**Abstract**

Quality Control (QC) is a guideline or set of procedures intended to ensure that a manufactured product or performed service adheres to a defined set of quality criteria or meets the requirements of the client or customer. Smart manufacturing is where the work is interfaced work pieces and associated tools that include logistics operations, Cyber Physical Systems, Artificial Intelligence, and Big Data Analytic tools. These form the norm of manufacturing operations to generate large amounts of data, which are used for analysis and prediction. Therefore, help to optimise the quality of manufacturing operations and manufactured products. The change in technologies have, however, altered the traditional way of manufacturing process as well as QC systems. Therefore, to address the challenge of data reliability, the sensors, actuators and instruments used at various levels of integration in the manufacturing process often operating under adverse physical conditions need to provide adequate levels of data accuracy and precision. Methodologically, the Chapter followed critical literature review on QC concepts and Industry 4.0 revolution, thereby culminating into conceptual framework of QC in Smart Manufacturing, which is the main contribution of this Chapter.

**Keywords:** Industry 4.0, quality assurance, total quality management, organisation, artificial intelligence, big data analytics, logistics management, supply chains

## **1. Overview**

The Chapter examines the Quality Control (QC) in Smart Manufacturing or Industry 4.0 Revolution. First, the Chapter begins with an examination of the historical development of QC from the Middle Ages (pre-Industry 1.0 revolution). Second, gives an overview of evolution of Smart Manufacturing and Quality Control, with emphasis on chronological trends of Industry revolutions up to date. Third, the concept of QC from the Middle Ages to 20th century. Fourth, it conceptualised the QC in Smart Manufacturing or Industry 4.0 revolution. Fifth, the Chapter gives an in-depth evolution of QC into Smart Quality Control Systems (SQCS) or Intelligent Quality Control Systems (IQCS), benefits, and lastly, summary of the Chapter.

#### **1.1 Introduction**

Historically Quality Control (QC) can be traced back to the times of pre-Industry 1.0 revolution when the modes of productions were still in their infant stage or Iron Age. During this period Human to Machine (H2M) interactions were still not

common in the manufacturing/assembly lines. And humans were not specifically and strategically positioned within the production lines to ensure that products, which do not meet specifications are eliminated before end of the production processes. This is because the production was always manned by Artisans working with some few workers using simple and less mechanised tools of production. The manufacturing processes continued with the Artisans being solely responsible for the product quality, while the consumer was expected to apply the principle of '*Caveat emptor*' when buying products [1]. Although it should be noted that, there were some punitive measures put in place to guard against unscrupulous traders who could take advantage of the customers. This method of quality control continued until the advent of Industry 1.0 revolution, which brought some remarkable improvement on the ways and methods of productions. However, it is not possible to discuss QC without discussing how the modern quality systems have evolved. First, modern "Total Quality Management" emerged as a subset of Quality Control (QC), whose sole purpose was to ensure that entire production systems (from inputs to outputs) followed set standards. Total Quality Management (TQM) origin can be trailed to the early 1920s, the time statistical theory was introduced to product quality control. TQM's idea was further advanced in Japan in 1940s by three Americans namely Deming, Juran and Feigenbaum [2]. To date QC has followed the same standards of inspections of inputs (raw materials) before the production/manufacturing processes (assembly lines) and outputs (final products) reach the market.

The development of the present QC can be traced to the period of Hawthorne studies between 1924 and 1932, which highlighted the significance of social and psychological work climate [3]. In the same period, Shewhart also developed Statistical process control, which later became known as "Statistical Quality Process" (SQP). Statistical Quality Control (SQC) emphasised the products' design and production. Over the years though the concept of quality has developed into a discipline, a complex set of principles and assumed truths that define quality of goods and services is to be assessed, managed, delivered and assured [3]. During and until late into Industry 1.0 revolution, quality could be best described as "*caveat emptor*", which means, let "*the buyer be aware*". The manufacturers, artisans and industrialists produced goods of certain quality, but it was up to the consumer/ buyer to appraise the quality of these goods. Thus, the consumer/buyer became responsible for the assurance of the goods they purchased [1, 2]. In the pre-industrial era, the quantity and quality of goods were the essential characteristics defining an economic transaction. In other words, "the qualities of the goods were known by their colour, sound, smell, taste, make, or shape [1, 4]. These forms of judgement made it problematic to differentiate the features that are the appropriate evaluators of those products. The problem is further compounded by the fact that people do differ very much; some person have clearer eyes, peculiar ears, noses, and tastes. In fact, the truth is every person having a good opinion of his/her faculties; therefore, it is difficult to find assessor to establish which is best" [4]. This approach makes quality to be more subjective and experiential. However, as progress made, through industrial evolution, and automation increased in the applications of manufactured goods, the level of product and process complexity, hence a new paradigm of quality control was borne, coinciding with a broader set of changes taking place under the realm of scientific management.

From Industry 1.0 to Industry 3.0 revolution, a lot of changes have been made in manufacturing lines/assemblies with the aim of producing products that meet customers specific needs. However, with the entry into Industry 4.0 revolution, organisations have moved from Human to Machines (H2M) production to Machine to Machine (M2M) intensive production, altering the way QC is managed in the manufacturing processes. Industry 4.0 revolution or Smart/digital manufacturing,

**19**

**Figure 1.**

*Chronology of industry revolution. Source: Author's own illustration.*

*Smart Manufacturing: Quality Control Perspectives DOI: http://dx.doi.org/10.5772/intechopen.95143*

successfully past behaviours [9].

with more emphasis on Big Data Analytic, Cyber-Physical Systems, 3-D printing, interface between M2M and Artificial Intelligence in the manufacturing processes [5]. Artificial intelligence (AI) can be conceived as the simulation of human intelligence in machines that are automated to think like humans and can imitate human behaviours [6]. This term may also refer to any machine, which displays attributes

The fourth industrial revolution, or Industry 4.0 revolution (I4.0R), has become a reality today (**Figure 1**). The political debate about the term Industry 4.0 revolution focuses equally on the important and abstract objectives. For its promoters, Industry 4.0 revolution, though coined in Germany is not only about improving Germany's international competitiveness, but also perceived as means for solving some of the urgent global problems for example, climate change that has created new demand for the increased consumption of renewable and non-renewable resources. While some of the problems are specific national challenges such as, labour supply that is ever-changing due to demographic shifts [7, 8], Industry 4.0 revolution is focused on smart products, procedures, and processes (smart production). A key element of Industry 4.0 revolution is, therefore, the Smart Manufacturing (**Figure 1**). Smart Manufacturing or Industry 4.0 revolution are Cyber-Physical Systems, physical systems integrated with ICT components. These are autonomous machines that can make their own decisions based on machine learning algorithms and real-time data capture, analytics results, and recorded

The Smart Manufacturing controls the fast-growing complexity, while also boosting production efficiency. Therefore, Smart Manufacturing is about direct communication between man, machine and resources to produce Smart products and services. Furthermore, Smart products know their manufacturing process and

related to human minds such as learning and problem-solving.

**1.2 Evolution of smart manufacturing and quality control**

*Smart Manufacturing: Quality Control Perspectives DOI: http://dx.doi.org/10.5772/intechopen.95143*

*Quality Control - Intelligent Manufacturing, Robust Design and Charts*

common in the manufacturing/assembly lines. And humans were not specifically and strategically positioned within the production lines to ensure that products, which do not meet specifications are eliminated before end of the production processes. This is because the production was always manned by Artisans working with some few workers using simple and less mechanised tools of production. The manufacturing processes continued with the Artisans being solely responsible for the product quality, while the consumer was expected to apply the principle of '*Caveat emptor*' when buying products [1]. Although it should be noted that, there were some punitive measures put in place to guard against unscrupulous traders who could take advantage of the customers. This method of quality control continued until the advent of Industry 1.0 revolution, which brought some remarkable improvement on the ways and methods of productions. However, it is not possible to discuss QC without discussing how the modern quality systems have evolved. First, modern "Total Quality Management" emerged as a subset of Quality Control (QC), whose sole purpose was to ensure that entire production systems (from inputs to outputs) followed set standards. Total Quality Management (TQM) origin can be trailed to the early 1920s, the time statistical theory was introduced to product quality control. TQM's idea was further advanced in Japan in 1940s by three Americans namely Deming, Juran and Feigenbaum [2]. To date QC has followed the same standards of inspections of inputs (raw materials) before the production/manufacturing

processes (assembly lines) and outputs (final products) reach the market.

The development of the present QC can be traced to the period of Hawthorne studies between 1924 and 1932, which highlighted the significance of social and psychological work climate [3]. In the same period, Shewhart also developed Statistical process control, which later became known as "Statistical Quality

Process" (SQP). Statistical Quality Control (SQC) emphasised the products' design and production. Over the years though the concept of quality has developed into a discipline, a complex set of principles and assumed truths that define quality of goods and services is to be assessed, managed, delivered and assured [3]. During and until late into Industry 1.0 revolution, quality could be best described as "*caveat emptor*", which means, let "*the buyer be aware*". The manufacturers, artisans and industrialists produced goods of certain quality, but it was up to the consumer/ buyer to appraise the quality of these goods. Thus, the consumer/buyer became responsible for the assurance of the goods they purchased [1, 2]. In the pre-industrial era, the quantity and quality of goods were the essential characteristics defining an economic transaction. In other words, "the qualities of the goods were known by their colour, sound, smell, taste, make, or shape [1, 4]. These forms of judgement made it problematic to differentiate the features that are the appropriate evaluators of those products. The problem is further compounded by the fact that people do differ very much; some person have clearer eyes, peculiar ears, noses, and tastes. In fact, the truth is every person having a good opinion of his/her faculties; therefore, it is difficult to find assessor to establish which is best" [4]. This approach makes quality to be more subjective and experiential. However, as progress made, through industrial evolution, and automation increased in the applications of manufactured goods, the level of product and process complexity, hence a new paradigm of quality control was borne, coinciding with a broader set of changes taking place under

From Industry 1.0 to Industry 3.0 revolution, a lot of changes have been made in manufacturing lines/assemblies with the aim of producing products that meet customers specific needs. However, with the entry into Industry 4.0 revolution, organisations have moved from Human to Machines (H2M) production to Machine to Machine (M2M) intensive production, altering the way QC is managed in the manufacturing processes. Industry 4.0 revolution or Smart/digital manufacturing,

**18**

the realm of scientific management.

with more emphasis on Big Data Analytic, Cyber-Physical Systems, 3-D printing, interface between M2M and Artificial Intelligence in the manufacturing processes [5]. Artificial intelligence (AI) can be conceived as the simulation of human intelligence in machines that are automated to think like humans and can imitate human behaviours [6]. This term may also refer to any machine, which displays attributes related to human minds such as learning and problem-solving.
