THE ROLE OF AI IN THERMOFORMING OPERATIONS
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Table of Contents
Investigation of approach to the management of AI-based projects and justification of Success 5
Impact of Superschedular on Technoform’s operational excellence 7
Issues encountered by Technoform regarding existing data in Nicim Database with their impact on operations
The provided case study displaying the introduction of AI-based systems in SMEs in association with Technoform Srl depicts certain issues of the Nictim Database. The company is one of the most notable companies in the thermoforming industry that aims to include AI systems to transform their operations. The original database comprises “Historical production data”, “process parameters”, “material specifications” and other necessary data. The main challenges include the following as mentioned below:
Data quality and maintenance: One of the main hurdles encountered by Technoform is the quality of data collected in the NICIM database. The company, during its operations and thermoforming methods, generates a number of essential information and other data (Laudon & Laudon, 2021). However, sometimes the data may be incorrect or flawed. In this case, the implementation of an AI-based system can face issues. Incorrect data can lead to the formulation of incorrect predictions. It leads to obstructing the thermoforming process.
Data accessibility: There are a lot of steps included in the thermoforming process. The challenge lies in the integration of data from various phases so that it can be made available in an accessible format (Won et al. 2023). It is quite difficult to integrate data from different types of design and material sourcing so that a proper understanding can be developed.
Standardization: One of the major issues that the company in question has to encounter is that data is collected utilizing different methods, procedures, and formats, However, AI implementation requires one standard data collection method. Therefore, it is a great hurdle for the company Technoform Srl.
Data privacy: There is another major issue faced by the company. It is regarding data privacy in the NICIM database (Technoformsrl, 2023). The implementation of an AI-based system means that the existing personal and sensitive data have to be shared with external partners. Therefore, there is the threat of data breaches.
In addition, there are also other issues such as inconsistency between data, missing data, disorganized management, and analyzing the same. These above-mentioned hurdles greatly affect the operational efficiency of the mentioned company. The impacts are elucidated below as follows:
Issues in quality control: The placing and utilization of inaccurate data in the thermoforming process, can create huge disruptions. It can enhance the “rejection rates” and increase production time. Therefore, there is more inefficiency regarding The company’s operational performance.
Resource utilization: Inadequate data can create great obstructions for the company to view the current resource allocation process and the means to improve the same in the future (Crockett et al. 2021). Therefore incorrect data gathered can decrease the overall efficiency of the “AI-based systems”.
Additional costs: Ineffective processes because of the failure of AI models can create certain disadvantages. It leads to additional operational costs (Market outlook, 2023). This is because there is an increase in material waste and extended production time. The company in question, therefore has to bear additional and unrequired costs, that can otherwise be avoided.
Competition: While effective utilization of AI models can bring better innovation and growth, the lack of the same in the NICIM database leads to a loss of the competitive edge (Hansen & Bøgh, 2021). Therefore, the company Technoform has become less competitive because of the issues faced.
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Evaluation of different factors used by the company and their benefits in implementing AI-based project
There are a number of factors that are considered by the company in the topic so that the implementation and integration of AI systems can be successful. All these ensure that the data collected is standard and complete. It is also ensured that the data collected is qualitative so that the company can materialize its requirements through AI models. These considerations include the following as discussed below:
Data development and quality maintenance: Technoform must make sure that the NICIM database is made accurate, prepared effectively, and revised before it is added to the AI algorithms. It ensures that the data is consistent and that proper quality is upheld (Bunte, Richter & Diovisalvi, 2021). It also ensures that no missing values and other types of inconsistencies are present.
Proper expertise: For the adequate development and positive utilization of AI tools, proper expertise must be developed. For this purpose, the existing workforce must be trained and developed. To maintain AI-based projects, people must be properly hired and educated.
Infrastructure suitability: Another consideration to be made while implementing AI-based models includes the viewing of whether the current infrastructure is adequate for supporting AI. Since AI requires strong computing resources for processing and modeling data, therefore, such considerations are extremely crucial for the company. Therefore, it is one of the main factors that the company views.
Data privacy: Since the company deals with the personal information and private data of the clients, therefore, data security must be maintained effectively (Meskó & Görög, 2020). The company in question must develop proper data-securing protocols and safeguards so that there are no breaches in personal data.
Change management: Another consideration of the factors that must be made while implementing AI is that it develops a number of changes. Not all the employees are comfortable with the change. Therefore, the implementation and its advantages must be spread effectively through proper communication.
Integration with the current systems: The AI tools must be utilized in such a manner so that they can provide better advantages to the existing database and operation. Therefore, considerations must be made on how the existing systems respond to the implementation of the AI tools. It must be considered so that an optimal flow can be developed. It results in fewer disruptions and a more streamlined workflow. Therefore, the operational hurdles can be effectively overcome.
The company in question, Technoform Srl also realizes different advantages associated with the implementation and proper utilization of the AI tools (Fosso Wamba et al. 2022). Some of the benefits of implementation of the AI tools in the company are elucidated below as follows:
More efficiency: The implementation of AI tools aids in bringing automation to different tasks. The data which had to be manually collected, as viewed in the case study. It enables the tasks to be more streamlined and with better efficiency. It leads to better and more optimization of resource allocation. Therefore, the production costs are reduced.
Better decision fabrication: AI tools can prepare proper forecasts based on the information provided to them. Therefore, it aids in making forecasts and assumptions. It makes business operations smoother and brings better innovation as well.
Better enhancement of quality: Different AI tools are able to analyze and scrutinize production data to understand different factors affecting production. Through the same proper standards can be maintained. It not only helps the business to operate smoothly but also provides a competitive edge and better customer satisfaction.
Better allocation of different resources: AI and tools associated with it can enable the proper allocation of resources by comparing and placing resources where they are required the most. Therefore, the company is able to optimize its resource use. In addition, the energy and machine time can also be improved. It facilitates lesser wastage and better resource use.
More innovation: AI tools also provide the company Technoform with the advantage of adaptability to market alterations (Flavián & Casaló, 2021). It enables the company to be more competitive and understand the current trends and wishes of the customers, Therefore, better product development can be ensured.
“Predictive maintenance”: AI algorithms are able to predict failures associated with machines. Therefore, maintenance costs and needs are effectively understood. It aids in understanding maintenance requirements and downtime. Therefore, there is less “wear and tear”. It enhances the longevity of the different equipment in the company.
Data-based insights: AI tools also provide insights into the current market and the most suitable decisions to be taken. Therefore, it enhances the company’s understanding of the required decisions.
Investigation of an approach to the management of AI-based projects and justification of Success
The implementation of an “AI-based project” at Technoorm Srl must be done in a systematic method so that the same can be made relevant to the goals, capabilities, and features of their “thermoforming production methods”. The selected approach as viewed here, includes several steps to ensure successful projects leveraging AI tools in an effective manner. The steps are elucidated below as follows with proper justification:
Step 1: Defining the main objectives and scopes”: The main aim of the company is to optimize the thermoforming process. It is done so that efficiency and quality can be improved (Youtube, 2020). The introduction of a superscheduler can be used with the objective of bringing automation to thermoform production and its different processes.
Step 2: Data collection and development: The data collected through the NICIM process is made ready so that it can be used to provide effective data. It ensures that clean, processed, and accurate data is available for utilization.
Step 3: Selection of the appropriate AI techniques: Based on the necessities of the company, the proper AI techniques are utilized. Based on the current needs, as viewed by the case study, superschedular is the one AI technique used by the company to meet its requirements.
Step 4: Infrastructure assessment: In this step, the already existing infrastructure is examined so that it is viewed if AI can be properly implemented. It supports the computational demands of the company effectively. Based on the same, the current lacks are identified so that they can be effectively eradicated.
Step 5: Data Privacy: The implementation of a superscheduler ensures that data security and privacy are maintained properly. It is because production deals with super sensitive security. Therefore, the same must be maintained.
Step 6: Model development and provision of training: The development of proper models ensures that superschedular is properly developed (Johnson, Stone & Lukaszewski, 2020). In addition, proper training is also provided to the workforce so that all the employees can equally and properly access the same.
Step 7: Integration: It ensures that the AI models are well used along with the existing equipment. It ensures better data flow and compatibility. Therefore, there is no disruption in the overall production process.
Step 8: Change management: Since the implementation of new tools brings changes in the overall organization, therefore, change management must be communicated and implemented properly. It is done so that all the employees can be made aware of the benefits brought by the implementation of AI tools.
Justification
This approach is the most suitable and appropriate for Technoform since the company’s goals and objectives can be easily and effectively achieved with the same. This approach ensures that the objectives are followed, proper data are prepared and privacy is ensured. Moreover, it prioritizes change management and ensures that there are no inaccuracies or any other issues in the data. Moreover, the business objectives ogf the company can also be sure since demand forecasting and optimization are effectively followed. These increase operational efficiency, which was otherwise a problem for the company discussed in the topic. Therefore, these steps are holistic and designed specifically to meet the requirements of the company in a more effective way.
Impact of Superschedular on Technoform’s operational excellence
The Superscheduler is an optimized system that is implemented by Technoform Srl to bring operational efficiency. It greatly aids in enhancing the thermoforming production method leading to better improvement and more resource utilization. The impact on the operational excellence of the company is elucidated below as follows:
Optimized production scheduling”: the superschedular utilizes advanced algorithms so that production schedules can be better optimized. It ensures that production runs smoothly and without any hurdles. There are no delays as well.
Resource utilization: Through better data-based decision development, the superschedular ensures that proper decision fabrication is done after understanding optimal resource utilization. Therefore, there is less downtime and material waste. It also enhances productivity.
Better quality control: Technoform ensures that only proper quality products are designed and provided to the customers. Manufacturing is done in an automated and smooth manner. Therefore there is consistent quality and standard data.
“Data-Based insights”: The superscheduler provides the required and most valuable insights based on the data provided. It allows the company in question to be more competitive and understand the current trends. Therefore a better production decision can be formulated.
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References
Provided materials
Laudon, K. C., & Laudon, J. P. (2021). Management information systems: Managing the digital firm. Pearson Educación.
Market outlook (2023): A look at inflation, stocks, the Fed, housing and more. Retrieved from: https://yhoo.it/2fGu5Bb
Technoformsrl, (2023). Technoform. Retrieved from: https://www.technoformsrl.it/
Youtube, (2020). How the THERMOFORMING PROCESS works? – Factories. Retrieved from: https://www.youtube.com/watch?v=LYfzl8eciG8&t=4s
Journals
Bunte, A., Richter, F., & Diovisalvi, R. (2021, February). Why It is Hard to Find AI in SMEs: A Survey from the Practice and How to Promote It. In ICAART (2) (pp. 614-620).
Crockett, K. A., Gerber, L., Latham, A., & Colyer, E. (2021). Building trustworthy AI solutions: A case for practical solutions for small businesses. IEEE Transactions on Artificial Intelligence.
Flavián, C., & Casaló, L. V. (2021). Artificial intelligence in services: current trends, benefits and challenges. The Service Industries Journal, 41(13-14), 853-859.
Fosso Wamba, S., Queiroz, M. M., Guthrie, C., & Braganza, A. (2022). Industry experiences of artificial intelligence (AI): Benefits and challenges in operations and supply chain management. Production planning & control, 33(16), 1493-1497.
Hansen, E. B., & Bøgh, S. (2021). Artificial intelligence and internet of things in small and medium-sized enterprises: A survey. Journal of Manufacturing Systems, 58, 362-372.
Johnson, R. D., Stone, D. L., & Lukaszewski, K. M. (2020). The benefits of eHRM and AI for talent acquisition. Journal of Tourism Futures, 7(1), 40-52.
Meskó, B., & Görög, M. (2020). A short guide for medical professionals in the era of artificial intelligence. NPJ digital medicine, 3(1), 126.
Won, J., Mendis, C., Emer, J. S., & Amarasinghe, S. (2023, January). WACO: Learning Workload-Aware Co-optimization of the Format and Schedule of a Sparse Tensor Program. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (pp. 920-934).