Information Superiority in Ukraine

In my work on Data Strategy, I have drawn heavily on the concept of information superiority / information advantage, which was originally developed in a military / defence context. Like many other innovations, there is significant potential for peaceful / civilian use of this concept.

There are now some early hints of information advantage / disadvantage emerging from the war in Ukraine. One obvious area is Information, Surveillance, Target Acquisition and Reconnaisance (ISTAR).

By utilizing its intelligence and surveillance assets in Eastern Europe, the United States was able to build a picture of Russia’s movements and strategically release information about Russia’s plans. US source quoted in Insinna

Another area where information is important is logistics. As already predicted before the invasion started (for example Vershinin), Russia's troops appear to have suffered significant problems in this area, at least initially.

In other areas, however, predictive computer models of the conflict seem to have been way off the mark, mainly because they failed to adequately represent the human factor.

John Naughton wonders why Russia appears to have abandoned the Gerasimov doctrine of new generation (nonlinear) warfare, which appears to have been executed successfully in the occupation of Crimea in 2014, and reverted to an older style of warfare. Ukraine, on the other hand, appears to have applied the Gerasimov doctrine more successfully, protecting against cyber attack while easily managing to intercept insecure Russian communications. By a strange historical irony, it is reported that one of the Russian generals killed after his geolocation was intercepted by the Ukranians was called Vitaly Gerasimov. Wikipedia advises us not to confuse him with Valery Gerasimov, the author of the doctrine.

No doubt there will be more clues emerging from this horrible conflict.

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Valerie Insinna, Top American generals on three key lessons learned from Ukraine (Breaking Defense, 11 March 2022) 

Martin Murphy, Understanding Russia’s Concept for Total War in Europe (Heritage Foundation, 12  September 2016)

John Naughton, Putin has a 21st-century digital battle plan, so why is he fighting like it’s 1939? (Guardian, 26 March 2022)

Dan Sabbagh, Russia solving logistics problems and could attack Kyiv within days – experts (Guardian, 8 March 2022)

Alex Vershinin, Feeding the Bear: A Closer Look at Russian Army Logistics and the Fait Accompli (War on the Rocks, 23 November 2021)

Wikipedia: ISTAR, New Generation Warfare, Valery Gerasimov, Vitaly Gerasimov

Previous posts on Information Superiority: Information Superiority and Customer Centricity (March 2017), Developing Data Strategy (December 2019), Information Advantage (not necessarily) in Air and Space (July 2020)

Automated Tetris

Following complaints that Amazon sometimes uses excessively large boxes for packing small items, the following claim appeared on Reddit.

"Amazon uses a complicated software system to determine the box size that should be used based on what else is going in the same truck and the exact size of the cargo bay. It is playing automated Tetris with the packages. Sometimes it will select a larger box because there is nothing else that needs to go out on that specific truck, and by making it bigger, it is using up the remaining space so items don't slide around and break. This actually minimizes waste and is on the whole a greener system. Even if for some individual item it looks weird. It's optimizing for the whole, not the individual." [source: Reddit via @alexsavinme]

Attached to the claim is a link to @willknight's 2015 article about Amazon's robotic warehouses. The article mentions the packing problem but doesn't mention the variation of box sizes.

The claim quickly led to vigorous debate, both on Reddit and on Twitter. Here are a selection of the argument and counter-arguments.

• Suggesting that the Reddit claim was based on a misreading of the MIT article.

• Asserting that people working in warehouses (Amazon and other) were unaware of such an algorithm. (As if this were relevant evidence.)

• Evidence that equally sophisticated algorithms are in use at other retailers and logistics companies. (Together with an assumption that if others have them, Amazon must definitely have them.)

• Evidence that some operational inefficiencies exist at Amazon and elsewhere. (What, isn't Amazon perfectly optimized yet?)

• Providing evidence that computer systems would not always recommend the smallest possible box. For example, this comment: "At Target the systems would suggest a size but we could literally use whatever we wanted to. I constantly put stuff in smaller boxes because it just made so much more sense." (Furthermore, the humans being able to frustrate the intentions of the software.)

• Suggesting that errors in box sizes are sometimes caused by mix-up of units - one item going in a box large enough for a dozen.

• Pointing out that the solution described above would only work for transport between warehouses (where the vehicle is full for the whole trip) but wouldn't work for "last mile" delivery runs (where the vehicle becomes progressively more empty during the trip).

• Pointing out that the "last mile" is the most inefficient part of the journey. (But this doesn't stop retailers looking for efficiency savings earlier in the journey.)

• Pointing out that there were more efficient solutions for preventing packages shifting in transit - for example, inflatable bags.

• Pointing out that an overlarge box merely displaces the problem - the item can be damaged by sliding around inside the box.

• Complaining about the ethics, employment policies and environmental awareness of Amazon.

• Denigrating the intelligence and diligence of the workers in the Amazon warehouse. (Lazy? Really?)

Some people have complained that as the claim is evidently false, it counts as fake news and should be deleted. But it is certainly true that retailers and logistics companies are constantly thinking about ways of reducing packaging and waste, and there are several interesting contributions to the debate, even if some of the details may not quite work.

It's also worth noting that the claim is written in a highly plausible style - that's just how people in that world would talk. So maybe someone has come across a proposal or pilot or patent application along these lines, even if this exact solution was never fully implemented.

Some may doubt that such a solution would be "greener on the whole". But any solution architect should get the principle of "optimizing for the whole, not the individual". (Not always so easy in practice, though.) 

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Will Knight, Inside Amazon’s Warehouse, Human-Robot Symbiosis (MIT Technology Review, 7 July 2015)

Wikipedia: Packing Problems

Requisite Collaboration

My blogpost on The Startling Cost of Inefficient Collaboration started from the observation that "too much collaboration can be bad". But how much is too much?

Too Little

Barely Enough

Just Right

More Than Enough

Too Much

We often need to make a judgement about the appropriate quantity and quality of something. Such judgements can be difficult ones, because they need to be sensitive to who, why and where. Even something as basic as the right amount of food will vary between members of the same family and at different times. And there is no simple formula that will tell you whether to spend more time preparing for tomorrow's meeting or to get an early night.


So how much is a good amount of collaboration - just enough but not too much? Obviously this depends on many situational factors including purpose and context. We can talk about requisite collaboration - in other words, the quantity and quality of collaboration that is appropriate for a given situation. And although we should not expect a simple formula, this is nonetheless something we should expect to be able to reason about.


It's also important to remember that the quantity and quality of collaboration is not necessarily under the control of a single stakeholder. People who work in large organizations may be able to initiate some meetings and duck out of others, and may sometimes be able to unplug phones and ignore email (although many people experience guilt or anxiety when they do this), but the actual quantity of collaboration emerges from the interaction with everyone else. The bosses at Lehman Brothers had elaborate routines to prevent ordinary staff talking to them, and some commentators (e.g. @markfidelman What if Peter Drucker Taught Enterprise 2.0?) have suggested this could have contributed to the collapse of the firm, but I'm sure even Lehman bosses couldn't cut themselves off completely from urgent demands from important stakeholders.


So Lehman bosses may sometimes have had more communication with other people than they felt they wanted at the time, but with hindsight even they might admit that they didn't have enough of the right kind of communication. Such a judgement is always relative to a particular stakeholder position - other people such as junior staff or minor customers might complain that Lehman bosses didn't listen to them, but to put this complaint in terms of requisite collaboration would be to imply that Lehman bosses didn't do enough listening to serve their own interests and the interests of the firm as a whole.

In some domains, there is a body of knowledge that may help us to determine the right level of collaboration. For example, John Gattorna uses the concept of requisite collaboration within enterprise supply chains, suggesting that collaboration makes sense only with those who truly want to collaborate. (See John Gattorna, Supply Chain Collaboration, Supply Chain Digest June 2007. See also review of Dynamic Supply Chain Alignment by Jan Husdal.)

In any case, we need some whole-of-context view to determine the requisite level of collaboration, as my friend @tetradian points out. For Lehman Brothers, inadequate collaboration led to inadequate organizational intelligence, which led to organization failure. The organizational intelligence methodology should help us think about the appropriate collaboration levels of different scenarios by working backwards from the consequences.


Related posts: What are Silos Good for? (June 2010), Organizational Intelligence After Drucker (August 2010)