There is a famous game in the business world called the Beer Game.  Developed at MIT’s Sloan School of Management, this game gives a taste of reality to novice and experienced managers alike.  In addition to lessons in management, the Beer Game illustrates an interesting similarity between business systems and industrial processes, and suggests to me how real-time data cloud services may be able to help business sidestep costly delays.

In the Beer Game, players representing retailers, wholesalers, distributors and producers of beer are responsible for keeping up with customer orders.  Everything goes pretty smoothly until there is a sudden increase in demand.  Due to a built-in request-and-response time delay at each level of supply, it takes a while for the increased orders to reach the factory, and still longer for the new supplies of beer to reach the retailers.

The delay in supply shipments causes a temporary shortage for retailers, so they keep sending in large orders for beer.  Eventually, when truckloads of beer finally start to arrive, their supplies overshoot demand, and the retailers now have to cut orders dramatically.  But the beer keeps coming.  Oscillations between supply and demand ensue, creating customer dissatisfaction, wasted resources, and loss of profit.  Hard feelings often arise between the game players at the various levels in the supply chain, as each blames the others for the losses.

The problem is, most players simply keep ordering beer as long as their customers or downstream distributors keep clamoring for it, not realizing the mistake until it is too late.  “If there were no time delays, this strategy would work well,” said MIT Professor John D. Sterman, who has run the game for many years.  So, in a way, the culprit here is the time lag.  Let’s see how a real-time approach might change the picture.

In the real-time world of industrial control, this is a familiar scenario.  If you have, for example, an oven running at a set temperature, and then turn a dial to raise the temperature, it takes a bit of time for the system to respond.  If it is tuned well, the heating mechanism will quickly bring the oven up to the newly set temperature, and maintain that setting.  If not, it may respond slowly, and possibly overshoot and undershoot the setting a few times until it finally stablizes on the new temperature.

This type of behavior can be plotted on a graph.  Here are a few examples:

In these images, the red line represents the newly set temperature, the green line is the ouput from the heating mechanism, and the blue line is the actual temperature inside the oven.

Poor Response shows delayed communication and overreaction.  Notice that the heating mechanism output (green line) doesn’t start decreasing until the oven temperature hits the proper setting.  Poor feedback between the actual temperature in the oven and the heating mechanism causes a number of oscillations.

So-so Response is better, but there is still some overreaction.

Quick Response is the best.  A combination of an immediate and strong initial response with a tightly coupled feedback loop between the heating mechanism and the oven temperature means that the new setting is achieved rapidly, with minimum waste.

How does this apply to the Beer Game and the business world?  Using real-time cloud technology, it should be possible to connect all the data related to the beer sales, ordering, distribution and production into a single, seamless flow.  Imagine if each player in the game had a window into actual production figures and supply inventories at every level, updated in real time.  The factory could see immediate spikes in demand and retailers could gauge supply levels, while distributors and wholesalers could monitor the flow of orders and shipments up and down the supply chain.

Of course, there will always be time constraints in the actual beer shipments.  But that doesn’t mean we have to settle for frustrations originating in the paper-based systems of the last century.  With a real-time cloud approach, many “inevitable” delays can simply be sidestepped.

Like any good mathematician, Joe Weinman in his book Cloudonomics lays out some definitions right up front.  He chooses to define the concept of “cloud” in cloud computing in a way that brings out five essential attributes that are common to other cloud-like systems in business and life in general.  To make it easy to remember he gives his definition as a mnemonic: C L O U D.

Let’s see how these five attributes of any cloud system fit in with our understanding of real-time cloud computing:

C – Common infrastructure - refers to the ability to share resources.  A city park is like a cloud in that it can meet the needs of millions of apartment dwellers for some quality outdoor space—gardens, walkways, playgrounds, and sports fields.  Nobody feels overly crowded because they don’t all use the park at the same time, or in the same way.

As Wienman explains in detail later on in the book, non-cloud computing resources are often underutilized, which becomes a cost.  For example, some industrial applications require their software to run alone, on a separate server.  As the number of this kind of application grows, the waste of resources increases.  Where possible, using virtual machines is one way to share the resources of a single server to reduce this kind of waste.  This approach to sharing infrastructure is often used in cloud systems, as well as private systems.

L – Location independence - means that the sevice is available pretty much everywhere.  You might not think of a fast-food franchise as a cloud service provider, but in a sense it is similar.  Just as you can get order-in or take-out service from your favorite burger outlet in many places around the country or even the world, so also can you access the cloud from practically any location.

The value of location independence for real-time systems is just beginning to be realized.  For decades data from industrial systems has been tightly locked down, behind firewalls and physically isolated systems.  But now, perhaps to the dismay of engineers and system integrators who rely on isolation for security reasons, upper management in many companies is waking up to the value of accessing that data from anywhere.

Of course, there is alway a need to keep raw process data secure and free from interference, but advanced methods of keeping firewalls closed and permitting read-only access can help bring key real-time peformance metrics to analysts and decision makers in the office, at home, or on the road.

At the same time, many embedded systems once lacked the power or connectivity to put their data online.  With the advent of the Internet of Things connecting cars, appliances, remote sensors, and a host of other devices directly to the Internet, we are witnessing a huge growth and interest in accessing live data from all kinds of sources, independent of location.

O – Online accessibility - is the availability of service via a network or the Internet.  Every service needs some form of access.  A restaurant needs an eating area, a movie theater needs seats and a view of the screen, a radio show needs transmitters and receivers.  As Wienman sums it up: “Without networks, there is no cloud.“  Real-time cloud systems can function well on private networks, and in many cases access to the Internet and public clouds will provide additional value.

U – Utility pricing - like the Water Works and Electric Company in the game of Monopoly, utility pricing means you only pay for what you use—be it water, electricity or computing power.  Usually this aspect of cloud computing goes hand-in-hand with on-demand resources.

D – on-Demand resources - the ability to bring in additional resources, or remove extra ones, to cope with variable demand.  For example, your house has plenty of space for your family and an occasional guest, but on special occasions like a big wedding you may need to engage the services of hotels or restaurants.

The flexibility to respond to market fluctuations is a real boon for retail and consumer-oriented companies who may see significant peaks and valleys of seasonal or irregular demand.  In our experience, most industrial and embedded real-time systems don’t undergo such large variations in demand for computing resources.  However, for systems too small or too dispersed to justify a dedicated, in-house SCADA system, (such as mentioned in our SCADA for the Masses discussion), on-demand resources and utility pricing may help make the cloud a viable solution.

Given the above C L O U D definitions, the economic value of any cloud computing system, real-time or not, depends on a number of variables and circumstances.  We need to consider these in their appropriate context to determine how real-time systems can benefit.

For the past few months we’ve been looking at the technical side of real-time cloud computing.  We’ve touched on some of the requirements for supporting real-time data communications on the cloud, looked at how SCADA and embedded systems might benefit from accessing the cloud, and even considered how the term “real time” may be best applied to cloud computing.

Going forward, I thought it might be a good idea to switch gears a bit, and take a deeper look at the business and economic side of cloud computing, and see how the latest thinking about cloud economics may or may not apply to real-time applications.

A new book, Cloudonomics, by Joe Weinman, Senior Vice President of Cloud Services and Strategy at Telx, gives a profound yet accessible overview of the business value of cloud computing.  Among other things, the book’s cover blurb says, “Weinman drills down past the hype and hysteria, the myths and misconceptions, to uncover the fundamental principles underlying how the cloud works, how it’s used, and how it will evolve in a business context.“

With the vision of a mathematician, Weinman strips away the non-essential features of the cloud and breaks it down into its basic elements and principles.  At that level, he can demonstrate how “cloudy” ideas and concepts have been used for centuries.  For example, he shows the similarities between cloud computing and the transportation and lodging infrastructure of ancient Rome, complete with multi-protocol wide-area networks, pay-per-use resources, value-added services, regulatory agencies, security tokens, branding, advertising, and more.

Weinman uses lots of real-world examples to show how we find cloud concepts in every facet of life, such as hotels, taxicabs, and movie theaters.  At the same time, he introduces some simple mathematical theories and models that sometimes uphold and sometimes contradict much of the conventional wisdom that has grown up around cloud computing.

Through it all, he strives to adhere to three goals: 1) present a multidisciplinary view from a number of fields of economics, mathematics, natural sciences, and system dynamics; 2) plant seeds of ideas in areas related to cloud computing, which may be cultivated and developed by others; and 3) take an evergreen approach, where the concepts are so fundamental and universal that they will serve to inspire research and application in business for many years to come.

Although I haven’t read it exhaustively, I’ve not yet seen much mention of the application or value or real-time systems in the cloud.  This is not surprising, as this topic is still on the distant horizon for many leaders of thought.  Or, it could be that what applies to cloud computing in general also applies to real-time cloud computing.

This raises an interesting question: Is there any significant difference between the economics of the more familiar cloud systems of business and consumer applications, and the less-well-known real-time cloud systems for industrial and embedded applications?  We know there are some unique technical requirements.  Is there a fundamentally different business model for real-time cloud?

In the weeks to come we’ll take a look at some of the ideas presented by Weinman in Cloudonomics, and see how they may or may not apply to the special case of real-time cloud computing.