Organisational Lessons from the COVID-19 Pandemic

Peter Reilly, Principal Associate, Institute for Employment Studies

One of the interesting aspects of the COVID-19 crisis is the extent to which it has highlighted the limitations of the UK’s health service, its structure, organization and management culture.

When we look back on the crisis, which
features of management have been emphasized as important. Here is a personal
selection of points:

Preparation/Foresight

A previous blog emphasized that organizations need to anticipate extreme events if they are reasonably likely to happen. They may not be able to cover all eventualities, but they need at the very least to think through the consequences of such an emergency and draw up a contingency plan. Better still is to have the capacity to respond when the problem strikes. The mistake is to assume that tomorrow will be the same as yesterday.

Epidemiologist, Dr Olivier Restif
captures this point well in relation to the UK government:

‘The irony is that we have been
expecting a pandemic like this for nearly twenty years…. So why didn’t this
knowledge translate into better preparedness? This is yet another example of
short-term political priorities getting in the way of planning for extreme
events’[1].

Modified JIT

Much modern management has rightly
emphasised the cost of warehoused spares or holding reserves of resources, but
the pandemic has also illustrated the risks of not having enough kit, people,
up-to-date technology in place when needed. At the national and local health
level the shortage of masks, ventilators, reagents for testing, etc has cost
lives, as has the shortage of doctors, nurses, care staff and so on.

Post crisis, we should look again at
screwing down organisational (and HR’s own functional) numbers to the maximum
possible. Just as a hospital can’t effectively run with 95% occupancy, so
HR/organisations should have some slack to be able to respond to unexpected
challenges.

Resourcing Flexibility

If that sounds too indulgent, how much
attention has been given to how quickly HR/organizational staff can be acquired/redeployed
in a hurry? The NHS example shows, with varying degrees of success, the way ex-employees
can be called back into work or experts in one field transferred to another
(from say pediatrics to intensive care). How much thought though has been
given to workforce/HR re-deployability? Has consideration been given to the downside
of resource specialization? And this is not just in numerical terms, but also
in terms of lead times – identifying type of need, hiring/finding, training and
deployment. The failure of G4S to understand the challenges of providing
security staff to the London Olympics under time pressure is illustrative of
the financial costs of getting this wrong[2].

The Limitations of Centralization

One of the features of HR and other ‘back
office’ transformation has been the centralization of resources into shared
services centres and centres of expertise, often remote from operational areas.
Only business partners are locally deployed, but in large corporations they
might not be the people on the ground either as they may well sit in business
unit headquarters away from the action.

What has been shown in the UK health
service is that this approach runs several risks: For example, it makes it
harder to redeploy staff to the frontline. These days there may not be such a
need to be physically present, but you do require a feel for the situation on
the ground. This has been especially demonstrated when so many problems with
government responses (and not just in the UK) relate to logistical/operational issues.
Or, if you HQ is hard hit by absence, you don’t have local staff who can easily
slot in to replace them – either because they are not there or because they
don’t have the skills/awareness.

Centre Knows Best

Another trend in multinational companies
linked to the above is to seek economies of scale from the global
standardization of processes based on the corporate centre’s perception of what
‘best’ looks like. There have always been fears about the risks of
ethnocentricity in such an approach. What the crisis has demonstrated is that your
ability to test and try is hampered by all your resources being in one place.
Moreover, the ability to innovate is curtailed if people are not able to pursue
their ideas. The risk of groupthink (allegedly found in UK government’s
response to the pandemic and apparently in the US government’s to a 1976 swine
flu outbreak[3])
is ever present.

Richard Coker, emeritus professor of public
health at the London School of Hygiene and Tropical Medicine, urges us to keep
open minds:

‘All of us involved in attempting to
control Covid-19, which means all of us, should reflect with humility, embrace
those who challenge our assumptions.’[4]

And it is harder to keep
a broad perspective if the generators of data and the same people who review
the data, as has been true of SAGE (the UK government science advisory group)[5].

One modern management dictum is relevant
here: fail fast. Controlled experimentation is a good thing so long as we learn
our lessons and do better at the next opportunity. Regarding testing, the UK
government is persisting with a centralized testing regime even though this did
not work at the outset of the crisis as has been pointed out by the House of
Commons Science and Technology Committee[6]

Modelling and Data

If we did not know it already the
provision, analysis and representation of quality data has been shown to be
vital in understanding a complex issue. Knowing the nature, progress and impact
of the virus has depended on data gathering. It has also shown the difficulties
of interpretation. To give a simple example, the BBC regularly quotes three
death rates for Covid.[7]
Or choosing a more challenging illustration, look at Public Health England’s
struggles with understanding the disproportionate death rates in the black and
minority ethnic community[8].
Indeed, collecting more data earlier and undertaking international benchmarking
have been some of the early criticisms of the government approach.[9]

There have also been disputes about
modelling the data. To those who think that the response to the pandemic is
overblown, they complain that the modelling of the consequences of inaction was
flawed[10].

Commenting on modelling in a Cambridge
University podcast on 14th May[11],
Professor Mike Hulme emphasised their value in aiding policy but also their
limitations in not capturing all the relevant matters that are necessary for
‘good and wise decision making’. Specifically, he warned about the ‘allure’ of
models that seemed to offer accurate predictions to politicians: they are not
‘truth machines’, he said (not least because modellers
often find it hard to ‘retain critical distance from their own creations’)[12]. Just as you
should have many ‘voices’ advising your policy, so you should not rely on a
single explanatory model because it will be ‘partial’ in some way.

As Hume argues, we should acknowledge that
decision makers in the end make their own judgements, hopefully informed by
evidence but the balancing of that evidence is their preserve. And in this regard technical people, like scientists, need to
be aware in presenting their evidence that they are much more used to dealing
with uncertain and complex data than their political counterparts who wish/need
to narrow down their thinking[13].

Communications

A final point to consider is that in crisis
management what people respond to is clear communication of the challenge and
the actions the government/organization will take as a consequence. Especially
in persuading people to do things they do not want to do; they need to be
confident of what the message is.

Hence the argument about the UK
government’s switch from ‘Stay home. Protect the NHS. Save Lives’ to ‘Stay
alert > Control the Virus > Save lives’ since readers were unclear about
what being ‘alert’ meant. There is a similar argument about changing the two metre
distancing regulation: people might get the substance, but might not grasp the
point of the change – what is it signaling?

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