**4.6 Statistical test of level agreement of responses on issues and challenges involved in adapting quality control systems**

Man Whitney U Test was carried out on the data in **Table 5** at significant level 0.05 two tailed, while the Test statistics results was presented in **Table 6**. The U-value is 24, the critical value of U at P < 0.05 is 8, therefore, the result is not significant at P < 0.05. Similarly, Z-score is 0.00. The P-value is 1.5 and 2.0, the result is not significant at 0.005. However, there is no significant difference in the opinion expressed by the respondents on the agreement level on Responses on issues and challenges involved in adapting quality control systems in intelligent manufacturing. It indicates high percentage of respondents in support of the fact that the KPI identified is in high order as presented on Likert scale 4 and 5 [23, 26].

From **Table 7** above, the Pearson Chi square results of the variables on Issues and Challenges Involved in Adapting Quality Control Systems is presented. From the table, the Pearson Chi-square row indicated that Chi value is 0.261, while P value is 0.05. This indicates that there is no statistical significance difference in association of respondents' opinion in the ranking of the issues and challenges involved in adopting control systems in intelligent manufacturing. The Null hypothesis is therefore accepted. It indicated that the factors were unanimously agreed upon. The implication lies in the fact that the listed factors in the tables were agreed upon by the respondents as critical challenge that poses threat and challenges to the intelligent manufacturing.

## **4.7 ANOVA of satisfaction level of facility managers on intelligent building systems' performance**

Pearson's T and Student T test was carried out on mean of responses collated from the post occupation managers of the intelligent building used for the study and results presented in **Table 8**. The test was carried out to compare the difference in

means of the post occupation facility managers and users. This is to check whether there variation among the group observed. The test results of equality of variance is as presented in the table. The test was performed at 95% confidence interval and all variables presented exhibited P-value higher than 0.05 i.e. P > 0.05. The result

*Chi-Square Test on Issues and Challenges Involved in Adapting Quality Control Systems.*

**Officers/managers Production**

**Officers/managers Production**

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

Machine–Machine interaction

Spare parts Management

**Table 6.**

*Systems.*

**manager**

**Quality control officer**

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

**Statistic items N Mean N Mean N Mean N Mean — —**

Man/machine Tradition 1 1.500 1 1.500 1 1.500 1 1.500 — —

Data Quality 1 1.500 1 1.500 1 1.500 1 1.500 — —

Cyber Security 1 1.500 1 1.500 1 1.500 1 1.500 — —

Data Acquisition 1 2.000 1 2.000 1 2.000 1 2.000 — —

Training Challenges 1 2.000 1 2.000 1 2.000 1 2.000 — —

Testing and Complexity 1 1.500 1 1.500 1 1.500 1 1.500 — —

**Production supervisor**

1 1.500 1 1.500 1 1.500 1 1.500 — — 2 1.500 2 1.500 2 1.500 2 1.500 — —

2 1.500 2 1.500 2 1.500 2 1.500 — —

2 1.500 2 1.500 2 1.500 2 1.500 — —

2 1.500 2 1.500 2 1.500 2 1.500 — —

1 2.000 1 2.000 1 2.000 1 2.000 — — 2 1.000 2 1.000 2 1.000 2 1.000 — —

2 1.000 2 1.000 2 1.000 2 1.000 — —

2 1.000 2 1.000 2 1.000 2 1.000 — —

2 1.5000 2 1.5000 2 1.5000 2 1.5000 — —

**ICT officer**

**Std. deviation**

**Std. error**

*Adapting Quality Control Systems.*

**Table 7.**

**71**

**manager**

**Quality control office**

**Statistic items PS KDT PS KDT PS KDT PS KDT** Machine–Machine interaction 0.261 0.000 0.261 0.000 0.261 0.000 0.261 0.000 Man/machine Tradition 0.261 0.000 0.261 0.000 0.261 0.000 0.261 0.000 Data Quality 0.261 0.000 0.261 0.000 0.261 0.000 0.261 0.000 Cyber Security 0.238 0.030 0.238 0.030 0.238 0.000 0.238 0.000 Spare parts Management 0.238 0.164 0.238 0.164 0.238 0.091 0.238 0.091 Data Acquisition 0.261 0.000 0.261 0.000 0.261 0.000 0.261 0.000 Training Challenges 0.261 0.391 0.261 0.391 0.261 0.261 0.261 0.204 Testing and Complexity 0.261 0.000 0.261 0.000 0.261 0.261 0.261 0.000 *Ho: There is no statistical difference in the managers' opinion as regards the Issues and Challenges Involved in*

*Man-Whitney U Student T-Test on Responses on Issues and Challenges Involved in Adapting Quality Control*

**Production supervisor**

**ICT officer**

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


#### **Table 6.**

*Man-Whitney U Student T-Test on Responses on Issues and Challenges Involved in Adapting Quality Control Systems.*


*Ho: There is no statistical difference in the managers' opinion as regards the Issues and Challenges Involved in Adapting Quality Control Systems.*

#### **Table 7.**

*Chi-Square Test on Issues and Challenges Involved in Adapting Quality Control Systems.*

means of the post occupation facility managers and users. This is to check whether there variation among the group observed. The test results of equality of variance is as presented in the table. The test was performed at 95% confidence interval and all variables presented exhibited P-value higher than 0.05 i.e. P > 0.05. The result


#### **Table 8.**

*ANOVA of Satisfaction Level of Facility Managers on Intelligent Building Systems' Performance.*

statistics implies that there is no significant difference in the means value the samples of the managers, therefore, the Null hypothesis is accepted, implying that there is no variation in the mean values of satisfaction level of production managers of the sampled companies. The reasons behind the difference could be linked to the background experience of the manager in intelligent manufacturing process.

Intelligent manufacturing is multidisciplinary in nature. This indicate that there are several intervening factors that influences success of intelligent manufacturing. Some of the significant factors are industrial chain factor, development factor, capital availability factor, political factor and socio-economic factors [44]. Submitted that chain factor, developing factor and capital factors influences the effectiveness of intelligent manufacturing system, the factors according to the study was described as engine to the effectiveness of intelligent manufacturing concept. Chain value system in supplying of materials for manufacturing is somehow significant, it borders about micro and macro supply chain system, and this toes the line of submissions in [45–47]. Similarly, there are issues that surrounds effective transition into intelligent manufacturing from traditional manufacturing system. While traditional manufacturing is transiting into intelligent manufacturing for the production of customized product involved principally. [46, 48] presented issues on important factors that should be taken into consideration in intelligent manufacturing. The study revolves around actions to be taken to eliminate the challenges in manufacturing. Some of the actions include benchmarking capital factor, development factor and chain factor, the factors listed according to [48–51] formed the basis of the ideals in intelligent manufacturing and has tendency to transform traditional manufacturing production to customized product often associated with intelligent manufacturing. Moreover, [36] Adnan (2017) posited that manufacturing process and manufacturing organization responds to factors such as organization performance, organization culture, socio-economic reality and supply chain

**Critical factors parameters PM Rank QCO Rank PS Rank ICTO Rank**

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

Cost of talent 0.885 1st 0.863 1st 0.785 1st 0.783 2nd Cost of failure 0.883 2nd 0.861 2nd 0.721 5th 0.853 1st Technological exchange 0.771 4th 0.757 5th 0.720 6th 0.759 4th Testing cost and complexity 0.776 3rd 0.751 6th 0.698 7th 0.654 6th

Government policy 0.766 7th 0.762 4th 0.657 9th 0.766 3rd Technological content regulation 0.653 8th 0.651 7th 0.589 10th 0.651 7th Provision of infrastructure environment 0.647 13th 0.643 8th 0.568 11th 0.457 14th Political will for approval 0.653 8th 0.765 3rd 0.765 3rd 0.743 5th

Interplay of micro-economic variables 0.557 11th 0.587 9th 0.431 13th 0.456 14th Interplay of macro-economic policy 0.766 7th 0.534 10th 0.678 8th 0.532 12th Inter-continental technology transfer 0.765 5th 0.447 13th 0.787 2nd 0.521 13th

Data acquisition and storage 0.567 10th 0.531 12th 0.532 12th 0.654 8th High regulation requirement 0.553 11th 0.534 10th 0.765 3rd 0.654 9th Large state space 0.463 12th 0.503 11th 0.763 4th 0.503 11th

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

*ICT Officer-Information Communication Technology Officer.*

*Critical Factors Influencing Intelligent Manufacturing Adaptation.*

Technological Related Factor

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

Political Related Factor

Economic Related Factors

Digital divide related factors

**Table 9.**

**73**

management. This fact is further corroborated in [52–55].

Critical factors influencing intelligent manufacturing adaptation is presented in **Table 9**. The factors were broadly divided into four main categories, the technical related factors, political related factors, economic related factors and digital divide related factors. Main factors that are rated high belong to the technological related factors and were ranked 1st 2nd, 3rd and 4th, for instance, some production managers, quality control officers, production supervisor and ICT officers ranked category of factors that are technological related high, such as cost of talent, cost of technological failure, technological exchange problem and complexity in testing cost. Similarly, political related factors such as political will, technological content regulation and infrastructural environment provision are ranked 4th, 5th and 6th while digital divide factors were ranked 7th, 8th, 9th and 10th.

**Critical factors parameters PM Rank QCO Rank PS Rank ICTO Rank** Technological Related Factor Cost of talent 0.885 1st 0.863 1st 0.785 1st 0.783 2nd Cost of failure 0.883 2nd 0.861 2nd 0.721 5th 0.853 1st Technological exchange 0.771 4th 0.757 5th 0.720 6th 0.759 4th Testing cost and complexity 0.776 3rd 0.751 6th 0.698 7th 0.654 6th Political Related Factor Government policy 0.766 7th 0.762 4th 0.657 9th 0.766 3rd Technological content regulation 0.653 8th 0.651 7th 0.589 10th 0.651 7th Provision of infrastructure environment 0.647 13th 0.643 8th 0.568 11th 0.457 14th Political will for approval 0.653 8th 0.765 3rd 0.765 3rd 0.743 5th Economic Related Factors Interplay of micro-economic variables 0.557 11th 0.587 9th 0.431 13th 0.456 14th Interplay of macro-economic policy 0.766 7th 0.534 10th 0.678 8th 0.532 12th Inter-continental technology transfer 0.765 5th 0.447 13th 0.787 2nd 0.521 13th Digital divide related factors Data acquisition and storage 0.567 10th 0.531 12th 0.532 12th 0.654 8th High regulation requirement 0.553 11th 0.534 10th 0.765 3rd 0.654 9th Large state space 0.463 12th 0.503 11th 0.763 4th 0.503 11th

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

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

#### **Table 9.**

*Critical Factors Influencing Intelligent Manufacturing Adaptation.*

Intelligent manufacturing is multidisciplinary in nature. This indicate that there are several intervening factors that influences success of intelligent manufacturing. Some of the significant factors are industrial chain factor, development factor, capital availability factor, political factor and socio-economic factors [44]. Submitted that chain factor, developing factor and capital factors influences the effectiveness of intelligent manufacturing system, the factors according to the study was described as engine to the effectiveness of intelligent manufacturing concept. Chain value system in supplying of materials for manufacturing is somehow significant, it borders about micro and macro supply chain system, and this toes the line of submissions in [45–47]. Similarly, there are issues that surrounds effective transition into intelligent manufacturing from traditional manufacturing system. While traditional manufacturing is transiting into intelligent manufacturing for the production of customized product involved principally. [46, 48] presented issues on important factors that should be taken into consideration in intelligent manufacturing.

The study revolves around actions to be taken to eliminate the challenges in manufacturing. Some of the actions include benchmarking capital factor, development factor and chain factor, the factors listed according to [48–51] formed the basis of the ideals in intelligent manufacturing and has tendency to transform traditional manufacturing production to customized product often associated with intelligent manufacturing. Moreover, [36] Adnan (2017) posited that manufacturing process and manufacturing organization responds to factors such as organization performance, organization culture, socio-economic reality and supply chain management. This fact is further corroborated in [52–55].


#### **Table 10.**

*Man-Whitney U Student T- Test on Critical Factors Influencing Intelligent Manufacturing Adaptation.*

Large state space 1 2.000 1 1.300 1 **2.000 1 2.000 — —**

2 1.000 2 1.000 2 **1.000 2 1.000 — —**

Literature review was conducted background for the study and set stage for identification of missing links and gaps. The Intelligent manufacturing has been noted to be fairly new in the developing countries [11–13]. Ten (10) areas of disruptions was identified in this study, include; process design, analytical technology, platform technology, operation technology, intelligent spindle system, sensor based control units, intelligent system powered psychometric system, 3D design system and

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

In the context of this study, some significant challenges were profiled and processed. Some of the challenges profiled include machine–machine interaction; man–machine interaction; data quality; cyber-security; spare part management; data acquisition/storage; training challenges and testing cost and complexity. The validity of the outcome of this research lies in applicability in expanding frontiers of knowledge in literary research, assistance to policy makers, assistance to the production managers and personnel among others. The study finally presents areas of disruptions in the quality assurance monitoring and calibration in production process, issues and challenges involved in quality control systems in manufacturing,

The support of Building Informatics group members of Building Technology Department of College of Science and Technology and Center for Innovation and Research Development Covenant University is appreciated for various contribu-

The authors declare no conflict of interest in this research work. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

emerging areas of application and recommendation for improvement.

intelligent sequencing system.

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

**Acknowledgements**

**Conflict of interest**

**75**

tions and funding the OAPF.

Man Whitney U Test was carried out on the data in **Table 9** and presented in **Table 10**, at significant level 0.05 two tailed. The U-value is 24, the critical value of U at P < 0.05 is 8, therefore, the result is not significant at P < 0.05. Similarly, Z-score is 0.00. The P-value is 1.5, the result is not significant at 0.005. However, there is no significant difference in the opinion expressed by the respondents on the agreement level by production managers, production supervisors, quality control officers, and ICT officers. it indicates high percentage of respondents in support of the critical factors influencing intelligent manufacturing adaptation which supports the fact that the KPI identified is in high order as presented on semantic rating Likert scale 5 and 4 [21, 22, 56].

### **5. Conclusions**

The study has censored and profiled the issues, facts, ideals and factors that influences the adoption of disruptions in creating adaptive intelligent manufacturing with a view to creating enhanced productivity in intelligent manufacturing.

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

Literature review was conducted background for the study and set stage for identification of missing links and gaps. The Intelligent manufacturing has been noted to be fairly new in the developing countries [11–13]. Ten (10) areas of disruptions was identified in this study, include; process design, analytical technology, platform technology, operation technology, intelligent spindle system, sensor based control units, intelligent system powered psychometric system, 3D design system and intelligent sequencing system.

In the context of this study, some significant challenges were profiled and processed. Some of the challenges profiled include machine–machine interaction; man–machine interaction; data quality; cyber-security; spare part management; data acquisition/storage; training challenges and testing cost and complexity. The validity of the outcome of this research lies in applicability in expanding frontiers of knowledge in literary research, assistance to policy makers, assistance to the production managers and personnel among others. The study finally presents areas of disruptions in the quality assurance monitoring and calibration in production process, issues and challenges involved in quality control systems in manufacturing, emerging areas of application and recommendation for improvement.

## **Acknowledgements**

The support of Building Informatics group members of Building Technology Department of College of Science and Technology and Center for Innovation and Research Development Covenant University is appreciated for various contributions and funding the OAPF.
