This is the end February 2015 update to let you know what we are doing, what we are thinking and seeking some help in setting priorities, especially in Big Data.
We need your help on making making two decisions. Please continue reading to help us vote.
For 2015, we set an extremely ambitious agenda for new consulting studies, new TCO-type shows and significant changes to the website. The good news is that we are on track so far with just 17% of the year completed.
Where do we stand:
Executive program studies x 4:
TCO-type shows x 3: On schedule
Website changes x 2: Ahead of schedule
As it stands, of the 9 major initiatives we have planned, we can clearly see at least 7 of them finishing on time while the other two are still possible but we need to make some changes to complete them all.
Fatigue among our executive clients is a big problem. Read one classic example that made it into the press. Horta-Osorio is actually a very capable executive and this did not derail him. Others are not so lucky.
At Firmsconsulting we have a dedicated partner working on leadership/confidence/motivational issues for the executives at the companies where we run our consulting studies. This is a big part of the behind-the-scenes work you never see about our studies. We discuss how we apply this work during the Power Sector Corporate Strategy Study Podcasts where the leadership team is playing a major role in working with the client.
This work is crucial. Most of our executive clients are under extreme pressure to perform. These are the tough conditions of the power sector study, for example.
The company is constantly in the press under a barrage of blinding criticism. The executive team is always in emergency meetings dealing with funding issues, operational problems and discussions with regulators. A typical day for an executive starts at 6am and runs until 11pm.
None of the meetings are pleasant and few care about the executive’s views or well being. Executives are routinely seen as highly paid and therefore should be able to handle the criticism.
The executive team is routinely insulted in the press and there is a real threat of termination. At the same time, while all of this is happening, our corporate strategy team is charting a new course for the company and this adds additional pressure on the leadership team.
They need to put out fires constantly while thinking ahead and planning for the future. That is exhausting to do.
Our leadership partner helps executives deal with the following issues.
The objective is simple.
Even if the study is a success, we would not want the leadership team to become burned out or marginalized through the process. They need to achieve personal career success just as much as the company achieves corporate success.
If you would like to know more about this work we do, please let us know in the comments section.
On our header and footer, we list all the studies we have scheduled for 2015 and 2016. Each time you subscribe for a study we count that as a vote for the study. In other word, how popular is the study. The exception is the Big Data Disease Management study for which we do not, yet, have a page to vote.
Right now, operations is the most popular study and is scheduled to be the 4th and final study of the year.
As it stands, this is the top 4 studies based on your votes:
We have to decide if the operations study is the 4th study we will do this year. To us, the options are not clear cut. We think BTO and Big Data Disease Management can teach more. We will explain each one below and let you decide.
Operations strategy begins by first determining if the corporate strategy is about competing on lower costs OR product differentiation. It cannot be both. Once you know that, as the operations executive, your job is figuring how to tweak the operations to meet that goal while increasing total factor productivity. The key thing is to know the goal. In fact, there are five key steps to follow. Every single operations technique/tool/approach you could possibly follow at any company anywhere in the world fits into this framework.
Any operations executive can increase productivity by lowering input costs while keeping output value constant. However, a luxury brand like Hermes, for example, will have such a high cost base to produce quality products that it must focus on increasing output value at a faster rate than rising input costs. Therefore, simply lowering the input costs raises Hermes’s productivity in the short term, but will decimate the company in the long-term. An operations executive who does not understand Hermes’s corporate strategy could conceivably make this mistake.
This applies to any luxury brand, even a luxury auto company. There are many ways to raise productivity, yet they must always support the primary corporate strategy. In the example below, the executive has multiple paths to raise productivity but may need to give up some productivity gains in the short term to keep the output value consistently high over the long term. In other words, how you achieve the productivity increase is just as important as the increase you achieve.
On the other hand, commodity producers like oil and coal companies must always be lowering costs since they are at the mercy of the lowest cost producer and the market, since low volume producers cannot set the commodity price. Unlike Hermes, a commodity producer must be trying to hack away at costs without lowering volume throughput. This is especially important during bull markets when rising markets will cause prices to rise and productivity to spike even when the costs are rising to unhealthy levels. High prices routinely mask poor operating practices in commodities.
Finally, if you are not the operations executive, but someone working in the operations department, you must understand how your individual work is linked to the effort to either lower costs or raise value, and which lever needs to be pulled to what extent based on the corporate strategy.
In fact, if you are an operations analyst, you can make a clear case for the value you bring by ensuring your work is always helping the operations team enable the corporate strategy.
IT strategy is generally poorly done. IT strategy is like corporate finance or organizational design. The corporate finance plan and organizational design must enable/support the corporate strategy. The same for the IT strategy. We will not publish any of the slides on BTO but there is a far more elegant and analytic way to develop IT strategy than the current approach.
Rather than re-writing an overview of IT strategy, it is best to read this detailed article we published earlier.
This the potential study that I am personally most excited about.We have the opportunity to do something very cutting-edge.
A few weeks back we published a very popular video demonstrating advances in corporate finance now that Big Data can be applied to measure risk and return in far more sophisticated ways.
Healthcare, not finance, is one field where this concept has enormous value. To learn more about this field, read this recent article in the New York Times and the excellent work of David Matheson. Matheson is the now retired BCG senior partner who pioneered a more data intensive way to manage healthcare programs and he basically invented the field of disease management in the 1980s and 1990s.
We think more can be done today, by taking Matheson’s work further. Much, much further.
The link here is that Matheson’s work can be refined and improved by borrowing quite a few concepts from financial analysis, which we just could not do 3 or even 5 years ago due to a lack of computational power.
When I was at the firm I was asked to also lead a group of about 20 PhD’s in physics and mathematics who where trying to solve a particularly important problem in measuring returns. We where successful to some degree but did not have the software now cheaply available to test all our ideas. I will explain the problem in a simple way and then explain the study where we want to apply this technique.
So the proposed study below is the culmination of several years of work and thinking.
Explaining the problem: Let’s assume there is a football team, Liverpool FC. The football team has a defender who by every conventional metric for a defender is pretty lousy. He is not great at stopping the other team from scoring, his tackles are weak and he basically is just hanging onto his role. Now, the manager benches him. The expectation is that the team will perform better if a new defender comes in who has better metrics. However, while the new defender does better, the overall team does worse.
The net effect is that the team loses the game 3 – 2.
Perplexed, the manager brings back the “weaker” defender after 3 straight losses.
Surprisingly, the team plays much better even though that “weak” defender is back and his metrics have not changed. Here you have a situation where the defender is terrible at his role, but the effect he has on the team means that the entire team performs much better when he is around.
If you selected defenders based just on defense metrics, you would cut this defender. However, if the manager selected a defender based on the defender’s ability to “somehow” make the team win, he would keep this defender.
The “somehow” part his key. Maybe the defender raises morale. Maybe the team is more relaxed around him. The correlation is clear but understanding causality is hard. It is close to impossible.
In finance we call this a portfolio selection problem. Rather than looking at each player individually, we need to see the impact he has on the portfolio of players – the team. In a team with 11 players being watched all the time by a coach and thousands of camera’s and screaming fans, this is hard to do. It is hard to determine the impact a player has on the team, and even if you could see the impact, it is very hard to determine the sequence of events that causes that impact. It is therefore, all but impossible to predict what will happen with certainty.
Now, lets make the problem more complicated.
Imagine if you had a team with 350 million players and 100,000 more rules. How could you possibly understand the causality?
This is essentially the problem in healthcare.
You have thousands of treatment options, medical options, payments options, specialist provider options etc., all impacting the level of care provided. Given the complexity of the healthcare system and the inability to determine how one drug/treatment/doctor/insurer impacts the overall end result, the healthcare sector creates metrics to measure how a drug performs as a drug, versus its impact on the overall treatment end goal.
This is the very same problem as in football.
In other words, using the current approach in healthcare you could conceivably cut a treatment since it fails on the metrics for that treatment protocol alone. However, doing so could hurt the entire health outcome if the treatment has an overall positive effect on the net system, even if it scores abysmally in its own category. That is tough to determine but it can be done today with the tools we have for analysis.
It is a completely different way to analyze healthcare options versus the current approach.
We have an opportunity to develop the national disease management strategy for one particularly costly and burdensome disease. The disease has been singled out as the most pressing problem for this economy and is directly impacting productivity and foreign direct investment in one of the most important sectors.
We want to tackle this problem by avoiding the per treatment least cost analyses approach that was pioneering in the 1980’s but now outdated. Yet, still used today.
Most times, consulting assignments zoom in on cost spikes in the chain of treatment and try to lower them. That is the early work of Matheson at BCG. At the time it was pioneering but now that his approach has been implemented widely, doing more of the same generates little incremental improvement. Yet, it is still the gold standard.
In recent times, economists have used health exchanges to pass the burden of costs to consumers so they are incentivized to help lower the costs. The tool is new but the goal is still the same: lowering costs.
The problem is that when consumers are dis-incentivized to spend money on treatment, the consumer may pick a needed treatment/pill to avoid. So healthcare costs go down initially, hopefully, but the overall health of the patient and patient pool is no better off. In fact, relapses from avoiding a needed treatment could cause costs to rise in the long-term.
Doctors attack this problem from a different way. They try to analyze the pathways of a disease to figure out which treatments work best and which can be discarded. They then recommend a treatment path and insurers have to figure out how to pay for it.
So there is this big disconnect. The insurer refuses to pay for treatment x, like physiotherapy, since the benefit is not clear to the system and there is no direct benefit when measuring the impact of the treatment alone. However, the physiotherapy may be necessary for the hip-replacement to work which makes the overall operation a success. Those linkages are hard to understand. They should be understood.
To prove the concept, we built a simple but radically different way to model this problem using Big Data. Rather than being obsessed with the costs spiking, we wanted to see if the return attached to that cost outweighed the costs. The key thing is that the return is not directly linked to the one treatment point. The return for treatment x may be spread all over the system and we need to hunt it down and add it up. The hunting down part cannot be a group of consultants poring over spreadsheets. Even if they could find all the data, the consultants could not understand all the linkages.
I can say that we arrived at this very elegant, and beautiful way to understand the costs, risks, returns and business case around a disease by moving beyond simple 2-dimensional correlations – in English that means plotting data on a x-y graph – to modelling the relationships that existed in a 3rd or 4th dimension.
Our governing hypothesis is that the wrong treatment decisions are being made since we do not correctly understand the risk, cost, return and benefit of each treatment step for the disease and the overall treatment protocol. The client’s eye’s popped out a little when we showed them the example of what we had in mind and the very different recommendations we could develop.
We think you will like this study too.
It is a way to analyze diseases and extract new insights and recommendations that you have never ever seen before. That, we can guarantee.
Operations study: vote here
BTO study: vote here
Big Data Disease Management: vote by posting a comment below
We will make a decision based on the number of votes and points raised in the comments below. As it stands the operations study has >400 votes, BTO ~ 100 and Big Data Disease Management is at zero since we have just announced it.