Big Data Analytics Applications in Product Design, Manufacturing & Supply Chain Design & Management

Google Analytics is a good example of big data analytics and its utility. Google analytics provides descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics.

It is big data because data of millions of websites is captured by the analytics application and it has to be processed in almost similar way. So number of computers using big data techniques are used to record and process data and create the dash boards that the user sees for his web site or web sites.

Applied Industrial Engineering - Industrial Engineering 4.0 - Process Steps
Monitor - Explore - Analyze - Develop - Optimize - Participate - Install - Improve
http://nraoiekc.blogspot.com/2018/05/applied-industrial-engineering-process.html

Data Analytics Period in Productivity Improvement - Productivity Engineering and Management
https://nraoiekc.blogspot.com/2017/06/data-analytics-period-in-productivity.html

Big Data - 5V Framework

What differentiates “big data” from the term “data”?

Big data includes data sets that can’t be analyzed by the common traditional data analysis tools Big data refers to a high volume of data from a variety of sources. This required  methods different from  the  ones used in conventional database systems for decision making. Big data enables processing of   a large volume of real-world data and generating information. 

Big Data was defined by the 3V model (Volume, Velocity, and Variety)  in a study by Laney (2001b). Volume refers to the amount of available data; Velocity refers to the timeliness of the data; and Variety refers to the diversity of the data types, including unstructured, semi-structured, and structured data sets.

Two other important Vs have been added to the definition of big data. Value is added by Idc-Vesset et al. ( 2012). Value refers to the profit gained by analyzing a huge volume of data and that is why big data analysis is done. Veracity refers to the uncertainty and imprecision in the real world data  (Schroeck et al., 2012). Wamba et al., (2015) integrated and introduced the 5V big data framework. 

Big data analysis is a process that transforms terabytes of data into a small amount of high-value information. A big data system has five consecutive phases: data generation (in the environment), data acquisition at the source and transmission to central devices), data storage, and data analytics and information communication to decision makers.

Big data applications in a business

In the early days of electronic data processing, businesses used their own data to make decisions. But now, new technologies give businesses access to various brand-new types of datasets. The usage of social networking is booming at a quick pace, and a huge volume of consumer data is being provided to businesses through point of sales terminals at retail outlets . Big data  has shown its useful applications in decision areas of business.  

Data plays a vital role in developing today’s operational systems. Big data can be used to increase business competitiveness in real time. A large volume of data is generated every minute. Big data can be used  for continual improvement. The data can be subjected to  descriptive analytics. Then advanced analytics such as predictive analytics, automated algorithms, and real-time data analysis can be used. 

Various techniques such as  data mining, machine learning, neural networks, pattern recognition, visualization, etc. are used to extract valuable information out of big data Cloud storage and computing are used to store, develop, and deploy big data in business processes.

Data management costs have decreased. In 2019, storing a terabyte of data using relational traditional databases could cost over $20000 for a company (Sonra, 2015), but storing the same amount of data could cost just $1000-$2000 using big data technology such as a Hadoop cluster (StatSlice, 2013). Hadoop gained popularity  because of its low price and capacity for data storage.

Not much research was published on big data before 2010.

What are the different categories of big data analytics that are used in supply chain management?

What are the factors that affect the attractiveness of using big data analytics in supply chain management studies?

What supply chain management research topics are studied more often by big data analytics?

What are the hurdles and advantages of using big data analytics in supply chain management research? What must be done in the future?

How Apple uses AI and Big Data

Ritesh Pathak, Jan 21, 2021

https://www.analyticssteps.com/blogs/how-apple-uses-ai-and-big-data

BMW develops AI-powered big data hub with AWS to boost manufacturing efficiency

December 8, 2020 

“We have a few hundred data scientists at BMW, but the aim is to make the data accessible to everyone.”

https://venturebeat.com/2020/12/08/bmw-develops-ai-powered-big-data-hub-with-aws-to-boost-manufacturing-efficiency/

How BMW uses Artificial Intelligence (AI)?

Ritesh Pathak, Mar 20, 2021

https://www.analyticssteps.com/blogs/how-bmw-uses-artificial-intelligence-ai

How Coca-Cola is going digital to better understand, serve customers

By Nibedita Mohanta - 07/20/2021

https://www.geospatialworld.net/blogs/how-coca-cola-is-going-digital-to-better-understand-serve-customers-location-bi/

Coca-Cola Bottler Digitizes Manufacturing Processes with AWS

by Justin Honaman | on 13 JUL 2021 

CCI is modernizing its manufacturing facility by creating a digital plant replica—a digital twin—in the cloud. It hopes to unlock value with advanced analytics, artificial intelligence (AI), and real-time asset monitoring. In fact, CCI has produced a repeatable playbook so other Coca-Cola bottlers can deploy the same digital manufacturing solution in their facilities.

https://aws.amazon.com/blogs/industries/coca-cola-bottler-digitizes-manufacturing-processes-with-aws/

If you are on the premium version of Google Analytics, you can use Big Query, a big data engine provided by Google, to sift through Google Analytics. Many companies on the premium version of GA have billions of rows of data in GA.  You need  Google’s big data tools. 

Smarter insights to improve your marketing decisions and get better ROI

14 October 2020

To help you get better ROI from your marketing for the long term, we're creating a new, more intelligent Google Analytics that builds on the foundation of the App + Web property we introduced in beta last year. It has machine learning at its core to automatically surface helpful insights and gives you a complete understanding of your customers across devices and platforms. It’s privacy-centric by design, so you can rely on Analytics even as industry changes like restrictions on cookies and identifiers create gaps in your data. The new Google Analytics will give you the essential insights you need to be ready for what’s next.

https://blog.google/products/marketingplatform/analytics/new_google_analytics/

https://towardsdatascience.com/google-analytics-is-digital-marketing-but-digital-marketing-is-not-analytics-ba73cbb4ee69

https://www.mainstreetroi.com/how-to-use-google-analytics-to-ensure-digital-marketing-success-in-2018/

Apr 5, 2019,

The Fascinating Ways PepsiCo Uses Artificial Intelligence And Machine Learning To Deliver Success

Bernard Marr

Enterprise Tech

https://www.forbes.com/sites/bernardmarr/2019/04/05/the-fascinating-ways-pepsico-uses-artificial-intelligence-and-machine-learning-to-deliver-success/

https://brainstation.io/magazine/pepsico-looks-to-analytics-to-improve-2-billion-e-commerce-business

Big Data in Manufacturing 

Manufacturing applies resources such as  machines, tools, and labor and converts raw materials into useful products. The manufacturing industry contains a huge volume of data created by sensors, electronic devices, and digital machines in factories (Zhong et al., 2015). 

Manufacturing plants collect data using different channels such as manufacturing processes, supply chain management systems, and tracking the products sold. Using big data can help to develop new products based on customer needs. Moreover, manufacturers have the opportunity to better plan out their supply chain with a more accurate demand forecast. Managers believe that using big data can help diagnose defective products, improve process quality, and better plan supply chains (Nedelcu, 2013).

Many of the logistics processes in manufacturing plants (storage, retrieval from the storage and transport) are now performed using radio-frequency identification (RFID) tags, which allows real-time tracking of the products. Using data analysis on the shop floor enables the system to efficiently implement real-time manufacturing instructions, planning, and scheduling based on the material delivery time and the real-time information coming from the manufacturing processes. Analyzing the big data can help the plant manager to better plan space limitations regarding material flow and warehousing operations.

There are a lot of process, personnel, and departments data generated during a product’s life cycle. The nine stages of a product’s life cycle were introduced by Tao et al. (2018): product concept, design, raw material purchase, manufacturing, transportation, sale, utilization, after-sale service, and recycle/disposal. In each stage, a lot of data is generated, and by collecting this data for all products, we can have a dataset with big data characteristics. 

Five areas of big data application in manufacturing are (Benhenni, 2017):
1. using data to forecast a complex process’s output;
2. using data to capture that which is difficult to measure under regular conditions,
3. developing algorithms which can more accurately control the quality and safety of the final product;
4. using image metrology to reduce the amount of human supervision required; and finally,
5. determining the optimal time periods for doing predictive maintenance.

The continued growth of the devices connected to  Internet of Things will increase the amount of data available to manufacturing companies. It has been forecasted that by 2025, about 175 trillion gigabytes of data will be available, and the manufacturing industry will be the second-fastest-growing sector for data generation, after the healthcare industry (Reinsel et al., 2018). Data mining has been used frequently in manufacturing decision making problems (Hanumanthappa & Sarakutty, 2011). Research into big data applications in manufacturing still needs to be carried out in a big way.

There are several different areas of manufacturing in which big data analysis was used, including new product development (Niebel et al., 2019; Zhan et al., 2018), smart manufacturing (O’Donovan et al., 2015), cloud-based manufacturing (Kumar et al., 2016), process improvement (Gupta et al., 2020), predictive manufacturing (Lee et al., 2013), and redistributed manufacturing (Zaki et al., 2019). 

Belhadi et al., (2019) studied the major contributions of big data analytics in manufacturing systems by examining several case studies.

Important areas of manufacturing in which big data analysis is reported are described below.

Operations improvement

A number of studies show that big data analytics can improve the entire operational performance in manufacturing systems.

Yadegaridehkordi et al., (2018) developed a hybrid approach to study the effect of the adoption of big data analytics on manufacturing companies’ performance. Popovič et al., (2018) showed that big data analytics’ capability, along with organizational readiness and certain design factors, could enhance a business’s performance. In another study, Guo et al. (2017) applied data visualization and machine learning algorithms to better inform the operations manager of the product’s market situation. Some other applications of big data analytics in manufacturing systems are covered in Dutta & Bose (2015).

(Huang et al., 2019) developed a theoretical approach to demonstrate the application of big data analytics in the area of production safety management.

Sustainability

Xu et al. (2019) showed how using the available big data on used products can increase the efficiency of remanufacturing systems and save more resources. Dubey et al. (2016) performed a survey 405 senior managers to develop a framework that could use big data to determine the most important factors for maintaining a sustainable manufacturing system. Lowering service costs, increasing the level of trust between stakeholders, respecting customers’ privacy, and increasing data-sharing security are among the benefits that big data analytics may bring to sustainable manufacturing systems (Rehman et al., 2016). 

The application of big data analytics in Bosch Car Multimedia’s (Braga-Portugal) organization (Santos et al., 2017) reviews the challenges of collecting, integrating, storing and processing the data in a manufacturing environment. It also shows the potential opportunity that is created  for sustainable innovations in a future manufacturing environment by big data analysis. 

Mani et al.  (2017),   showed that applying big data analytics in order to mitigate the supply chain’s social risk can help improve social and economic sustainability.

Smart Manufacturing  and Agile Manufacturing

Big data analytics can be used in smart manufacturing to solve shop floor problems at speed.  Big data analytics has been proven to be a valuable tool for manufacturers to help them develop strategies, share data, design predictive models, and connect factories in order to control processes (Kusiak, 2017). A study by Bumblauskas et al. (2017a) found big data applied to designing a smart maintenance decision support system, improved an asset’s lifecycle. Liu et al. (2019) used big data analytics for routing order pickup and delivery as well as assigning orders to laundry terminals in smart laundry service enterprises. Big data applications in strategy development and agile manufacturing have also been studied by Opresnik & Taisch (2015), Waller & Fawcett (2013), Guha & Kumar (2018), and Gunasekaran et al. (2018). 

Ren et al. (2019) reviewed the available research in big data applications that support sustainable smart manufacturing. Big data applications to facilitate agility in a manufacturing system, the capability to better deal with unpredictable events, and turn these events into benefits were also noted (Swafford et al., 2008).

https://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132020000100403

https://www.intechopen.com/books/new-trends-in-the-use-of-artificial-intelligence-for-the-industry-4-0/big-data-analytics-and-its-applications-in-supply-chain-management

Ud 5.10.2021

Pub 27.7.2020