Data Science and Big Data
Big data analytics methodologies make it possible for organizations to advance in information and communication technologies. 1 Though it has numerous advantages to the manufacturing sector, big data analytics in the huge manufacturing data can also result in research challenges associated with the heterogeneous data type, the real-time velocity of manufacturing data, and enormous volume (Haddud et al. 2017). 3 This paper will look at the benefits and challenges of Big Data Analytics for Manufacturing the IoT.
4 Benefits Big Data Analytics for MIoT
Among the first key advantages of big data analytics is improving factory production and operations (Witkowski, 2017). 5 The predictive analytics of customer demands, and manufacturing data can improve machinery utilization in a production plan while improving factory operations. For example, certain products' demands are directly related to seasonal and weather conditions like down coats directly related to cold weather. The cold weather forecasting can be applied to make pre-purchasing raw materials and advance allocation of appropriate machinery resources to the full upsurge demands (Dey et al. 2018).
6 Big data analytics reduce machinery downtime. The prevalent sensors placed in different production sections can collect important data relating to the machinery's status. 1 For example, the continuous analysis of machinery data can help identify the failure's root cause, reducing the machinery downtime. Besides, the sensory data from automatic production and assembly lines help determine every machine's excess load so that this load is balanced among several machines (Witkowski, 2017). Big data analytics help energy companies, which are the main energy producers in the production industry, prevent turbine failure and ensure minimal downtime.
Either big data analytics improves production quality (Witkowski, 2017). On the one hand, continuous market analysis and frequently changing customer demands, and requirements help improve product design hence general product improvement. At the production stage, the manufacturing data analysis helps minimize the defective ratio by identifying its root cause (Haddud et al. 2017). A s a result, the quality of the product also improves. 5 Big data analytics also enhances the efficiency of the supply chain. 7 The increase of various sensors, tags, and RFID during supply, product manufacturing, and transportation produces huge supply chain data used to analyze supply risk, plan optimal logistic routes, and predict the delivery time. The analysis of analysis data is vital in the supply chain and help minimize the holding costs and full dynamic demands by initiating safe stock levels.
Finally, big data analytics improves the experience of customers. 8 Companies obtain customer data from different sources, such as partner distributors, sales channels, social media platforms, and retailers (Dey et al. 2018). 9 Big data analytics on customer behaviors provide predictive, descriptive, and prescriptive solutions to enable production companies to improve product designs, warrant, delivery, and after-sale support of different products. As a result, customer experience in different products is improved. 7 For example, The IoT data in the supply chain assist in preventing malicious actions and guarantee food safety.
Challenges of big data analytics for MIoT
10 Manufacturing internet of things data has different characteristics consisting of massive volume, being generated in a real-time fashion, heterogeneous data type, and bringing both huge social value and business value (Haddud et al. 2017). 4 The unique features cause research challenges in big data analytics for manufacturing the internet of things.
11 challenges of data acquisition Data acquisition handles issues, including data transmission and data collection, during which there are the following challenges. 12 The first challenge is difficulty in data representation. 11 MIoT entails different types, various dimensions, and heterogeneous structures (Witkowski, 2017). For example, manufacturing data can be classified into structures, semi-structured and unstructured data. 7 How to represent these structures, semi-structured and unstructured data remains one of the primary challenges in big data analytics for MIoT.
13 Efficient data transmission another challenge associated with data acquisition. How to transmit huge volumes of data to data storage infrastructures efficiently becomes a major challenge due to energy efficiency, which is a common challenge in wireless industrial systems (Dey et al. 2018). The challenge of efficient data is also caused by high bandwidth consumption because big data transfer becomes a big issue of wireless communication systems.
7 Challenges in the data processing.
The first challenge related to data processing is difficult in data representation. 14 Data generated from MIoT results in different challenges in the data processing. It is important to incorporate different types to implement efficient data analytics (Witkowski, 2017). It is, however, difficult for the researcher to bring in different MIoT data.
When it comes to the issue of data processing, researchers face the challenge of Redundancy reduction. 11 The raw data generated is primarily characterized by spatial and temporal redundancy, which frequently leads to data inconsistency, affecting the effective analysis of data. How to mitigate the challenge of data redundancy has always remained a challenge to many researchers. 1 Besides, researchers also face a challenge in data cleaning and data compression. Apart from data redundancy, MIoT data is frequently noisy and erroneous to the error of sensors or machinery (Haddud et al. 2017). 4 The large amounts of data make the cleaning process of data more challenging. 14 Therefore, it's essential to develop an effective scheme to clean errors of MIoT data and compress MIoT data.
15 Challenges related to data storage include reliability and persistence of data storage, scalability, and efficiency. Data storage must ensure that there are data persistency and data reliability of MIoT data. However, it is challenging to meet these qualities while still balancing on cost due to huge data amounts. Besides the storage reliability, another challenge in MIoT data storage in the scalability of the storage infrastructure to accommodate the available huge amounts of data (Witkowski, 2017). The huge amounts of data result in the in-feasibility of a standard database for big data analytics.
1 For this reason, there is a need for new storage paradigms to accommodate large-scale data storage systems. 5 Another major concern for data storage systems is efficiency. For storage infrastructures to support huge volumes of data, there is a need to have data efficiency, which is made difficult by both reliability and scalability.
It is quite challenging to analyze data for MIoT because of its large volume, high dimension, and heterogeneous structure. 15 Data analysis's major challenges are data temporal and spatially correlation, efficient data mining schemes, privacy, and security (Witkowski, 2017). As is the case with the conventional house, data from MIoT is usually temporally and spatially correlated. How to manage these data and acquire relevant means always remains a challenge. The huge data volume creates a new challenge in coming up with an efficient mining scheme (Haddud et al. 2017). Large amounts of data acquired make it not feasible to make use of conventional multi-pass data mining schemes. Finally, the existing conventional privacy-preserving data analytical schemes do not apply in MIoT data, making it challenging to maintain privacy and ensure data security during the analytics process.
Conclusion
For a long time, businesses have used data analytics to direct strategies to support the decision-making process and maximize profit. Today it is widely accepted that big data technologies and cloud computing are two major dominant technologies that shape up the business world. Big data technologies supported through cloud computing make it possible for businesses to develop proactive and knowledge-based decisions that allow them to have future behaviors and trends predicted. Businesses are better positioned to store their data locally and access services and data anytime and anywhere.