Benefits of big data analytics in Manufacturing Internet of Things
The exponential growth in information technologies and advances in communication technology has enhanced the evolution of smart-data driven manufacturing. The Internet of Things is one of the modern technologies that is disrupting the functionality of the manufacturing industry. Organizations and businesses are experiencing a paradigm shift towards smart manufacturing, a move that is enhancing the functionality of automated manufacturing. Manufacturing Internet of Thing (MIoT) covers an extensive range of manufacturing equipment such as smart meters, sensors, controllers, RFID tags and actuators. The equipment are interconnected using wired or wireless communication networks and work as a unit. MIoT processes massive amounts of data that is featured with various types, that is, structured, unstructured and semi-structured. The MIoT data is processed in a real-time fashion and designed to help the manufacturing sector to respond to changes in the environment in a real-time manner. The analytics of MIoT big data has brought many benefits to the manufacturing sector, such as improving product quality, reducing machine downtime and improving production. The implementation of big data analytics in manufacturing the Internet of things is also faced with a lot of challenges, such as challenges in data acquisition and in data preprocessing and storage.
Benefits of big data analytics in Manufacturing Internet of Things
Reducing machine downtime
MIoT is characterized by integrating the production line with sensors that collect information regarding the status of the machinery and other production equipment. For instance, the sensors are deployed during the analysis of machinery health data and enhance the recovery process by helping the engineers identify the failure, thus reducing machine downtime. In addition, the sensors are used to determine the amount of load on the machines and help to balance the loads among the various productions lines, consequently reducing the possibility of the machines failing.
Improving factory operations and production
Manufacturing IoT big data analytics are using predictive analytics algorithms to determine the manufacturing requirements of tomorrow. Mining big data from manufacturing data and customer requirements generates useful insights that help businesses and organizations to enhance machinery utilization and thus improve factory operations. For instance, in the textile industry, the production of certain products such as raincoats and beach shorts are related to specific weather conditions and seasons. Big data analytics help to forecast such conditions and manipulate the production line to produce relevant products.
Improving product quality
The analysis of customer requirements and market demands provide knowledge on how to improve the product design. Analysis of big data in other sectors such as healthcare and banking has helped institutions and manufacturing firms to reduce defective products and identify the root cause of producing low-quality products. For organizations and businesses to remain competitive in the hostile business world, they must up the quality of their products.
Enhancing supply chain efficiency
The deployment of RFID, sensors and tags during supply generates massive amounts of supply chain data which can be used to predict the most appropriate delivery time and plan an optimal logistic route. In addition, the analysis of inventory data can help in the establishment of safety stock levels and intelligent manufacturing shops.
Improving customer experience
One of the significant boosts of MIoT big data analytics is the availability of large amounts of data from the Internet, social media platforms, partner distributors and sales channels. This data provides insights about customer perspectives about the product quality, design, after-sale services and delivery warrant. Based on the knowledge generated, organizations can act on their production line to enhance customer experience.
Challenges of big data analytics for Manufacturing Internet of Things
- Challenges in data acquisition – some production lines require the acquisition and processing of big data in a real-time fashion. Challenges in data acquisition describe the problems experienced during the collection and transmission of big data. Some of the challenges include:
Challenges in data representation – big data is available in a variety of structures and dimensions. It is quite challenging to express and represent data that is structured, semi-structured or unstructured using the same representation methods.
Efficient data transmission – the transmission of tremendous volumes of data to data repositories requires a dedicated communication network that has high bandwidth and energy efficiency, especially when using a wireless infrastructure.
- Challenge in data preprocessing and storage
Some of the primary challenges in data storage and preprocessing include redundancy reduction and data compression. The raw data collected from the various sources is typically characterized by temporal and spatial redundancy that lowers the quality of data and results into inconsistency. Also, during the collection process, machinery defects and errors of the sensors result in storage of erroneous and noisy data. Cleaning massive amounts of data for compression and storage purposes requires skilled labour and a vast amount of processing power.
- Challenges in data analytics
The primary challenges in this phase is as a result of the massive amounts (volume) available for analysis, the heterogeneous nature (variety) and velocity of data acquisition. The major challenges faced during data analytics is enforcing the privacy and security of analytical data schemas as well as designing efficient data mining schemas.