All Posts in Organizational Effectiveness

|In: Cognitive Enterprise|Last Updated:

What’s the difference between a “Digital Twin” and a simulation? Or a model?

Not much, except maybe Digital Twins have a more robust connection between production data and the simulation’s behavior.

Or, as explained in a worth-your-while-if-you’re-interested-in-the-subject article titled “How to tell the difference between a model and a Digital Twin,” (Louise Wright & Stuart Davidson, SpringerOpen.com¸ 3/11,2020), “… a Digital Twin without a physical twin is a model.”

Which leaves open the question of what to call a modeled or simulated physical thingie.

Anyway, like models, simulations, and, for that matter, data mining, “Digital Twins” can become little more than a more expensive and cumbersome alternative to the Excel-Based Gaslighting (EBG) already practiced in many businesses.

If you aren’t familiar with the term EBG that isn’t surprising as I just made it up. What it is:

Gaslighting is someone trying to persuade you that up is the same as down, black is the same as white, and in is the same as out only smaller. EBG is what politically-oriented managers do when they tweak and twiddle an Excel model’s parameters to “prove” their plan’s business case.

Count on less-than-fully-scrupulous managers fiddling with the data cleansing and filtering built into their Digital Twin’s inputs so it yields the guidance the manager in question’s gut insists is right. Unless you also program digital twins of these managers so you can control their behavior, Digital Twin Gaslighting is just about inevitable.

Not that simulations, models, and/or Digital Twins are bad things. Quite the opposite. As Scott Lee and I point out in The Cognitive Enterprise, “If you can’t model you can’t manage.” Our point: managers can only make rational decisions to the extent they can predict the results of a change to a given business input or parameter. Models and simulations are how to do this. And, I guess, Digital Twins.

But then there’s another, complementary point we made. We called it the “Stay the Same / Change Ratio.” It’s the gap between the time and effort needed to implement a business change to the time the business change will remain relevant.

Digital Twinning is vulnerable to this ratio. If the time needed to program, test (never ignore testing!) and deploy a Digital Twin is longer than the period of time through which its results remain accurate, Digital Twinning will be a net liability.

Building a “Digital Twin,” simulation, or model of any kind is far from instantaneous. The business changes Digital Twinning aspires to help businesses cope with will arrive in a steady stream, starting on the day twin development begins. And the time needed to develop these twins isn’t trivial. As a result, the twin in question will always be a moving target.

How fast it moves, compared to how fast the Digital Twin programming team can dynamically adjust the twin’s specifications, determines whether investing in the Digital Twin is a good idea.

So simulating a wind tunnel makes sense. The physics of wind doesn’t change.

But the behavior of mortgage loan applicants, is, to choose a contrasting example, less stable, not to mention the mortgage product development team’s ongoing goal of creating new types of mortgage, each of which will have to be twinned as well.

Bob’s last word: You might think the strong connection to business data intrinsic to Digital Twinning would protect a twin from becoming obsolete.

But that’s an incomplete view. As Digital Twins are, essentially, software models of physical something-or-others, their data coupling can keep the parameters that drive them accurate.

That’s good so far as it goes. But if what needs updating in the Digital Twin is its logic, all the tight data coupling will give you is a red flag that someone needs to update it.

Which means the budget for building Digital Twins had better include the funds needed to maintain them, not just the funds needed to build them.

Bob’s sales pitch: All good things must come to an end. Whether you think KJR is a good thing or not, it’s coming to an end, too – the final episode will appear December 18th of this year. That’s should give you plenty of time to peruse the Archives to download copies of whatever material you like and might find useful.

On CIO.com’s CIO Survival Guide: “6 ways CIOs sabotage their IT consultant’s success.” The point? It’s up to IT’s leaders to make it possible for the consultants they engage to succeed. If they weren’t serious about the project, why did they sign the contract?

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|In: Leadership|Last Updated:

Effective IT leaders pay attention to four core organizational effectiveness “levers”: Business integration, process maturity, technical architecture, and human performance.

Ranked in order of importance, human performance comes first. Next comes human performance. Human performance comes after that, followed by human performance.

As evidence: Outstanding employees can overcome poor business/IT integration, while even the best-integrated IT organization won’t withstand poorly performing employees.

Top-notch employees can also overcome badly designed and implemented processes. The reverse is not true: No matter how good your process designs and management are, inept employees will cause them to fail.

The best technical architecture can, perhaps, limit the damage incompetent employees can wreak, but even that weak outcome is optimistic; meanwhile, “code gods” can overcome technical architecture that’s a complete mess.

No matter what your goals, strategies, hopes and vision, are, when it comes to getting the results you’re responsible for nothing comes close to the importance of how well the humans you’ve recruited, encouraged, coached, retained, and promoted perform.

As a leader and manager, it’s up to you to create an environment that fosters strong performance. Fostering it entails:

Leadership: In this context, leadership includes such techniques as listening (and especially organizational listening), followership, persuasion, and facilitation. Never fear – it also includes setting direction, but in this IT effectiveness model that’s covered under the IT/Business Integration banner.

Staffing and skills management: You need the right people, with the right skills. This entails effective recruiting, and treating employees as well after you’ve recruited them as you do while recruiting them. It also calls for training and education so staff bring the skills you need to the work they do.

Oh, and, by the way, “recruiting” really means “sourcing” – if you need a particular skill in the short term, but expect that need to go away, bringing contractors on board should be part of staffing as well.

Compensation and rewards: Designing compensation so it encourages strong performance and not perniciously embedded dysfunction isn’t easy. Here’s a link to get you started: “Poor Joe,” 10/22/2007.” To put a bow on it, constantly remind yourself of the role money plays in business communication. It isn’t an incentive, or a reward. It’s the company’s loudest voice, explaining what the company values most far more effectively than the best speechifying and executive charisma have to offer.

Organizational structure: The org chart, but not only the org chart. Beyond this are such elements as corporate infrastructure, key performance indicators and other corporate metrics, and accounting systems and what they inhibit or encourage.

Team dynamics: It’s rare for any employee to work in isolation. More often, employees work in teams, which is to say interdependence is the norm. Which is also to say business processes and practices are vulnerable to distrust among the team members who have to make them work.

Culture: We keep coming back to culture, and for good reason. Culture is how we do things around here, making some courses of action implicitly approved and others intrinsically unacceptable. Culture defines the social landscapes within which employees operate.

Beyond this, culture defines affinities and group memberships. In that guise, culture defines which teams are automatically trustworthy and which ones to view with suspicion no matter what they do.

Bob’s last word: The logic in favor of viewing human performance as the most important factor in driving organizational success is compelling. As stated earlier, it’s that great employees can overcome everything else, while poor ones can make failure unavoidable.

There’s been a lot of discussion as to whether generative AI can replace human beings, much of it little more than whistling in the dark. Example: “Artificial intelligence cannot replace human talent and creativity, it can only mimic the human brain.”

Putting on my Captain Obvious hat, if generative AI can mimic the human brain then by definition if can replace what human brains can do.

The better news is something discussed less often – whether generative AI can mimic human initiative. Eventually it will; I’m hoping I won’t be around to see it when it does.

Bob’s sales pitch: Speaking of not being around when it does, it’s time. Looking at the level of correspondence, comments, and declining subscriptions, I’m declaring 2023 to be my victory lap. So if there’s a topic you’d like me to cover in KJR, let me know via the Contact form.

This week on CIO.com’s CIO Survival Guide: “7 IT consultant tricks CIOs should never fall for.” It’s about how many consultants fix what’s broken by breaking what’s fixed, plus 6 other common consulting misdeeds.

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