Improve Decision Quality With Reciprocal Human-Machine Augmentation With Noha Tohamy
Nov 29, 2021
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Supply chain expert Noha Tohamy joins host Thomas O'Connor to discuss improving decision quality with reciprocal human-machine augmentation. They explore strategies such as crowdsourcing and data literacy, and provide real-world examples from Cisco and Western Digital. The podcast highlights the importance of leveraging the combined intelligence of humans and machines for better decision making in supply chain management.
Leveraging crowdsourcing principles that involve humans and machines as equal contributors can enhance decision quality in supply chains by combining domain knowledge and advanced analytics.
Investing in comprehensive data literacy training programs can enable supply chain staff to speak and understand data, thus augmenting decision-making capabilities and improving distribution quality.
Deep dives
Harnessing the Combined Intelligence of Humans and Machines
The podcast episode explores the concept of reciprocal human-machine augmentation and how it can drive better distribution quality in supply chains. Automation and advanced analytics are being widely adopted in supply chains, resulting in a need to capture and preserve human domain knowledge. The key is to develop a framework of reciprocal human-machine augmentation, which involves the mutual sharing of knowledge between humans and machines to enhance decision-making abilities. The goal is to simultaneously capture and improve domain knowledge while upskilling supply chain staff to effectively use data and analytics.
Effective Strategies for Reciprocal Human-Machine Augmentation
One effective strategy highlighted in the episode is crowdsourcing, where humans and machines are treated as equal contributors. Companies can leverage crowdsourcing to collect insights, predictions, and information from a dynamic set of participants. An example from Cisco demonstrates how different forecast streams, combining both human domain knowledge and advanced analytics, can enhance demand forecasting accuracy. The use of gamification further engages planners in the crowdsourcing process, capturing their experience and expertise. Another key action suggested is to invest in comprehensive data literacy training programs to ensure staff can speak and understand data, ultimately augmenting decision-making capabilities.
Investing in Analytics Coaches and Citizen Data Scientists
The podcast recommends companies to invest in analytics coaches who work closely with staff to capture domain knowledge, define analytics problems, and guide decision-making augmentation. These coaches, with expertise in both supply chain and data analytics, play a critical role in driving reciprocal human-machine augmentation. Additionally, organizations are encouraged to develop citizen data scientists among their supply chain staff, enabling them to independently create analytics solutions while relying on these applications to support decision-making. This approach fosters a virtuous cycle of reciprocal augmentation and empowers staff to leverage their own knowledge to enhance supply chain operations.
In this podcast, host Thomas O’Connor and guest Noha Tohamy explore improving decision quality with reciprocal human-machine augmentation. A confluence of forces is eroding supply chain domain knowledge while increasing the reliance on advanced analytics in making supply chain decisions. CSCOs and heads of strategy can adopt innovative approaches to harness the combined intelligence of humans and machines for better decision quality such as:
Crowdsourcing: Extend crowdsourcing principles to include humans and machines as equal contributors. Humans can bring their insights and predictions based on domain knowledge, while machines and algorithms can contribute data and analytics-based insights. Use this combination to improve decision quality.
Data Literacy: Develop staff to understand and speak data and analytics. This will allow humans to better augment machines. By understanding data, humans can augment their decisions with analytics insights and recommendations.
The discussion provides two real-world examples of leveraging these strategies from Cisco and Western Digital.