It is not innovation that matters, it is agreement. And we might need innovation to reach agreement.
In today’s world, trading systems are global and with their global reach, they are complex. Each of us has to find our niche, and the big question is how do we “insert” ourselves into a vibrant and rich value chain.
We aren’t interested in every value chain in the world, but for those that fascinate and attract our attention, we want tools to understand who does what and how to find our place.
These are notes I made from Global Value Chain Analysis: A Primer. They should be helpful when you are thinking ahead about thorny issues of developing a supply chain. Once you have the basics, they it would be best to go back to the original source at Duke University.
Our value chain includes everyone who is in it – from people who think up ideas, to people who supply raw materials, to the people who make things, move things and sell things to the people, yes, who pick up the waste and recycle what we throw out.
We map out everyone in the system, initially simply, and then in more detail showing what each person needs and use and what they get back in terms of wages, profits and new possibilities.
Value chains are global but the different parts of the value will happen in different places? Where? Can we show the value chain on a map?
And is there a good reason why things happen in any place? Are the natural resources there? Do they have a long history in making what is made? Is the market there? Are transport lines particularly good? Does the government give the players special privileges?
What are the opportunities for capturing parts of the value chain and moving them elsewhere? And who else is looking at the value chain seeing the same opportunities for themselves?
Sometimes it is easy to spot a big player like Walmart who dominates the entire chain? Knowing the ‘type’ of chain that we are in also helps us learn from chains in other industries that we might think are different but are organized in the same way.
Governance structures do three things: they express power differentials – who depends upon whom, they provide mechanisms to coordinate ourselves for our mutual prosperity, and they define relative profit margins within our value chain. Our natural inclination is to manoeuvre ourselves in to a better position and we will do so whenever we can. So as with political government, good governance is not static and rigid. It is dynamic, it is aware of shifting sands and it is fair. Nothing ruins a business relationship faster than the sense that the spoils are divide unfairly.
Sometimes we dismiss governance as ‘politicking’ and sometimes, it is. But it is as important as doing the work. It is every changing and we are doing business at a time when the rise of the BRICS and the growth of IT and web technology is changing business models. We need to pay attention and see where our value chain is going.
The relationship between our value chain and the wider world can be thought through using a standard PEST analysis. In each place where any part of value chain operates, what are the political, economic, social and technological issues and how are these changing?
Everyone taking part in our value chain is there to make a living and the best living they can. Hopefully, it is well governed and we can be competitive and innovative without destroying each other and destroying our value chain at the same time.
But the prosperity of the entire value chain does change in time and so does our position in it.
At first, obviously we know little about the value chain. But we can learn about the chain as a whole. We can park out the parts that we do know. And we can mark out who else knows what.
And we can be particularly alert to the best order of learning more and learning about the governance of the chain.
The best example of taking over a value chain was the move by Indian IT firms into software.
At first, we might be able to bid easily for repetitive work. Then we can gradually increase our skills to handle more difficult work that commands a higher price.
Some sectors are well documented and we can even get government statistics to understand how the value chain works. In others, we have to resort to special reports and even proxy metrics. The important thing is to keep paying attention and to keep learning.
There are three neat tricks to anticipating where a value chain will go.
So that is it in nutshell.
This is a brief post to remind myself of ideas from “Agile Sense-Making in the Battlespace”. So what relevance do weary soldiers have for those of us back home?
We spot some technical jargon immediately. Computer people like “agile”. What they mean is doing just as much as we can to be able to get some feedback.
Imagine it this way, when we begin a journey from London to Edinburgh, we ask the SatNav for a route and then we tend to assume the journey will then be pain free. Often the journey is not and the real outcome involves looking for another route in a mild panic when the inevitable happens and we are diverted.
The alternative is a SatNav that works like this.
Agile is simple getting on with the first task but allowing that the overall route to the destination may change.
Sense-making is best understood through the SIR COPE acronym of Karl Weick.
Sense is not about truth. Sense is about piecing together whatever information we have so that there are no discrepancies and so that we are willing to ‘stay in the game’.
Sense-making is an ongoing process; it is a confusing process; and it is ultimately a social process because a key factor in our decision to stay or go is our judgement of the people around us and their loyalty and commitment. In military terms, it is ‘morale’ – do I even want to belong to ‘this man’s army’?
So jargon aside, what new does William Mitchell add in his description of thinking clearly in the battle space? I will be using my words now because these are my notes. I hope you find them useful but if you do, check back to the original article.
Technically, we call imagination in “thinking about the systems of systems” or in Mitchell’s words “network philosophy”.
In practice, we think like this: I want to attract more customers to my business. They either don’t know I exist, or barely pay me any attention, and when they notice me, don’t trust me. I want to win their trust.
Of course, I can woo them directly and sometimes I will. But they already have relations among themselves. So when I woo the fellows who, say, wear hats, the fellows who don’t wear hats don’t want to take part. That second level effect is systems thinking.
When we are busy, or in goal mode, our systems thinking tends to get turned off. Let’s go back to driving from London to Edinburgh. When I set my SatNav and I head out onto the motorway, I know the trip will be boring, so I don’t want to know about all the wonderful places I could visit just 5 miles off the motorway, or I will not stick to my task.
But I also don’t know about the inter-schools football championship that is about to disgorge a flood of cars into the junction ahead of me. That’s what management intelligence is for. To make a system that scans for the opportunities or threats that we aren’t scanning for, and should not be scanning for, because we are in executive-mode and concentrating on something else.
But the key takeaway is not that we have lookouts. The key takeaway is that we have lookouts how understand second order effects – what causes what. And for there to be any point to having intelligent lookouts, we need managers who understand the messages from lookouts. That’s why managers must be fluent in systems of systems thinking. They must be able to follow the briefings and ask the right questions.
Technically, we call this state “iterative modelling”. We write down what we think to build a bridge from our brainstorming to our action.
In practice, we log our interactions with potential customers and we see how well we are doing. We calculate our open rates and click through rates and sales. We use numbers to focus our attention on what must be done and to learn how to do what we do even better.
Very simply, when we drive from London to Edinburgh, part of the system is written down for us. The SatNav is doing the map calculations for us using a straightforward A* algorithm and some detailed information from maps. Then it presents it on a map annotated with voice commands.
We do the rest. We look at our clock. We note the time to destination on the SatNav. And we note what time we ‘must’ arrive and make our decisions accordingly. We can see immediately that SatNavs are going to become much, much better at learning.
There are several skills involved in modelling dynamic information. We have to know what to model. We have to capture data. We have to write programs of very many sorts. We have to lay out information. And we have to learn, a lot, about how to make the whole system better.
And in that morass of work, we might forget what all this is about: to bridge the dynamism of systems about systems thinking with action that has to be taken in some instances, in a split second. This is what we are doing this for!
Technically, the third stage is called “hypothesis generation and testing” or “scenario planning”. Oh my, how we hate to do this when we are in the thick of action! To be goal-oriented means to be confident of what we are doing. And we resist any undermining of our confidence including thinking about what else might be a good idea!
But snap decisions are dangerous and unwise. A good MI system delivers the correct information to make choice at the right time. We slow down thinking to speed up work – or avoid false starts and over commitment to unwise courses of action.
Let’s imagine, for example, that we are very attracted to selling big ticket items to wealthy customers. And that we are reasonably successful. But that our smaller items fly off the shelves in our ‘outlet’ shop around the corner. Now imagine we have a choice: spend the next hour serving the high value customer, or spend the next hour helping move the queue around the corner. It’s helpful to have a display that shows our two choices and their consequences so we can make the choice in terms of what we will achieve and not simply our personal preference.
Equally, when we are driving from London to Edinburgh and we are diverted, in the time we have to reroute, it would be helpful to have a display that shows the best 5 choices rather than requires us to step through them painfully – a task that cannot be done until we find somewhere to pull over.
Every MI system has assumptions built into them. And though we use the systems in a very trusting way on a day-to-day basis, we should know what those assumptions are and what information we are not seeing. Yes, the data must come packaged ready for action. But we must have people in the background looking at alternatives and produces displays for those too. Caveat emptor: If we rely on computer systems that we don’t understand and don’t insist on getting better and better, then we only have ourselves to blame.
So this is it agile sense-making –
This is the new world of management consulting folks – data driven. Now let’s find the clients to match!