**2.4 Challenges in quality control system research (quality control implementation in**

Right from the ancient days of product manufacturing, industrial application has experienced a lot of changes ranging from design parameters configuration of product design implementation. Companies and design expert has labor extensively to come up with perfect system, but there has always been one challenge or the other. Quality control system in intelligent manufacturing gas come with challenges. [31] Deloitte (2014) opined that, there are challenges associated with industry 4.0 digitalization and digital transformation.

Challenge of making reliable forecast is one of the major challenges often encounter in quality control management, however, [32] opined that the use of cyber technology equipment, physical system, artificial intelligence, big data would increase efficiency of running production system. Similarly, ineffective flow of materials and adequate planning has always been the bane of effective production

system. [33, 34] argued that effective monitoring of flow system may not impart much on the production effectiveness unless match up with flow stream mapping. respondents were used in the study, they include the managers, officers and super-

manufacturing, located at the at Federal Capital Territory in Abuja and Lagos state Nigeria. The questionnaires were initially pretested on five (5) respondents for content validation, their observation were later used to recalibrate the questionnaire

Summarily, the unique group of sample used in the study include sample size of seventy three (73) respondents that cut across the cadre of managers and supervisors in product manufacturing companies, i.e. Production Manager-PM; Quality Control Officer-QCO, Production Supervisor-PS; ICT Officer-Information Com-

The data collection instrument adopted is a structured questions designed in Likert scale format of semantic rating scale 1–5. The questionnaire was designed in a way that allow for easy collation of data. It was divided into five sections. Section 1 was centered on the Bio-data information of the respondents, Section 2 was about categories of production manager, Section 3 was on Investigating the state of disruption in quality monitoring in industrial manufacturing, Section 4 was about the drivers of effective quality control system monitoring in intelligent manufacturing. Section 5 was centred on issues and challenges involved in quality control systems in Intelligent manufacturing while Section 6 focused on critical factors that influences

**Question, analytical method Scale Variables Reference**

Professional cadre, Gender, Types of managers, Qualification and cadre of managers on intelligent manufacturing system

Perception on state of adoption of quality control system for intelligent manufacturing

Drivers of Effective Quality Control System Monitoring in Intelligent Manufacturing system.

Issues and Challenges Involved In Quality Control Systems in Intelligent Manufacturing

[21]

[20, 23]

[21]

[20, 21]

Ordinal, Numeric, Likert Scale

Numeric Likert scale

Numeric Likert scale

Numeric Likert scale

visors engaged in companies that are involved in consumer good products

*Adapting Disruptive Applications in Managing Quality Control Systems in Intelligence…*

into final form used for the analysis the view was supported in [13–15].

munication Technology Officer.

*DOI: http://dx.doi.org/10.5772/intechopen.93979*

an effective intelligent manufacturing system.

**3.3 Operationalization of research variables**

**3.2 Questionnaire design**

Q1–5 Respondents Bio-data information, Work experience.

Descriptive Statistics, Percentage.

Effectiveness of control systems

Pearson's Chi-square, Kendal Tau teat and Relative Agreement Index and

Q15–23 Examine the Drivers of Effective Quality Control System Monitoring in

Q6–14 Investigate the state of disruption in quality monitoring in industrial

Analytical Methods:

Spearman Ranking.

manufacturing

Satisfaction level. Analytical method:

Spearman Ranking

Intelligent Manufacturing Analytical Method:

Man-Whitney U Test.

Intelligent Manufacturing Analytical Methods: Pearson's Chi-square, Relative

**65**

Pearson's Chi-square, Relative Importance Index, Cronbach Alpha test,

Q24–32 Study Issues and Challenges Involved In Quality Control Systems in

Moreover, managing logistic tasks with material handling machine was suggested by [33, 35] that there is negative consequence that could come up on account of in appropriate adoption of material handling machine. Some of the consequences include absence: absence of route temperature, fluctuating environmental temperature, half-life of machine parts, and inadequate level of machine parts' management.

Furthermore, inadequate manpower planning, inadequate system calibration and continuous data transfer are some of the challenges that should be surmounted for effective quality control system during intelligent manufacturing [22, 36]. Finally, major challenge with the use of Artificial intelligence (AI) in creating intelligent manufacturing in recent time is the one that influence negatively, the variable of decision making and data inter- operability. [32] described parameters that influences AI adoption in control system to include: data parsimony, data transfer, data processing and manipulation, process speed, system efficiency and interoperability. Similarly, high data affinity or fidelity expectation, smart storage capacity and smart maintenance are components of smart and intelligent manufacturing that should be managed.

## **3. Materials and methods**

In the context of this study, primary data was engaged and was collected from sampled Production managers, Production supervisors, Quality control officers and Information communication officers that are on ground at the selected locations of the research while Survey materials adopted structured questionnaire design in a closed structure manner as carried out in similar previous studies such as [13, 14, 16].

#### **3.1 Material and tools**

In this study different materials and tools were used, part of the materials used are A-4 papers for questionnaire production, Google forms, Google spread sheet, markers, pencils and biro. Analytical package of Statistical tools of SPSS was engaged in the processing of data collated from the respondents. Some of the tools include Relative Agreement Index (RAI), Mann–Whitney U-Test, Pearsons's Chi-square test and Student's T-test.

The Relative Agreement Index was calculated using the following relation.

$$\mathbf{RAI} = \sum \underline{\mathbf{W}}\,\tag{1}$$

$$\mathbf{A} \times \mathbf{N}$$

where RAI = Relative Agreement Index, Wi = Weighted Sum, A = The number of items on Likert scale of 1–5.

N = individual weight of the scale item on Likert scale 1–5. The component of the Likert Scale include (SA: Strongly Agree (5), A: Agree (4); SD: Strongly Dis-agree (2); D:Dis-agree (1); N:Neutral (3)).

Survey design method was used in the study with population comprised of 100 manufacturing company both small and large scale. Similarly, sectionalized category of client were profiled and censored for data collection purpose, they include: Production Manager-PM; Quality Control Officer-QCO, Production Supervisor-PS; ICT Officer-Information Communication Technology Officer, the category of

*Adapting Disruptive Applications in Managing Quality Control Systems in Intelligence… DOI: http://dx.doi.org/10.5772/intechopen.93979*

respondents were used in the study, they include the managers, officers and supervisors engaged in companies that are involved in consumer good products manufacturing, located at the at Federal Capital Territory in Abuja and Lagos state Nigeria. The questionnaires were initially pretested on five (5) respondents for content validation, their observation were later used to recalibrate the questionnaire into final form used for the analysis the view was supported in [13–15].

Summarily, the unique group of sample used in the study include sample size of seventy three (73) respondents that cut across the cadre of managers and supervisors in product manufacturing companies, i.e. Production Manager-PM; Quality Control Officer-QCO, Production Supervisor-PS; ICT Officer-Information Communication Technology Officer.

## **3.2 Questionnaire design**

The data collection instrument adopted is a structured questions designed in Likert scale format of semantic rating scale 1–5. The questionnaire was designed in a way that allow for easy collation of data. It was divided into five sections. Section 1 was centered on the Bio-data information of the respondents, Section 2 was about categories of production manager, Section 3 was on Investigating the state of disruption in quality monitoring in industrial manufacturing, Section 4 was about the drivers of effective quality control system monitoring in intelligent manufacturing. Section 5 was centred on issues and challenges involved in quality control systems in Intelligent manufacturing while Section 6 focused on critical factors that influences an effective intelligent manufacturing system.


#### **3.3 Operationalization of research variables**


#### **3.4 Processing and distribution of questionnaire**

In processing and administration of the data collection, survey design method was used while purposive sampling technique was adopted to pull together the respondents, as mentioned earlier in Section 2.2, the respondents used for this study are the production personnel, a total of eighty (80) questionnaire designed in Likert scale was sent to one eighty(80) managers and supervisors and officers that constitute respondents among which the completed and valid seventy three (73) questionnaire were used, this is similar to method adopted in studies of [13–15].

**4.3 Adaptable areas of disruptions in the quality assurance monitoring**

*Legend: Production Manager-PM; Quality Control Officer-QCO, Production Supervisor-PS;*

In **Table 3**, adaptable areas of disruptions in quality assurance monitoring was presented highlighting the responses of respondents. There are a lot of areas in intelligent manufacturing that disruptions has taken place. Some of the areas includes system design, implementation and monitoring. Areas covered in the study, 10 areas of disruptions was identified they include; process design, analytical technology, platform technology, operation technology, intelligent spindle system, sensor based control units, intelligent system powered psychometric system, 3d

**Adaptable areas in intelligent manufacturing RAI Rank** Process Design 0.869 1st Sensor based control units 0.786 2nd Platform technology 0.771 3rd Analytical technology 0.754 4th Operation technology 0.732 5th Intelligent Sequencing System 0.679 6th Intelligent system powered psychometric system 0.657 7th Intelligent spindle system 0.631 8th 3D Design System 0.579 9th

*Adaptable Areas of Disruptions in Quality Assurance Monitoring for Intelligent Manufacturing.*

**Respondent cadre Frequency Percentage (%)** Production Supervisor 23 31.50 Production Manager 20 27.40 Quality Control Officer 20 27.40 ICT Officer 10 13.70 Total 73 100

*Adapting Disruptive Applications in Managing Quality Control Systems in Intelligence…*

**Year experience PM QCO PS ICTO** BSc.[Bachelor] — 55 3 MSc.[Masters] 5 5 10 7 HND[Diploma] 5 5 4 — Trade Certificates 10 5 4 — Total 20 20 23 10

**and calibration in production process**

*ICT Officer-Information Communication Technology Officer.*

*Respondents Qualification Manufacturing Experience.*

*DOI: http://dx.doi.org/10.5772/intechopen.93979*

*Legend: RAI—Relative Agreement Index.*

**Table 3.**

**67**

**Table 1.**

**Table 2.**

*Respondents Category.*

#### **4. Results and discussion**

#### **4.1 Category of respondents**

In the context of this study, different category of respondents were engaged in the study (**Table 1**). Twenty three (23) production supervisor are engaged, twenty (20) production managers, twenty (20) quality control officer and ten (10) ICT officers. All respondents are directly involved in production process of their various company at the research location.

#### **4.2 Qualification of respondents**

Manufacturing education background matters in the context of this research, level of enlighten would enable a respondent to contribute adequately towards valid response to the administered questionnaire or interview, Therefore, respondents qualification in this study is as presented in **Table 2**. It is obvious that the managers, supervisors and officers has more than one qualification. Among the 20 production managers sampled, 10 possessed trade certificates, 5 possessed masters and higher national diploma degree, this trend cut across the QCO, PS and ICTOs. The ICTOs has predominantly masters and bachelor degrees beside other certifications that are not classified in this category. Most of the production managers and supervisors has adequate practical background which enables them to be suitable for the purpose of the research.

*Adapting Disruptive Applications in Managing Quality Control Systems in Intelligence… DOI: http://dx.doi.org/10.5772/intechopen.93979*


#### **Table 1.**

*Respondents Category.*


*Legend: Production Manager-PM; Quality Control Officer-QCO, Production Supervisor-PS; ICT Officer-Information Communication Technology Officer.*

#### **Table 2.**

*Respondents Qualification Manufacturing Experience.*
