inventory management in real time, monitor indicators such as productivity and profit margin, and quickly identify areas for improvement. This enables more efficient production management, such as improving product production lines, optimizing inventory, and reducing production costs.

Furthermore, smart factories are expected to introduce hourly settlement functions linked to ERP. The hourly settlement function is a function that allows you to analyze data accumulated in ERP in real time and understand the status of business performance. This enables faster and more accurate management decisions, leading to improved competitiveness for companies. In addition, the timely settlement function makes it possible to understand a company's financial situation and manage future risks.

AI weather information linked analysis

In smart factories, possibilities include optimizing operating schedules using weather information and reviewing raw material purchasing prices.

The AI weather information linked analysis function uses data from local weather stations and satellites, as well as APIs provided by weather information distributors, to collect weather information. By using AI to analyze and predict data obtained from these information sources, it can be used to adjust factory production plans and logistics schedules. In addition, by combining data from IoT sensors and cameras, it is possible to obtain more accurate weather information.

Predictive monitoring of delivery dates for materials and components, sea routes, air routes, and land routes

A function to check the delivery schedule status that is expected to be used in smart factories is a delivery date status confirmation management function based on the supplier, delivery company, traffic information, etc.

In the case of sea routes, the estimated time of arrival may change depending on weather and ship conditions. To respond to such situations, GPS and weather information can be used to monitor the ship's position, course, and weather conditions in real time, and calculate an estimate of the estimated time of arrival.

This function collects various information such as how long it will take to deliver ordered parts and materials, how the delivery is progressing, whether it may be affected by traffic information or weather, etc. You can understand the situation in real time.

When traveling by air, cancellations or delays may occur depending on the flight schedule. In these cases, you can monitor airline information and airport conditions and make changes to your estimated arrival time or suggest alternative routes.

This function also allows you to predict the risk of delays and stockouts in advance and take countermeasures. For example, for parts that are likely to be delayed in delivery, you can take measures such as securing other substitutes.

When traveling by land, delays may occur due to road conditions or traffic accidents. In these cases, you can monitor traffic information in real time and calculate estimated time of arrival. We can also suggest alternative routes and change delivery schedules for purchase orders to avoid traffic congestion.

In this way, the delivery status confirmation management function can prevent problems such as delivery delays and stockouts in advance, leading to improved production planning accuracy and production efficiency.

Terminal store sales trends

The following functions are expected to be used in next-generation smart factories, including sales trend functions linked to sales information at stores where products are sold.

With the product physical store sales trend function, it is important to understand the product sales status at physical stores in real time. Therefore, possible information collection sources include data from POS registers and sensors. At the POS register, information such as sales quantity, amount, and time can be collected. Additionally, by using sensors, it is possible to collect information such as customer flow lines and product residence times. This information can be analyzed in real time and used for inventory management and sales promotion. It is also important to collect information on competing stores, and possible sources of information may include the POS registers of competing stores and the results of surveys conducted by market research companies.

First, it will be equipped with a system that captures real-time sales data from retail stores. This data is categorized by product or product type and visualized according to conditions such as time of day and region.

Next, sales trends are analyzed using AI. This analysis comprehensively considers past sales data, seasonality, weather, and other information to predict future sales trends.

Finally, manufacturing planning and inventory management are optimized based on the predicted sales trends. For example, if demand for a popular product increases, you can respond by increasing the production volume of that product. This kind of optimization reduces risks such as overstocking and stockouts, and enables efficient production and sales.

News information collection Delivery destination impact Foreign exchange raw material costs AI analysis

The AI function that collects news and information that may affect factory operations and analyzes how it affects them is considered to be one of the important functions in smart factories.

News sites, financial information sites, industry specialized sites, etc. are generally used to gather news information and information on exchange rates and fluctuations in raw material costs. To automatically collect this information, we may use a technology called web scraping. In addition, to collect information on the impact of delivery destinations, you can use systems such as customer information management systems (CRM) and supply chain management (SCM) to collect information from customers and suppliers. These systems provide communication capabilities with customers and suppliers to help share information and resolve problems. The AI analysis function can collect and analyze this information and reflect it in production planning, inventory management, procurement planning, etc. This is expected to lead to more accurate production planning and inventory management, leading to cost reductions and improved productivity.

For example, fluctuations in the price of raw materials, natural disasters, changes in the political situation, etc. can affect factory operations. By collecting such information and analyzing it using AI, factory operators can respond faster and more accurately.

Analyzing this information is also useful for predicting product demand and analyzing purchasing trends. This allows us to respond quickly, such as by expanding our production lines, when demand increases.

Waste reuse check

The waste reuse check function is a function that monitors the type and amount of waste generated within a factory in real time, and if there is waste that can be reused, it can be reused. is.

It is envisaged that dedicated robots will be used for waste separation and collection work. Examples include self-driving garbage collection vehicles and robotic arms for separating garbage. These robots use sensors and cameras to recognize waste and separate and collect it appropriately. It is also possible to use AI technology to perform more accurate separation. When reusing waste, these robots will manage the sorted waste to ensure that it is reused appropriately. For example, if the waste reuse check function is linked with ERP (Enterprise Resource Planning) in a broad sense, the following flow is expected. 1. Data acquisition with the waste reuse check function: The waste reuse check function collects information on waste generated at factories. 2. Processing and formatting of waste information: Process and format the waste information collected by the waste reuse check function as necessary. For example, organize the type and amount of waste, location of generation, processing method, reusability, etc. 3. Registration and linkage of waste information to ERP: Information on processed and shaped waste is registered and linked to ERP. Since ERP integrates various factory information, it can be managed comprehensively, including waste information. 4. Analysis and utilization of waste information: Analyze the waste information registered in ERP to understand the amount and type of waste that can be reused, the location and frequency of generation, processing methods and costs, etc. Based on this information, we develop waste reuse plans and improvement measures for treatment methods, leading to improvements in production processes. As described above, by linking the waste reuse check function and ERP, it is possible to comprehensively grasp information regarding waste management and utilization, and formulate efficient reuse plans and improvement measures for treatment methods. I can.

For example, waste generated during manufacturing processes may include parts and materials that can be reused. The waste reuse check function can improve recycling rates and reduce environmental impact by identifying these reusable wastes and managing them appropriately.

Real-time power mix control

Energy conservation is an important issue in smart factories, and power consumption can be optimized by controlling the power mix in real time.

This feature minimizes power consumption by monitoring power demand and supply and automatically optimizing the power supply from power plants and utilities and the use of renewable energy. It is also possible to automatically reduce the device's power consumption if the power consumption exceeds expectations. This makes it possible to reduce costs by saving energy.

air conditioning control

Air conditioning control functions play a very important role in smart factories. By efficiently managing heat and cold sources, this function maintains a comfortable temperature environment while reducing energy consumption.

For example, on hot summer days, you can reduce power consumption by using outside air for cooling. Additionally, if the outside air is dry, you can use the humidifier function to adjust the indoor humidity.

Specifically, smart factories use sensors and actuators to control heat and cold sources to automatically adjust indoor temperature, humidity, airflow, etc. We also expect AI functionality to analyze these sensor data and provide optimal control to improve the efficiency of air conditioning systems.

Furthermore, smart factories can also capture weather data such as outside temperature and humidity of buildings and use this information to optimize air conditioning systems.

As mentioned above, the air conditioning control function in smart factories is one of the important functions that not only provides a comfortable working environment, but also contributes to energy saving and reducing environmental impact.

Abnormal odor detection Danger prediction

The odor detection and danger prediction function that is expected to be installed in smart factories is a function that detects the occurrence of odor in the factory and predicts danger before it occurs.

There are various types of sensors used for abnormal odor detection and danger prediction functions. Examples include gas sensors, VOC (volatile organic compound) sensors, temperature sensors, humidity sensors, pressure sensors, and smoke sensors. By combining these sensors, it is possible to detect the occurrence of strange odors and harmful substances and predict danger.

This feature helps reduce the risk of occupational accidents and health hazards. Sensors and monitoring systems are used to detect abnormal odors, and if abnormal values are detected, countermeasures such as alarms and automatic shutdowns may be taken. In addition, by identifying the cause of the odor, it is possible to consider improvement measures, which leads to quality improvement.

Temperature monitoring Hazard prevention

It is expected that smart factories will be equipped with temperature monitoring and hazard prevention functions. This feature allows you to monitor the temperature of equipment and products on the production line in real time, and quickly take action if an abnormality is detected.

Various types of sensors may be used for temperature monitoring and hazard prevention functions. For example, thermocouple sensors and infrared sensors are sometimes used to measure the temperature of machines and products in factories. Also, if the temperature rises too much, the risk of fire or explosion may increase. In these cases, temperature sensors may be used that issue an alarm if the temperature exceeds a certain threshold. Additionally, sensors to detect smoke and fire gases may also be used to prevent fires.

Specifically, temperature is measured using a temperature sensor and the data is monitored in real time. If the abnormal temperature detected by the sensor exceeds the set value, an alarm will be issued and the operator will be notified. In this way, you will be notified of temperature abnormalities before they occur, allowing you to respond more quickly.

Temperature monitoring and hazard prevention functions play an important role in the quality assurance of equipment and products. For example, on an electronic device manufacturing line, high temperatures can cause equipment failure, so appropriate temperature control is required. Additionally, on food production lines, improper temperature control can lead to poor product quality. In this way, temperature monitoring and hazard prevention functions have become one of the essential functions for maintaining product quality.

Emergency response

The emergency response AI functions that will be installed in next-generation factories include the following.

Automatic emergency contact notification function This function automatically notifies contact information for accident response personnel, emergency services, etc. when an abnormality is detected in the factory.
Automatic evacuation guidance function This is a guidance system that allows personnel in the factory to safely evacuate in the event of an emergency such as a fire or earthquake.
Employee safety confirmation function This function is used to confirm the safety of employees in the factory in the event of an emergency.
Vehicle guidance function This function provides appropriate guidance for vehicles within the factory in the event of an emergency.
Automatic remote control function This function allows managers who are not on site to remotely control equipment in the factory and check the status in the event of an emergency.

By implementing these functions, it will be possible to respond safely and quickly even in emergencies.

Inter-factory cooperation network

A smart factory requires mutual cooperation not only with the company's own factories but also with other companies' factories and distribution centers involved in the supply chain. Therefore, mutual cooperation network functions between smart factories are required.

Mutual cooperation network functions between smart factories are generally realized using IoT (Internet of Things), cloud computing, and 5G communication technology. Specifically, sensors and devices installed within each factory will be connected via IoT and share data on a cloud-based platform. In addition, by utilizing 5G communication technology, faster communication and connection of more devices will be possible. By utilizing these technologies, it is possible to grasp the production information and equipment status of multiple factories in real time, create optimal production plans, and improve the efficiency of equipment maintenance. Additionally, by building networks between factories located in different regions or countries, it is also possible to build a global production network.

The following functions can be considered for the inter-factory cooperation network.

Data sharing function between smart factories using IoT technology
A function to understand the production status of other companies in real time and reflect it in your own production plans and inventory management.
A function to understand the logistics situation of other companies in real time and reflect it in your own logistics plan.
Mutual sharing of quality information and recall information
Providing a collaborative platform between smart factories
Integrated management of manufacturers and collaborating companies using cloud-based services
Coordination of automated transportation management systems using electronic data interchange (EDI)

By realizing these functions, mutual cooperation between smart factories will be strengthened, and it is expected that the efficiency and productivity of the entire supply chain will be improved.

Inter-factory raw material adjustment

The inter-factory raw material adjustment function is for when multiple factories share raw materials, each factory automatically determines the optimal raw material mix according to changes in raw material quality, quantity, price, etc. With the function of.

Smart trucks travel between factories and transport raw materials. Smart trucks are equipped with features such as autonomous driving, optimizing driving schedules, and optimizing fuel efficiency, making it possible to transport materials more efficiently and quickly. In addition, vehicle dispatch schedules are automatically created taking into consideration factors such as in-factory production plans and demand forecasts, and determine the optimal routes and dispatch volumes. In this way, automating vehicle dispatch schedules can reduce human errors and operational waste, and stabilize the supply of raw materials. In addition, the automatic receiving robot uses a robot arm mounted on a smart truck to load and unload raw materials. Automatic receiving robots can be expected to reduce the burden on workers, shorten work time, and improve work accuracy.

The inter-factory raw material adjustment function is thought to include the following elements:

Sharing of raw material data: By creating a database of information on raw materials used at each factory and sharing it, we can share raw material information between factories.
Raw material quality control: We regularly evaluate the quality of raw materials and, depending on the quality, change the factory that uses the raw materials in order to maintain production quality.
Respond to fluctuations in raw material prices: In order to respond to fluctuations in raw material prices, it is equipped with an algorithm that automatically determines the optimal raw material mix.
Determining the optimal blend using AI: AI automatically determines the optimal blend of raw materials based on raw material information sharing, quality control, and response to price fluctuations.

In this way, the inter-factory raw material AI adjustment function is expected to improve production efficiency and quality by automatically sharing raw materials between factories, controlling quality, and responding to price fluctuations.

Inter-factory production adjustment

The inter-factory production adjustment function includes functions for adjusting production plans between different factories. With this feature, if one factory has excess production, the other factory can increase production. This allows us to respond quickly to market needs and maximize production efficiency.

Specifically, we collect data such as production line operating status, inventory status, and order status, and adjust production volume based on AI-based production plan optimization. Additionally, a network connecting multiple factories allows the status of production lines to be shared in real time. Therefore, it is expected that production adjustments will be made more smoothly and quickly.

central control room

In a smart factory, a central control room is required for machines, robots, sensors, and other devices to collect, process, and control data. The central control room can monitor and manage the status and operating status of equipment and devices in real time, and control them as necessary.

The central control room collects and analyzes sensor data and IoT device data, detects signs of equipment failure and malfunctions, and is able to develop appropriate maintenance plans and optimize production lines. Masu. Additionally, production data can be visualized and analyzed, which can contribute to improving productivity, reducing defective products, and optimizing the workforce.

A central control room can also be built using the cloud. By consolidating data in the cloud, you can grasp the status of the production site in real time and control and monitor the production line even from a remote location, leading to improved operational efficiency.

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