Monthly Archives: February 2019
Digital twins are emerging new technology that enables companies to solve problems faster and improve operational efficiency. Think about performance effect when every supervisor has world’s best leader as a personal advisor.
Digital twin is a near-real-time digital model of reality that helps optimize business performance. We have created AI powered digital twin that simulates business unit’s performance. It will provide help with human asset management and improving business performance. There are sophisticated algorithmic analyzing techniques that simulate human capital productivity. Digital twin can be in connection with organization ERP-system, collecting various data. We have scientifically approved the algorithms and rules, so it is not an AI-black-box.
“Over time, digital representations of virtually every aspect of our world will be connected dynamically with their real-world counterparts and with one another and infused with AI-based capabilities to enable advanced simulation, operation and analysis” David Cearley, Gartner Research.
Digital twin is continuously evolving digital model of organization system that helps optimizing business performance. It simulates human performance and includes real-life data that improves prediction accuracy. Digital twin models complicated human assets that interact in many ways with organization environment, thus making the outcomes that are difficult to predict by human mind. With the digital twin, the manager can learn by simulating the problems and actions outcomes.
Twin provides near-real-time comprehensive link between physical and digital worlds. Digital twin is a virtual replica of what is actually happening in the organization performance. It knows how the organization performance-system works. There is artificial intelligence assistant that you can ask advice. This AI-assistant will simulate the future using Bellman function and suggest the leadership activities that produce sustainable business value. Digital twin simulates specific complex human assets utilization in order to monitor and evaluate human capital productivity. Simulation may uncover insight into operational inefficiencies, that otherwise would remain unseen.
How does the performance digital twin create measurable business value? With better human assets management there are multitude positive effects on organization performance:
- Improve employee quality of working life as a production parameter
- Enhance change management process undependable of the change in hand
- Reduce sickness leave
- Reduce employee turnover
- Increase effective working time to make more revenue and profit
- Improves customer satisfaction
- Predict work and staff related problems
Besides the business values mentioned above, there are strategic benefits, which create competitive advantages in longer period. We are looking for companies for testing and further developing the emerging new technology of digital twin.
Kesti M. (2019) Architecture of Management Game for Reinforced Deep Learning. In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham
Marr, Bernard (2017). What is digital twin technology – and why is it so important. Forbes Mar 6 2017, https://www.forbes.com/sites/bernardmarr/2017/03/06/what-is-digital-twin-technology-and-why-is-it-so-important/#6ea8eea92e2a
Panetta, Kasey (2017). Gartner Top 10 Strategic Technology Trends for 2018. Gartner October 3, 2017, https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2018/
Parrott, Aaron and Warshaw, Lane (2017). Industry 4.0 and the digital twin. Deloitte University Press. https://www2.deloitte.com/content/dam/Deloitte/cn/Documents/cip/deloitte-cn-cip-industry-4-0-digital-twin-technology-en-171215.pdf
Human mind has certain brain structure for pattern recognition (neocortex). Pattern recognition forms cognitive biases that helps fast decision making in different contexts. It seems that cognitive biases are important because they reduce the brain energy consumption. When mind forms a pattern, it is easy to use it in similar cases intuitively and save thinking energy to more challenging situations. This pattern recognition is hidden and very effective. If we (humans) are in a hurry, we intuitively utilize these hidden patterns in decision-making. However, if the problems are complex the intuition may lead to wrong decision (1)(2)(3).
Here are nine leadership related cognitive biases that prevent organization performance
- Observation selection bias
Leader’s focus is on operative scorecards rather than listening to opinions on how to achieve the targets.
- False causality bias
Leader thinks that maximum result (profit) comes by maximizing working-time. In reality, the maximum result is more likely to come from improved work motivation.
- Confirmation bias
Executives give reward to supervisors from short-term results, however the long-term profit is sacrificed.
- Current moment bias
Leader thinks that his own work tasks are currently more important than spending time with the team.
- Self-enhancement bias
Leader assumes to be better leader than average, thus not seeing the development needs.
- Plunging-in bias
Leader makes conclusions too fast and gives solutions to wrong problems.
- Fundamental attribution bias
Leader thinks that the reason for poor performance is in team members or unrealistic targets, not leader’s own management.
- Ambiguity bias
Leader does not want to change management style because the result is uncertain.
- Status-Quo bias
Leader wants to stick to the past, because he/she sees change as a threat.
These cognitive leadership biases are human, thus leader is not to blame. However, wise leader can learn to avoid these harmful biases in workplace decision-making. In addition, there are new emerging science for solving this wicked performance problem. Shortly, new solutions include three methods:
- Problem: Supervisors have too much trivial operative workload. Solution: Arrange more time for collaborative leadership practices utilizing Robotics Process Automation.
- Problem: Operative scorecards takes leaders’ focus from people management. Solution: Measure QWL-index and make it important as a human performance scorecard. Start measuring QWL-index continuously (Robotic-QWL measurement), and this way gamify leadership development.
- Problem: Traditional leadership trainings do not eliminate harmful biases. Solution: Utilize Artificial Intelligence powered simulation for experience-based learning for eliminating harmful biases. (4)(5)
Improving organization leadership quality is not easy, thus it forms competitive advantage for those who master it. Economic benefits are substantial. We are looking for organizations to participate in our research and to test these emerging new solutions.
- Kurzweil, R. (2013). How to Create a Mind: The Secret of Human Thought Revealed. New York, NY, USA: Penguin Books.
- Kahneman, D.; Tversky, A. (1972). “Subjective probability: A judgment of representativeness”(PDF). Cognitive Psychology. 3 (3): 430–454.
- Kahneman, D (2011). Thinking, Fast and Slow. Penguin Books, UK.
- Kesti M. (2019) Architecture of Management Game for Reinforced Deep Learning. In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham.
- Kesti M., Leinonen J. and Kesti T. (2017). “The Productive Leadership Game: From Theory to Game-Based Learning.” Public Sector Entrepreneurship and the Integration of Innovative Business Models. IGI Global, 2017. 238-260.