Knowledge Management and AI-Based Technologies in Manufacturing

This paper discusses the importance of knowledge management practices and AI-based technologies in manufacturing for achieving long-term objectives. It covers topics such as tacit and explicit knowledge, AI-eye, predictive maintenance, and generative design. The paper also provides examples of how AI-based technologies have helped organizations streamline their work and increase their returns. Desklib offers solved assignments, essays, and dissertations for various courses and universities.

Knowledge information management
KIM
[DATE]
Hewlett-Packard
[Company address]

Table of Contents
1. Introduction.........................................................................................................................................2
2. Knowledge management practices......................................................................................................2
3. The strategic importance of knowledge management practices with reference to Tacit and Explicit
knowledge in helping organizations to achieve their long term objectives‐ ................................................3
4. AI based technologies..........................................................................................................................5
5. Impact of AI based technologies on operational excellence of manufacturing process..................5
6. AI-Eye...........................................................................................................................................6
7. Predictive maintenance...............................................................................................................6
8. Generative design........................................................................................................................7
Conclusion...................................................................................................................................................7
9. References...........................................................................................................................................8

1. Introduction
The main aim of the paper is to discuss the knowledge management and how it can be used
by organizations in order to achieve long term objectives. Knowledge management helps the
organizations in finding, selecting, organizing and disseminating the vital information in order to
improve their problem solving and decision making. Organizations these days spend a hefty
amount and significant resources for managing knowledge for regular and sustained use.
Therefore, these projects are also becoming quite exclusive and expensive and time consuming.
IBM, for instance, is using resources to code the knowledge (Hislop, Bosua, and Helms, (2018).
The paper also aims to further analyze the artificial intelligence being used in manufacturing
units and how this technology helps the manufacturing industry people gain efficient and
effective results through the implementation of the AI technologies. Artificial Intelligence is a
computer science branch that develops machines with intelligence, capable of thinking and
processing as humans do. Examples would include, speech recognition, learning and resolving
issues (Price, and Flach, 2017).
2. Knowledge management practices
The knowledge management practice (KMP) refers to the process of dealing with
developing the information and expertise to an organization, safe storing of the information,
retrieval and transferring and disseminating to the required departments and people this process
helps the organization in supporting the business performances. For the fact that knowledge is a
useful and imperative resource for any organization, therefore it must be sustained and
judiciously managed. The important factor behind KMP us is to stay competitive in the market as
well as being innovative. In order to make it work at each level in the organization requires a
cultural shift and commitment from each level. Through efficient and effective knowledge
management the organizations can fetch collectively the wisdom and knowledge in order to curb
any issue, at any given point of time (Hislop, Bosua, and Helms, 2018). It is the practice
undertaken to promote the innovation and creation in the undertaken business activities and also
strengthen the business outcomes (Cvitanovic, et al. 2015). There should be proper coordination
among the employee and proper code of conduct is required to set up proper promotional plans.

3. The strategic importance of knowledge management practices with
reference to Tacit and Explicit knowledge in helping organizations
to achieve their long term objectives‐
The Strategic importance of knowledge management with ref to Explicit knowledge in helping
the organizations to achieve long term objective
In order to achieve long term objectives it is imperative for the organizations to apply the
knowledge management tools and techniques and also to use both tacit and explicit knowledge.
Each organization, however, has a unique way of applying the knowledge using varied
perspectives to view to analyze the problems and probable solutions. The manner in which the
organizations can use the tacit and explicit knowledge are categorized in the following ways
(Rowley & Fullwood, 2017). Tactic Knowledge has a practical approach towards decision
making, having a know how practice before based upon the personal and professional
experiences and intuitions. On the other hand, Explicit knowledge is based upon academics and
is a more formal and print language having a documented approach towards decision making.
Park, Vertinsky & Becerra, 2015).
Tacit Explicit
Work
practice
1. Impulsive
2. Improvised
3. Unpredictable environment
4. Channels individual
expertise,
1. Organized tasks
2. Routine
3. Predictable environment,
4. Create knowledge objects
Learn
1. Team leaders supervise and
facilitates an open
environment.
2. More sharing of knowledge
amongst the employees
1. Learning is on the job and by
trial and error approach
2. Objectives and goals are set by
the organization
Teach 1. Teaching is generally one on
one through mentors.
2. Coaching, brainstorming and
apprenticeships, are other
1. Trainers may be outsourced or in
house, but the course content is
well specified by the
organization.

methods used. 2. There are set formats for training
Motivation
1. Leadership inspiration
2. Personal connection with the
employees
1. Motivation is based upon the
requirement as an when it
generates to meet any specific
given target or goal
Reward
2. The rewards are non
monetary and intrinsic.
3. Creativity and innovation in
performance are rewarded
4. There is a reward for
information sharing
1. The rewards are tied straight to
goals and targets
2. There is internal competition to
achieve more
3. The rewards are scarce,
therefore, the employees need to
fight to achieve them there may
or may not be a reward for
innovation and information
sharing
Relationships
1. Employees and employers
share a friendly and open
relationship
2. The relationship is based
upon instant knowledge
sharing
1. The relationship is also
structured
2. Follow a top down approach
3. There are supervisors above
team leaders and then team
members
The knowledge management practices are important both in terms of tacit and explicit
knowledge in order to achieve long term organizational objectives, the tacit however, has a more
informal approach which is required to keep the working environment friendly and conducive,
open for everyone to discuss upon matters leading to goal achievement, which permits the
employees to align their goals to the organizational goals which in turn motivates them to work
more to achieve more. On the other hand it has been seen that explicit knowledge is more formal

and structured which helps an organization achieve uniformity, discipline and structured
approach (Avasthi, Dey, Jain, & Mishra, 2015).
4. AI based technologies
A technology which has the capability to enhance the human capacity and potential to
complete the task with high precision and accuracy is Artificial Intelligence. This technology
also helps the business to work efficiently and effectively. However, AI is an old concept, but
continuous, sustainable and recent developments in the field of IT has helped AI to become more
competent. The examples of recent developments include, Cloud computing, and machine
learning algorithms, the widely used technology is voice recognition in iOS, called Siri. This has
been helping out business owners to streamline their work, especially in the manufacturing
industry, where the amount of errors is too high to manage. It has also helped business to grow
their returns and share in the market. According to the Manufacturer’s Annual Report, 2018 the
92% executives have a notion that their productivity and empowerment will be enhanced by
employing Artificial Intelligence. According to Boston Consulting Group the ratio between firms
not employing AI to employing AI is 5:1 therefore, there lies a gap in aspiration and execution.
In a similar report by the Global research firm states that out of 58% business who inquire
regarding the AI processes and implementation only 12% turn up with an intention to actually
implementing and using the service (Manufacturer, 2018). This has not only set up automation in
the business process but also helps in increasing the business outcomes (Cvitanovic, et al. 2015).
5. Impact of AI based technologies on operational excellence of
manufacturing process
Though expensive, it has been found that the AI is capable of making informed decisions
by working closely at each stage of the manufacturing process. The impact of AI in the
manufacturing industry to improve the efficiency and effectiveness is as follows. It will also help
in creating the core competency in the business outcomes. This has developed the operational
excellence of manufacturing process by adding more advancement in the functional acts and
technologies system.
In the manufacturing unit, there are certain sensors being installed where the production
of the products is being done. The data, however, will be sent for verification to the cloud. With

this any defective part of the production line will be separated out, either to create another
product, add as colors to another product. This immediate on spot removal and correction saves
the time and cost of the manufacturers in recalling and repairing the entire finished products
(Verghese, Shah, and Harrington, 2018). By using the more accurate data and advancement in
the technologies, company could easily determine the minimum order quantity, economic order
quantity and also helps in lower down the capital blockage in its production process.
6. AI-Eye
It is difficult for a person to stand in one place looking for the issues and flaws in the
product, remove the same almost immediately as soon the faulted product is observed. The high
resolution camera used in the Artificial Intelligence is highly sensitive to the programming
made in the AI (Cvitanovic, et al. 2015). This will set up automation in the process and also cut
down the costing involved due to the high employee indulged in the process.
A Silicon Valley veteran, Andrew Neg has successfully developed a tool which can be
used through vision. This helps in identifying defects which otherwise is not possible, the
machines provide an alert to the manager, the manager can either manually remove the product
or it can automatically send it to the other unit to process for another product. The automated
issue identification feature Provides an alert (Qin, Liu, and Grosvenor, 2016). This example has
given how issues and discrepancies in the product process could impact the undertaken work of
the process (Cvitanovic, et al. 2015).
7. Predictive maintenance
The machines automatically report any fault in their system and update on their current
situation on minute to minute to minute basis, in order to remove any machine fault as soon as
possible to avoid any possible faults in the products due to poor machine functioning. By using
the advance technologies and system process of AI, it could lower down the discrepancies and
issues in the process. This method helps save time and cost which also involve hefty fixed costs
such as labor wages. This process guarantees the efficient and effective manufacturing of the
products. The other features would include, digital twins, the sensors being used and embedded
analytics (Stock, and Seliger, 2016).

8. Generative design
The manufacturers use Artificial Intelligence using a new technology called Generative
Designs. In this technology, the organizational hired designers, along with the engineers create
and input design goals in the software. The designers also input the material to be used, the size,
shape and color of the same, the methods of manufacturing the product and the organizational
cost constraint. The software is designed in a manner to calculate all the feasible variations of the
required solution. The software generates the possible and feasible designs to the user. Such
Generative design software using Artificial Intelligence to sort the issue and help reaching the
decision making process is also used by IBM for Asset management. This process permits the
users to communicate with the software in order to make required and necessary changes in
order to identify and solve problems and smoothens the decision making process. It has been
identified that after using this the reduction in defect rate has gone down to 48% (Francalanza,
Fenech, and Cutajar, 2018).
Conclusion
After assessing all the details and information related to knowledge management practices, it
could be inferred that if business organization uses artificial intelligence practice in its business
process then it will not only strengthen the business outcomes but also increase the overall
outcomes of the organization. this has also given other main features such as, digital twins, the
sensors being used and embedded analytics which would be more beneficial for organization.

9. References
Avasthi, V., Dey, S., Jain, K. K., & Mishra, R. (2015, October). The evolution of knowledge in
communities of practice. In Proceedings of the 2015 Conference on research in adaptive
and convergent systems (pp. 96-101). ACM.
Francalanza, E., Fenech, A., and Cutajar, P. (2018). Generative design in the development of a
robotic manipulator. Procedia CIRP, 67(1), 244-249.
Hislop, D., Bosua, R., and Helms, R. (2018). Knowledge management in organizations: A
critical introduction.Australia: Oxford University Press.
John. H., (2018) Adopt or Die: AI Leaves Manufacturing No Choice [Online], Retirved from
https://www.industryweek.com/technology-and-iiot/adopt-or-die-ai-leaves-
manufacturing-no-choice
Manufacturer., (2018) The power of Artificial Intelligence in manufacturing [Online], Retirved
from https://www.themanufacturer.com/articles/power-artificial-intelligence-
manufacturing/
Park, C., Vertinsky, I., & Becerra, M. (2015). Transfers of tacit vs. explicit knowledge and
performance in international joint ventures: The role of age. International Business
Review, 24(1), 89-101.
Price, S., and Flach, P. A. (2017). Computational support for academic peer review: A
perspective from artificial intelligence. Communications of the ACM, 60(3), 70-79.
Qin, J., Liu, Y., and Grosvenor, R. (2016). A categorical framework of manufacturing for
industry 4.0 and beyond. Procedia Cirp, 52, 173-178.
Rowley, J., & Fullwood, R. (2017, September). Knowledge Sharing in Context: The Case of
Volunteer Development at a Heritage Site. In European Conference on Knowledge
Management (pp. 856-862). Academic Conferences International Limited.
Stock, T., and Seliger, G. (2016). Opportunities of sustainable manufacturing in industry
4.0. Procedia Cirp, 40, 536-541.

Verghese, A., Shah, N. H., and Harrington, R. A. (2018). What this computer needs is a
physician: humanism and artificial intelligence. Jama, 319(1), 19-20.