CHAPTER 10
The Promise of the Real-Time Supply Chain
After reading this chapter you will be able to
- Appreciate the “always-on” connection and what it means
- Assess the profit potential inherent in the self-adjusting feedback loop and explore how it can be harnessed to drive your supply chain
- Discuss the concept of emergent systems
The pace of business change and innovation is both exciting and relentless. Over the next decade, innovative companies in different market segments will learn to design and deploy their supply chains to improve their competitive positions in the markets they serve. They will create supply chains that enable them to develop and deliver products and provide levels of service at price points that their competitors cannot match.
We all sense that something profound has happened in the last 10 years or so. The Internet is a part of it, but it is not only about the Internet. We learned that in the “dot com” bubble of the late 1990s and early 2000s. It is more about what we can do by using the Internet than it is about any particular technology.
The Start of Something Big
As a historical analogy, consider what happened some 200 years ago at the beginning of an age that came to be known as the Industrial Age. The people of the time sensed that a powerful potential had been released by the invention and spread of the steam engine.
The steam engine for the first time provided a movable source of power that could be generated on demand and efficiently harnessed to perform a wide variety of tasks. The Industrial Age was not so much about the steam engine as it was about the things that could be done and were done with the power that the steam engine made available. Once it was born, the Industrial Age went on to outgrow the steam engine as it evolved more advanced engine technologies such as internal combustion, the jet, the electric motor, and atomic power.
The rise and spread of the Internet has created for the first time a global, multi-directional communications network that is “always-on.” The cost of connecting to this network is so cheap that there is no need for companies to save money by staying off-line and only connecting periodically. The normal state for companies is transitioning from being off-line and unconnected to one of being on-line and connected.
As more and more companies use the Internet and other communications networks to create always-on connections with each other, they will find ways to share data that enable them to better coordinate their interactions. They will also learn faster and adapt to changing conditions faster. These capabilities will clearly result in efficiencies that can be turned into business profits.
The always-on connection is a new light that sheds steady illumination on a landscape that had before been seen only in periodic snapshots. We are experiencing something similar to seeing a sequence of still photos turn into a moving picture. As more pictures are taken at shorter intervals, you cease to see a sequence of still photos and instead come to see a continuous, moving image. This continuous, moving image is what we see as we move from the snapshot or batch-time world into the real-time world.
Supply chain management is a process of coordination between companies. Those companies that learn to coordinate in real time will become incrementally more and more efficient. They will become more profitable and quicker to see new opportunities than their competitors who are still working in a batch-time world of snapshot pictures.
The Profit Potential of the Self-Adjusting Feedback Loop
The self-adjusting feedback loop is a very useful phenomena. An example is the cruise control in an automobile. The cruise control constantly reads the vehicle's actual speed and compares that to the speed it was set for. It responds to bring the actual speed in line with the desired speed. It causes the engine to either accelerate or decelerate. The cruise control's goal is to achieve and maintain the desired speed. As the vehicle travels down the highway it continuously monitors speed and operates the engine to achieve its goal.
Other examples of a self-adjusting feedback loop at work are a thermostat that controls the temperature in a room, or a guided missile that zeros in on a heat source or a radar-emission source. Self-adjusting feedback loops use negative feedback to continuously correct their behavior. Negative feedback occurs when a system compares its current state with its desired state (or goal) and takes corrective action to move it in a direction that will minimize the difference between the two states. A continuous stream of negative feedback guides a system through a changing environment toward its goal.
Companies can learn to work together to achieve supply chain performance targets that are profitable to all of them. They can learn to constantly adjust their behavior day after day, hour by hour to respond to events and continue to steer toward their performance targets. The bullwhip effect can be controlled by the introduction of negative feedback to dampen down the wild demand swings that otherwise result.
The opportunity now exists to leverage the power of the self-adjusting feedback loop across entire supply chains. Real-time data sharing and close coordination between companies can be employed to deliver operating efficiencies that result in significant profits over time. The result of these continuous incremental adjustments to supply chain operations is analogous to the growth of capital over time due to the miracle of compound interest.
Harnessing the Feedback Loop to the Supply Chain
How can the power of the self-adjusting feedback loop be brought to bear in a supply chain? The answer is beginning to appear. As companies link up using always-on communication networks to conduct business with each other, they begin to automatically collect useful data as a by-product of their interactions: electronic purchase orders, order status, order receipts, invoices, and payment status. It is no longer a huge administrative chore to regularly track performance in the areas of customer service, internal efficiency, demand flexibility, and product development.
Customers are starting to use supply chain “report cards” to grade the performance of their suppliers. The report cards are more accurate and more frequently produced than was previously possible. The next step is for companies to move beyond the use of these report cards as merely convenient tools for beating up their suppliers. The opportunity exists for customers and suppliers to use this data to work together to meet mutually beneficial performance targets. Companies can select performance targets that will generate quantifiable benefits and profits to reward them for the effort needed to achieve the targets.
Either one dominant company can set the performance targets or groups of companies can negotiate among themselves to set targets. The important thing is that all participating companies in a supply chain believe the targets are achievable and that when they are achieved there will be rewards as a result. The desire to receive these rewards is what brings the self-adjusting feedback loop into being.
The feedback loop happens when peoples' interactions with each other are cast in the form of a game whose object is to achieve the performance targets. If companies and people in a supply chain have real-time access to the data they need then they will steer toward their targets. If they are rewarded when they achieve their targets then they will learn to hit these targets more often than not. The profit potential of negative feedback and the self-adjusting supply chain is now unleashed.
Playing the Game of Supply Chain Management
Human beings are social creatures who love to play games. This is a good thing because through playing games we constantly learn and improve our skills and our performance. Companies such as Wal-Mart and Dell and their supply chain partners have in many ways begun to create an evolving game out of managing their supply chains. They have steadily learned and developed supply chains that are better than those of their competitors and that are clearly business advantages for them.
There are only a few things required to start a game. In his book, The Great Game of Business, Jack Stack lays out four conditions that are needed (Stack, Jack, 1992, The Great Game of Business, New York, NY: Currency/Doubleday). They are:
- People must understand the rules of the game and how it is played. They must know what is fair and what is not fair and how to score points.
- People must be able to pick the roles or positions they want to play in the game. They also need to get the training and experience necessary to keep developing the skills they need to succeed in their positions.
- All players must know what the score is at all times. They need to know if they are winning or losing and they need to see the results of their actions.
- All players must have a personal stake in the outcome of the game. There must be some important reward, either monetary or psychological, that provides a reason for each player to strive to succeed.
Basically, the game of supply chain management is a relatively simple game, as is soccer or basketball. Which is not to say that any of these games can be mastered without years of practice and play. The main techniques and operations of supply chain management are well enough understood to be taught to a wide range of people in different supply chain positions (see Chapters 2 and 3). The Internet is the way for everyone to know the score at all times and see the results of their actions. Profits generated by operating efficiencies provide people with rewards and the reason to strive to succeed.
In supply chain management, everyone can acquire and install technology, so technology alone cannot constitute a significant competitive advantage. The advantage lies in the way the game is played. Let's go back to the example of Alexander the Great (see Chapter 1). His army did not have any technology that was not also possessed by his opponents. In fact Alexander deliberately used less technology. He simplified his army's operations and equipment in order to make it more mobile and more efficient. His army could travel faster and lighter than those of his adversaries.
Advantage goes to those players who learn to use simple technology and simple tactics extremely well. Alexander's soldiers were well trained in how to use their technology and because of the simplicity of their tactics, they could remember and use them effectively in the heat of the moment when it really counted. After all is said and done, success is often just a matter of consistent performance and making fewer errors than your competition.
An example of the kind of system that makes the supply chain game into a reality in the operation of global and regional supply chains is the SCM Globe system described in Chapter 7. This system can be put to several uses. In can train people in supply chain operations; it can be used to design new supply chains and improve existing ones; and it can be used as a collaboration platform between companies to monitor and manage the workings of actual supply chains.
Strengthening Supply Chain Alliances
Strengthen supply chain alliances by making sure three conditions are present. These conditions are interdependent and all three must exist in order for any of them to truly be effective. They are: (1) all parties in the supply chain have easy access to relevant information and performance measures updated on a real-time or near real-time basis so they know what the score is; (2) people know how their actions influence the score and they have the skills and opportunity to act effectively; and (3) people have a stake in the outcome so that they will act to achieve the performance targets and continuously learn to improve. This is illustrated in Exhibit 10.1.
Supply chain alliances depend on close coordination between companies, and effective coordination can only happen when all parties have easy access to the information they need to do their jobs. These alliances are much like a game whose goal is to achieve the predefined performance targets. In order to play this game people need to know what the score is at all times. They need to know if they are moving toward the goal or away from it. They need current information that reflects events as they happen, not batch reports delivered 30 days after the end of the last quarter. This allows them to make good, timely decisions and coordinate effectively.
Strengthening Supply Chain Alliances
Once people are able to see the score and track events as they happen, they need to understand how their actions influence the score. If operating results are trending away from performance targets, people need to know what to do to bring operations back on track. If results are on target people need to know how best to sustain them. That means people get the training they need to do their jobs well. It also means that people have the opportunity and authority to act as they see fit when the need arises. If no action can happen until requests and permissions are passed up and down a chain of command, then responses will be too slow and people will become frustrated with the poor results.
When people can see the score at all times and when they know how to act in order to achieve predefined performance targets, there is one more condition that must be present in order for a strong alliance to emerge. That condition is that people have a stake in the outcome. Often this is in the form of a monetary reward when performance targets are achieved. Without a stake in the outcome, people become bored or indifferent and they will not make the effort to constantly improve and adjust operations to respond as the world changes. And without this constant effort, challenging performance targets cannot be achieved month after month, year after year.
EXECUTIVE & INSIGHT
Emergent behavior is what happens when an interconnected system of relatively simple elements begins to self-organize to form a more intelligent and more adaptive higher-level system. Steven Johnson in his book, Emergence: The Connected Lives of Ants, Brains, Cities, and Software, explores the conditions that bring about this phenomenon.
In an interview with Steven Johnson I posed six questions and asked him to share his insights on a range of topics. These topics range from what gives a system emergent characteristics to how could companies organize their supply chains so as to encourage and benefit from emergent behavior.
What is an “emergent system”? How is an emergent system different from an assembly line?
The catchphrase that I sometimes use is that an emergent system is “smarter” than the sum of its parts. They tend to be systems made up of many interacting agents, each of which is following relatively simple rules governing its encounters with other agents. Somehow, out of all these local interactions, a higher-level, global intelligence “emerges.” The extraordinary thing about these systems is that there's no master planner or executive branch—the overall group creates the intelligence and adaptability; it's not something passed down from the leadership. An ant colony is a great example of this:
colonies manage to pull off extraordinary feats of resource management and engineering and task allocation, all by following remarkably simple rules of interaction, using a simple chemical language to communicate. There's a queen ant in the colony, but she's only called that because she's the chief reproductive engine for the colony—she doesn't have any actually command authority. The ordinary ants just do the thinking collectively, without a leader.
A key difference between an emergent system and an assembly line lies in the fluidity of the emergent system: randomness is a key component of the way an ant colony will explore a given environment—take the random element out, and the colony gets much less interesting, much less capable of stumbling across new ideas. Assembly lines are all about setting fixed patterns, and eliminating randomness; emergence is all about stumbling across new patterns that work better than the old ones.
You say that such systems are “bottom up systems, not top-down.” These systems solve problems by drawing on masses of simple elements instead of relying on a single, intelligent “executive branch.” What does this mean for people who are trying to design and build emergent systems?
One of the central lessons, I think, is that emergent systems are always slightly out of control. Their unpredictability is part of their charm, and their power, but it can be threatening to engineers and planners who have been trained to eliminate unpredictability at every turn. Some of the systems that I've looked at combine emergent properties and evolutionary ones: the emergent system generates lots of new configurations and ideas, and then there's a kind of natural selection that weeds out the bad ideas and encourages the good ones. That's largely what a designer of emergent systems should think about doing: it's closer to growing a garden than it is building a factory.
What does it mean when you say that emergent systems display complex adaptive behavior?
The complexity refers to the number of interacting parts, like the thousands of ants in a colony, or the pedestrians on a street in a busy city. Adaptive behavior is what happens when all those component parts create useful higher-level structures or patterns of behavior with their group interactions, when they create something—usually unwittingly—that benefits the members of the group. When an ant colony determines the shortest route to a new source of food and quickly assembles a line of ants to transport the food back to the nest; when thousands of urbanites create a neighborhood with a distinct personality that helps organize and give shape to an otherwise overwhelming city—these are examples of adaptive behavior.
What is negative feedback as opposed to positive feedback? What role does negative feedback play in the ability of a system to exhibit adaptive behavior?
Negative feedback is crucial, and it's not at all negative in a value-judgment sense. Positive feedback is what we generally mean when we talk about feedback, as in the guitar effect that we first started to hear as music in the 60s: music is played through a speaker, which is picked up by a microphone, which then broadcasts it out though the speaker, creating a sound that the microphone picks up, and so on until you get a howling noise that sounds nothing like the original music. So positive feedback is a kind of self-perpetuating, additive effect: plug output A into input B which is plugged into input A. Negative feedback is what you use when you need to dampen down a chain like this, when there's a danger of a kind of runaway effect, or when you're trying to home in on a specific target. Think of a thermostat trying to reach a preset temperature: it samples the air, and if the air's too cold, it turns the heat on, then samples it again. Without negative feedback, the room would just keep getting hotter, but the thermostat has been designed to turn the heat off when the air reaches the target temperature. Ants use a comparable technique to achieve the right balance of task allocation throughout the colony: an individual ant who happens to be on foraging duty will sample the number of ants also on foraging duty that she stumbles across over the course of an hour—if she encounters a certain number, she'll switch over to another task (nest building, say) in order to keep the colony from becoming overrun with foragers.
In your book you mention a designer who has proposed building a learning network of traffic lights that will find an optimal solution to continually changing traffic conditions. You observe that, “You can conquer gridlock by making the grid itself smart.” What is it that would make the grid smart? Is this grid an example of an emergent system?
The idea proposed in the traffic model is not to take the traditional engineering, top-down approach and say: “let's look at the entire city and figure out where all the problems are, and try to design the roads and the light system to eliminate the problems.” The smart grid approach is to give each light a local perspective with a little bit of information, and give it the goal of minimizing delays at its own little corner. So the light would be able to register the number of cars stacked up at the intersection, and it would be able to experiment with different rhythms of red and green, with some feedback from its near neighbors. When it stumbles across a pattern that reduces delays, it sticks to that pattern; if the delays start piling up again, it starts experimenting again. The problem with this sort of approach is that on Day One it's a terrible, terrible system, because it doesn't yet know anything about traffic flows. (You'd have to teach it quite a bit before you could actually implement it.) But it would learn very quickly, and most importantly, it would be capable of responding to changing conditions, in a way that the traditionally engineered approach would not. That's a hallmark of adaptability.
Consider a system composed of many different companies whose goal is to provide a market with the highest levels of responsiveness at the lowest cost to themselves. High levels of responsiveness require that these companies work together to design, make, and deliver the right products at the right price at the right time in the right amounts. What are some of the things that these companies could do to organize themselves into an emergent system?
There's a telltale term in supple chain systems, which may well be unavoidable—the term “chain” itself. Almost all emergent systems are networks or grids; they tend to be flatter and more horizontal, with interaction possible between all the various agents. The problem that supply chains have with positive feedback revolves around the distance between the consumer and those suppliers further down the chain—because the information has to pass through so many intermediaries, you get distortion in the message. Most emergent systems that I've looked at have a great diversity of potential routes that information can follow; the more chain-like they become, they less adaptive they are. The other key here is experimentation: letting the system evolve new patterns of interaction on its own, since these can often be more useful and efficient than the pre-planned ones. Of course, you don't want to waste a few economic quarters experimenting with different supply chains, most of which are a disaster. But that's where some of the wonderful new modeling systems for complex behavior can be very handy: you can do the experimenting on the computer, and then pick the best solutions to implement in real life.
Emergent Behavior in Supply Chains
In the workings of a system such as a free market, we witness emergent behavior. This behavior is what the great British economist Adam Smith referred to as the “invisible hand” of the market. This invisible hand emerges to set product prices so as to best allocate available supplies to meet market demands. Local interactions between large numbers of agents, governed by simple rules of mutual feedback, produce a macro effect for the system as a whole that results in what we call emergent behavior.
As we begin to practice supply chain management as a game between companies and people who are motivated to achieve certain performance targets, we will see emergent behavior in supply chains. Good players in the supply chains of particular markets will seek each other out, because by playing together they can create more efficient supply chains and generate better profits.
Supply chains will form like sports teams and these teams will compete with each other for market share. Just as the game of basketball or soccer evolves over time, so too will the game of supply chain management. New tactics, techniques, and technology will come about. Market demands and the desire for competitive advantage will drive companies to collaborate and innovate with each other to win at the game of supply chain management.
Computers are best used to automate the rote, repetitious activities that humans find to be dull and boring. These are all the ongoing and routine activities of recording and monitoring supply chain operations. Computers do these tasks very well. They do not fall asleep, they do not miss details, and they can handle enormous volumes of data without complaint.
People are best used to do the creative and problem-solving activities. These are the activities that do not have clear right or wrong answers. These are the activities that call for people to collaborate with other people and share information and try out different approaches to see which ones work best. People are good at these activities and they like doing them so they learn and keep getting better.
At a macro level, this will give rise to supply chains that, in effect, learn and grow smarter. Computers will listen to the hum and crackle of data flowing through the real-time, always-on supply chain. They will employ pattern recognition algorithms to spot exceptions and events that need to be brought to the attention of human beings. Like good pilots and navigators, people will learn to respond effectively to these developments as they happen. People will learn to keep steering the supply chain on a course toward its desired performance targets.
Adaptive Networks and Economic Cycles
As we learn to recognize and effectively respond to developments in our supply chains, it will tend to lengthen the periods of market growth and stability. Any industry or market where there is a boom-to-bust cycle is an opportunity for us to apply the self-adjusting feedback loop to smooth out the economic ups and downs. The boom-to-bust cycle is caused by the same dynamic that results in the Bullwhip effect in individual supply chains (see Chapter 6).
In industries ranging from manufacturing to real estate development and telecommunications, the boom-to-bust cycle causes economic waste and disruption. It also brings with it all the related human hardships that are caused by the cycle. Examples of this cycle are the “Dot Com” bubble of 1997 to 2001 and the Real-Estate bubble of 2003 to 2008. The ability to recognize and smooth out excessive swings in demand, prices, and productive capacity in different areas of the economy will create greater stability and more sustainable prosperity. Through this stability more wealth will be both generated and preserved. Think of the wealth that was destroyed by the excessive investments that created more dot com companies and more real-estate developments than were really needed. Think of the wealth that disappeared in company closures and job losses that happened when these companies and their suppliers finally had to face the consequences of too much supply and not enough demand.
Adaptive supply chain networks using real-time information and feedback loops can effectively dampen excessive market swings. This ability alone will have a wealth creation effect that is even more powerful than what was created by the effect of the steam engine and the industrial revolution.
Chapter Summary
The “always-on” connection of the Internet and other communication networks allows us to see ourselves in real-time. We can now see the supply chain as a continuous moving picture, whereas in the past we could only see it as a collection of snapshots taken at periodic intervals. This always-on, moving picture makes it possible to constantly adjust supply chain operations week to week and day to day to get significant new efficiencies.
This self-adjusting feedback loop is harnessed to the supply chain through the daily actions of the people who carry out supply chain operations. First motivate people by providing them with monetary or psychological rewards for achieving predefined performance targets. Then provide people with real-time information that shows them whether they are moving toward or away from their targets. People will steer toward their targets and they will learn to hit these targets more often than not.
The effect of this dynamic will be to give rise to supply chains that are both highly responsive and very efficient. Real-time operating adjustments will result in supply chains that can better adapt to business changes and deliver performance and profitability that is of a higher level than anything that has been seen before.