Following the bright spots and its implication for designing a strategy

In an interview Andrew Grove narrates his experience at one of the exit interviews while being CEO of Intel. Steve, a young employee who is leaving Intel, said, “Andy, if I were you, I would take microprocessors seriously. We should learn how to use microprocessors, how to develop applications and become experts.” Andy said, “Sure” and never paid any further attention to the remark. In fact he says, “It was inconceivable for me to think of it.” Looking back Andy feels, “He was so right and I was so wrong.” What Steve was pointing to was an internal bright spot – the microprocessor business – doing well at the time but without any strategic attention from management. Question is: Can focusing on bright spots be a good option while designing strategy? If so, can it be done systematically??

Strategy answers 2 questions: What game are we playing? How will we win it? At the time of Steve’s exit interview with Andy, Intel was playing memories game and losing it badly. Many companies are in a situation like Intel and figuring out what to do next. Most of the time the attention is put on the questions “What is not working? And why?” In this article I want to explore what happens when the attention is put on the questions, “What is working? And can we replicate it elsewhere?”

For Intel the bright spot Steve alluded to was already quite bright. In most organizations it is much dimmer and needs some digging like that done by archaeologists. When Sudhir Kumar started working with Lalu Prasad Yadav in 2004, Indian Railways was heading towards bankruptcy, fast. Dr. Rakesh Mohan committee had already submitted an eight volume report on the causes of the failure and possible remedial actions. The committee had attributed the falling market share of Railways to high freight rates that subsidized low passenger fares.

When Sudhir Kumar studied the data on freight traffic, he discovered an interesting anomaly. Market share of some of the commodities (like steel and cement) had gone down. However, some other commodities (like iron ore and coal) had held on. What was happening? After analysing it further, Sudhir Kumar discovered that the Railways was providing door-to-door service for the winning commodities. On the other hand, it was doing station-to-station service for the losing ones. This lead to the insight of creating differentiated freight rates based on the value created for the customer. This became a key element of the strategy designed by Railways and it paid handsomely. Point to be noted in this story is that Sudhir Kumar followed the bright spots i.e. he asked “What is working? And can we clone it?”

Peter Drucker referred to this approach as pursuing the “unexpected success” in his book “Innovation and Entrepreneurship” written more than a quarter of a century ago. He begins chapter 3 as follows:

No other area offers richer opportunities for successful innovation than the unexpected success. In no other area are innovative opportunities less risky and their pursuit less arduous. Yet the unexpected success is almost totally neglected; worse, management tend actively to reject it.

Of course, the knowledge of an unexpected success may not come to you on a platter like it did for Andy Grove. Like Sudhir Kumar you might have to go hunting for it. The good news is that there is a systematic approach on how one can go about hunting for the bright spots. See the figure below adapted from Chip & Dan Heath's Switch.

Sources:

Sudhir Kumar story is from “Changing tracks: Reinventing the spirit of Indian Railways” by V. Nalakant and S. Ramanayaran, Collins Business, 2009, pp 112-115.

“Follow the bright spots” approach is explored in detail in “Switch: How to change things when change is hard” by Chip and Dan Heath, Broadway Books, 2010, chapter 2 titled “Find the bright spots”.

Sudhir Kumar’s photo is from IIM Indore site.

Baseline rates in innovation management

Wikipedia says that Fetal Heart Rate (FHR) should be between 110 beats per minute (bpm) 160 bpm. Anything beyond this range is considered abnormal. These rates are called baseline rates. FHR baseline rate is the same no matter which culture or nation the baby is born into. Are there any baseline rates in innovation management similar to FHR baseline rates? I don’t know. However, I feel that we need to establish them as they are going to be very useful in making various decisions in managing innovation. Nobel Laureate Daniel Kahneman highlights in “Thinking, fast and slow” that baseline rates are a good starting point while making risky decisions. In this article, I present my current view and a first attempt at the baseline rates relevant for innovation management.

Let me qualify the data set first. These rates are based on the data from 25 to 50 organizations depending upon the parameter. There are some parameters like the “idea per person per year” and “participation” where data is available from more organizations (50). And there are parameters like “response time” and “success rate” for which data is available from fewer organizations (25). Moreover, these numbers are not averages. Like FHR baseline, they are linked to the health of the innovation engine. Currently I have used my judgement in calling some rate “poor, OK or Good”. I have used publicly available information such as INSSAN benchmarks as well as data published from companies like Toyota and P&G. Moreover, I have also used data from a dozen odd organizations where I have seen the innovation engine personally.

Let’s look at each parameter briefly:

Idea pipeline (General): This parameter says that if you are 1000 people organization and if you have an idea box (physical or on intranet), then you should get at least 1000 ideas in a year to qualify for “good” category. If you get, say 150 ideas, then you are OK. And if you get 70 ideas in a year then you are poor. The maximum number I have seen is from Brasilica (a Brazilian firm) is at 143.

Big idea pipeline: Many organizations manage a separate pipeline for large impact ideas. Take each idea in the pipeline and identify how much business impact (annual) it projects today. Let’s say your big idea pipeline has 3 ideas with following potential revenue: 1 crore, 3 crore, 1 crore and if your revenue is 100 crore then total business impact of the pipeline is: 1+3+1 = 5 and the ratio of total business impact to revenue is 5/100 = 0.05. The table says it is “poor”. GE’s breakthrough imagination has 100 ideas each with a minimum potential of $1 billion. That makes the ratio at least 0.67 (perhaps the actual ratio is > 1).

Participation: Less than 5% employees giving at least one idea in a year is “poor”. More than 30% doing the same is “good”. See here how this parameter evolved in Toyota over 40 years.

Response time: How soon are you getting in touch with the person who submitted an idea? Less than a week is “good” and more than a month is “poor”. For example, Shell Gamechanger process promises to communicate input on your idea within 48 hours.

Success rate: This is the trickiest parameter. Too high of a success rate may mean nobody is taking any risk. Check out my article “Lower your batting average to improve innovation productivity”.

Will these baseline rates change as we get more data? Yes. Will the role of baseline rates diminish? I doubt it. As I mentioned this is my first attempt and your inputs would be greatly appreciated.