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).
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.
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 methods used. |
1. Trainers may be outsourced or in house, but the course content is well specified by the organization. 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).
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).
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.
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).
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).
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.
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.
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