This four-part series looks at what recent fatal Boeing 737 MAX aircraft crashes have in common with the Toyota and Takata automotive recall scandals, and proposes a solution.
In the first article of this series, I look at medicine’s aspirations to be more like aviation in how it manages the serious problems that are arising at the complex interface between people and technology. However, as I revealed, the shocking fatal crashes of new Boeing 737 MAX aircraft show that aviation really isn’t the role model that medicine thought it was.
Boeing failed to achieve the right balance between providing too much and too little information to aircrew in relation to the new Maneuvering Characteristics Augmentation System (MCAS) that has been implicated in the 737 MAX crashes. Further, Boeing failed to respond to the criticisms of pilots who pointed out that it hadn’t got the balance right.
- Boeing’s KM program is internally focused, to the neglect of external knowledge flows between Boeing and the users of its products, being the many pilots in the many airlines around the world
- Boeing’s KM program lacks mechanisms to adequately deal with the complexity that occurs at the interface between its products and the users of its products
Then in the third article, I reveal that unfortunately it’s not just aviation and medicine that are experiencing life-threatening problems at the complex interface between technology and people. There are disturbing parallels between the situation with Boeing and two of the most notorious automotive industry recalls – the Toyota sudden unintended acceleration recalls and the Takata airbag recall.
This is despite Toyota having pioneered The Toyota Way, a very famous quality framework built on two pillars: continuous improvement and respect for people. The Toyota Way is often praised by knowledge managers, but as is the case with Boeing’s KM program, The Toyota Way is internally focused to the neglect of the complex external environment and Toyota’s interface with that environment.
Also in the third article of the series, I alert that cognitive biases are the reason why the circumstances of the safety failures of Boeing, Toyota, and Takata are so remarkably similar. Cognitive biases impact directly on the complex interface of technology and people. This means that internally-focused linear quality improvement and KM programs like those of Boeing, Toyota, and Takata can only ever be of the limited success.
An issue reported in the national media in Australia since the publication of the third article confirms this conclusion. As I reported in the third article, Toyota CEO Akio Toyoda had acknowledged the company’s neglect of external knowledge flows, stating in testimony that “what we lacked was the customers’ perspective. To make improvements on this, we will … devise a system in which customers’ voices around the world will reach our management in a timely manner.”
But this clearly hasn’t happened. As early as 2017, the Australian owners of Toyota Hilux, Fortuner and Prado vehicles have been reporting problems with faulty diesel particulate filters (DPFs). After receiving numerous reports, Berrima Diesel, a repair garage located south west of Sydney, began to discuss the problems on their Facebook page. However, rather than respond to “customers’ voices” as promised, Toyota’s response was to threaten Berrima Diesel with a Supreme Court injunction. This action looks very much like the knee-jerk reaction of managers who are fearful of having a public scandal damage their reputation or that of the Toyota brand – such reactions are the result of cognitive biases.
Bannister Law has since initiated a class action against Toyota on behalf of affected owners. The class action includes the claims that “certain models of Toyota motor vehicle … fail to comply with the statutory guarantee as to acceptable quality provided under the Australian Consumer Law and Toyota Australia has engaged in conduct that was misleading or deceptive and, in the circumstances, unconscionable.” So much for the pillars of continuous improvement and respect for people in The Toyota Way.
Also in the time since the publication of the third article in the series, reports have emerged that concerns in regard to the safety of 737 MAX had been expressed within Boeing for some time, but production pressures meant that these concerns did not result in appropriate action being taken.
An exchange of text messages in 2016 reveals that a senior Boeing test pilot had described the MCAS as “running rampant,” but the same pilot had also lobbied the Federal Aviation Administration (FAA) to remove mention of the MCAS from the operating manual and pilot training as a cost-cutting measure. A former Boeing manager has also revealed that he advised the senior management of Boeing to shut down the 737 MAX factory because of what he describes as the unsafe chaos caused by production pressures, but his pleas were rejected. Yet another former Boeing manager has asserted that what he describes as Boeing’s focus on “putting profits above quality and safety” has also caused safety issues in another of Boeing’s newest aircraft, the 787 Dreamliner.
In all of these cases, production pressures caused people to play down the safety risks in their minds – such thinking is the result of cognitive biases. Boeing has sacked its CEO Dennis Muilenburg in an attempt to restore confidence in the embattled aircraft maker, but this does nothing to address the real problem. If Boeing continues with the same approaches to KM and quality improvement, then cognitive biases will continue to cause chaos.
However, a different approach to KM offers a pathway forward, and this is the focus of this fourth and final article in the series.
Complex adaptive systems and community engagement
Reinforcing my conclusions, this particular study is not supportive of the idea that medicine can learn lessons from aviation, stating that “The decision-making processes of a clinician … are perhaps more akin to those of a firefighter brigade commander than to the pilot of an airliner.” However, the way forward proposed in the study can be applied to both medicine and aviation, and also to other industries where similar problems are occurring such as the automotive industry.
The study authors advise that:
In our view, errors are the product of cognitive activity in human adaptation to complex physical, social, and cultural environments. How well the design of HIT [health information technology] complements its intended setting and purpose is critically important for safe and effective performance …
We believe that the most suitable approach to error management is to develop adaptive systems that anticipate errors, respond to them, or allow intervention before an adverse event results.
The idea of complex adaptive systems that allow us to anticipate errors and respond to them before cognitive activity leads to an adverse event is further explored by Dave Snowden in the following TEDx University of Nicosia presentation. I introduced Dave Snowden’s landmark work3 on understanding and responding to complexity in the second article of this series.
As Dave Snowden states in the video, there’s a third type of system beyond the ordered systems that we attempt to impose on complex environments, and the chaotic systems that often arise when our attempts to impose order fail. These are called complex adaptive systems. He also alerts to what we need to do to understand complex systems:
For all of our history as a species, we’ve contrasted two types of system – ordered systems and chaotic systems … We need to move away from a primitive dichotomy in which we contrast one highly structured system with an absolutely chaotic system, into something more sophisticated. Now there is actually a third type of system which exists in nature – it’s a complex adaptive system. It’s a system defined not by its structure, but by its connectivity. In a complex system, everything is connected with everything else, but many of the connections can not be known … understanding these and understanding how we manage them is critical, and it’s not about control, it’s about understanding the connections, about changing the linkages …
You can only understand a complex system by understanding the small particular parts of day to day interaction. For humans, those are the anecdotal data of the school gate, the street story, the beer after work. They’re not the grand narratives of workshops, it’s the day to day anecdotes of people’s existence. We need to understand them through the voice of the people that tell them …
In order to do that we have to engage people.
Dave Snowden further expands on this understanding in a July 2019 call with the SIKM Leaders Community titled “Let’s start to manage knowledge, not information.”
He argues in the call that the empirical approaches commonly used in KM, for example retrospectives and lessons learned, can’t adequately or appropriately respond to complexity because of the contextual nature of knowledge. In response, he argues that people need to be engaged in natural sensemaking that is based on the following seven principles:
- Knowledge can only be volunteered, it cannot be conscripted
- We only know what we know when we need to know it
- In the context of real need few people will withhold their knowledge
- Everything is fragmented
- Tolerated failure imprints learning better than success
- The way we know things is infrequently the same as the way we report we know things
- We always know more than we can say, and we always say more than we can write down.
I recommend both watching the video above and listening to the call recording before continuing to read this article.
The case study of Xiaomi and a growing research base
Dave Snowden provides case study examples of the application of natural sensemaking in both the video and call recording.
However, one of the most significant global examples of this approach was brought to my attention by one of the PhD students in my KM & Innovation class4 at Shanxi University in China. This is the iterative innovation approach practised by Chinese electronics maker Xiaomi. As I reported in a previous research summary5, Xiaomi engages directly with its customer support base to listen to and directly respond to the problems this large and growing community identifies. Because the iterative cycles are rapid, direct, and public, cognitive biases and the potential for dark side KM tactics are effectively neutralised.
As well as being a notable example of natural sensemaking, the cyclical aspect of Xiaomi’s approach is an example of the application of yin-yang thinking in dealing with unknown unknowns:
Yin-yang is reflected in the Chinese way of thinking that is characterized by a non-linear worldview, where there is no pre-defined and final goal but patterns are changing, being ‘repeated’ in a circular fashion. This thinking can provide at least three important insights for a better understanding of unknown unknowns.
Xiaomi’s community engagement approach is pretty standard within China, and with relatively minor variations is used by many large Chinese companies. This isn’t surprising because this community-based decision-making approach has a long history of success in China, dating back hundreds of years at least, and also now forms part of the National People’s Congress (NPC) governance structure. As I detailed in a previous feature article6,7, I’ve observed the effectiveness of the local-level village committees under this structure in the megacity of Shenzhen in southern China.
I’ve also participated in and facilitated effective natural sensemaking in Australia, for example in the Ipswich Heritage Program and Sustainable Management of the Helidon Hills Project8. However, as Dave Snowden discusses in his SIKM Leaders Community call (above), empirical approaches to KM still dominate in the Global North.
This means that companies such as Xiaomi should be studied by the global KM community, with the aim of supporting companies like Boeing and Toyota to establish similar communities where the users of their products – pilots and drivers respectively – can be directly engaged in reporting issues in real time and collaborating to develop and implement solutions. We owe it to the 346 people who died in the Boeing 737 MAX crashes and the 89 people who died as a result of sudden unintended acceleration in Toyota vehicles to do this.
This is particularly the case as Xiaomi is the first company to export this community-based decision-making approach globally at a large scale, for example to India where Xiaomi is the mobile phone market leader. It needs to be recognised that what we know as KM originated in a relatively small part of the world9 with generally uniform culture and values. While much of the rest of the world may not have practiced KM as such, it has been successfully managing knowledge for a very long time, and has learnt much in the process. Given this, it would be very wrong to assume that what we know as KM constitutes all there is to know about managing knowledge, or has even found the best ways of doing it.
The growing research base also very much supports the need for the improved use of multi-stakeholder knowledge10,11 in decision-making. For complex decisions, the research shows that stakeholders have an essential role in making complexity evident12 and ensuring that the knowledge needed to resolve complex problems13 is available. The four Overseas Development Institute (ODI) complexity studies that we’ve been republishing as article series also emphatically reinforce this, particularly the taking responsibility for complexity series14.
As I argued in response to the KM standard controversy, even the KM community itself can do better in regard to community engagement.
Header image source: NewsBeezer reports on 4 January 2019 that “Recently, world-class technology leader Siai (Xiaomi) hosted the “Mi Fans Thank You 2018″ event to celebrate the 100,000-member Mi Thailand Community.” © NewsBeezer.
- Alter, S. (2006, January). Goals and tactics on the dark side of knowledge management. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS’06) (Vol. 7, pp. 144a-144a). IEEE. ↩
- Horsky, J., Zhang, J., & Patel, V. L. (2005). To err is not entirely human: complex technology and user cognition. Journal of biomedical informatics, 38(4), 264-266. ↩
- Snowden, D. J., & Boone, M. E. (2007). A leader’s framework for decision making. Harvard Business Review, 85(11), 68. ↩
- Boyes, B. (2018). Educating knowledge managers. Information Professional, March 2018. ↩
- Ortiz, J., Ren, H., Li, K., and Zhang, A. (2019). Construction of Open Innovation Ecology on the Internet: A Case Study of Xiaomi (China) Using Institutional Logic. Sustainability 11(11): 3225. ↩
- Ma, H. (2006). “Villages” in Shenzhen—Persistence and Transformation of an Old Social System in an Emerging Mega City (Doctoral dissertation, Bauhaus University, Weimar, Germany). ↩
- Da Wei David Wang. (2013). Shenzhen’s Urban Villages: Surviving Three Decades of Economic Reform and Urban Expansion (Doctoral dissertation, University of Western Australia). ↩
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- Wang, P., Zhu, F. W., Song, H. Y., Hou, J. H., & Zhang, J. L. (2018). Visualizing the Academic Discipline of Knowledge Management. Sustainability, 10(3), 682. ↩
- Cummings, S., Regeer, B. J., Ho, W. W., & Zweekhorst, M. B. (2013). Proposing a fifth generation of knowledge management for development: investigating convergence between knowledge management for development and transdisciplinary research. Knowledge Management for Development Journal, 9(2), 10-36. ↩
- Cummings, S., Kiwanuka, S., Gillman, H., & Regeer, B. (2018). The future of knowledge brokering, perspectives from a generational framework of knowledge management for international development. Information Development, https://doi.org/10.1177/0266666918800174 ↩
- Bammer, G. (2019). Key issues in co-creation with stakeholders when research problems are complex. Evidence & Policy: A Journal of Research, Debate and Practice, 15(3), 423-435. ↩
- Gagnon, R., Ferreira, B. S., & dos Santos, G. L. (2019). Towards complete knowledge for complex problems resolution. Journal of Applied Learning and Teaching, 2(Sp. Iss. 1), 8-16. ↩
- Jones, H. (2011). Taking responsibility for complexity: How implementation can achieve results in the face of complex problems. Overseas Development Institute (ODI) Working Paper 330. London: ODI. ↩