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Submission declined on 13 April 2024 by Taking Out The Trash (talk). This submission is too technical for most readers to understand. Please improve it to make it understandable to non-experts before resubmitting. Declined by Taking Out The Trash 5 months ago. |
Introduction
editThe First Human Touchpoint (FHT) is a business management concept developed by Alessio Sinisi that emphasizes the strategic timing of human intervention in automated processes to maximize efficiency and innovation. The concept has been registered under the intellectual property rights by Sinisi, acknowledging his authorship and theoretical contributions.
Overview
editThe FHT theory posits that the integration of human decisions and actions should occur as late as possible in an automated sequence, thus ensuring that human creativity and judgment are reserved for tasks that require such unique capabilities. This approach is intended to enhance operational efficiency and foster innovation by allowing technology to handle repetitive and predictable tasks, thereby freeing human resources to focus on more complex problems.
Historical Background
editSinisi first proposed the First Human Touchpoint concept in 2024, amidst growing trends towards automation and artificial intelligence in industry[1]. His theory was a response to the inefficiencies he observed in the premature integration of human oversight which often led to bottlenecks and reduced operational speed.
Mathematical Formulation
editThe formulation of the FHT involves a correlation formula presented by Sinisi:
where:
- S represents the success of the business process, measured in terms of output quality, customer satisfaction, or financial gain.
- H denotes the time delay (in hours or relevant units) until the first human intervention.
- A is the efficiency of automated operations prior to human input.
- Q measures the quality or error rate of the process.
- k is a proportionality constant that can be empirically derived.
Theoretical Basis
editThe core idea behind FHT is rooted in the principle of Digital Humanism[2], which seeks to balance technological advancement with human-centric values. This theory advocates for the use of technology to enhance human capabilities rather than replace them.
Example
editConsider an automotive factory that has implemented a new automated system for producing cars. The system is designed to operate autonomously for the first 8 hours of each shift, producing cars at a rate of 100 cars per hour with a 98% defect-free rate. Human intervention is only required for quality checks and adjustments after these 8 hours. Using the FHT formula:
where A (efficiency of automated operations) is 100 cars per hour, Q (quality) is 0,98 and H (time until first human intervention) is 8 hours, the success index S calculates as:
This index value of 12,25 suggests a high level of process efficiency and quality due to the strategic delay in human intervention. This metric helps the factory measure the effectiveness of different shifts and make informed decisions about potential adjustments to the automation setup.
Practical Implications
editBy analyzing such indices, companies can determine the optimal balance between automated and human tasks, ensuring both resources are utilized where they are most effective. For instance, increasing automation in the initial phases might boost the success index if it allows human workers to focus on more critical or complex issues later in the process.
Applications
editFHT has been applied across various sectors:
- In automotive manufacturing[3], automation handles initial production stages with humans intervening only for final quality checks and complex assemblies.
- In e-commerce, algorithms manage most of the order processing while humans handle customer service and quality assurance roles.
Case Studies
edit- Amazon: Extensive automation from order reception to packing, with FHT applied in quality control steps.
- Ferrari: Despite high automation levels, critical finishing processes are performed by humans to ensure premium quality.
Criticism and Controversy
editWhile FHT has been praised for its innovative approach, it has also faced criticism, particularly regarding the heavy initial investment in technology and potential job displacements. Critics argue that the theory may overlook the nuanced needs of different industries and the inherent value of early human engagement.
Future Directions
editFuture research in the FHT framework is directed towards refining the interaction models between humans and machines[4], particularly how AI can further enhance decision-making processes in real-time business environments.
References
edit- ^ Brynjolfsson, Erik; McAfee, Andrew (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W.W. Norton & Company. ISBN 978-0393239355. Retrieved 2024-04-13.
- ^ Werthner, Hannes (2022). Perspectives on Digital Humanism. Springer Nature.
- ^ Tubaro, P.; Casilli, A. A. (2019). "Micro-work, Artificial Intelligence and the Automotive Industry" (PDF). Journal of Industrial and Business Economics. 46 (3): 333–345. doi:10.1007/s40812-019-00121-1.
- ^ Bolton, C.; Machová, V.; Kovacova, M.; Valaskova, K. (2018). "The Power of Human–Machine Collaboration: Artificial Intelligence, Business Automation, and the Smart Economy". Economics, Management, and Financial Markets. 13 (4): 51–56. doi:10.22381/EMFM13420184.
Bibliography
edit- Colvin, Geoff (2015). Humans are Underrated: What High Achievers Know That Brilliant Machines Never Will. New York: Portfolio. ISBN 978-1591847205. Retrieved 2024-04-13.
- Ford, Martin (2015). Rise of the Robots: Technology and the Threat of a Jobless Future. New York: Basic Books. ISBN 978-0465059997. Retrieved 2024-04-13.
- Wright, Scott A.; Schultz, Ainslie E. (November 15, 2015). "The Rising Tide of Artificial Intelligence and Business Automation: Developing an Ethical Framework". Harvard Business Review. Retrieved 2024-04-13.
- Davenport, Thomas H.; Brain, David (June 13, 2018). "Before Automating Your Company's Processes, Find Ways to Improve Them". Harvard Business Review. Retrieved 2024-04-13.
- Pinski, Marc; Tarafdar, Monideepa; Benlian, Alexander (2024-04-09). "Why Executives Can't Get Comfortable with AI". MIT Sloan Management Review. Retrieved 2024-04-13.
- Altman, Elizabeth; Kiron, David; Read, M. Shawn (March 28, 2024). "How AI Changes Your Workforce". MIT Sloan Management Review. Retrieved 2024-04-13.
- Mollick, Ethan (2024-04-02). "Reinventing the Organization for GenAI and LLMS". MIT Sloan Management Review. Retrieved 2024-04-13.
- Murchison, Mike (March 8, 2024). "Do We Need HR for AI Employees?". Forbes. Retrieved 2024-04-13.
- Sjödin, David; Parida, Vinit; Palmié, Maximilian; Wincent, Joakim (June 2, 2021). "How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops". Journal of Business Research. 134: 574–587. doi:10.1016/j.jbusres.2021.05.053. Retrieved 2024-04-13.
- Duranton, Sylvain (2019-09-01). "How humans and AI can work together to create better businesses". TED Talks. Retrieved 2024-04-13.
- Khodabandeh, Shervin (2022-02-01). "Why People and AI Make Good Business Partners". TED Talks. Retrieved 2024-04-13.
- Heyer, Clint (December 3, 2010). "Human-robot interaction and future industrial robotics applications". 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 4749–4754. doi:10.1109/IROS.2010.5651294. ISBN 978-1-4244-6674-0. Retrieved 2024-04-13.
{{cite book}}
:|journal=
ignored (help) - "Big Tech Companies Form New Consortium to Allay Fears of AI Job Takeovers". TechCrunch. Verizon Media. April 4, 2024. Retrieved 2024-04-13.
- Josh Dzieza (June 20, 2023). "AI artificial intelligence data notation labor scale surge Remotasks OpenAI chatbots". The Verge. Vox Media. Retrieved 2024-04-13.
- Alex Singla, Alexander Sukharevsky, Bryce Hall (August 1, 2023). "The State of AI in 2023: Generative AI's Breakout Year". McKinsey & Company. Retrieved 2024-04-13.
{{cite web}}
: CS1 maint: multiple names: authors list (link) - Mendling, J.; Decker, G.; Hull, R.; Reijers, H. A.; Weber, I. (2018). "How do machine learning, robotic process automation, and blockchains affect the human factor in business process management?". Communications of the Association for Information Systems. 43 (1): 19. Retrieved 2024-04-13.
- Volik, Mariya; Kovaleva, Maria (2020). Features of automation of business processes of interaction with customers. pp. 1–6.
- Ter Hofstede, A. H.; Van der Aalst, W. M.; Adams, M.; Russell, N. (2009). Modern Business Process Automation: YAWL and its Support Environment. Springer Science & Business Media.
- Pušnik, M.; Jurič, M. B.; Rozman, I. (2002). "Evaluation of technologies for business process automation". Informatica. 26: 373–380.
- Casati, F.; Shan, M. C. (2000). Process automation as the foundation for e-business. pp. 688–691.
- "Detailed Documentation for the Concept of First Human Touchpoint". European Copyright Office. Copyright.eu. April 13, 2024. Retrieved 2024-04-13.
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