**4.3 Adaptable areas of disruptions in the quality assurance monitoring and calibration in production process**

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


#### **Table 3.**

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

design system and intelligent sequencing system. This Process design is ranked 1st with RAI of 0.869; analytical technology is ranked 2nd with RAI value 0.786, platform technology with RAI 0.771 is ranked 3rd, operation technology is ranked 4th with RAI value of 0.754, intelligent spindle system also ranked 5th with RAI 0.732, sensor based control units ranked 6th, while intelligent system powered psychometric system and 3D design system and intelligent sequencing system were ranked 8th and 9th respectively with RAI values 0.631 and 0.579. In disruption that relates to intelligent manufacturing, there is always benefits often associated with paradigm shift in innovation technology. [36] opined that there are a lot of opportunities accrued to disruption that are beneficial although there is always negative resistance usually connected with a new change. There are risks associated with disruptions on account of new innovations often involved with the change.

rectification system RAI 0.771 is ranked 2nd, Effective design and production calibration system with RAI 0.769 ranked 3rd, Intelligent cost control system with RAI 0.754 ranked 4th, while Effective data extraction and transfer with RAI value of 0.754 is ranked 5th. However, Adequate machine part management with RAI 0.623 and Half live of machine parts with RAI value of 0.541 were ranked least with 8th

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

Developing quality control is one of the important phase of manufacturing process. Quality control of product is very significant to the users and manufacturers. Quality to the manufacturer borders about correct design, authentic system calibration. Quality to consumer refers to manufactured product meeting users demand and expectation, therefore subjective in nature. ENQA (2009)in an adaptive study on standard and guidelines for quality assurance posited that there should be common reference point for quality assurance, keeping of quality register, procedure for quality assurance, keeping of quality register, documentation of procedure for the recognition of quality qualification and exchange of view among quality personnel. This could be encapsulated as a quality culture which should be keenly observed. Quality culture should be maintained so as to create a healthy production environment which guarantees customers production satisfaction. [40, 41] submitted further that quality culture would fine tune quality control value in a product system and this will lead to the reduction of political and geographical barrier). There are parameters that controls production in manufacturing industries, some of them includes: specification marketing, scheme and specification from purchasing units and agencies. Moreover, quality calibration at the outset of a manufacturing process usually ensures that quality product emerge from production system. Also, adoption of simple inspection table, and adoption of basic principles of sampling through sampling lot are cutting edge techniques that facilitates adoption of quality culture in product production, this toes the line of submissions

**4.5 Issues and challenges involved in adapting quality control systems in**

In this section challenges involved in the adapting of disruption application in intelligent manufacturing is outlined in **Table 5**. There are methods that could be used to control quality during manufacturing process [20, 21, 43] alluded that discrepancies in the document and. Process involved in manufacturing could be

**Issues and challenges PM Rank QCO Rank PS Rank ICTO Rank** Machine–machine interaction 0.865 2nd 0.852 2nd 0.785 1st 0.773 1st Man–machine interaction 0.873 1st 0.869 1st 0.721 2nd 0.653 2nd Data quality 0.762 4th 0.754 4th 0.720 3rd 0.589 3rd Cyber-security 0.771 3rd 0.771 3rd 0.698 4th 0.554 5th Spare part management 0.762 4th 0.732 5th 0.657 5th 0.467 6th Data Acquisition/Storage 0.657 5th 0.631 6th 0.589 6th 0.573 4th Training Challenges 0.634 6th 0.543 7th 0.568 7th 0.457 7th Testing cost & complexity 0.537 7th 0.557 7th 0.431 8th 0.456 8th

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

*ICT Officer-Information Communication Technology Officer.*

*Challenges involved in Adapting Quality Control System.*

and 9th position respectively.

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

in [20, 42].

**Table 5.**

**69**

**intelligent manufacturing**

However, any visionary organization should learn how to strategize in situation of associated risks mitigation. Intelligent manufacturing disruption has impacted strategic aspect of intelligent manufacturing that is changing the trend of games in product manufacturing. For instance, the following areas of intelligent manufacturing has been duly impacted; economy of product, economy of value chain, demand for product and changing faces of product as supported in [36, 37]. The associated risk in the disruption in intelligent manufacturing was modelled by [37–39] for monitoring of systemic sequencing of operation in product manufacturing. Disruption risk and pattern was modelled in [37] with Poisson ump process using random multiple system. Similarly parameters were sequenced and stochastically correlated to monitor quality diffusion process. However, internet of things (IoT), cyber-physical system, logistic 4.0, manufacturing 4.0, and hospital 4.0 are some of the aspect of disruption in intelligent manufacturing that formed the embodiment of application that has enhanced product production and manufacturing.
