The Need for Smart Enough Systems (Part 2): Introducing Smart Enough Systems
Introducing Smart Enough Systems
Last time we introduced the idea of the "smart enough" system. In this instalment we discuss the kind of system that would deliver this vision of operational decisions. What kind of systems would deliver this vision of operational decisions? The term "smart enough systems" is used in this book to describe them. A smart enough system is not some kind of artificial intelligence device like HAL 9000 (from 2001: A Space Odyssey). Equally, a smart enough system can't be developed the same way you build traditional "dumb" information systems.
Building smart enough systems means taking a new approach to bringing automation to operational decisions. Instead of hard-coding decision rules into systems, it means using separate tools to build, manage, and carry out decisions in concert with other operating processes. It means developing new services that can deliver operational decisions that perform well enough to be used in real-time front-line systems and processes. It means developing services that are agile enough to keep up with a changing world and, indeed, learn from it. It means services that make customer-centered (associate-centered) decisions and services that can support an extended enterprise.
Characteristics of Smart Enough Systems
Smart enough systems have some key characteristics. In particular, they are operational and capable of real-time performance. They are agile, capable of learning, and customer- (associate) centered as well as compliant and supportive of an increasingly extended enterprise.
The increasingly distributed and always-on nature of organizations puts a premium on high-performance execution, which means operating quickly, flawlessly, legally, and profitably at every level. It's about more than how your employees perform; it's about how your systems perform.
For organizations to exhibit high-performance execution in day-to-day operations, their top performers' expert judgment must be made available everywhere. This means making sure the systems everyone uses embody that expertise -- not in an expert system, ask-if-you-get-stuck way, but with systems that are embedded in the operational processes necessary to the business. This expertise, however, must be balanced by analytic insight developed by a careful analysis of the organization and its operational history. As Malcolm Gladwell said:
"Truly successful decision making relies on a balance between deliberate and instinctive thinking." 
~ Malcolm Gladwell
Not only must the organization's expertise be balanced with an effort to run the organization "by the numbers," but the interaction skills of those who serve as the point of contact with associates also must be considered. For the moment, no system can replace human interaction. Ensuring that these interactions make use of decisions informed with an analysis of past success and experts' judgment can ensure that customers get the best possible experience and organizations can get the best possible results. Smart enough systems make organizational knowledge "explicit, executable, actionable, and adaptable."
Capable of Real-Time Performance
Smart enough systems must operate in a no-wait, multichannel world where customers and other associates expect responses, actions, and decisions immediately. Suppliers expect immediate updates on the demand chain, and retailers and distributors expect to know about problems in the supply chain instantly. Real-time connections between organizations are also essential, because organizations must become more loosely coupled. They must deliver their products and services by coordinating and orchestrating many distinct organizations, both internal and external.
In the past it was sufficient to coordinate operations within an enterprise, but today successful organizations must be able to operate with both known and unknown entities without delays. Systems can't wait for someone to wake up before acting, and people want to be told what has been done to make their life easier, not asked for decisions. Smart enough systems must make decisions fast enough to be used in operational, real-time systems.
An agile organization can effectively change the way it operates when it needs to, but only if it has a good understanding of how it's operating and why it operates that way. Smart enough systems support this agility by making how they operate explicit, easy to understand, and easy to modify. Agility is a measurement of the total time and cost in getting from having the data that means you should change your business to actually making the change. [See Sidebar]
Gartner Group Inc. defines agility as "the ability of an organization to sense environmental change and to respond efficiently and effectively to that change." Gartner uses an agility cycle, shown in Figure 1, to show how agility is achieved and to indicate that it's ongoing. The basic steps are sensing a threat or opportunity, strategizing about options, deciding on the most appropriate action, and then communicating it before acting. This cycle must be continuous, because each change must be monitored for subsequent changes.
Figure 1. The agility cycle
Human Latency People will always be a bottleneck when it comes to change. Gartner defines "human latency" as something that reduces agility, for instance. Some technology approaches make it harder to be agile, and some make it easier. Some help you persuade people to change; others help you make the changes after they have been agreed on. Organizations in the future will have to improve their agility organizationally and in terms of the technology they use and how they use it.
Capable of Learning
Smart enough systems need to "learn" as new data is collected. Organizations collect an enormous quantity of data, and the volume of data is increasing steadily. Generally, organizations don't have systems that are smart enough to take advantage of this data. For instance, a PricewaterhouseCoopers Barometer survey in late 2006 gave an accurate summary of how organizations think their data should give them a real competitive edge and why it currently doesn't:
- 71 percent of senior executives describe the data in their company's information systems as potentially very valuable.
- 68 percent of these executives expect this data will become even more valuable as a source of competitive advantage during the next 12 to 18 months.
- 84 percent cited their inability to mine and interpret data as the highest-ranked obstacle to achieving value.
- 75 percent said that an ability to mine and interpret data was key to getting value from data.
You have to do more than collect, organize, and report on your data. You, and your systems, have to learn from it, mine it for insights, and interpret what it means for the future. Doing so is a key to taking advantage of one of your last remaining areas of competitive advantage -- knowledge of your associates -- and is a way to improve performance by insisting on realism. If your business decisions are based on what your data tells you, you're more likely to get realism than if you rely on hunches and how you have always done something. Even Malcolm Gladwell, in his paean to instinctive reactions, noted that informed snap judgments outperform uninformed ones. [See Sidebar]
You must also realize that a generation of workers, the baby boomers, is retiring. The new generation of workers is more technology-literate but is unlikely to take the kind of jobs their parents and grandparents took. Even if they did, they lack the depth of experience on which organizations have been relying. Those retiring baby boomers know all the tricks, exceptions, and workarounds that make your manual decisions work. Without them, you need some other way to get this knowledge to your workers, and these workers will look to information systems for that knowledge.
"Customers can access more information about more vendors, negotiate more effectively with still more vendors, and switch from one vendor to another whenever they find greater value." 
Much money and energy have been spent using technology to improve customer relationships, yet much of it has been used as a technological alternative to talking with customers, not to empower customers.
Many organizations fail to respond to customers in a consistent, focused, targeted way and have customer processes that are costly in terms of customer satisfaction, operating costs, and profits. As the world moves faster and gets more complex in terms of regulations and competition, this situation will get worse. Customers expect quicker decisions and are no longer willing to wait for them. With all the information about competitors and quick Web-based access to them, they can find an alternative easily.
With the many channels now available, the potential for annoying or ignoring customers unintentionally is rising. Competitors are constantly forcing reactions, because customers might find another supplier who offers them something more compelling. These customers want to self-serve, to actively manage their relationship with their suppliers, and the organization of the future must make it possible for them to do so. As interactive web applications get better, many people will prefer "self-service" over "customer service."
The information an organization has about its associates is widely regarded as one of the few advantages an "incumbent" has. The current frenzy for customer data integration (CDI) is clear evidence that more attention is being paid to managing the resource of customer data. However, it doesn't matter how well managed and integrated this information is unless it contains customer preferences, and unless their preferences and your insights are used to tailor interactions with them.
The information you have about associates is a critical advantage only if you can learn from it. This learning can't be static, either; you can't discover an interesting piece of information about your associates and then stop. This insight must make it into your systems. Smart enough systems focus on better decisions for how to treat associates.
Support for an Extended Enterprise
The growth of outsourcing and smartsourcing is leading to more loosely coupled organizations or groups of organizations. As Hagel and Seely-Brown said, "Loose coupling represents a more modular approach to process management." Loose coupling means creating independent activities with clear owners and interfaces and performance guidelines. These activities can then be assembled and disassembled more easily to meet changing needs. This kind of business structure parallels the more flexible approach to information systems represented by a service-oriented architecture (SOA). [See Sidebar] This approach implies trusting relationships.
Organizations adopting this approach need systems smart enough to work in this environment and smart enough to allow associates to change how decisions are made in the processes that span organizations -- that is, processes that require multiple organizations to deliver. Business processes, which once belonged to a single organization, are now composed of agile mini-processes that must be configured dynamically across organizational boundaries. This is impossible without the handshake of industry standards, directory services, and orchestration -- and, once again, loose coupling in a service-oriented architecture. The systems supporting these processes must also be smart enough to generate the kind of audit trails and decision outcome logs that build trust between companies and between companies and their regulators.. [See Sidebar]
Organizations must also handle more jobs that aren't located in one building or even one country but are outsourced or "homesourced" by using the Internet and related technologies to connect workers. The systems these workers use must be smart enough to let them do their jobs effectively and to act on behalf of the organization yet ensure compliance with company policy and more.
Thomas Friedman says, "There are currently about 245,000 people in India answering phones from all over the world or dialing out to solicit people for credit cards or cell phone bargains or overdue bills." He describes a series of trends and technologies that have, in his words, "flattened" the world by making it more interconnected. He explains how this flattening fits with globalization and how companies are reinventing themselves in the face of these changes and describes some of the problems, risks, and effects on political and public policy.
For example, deciding where to locate work is becoming more complex. More options, with advantages and disadvantages, are available, thanks to the overall increase in interconnectedness. Friedman explains that work will go where it can be done most effectively.
Another concept emphasized in the book is that of global, dynamic supply chains that "[coordinate] disruption-prone supply with hard-to-predict demand." For most of history, location has been critical for businesses of all kinds: where to open a store, where to put a factory, where to find customers. Improvements in connectivity and network bandwidth, however, mean that location is no longer a factor. Now the trend is work taking place where it can be done best and for the lowest cost. In addition, organizations find customers as well as suppliers and staff all over the world. They can reach out to new markets, take advantage of new opportunities, and collaborate with new partners worldwide. The parallel growth in information content of products and the overall shift from products to services in the world economy have forced organizations to consider their "digital supply chain." You can no longer consider just how and when physical goods are moved through your supply chain; you must also manage the knowledge and information that flow through it.
This ability to build a more distributed, electronically connected organization has consequences, however. In particular, how do you control it? When you outsource work to India or homesource it to Peoria, how do you make sure the work is done the way you want it done, following your policies? You need to be able to ensure that people working all over the world for you and your partners or suppliers treat your customers, your products, and your employees the way you want them to. You must equip them to act as though you were sitting in the next cubicle, even though they are geographically dispersed and perhaps brought together only temporarily to meet a business need.
Will you rely on just policy manuals and training? Will you assume that the people making decisions on your behalf can interpret data correctly from their reports and apply your business strategy to what the data tells them? With homesourced booking agents, for example, you want to make sure they offer your best travelers upgrades when they can and know how to prioritize customers who need rerouting. Those 245,000 phone operators in India need an automated system for approving credit and recommending what kind of collections strategy will work. They need smart enough systems.
Each new scandal seems to result in a new piece of regulation. Government and nonprofit organizations struggle under their own burden of reporting and compliance, and the penalties for noncompliance grow for organizations and individuals. For these reasons, governance and compliance are popular topics on the conference circuit.
Not only do organizations face more restrictions, but also many restrictions now demand demonstrating compliance. Organizations must be able to show that they are compliant with regulations. No one has to sue them or demand the information; they must report it annually, quarterly, or more often. In this environment, allowing front-line workers to make critical decisions is risky. They are less likely to be well trained, more likely to have high turnover, and most likely to be employed by third parties in the form of outsourcing. They don't necessarily make the best decisions. More important, showing that they made legal, appropriate, compliant decisions isn't easy. If more of your decisions are embedded in your information systems, however, you risk pushing the enforcement of these rules onto programmers who don't understand them, not onto businesspeople who do.
Additionally, more organizations must contend with multiple layers of regulation. They are obliged to follow local and national regulations, as they always have, and doing business on the Internet or using outsourcers around the world increasingly involves new sets of national regulations. Many international organizations, from the European Union to the World Trade Organization, also have rules that must be followed. Even knowing which set of local, national, and international rules must be applied to a specific transaction becomes a problem, let alone actually enforcing and demonstrating compliance with those rules.
Formal regulations are not the only rules an organization might need to follow. Socially conscious consumers, activist shareholders, and nongovernmental organizations also play a role. An organization might need to enforce rules to show that it's "green" or to defuse an unpopular perception of it. These "rules" must be enforced just like regulations, but they will be truly valuable only if made public. Those who care about these rules want to know exactly what the organization is planning to enforce. They want accountability -- knowing what you did with their money, goods, and so forth. Managed transparency becomes important for most, if not all, organizations. Demonstrated compliance with publicly auditable rules creates new demands on systems and people.
Regulations are also becoming more sophisticated. No longer are they simply a set of rules to be enforced; some are starting to embody best practices and statistical measures. Two examples are Basel II, with its enforcement of best practices in risk management, and court rulings forbidding personnel actions that might reasonably result in discrimination against a class of employee, even when no actions specifically do so. Being compliant won't get any easier.
The push toward compliance has a cost, however. As Taylor notes, "The biggest problem with SOX ... and [other regulations] is that it assumes a relatively static mode of business operations, and today, to be static is to be dead."
Organizations must deliver agile compliance; they must maintain business agility despite the burden of increased compliance. The increase in regulation tends to slow the rate of change in organizations by making it more expensive to make changes, but it can't stop change. Some organizations will find a way to evolve and be agile despite the regulations they operate under, and their competitors will need to do likewise. Achieving agility despite regulatory burdens requires smart enough systems.
"We have established that you cannot code your way into the future." 
"The problem is that computers and the software that runs on them ... are notoriously difficult to change with any speed or accuracy."
First, decision logic (policy rules, formulas, thresholds, regulatory mandates, and other elements used to make decisions) traditionally has been hard-coded into operational systems. As a result, development is time-consuming and costly. Developers have to translate business requirements ("If this condition is encountered, respond in this manner") into abstract representations in programming. This is a laborious process full of possibilities for error through misinterpretation. Developers have to try to anticipate all possible requirements and conditions because any changes after deployment could affect other parts of the program and require unraveling a good part of their work. Businesspeople requesting a change usually have to wait weeks or even months for the change to be coded and deployed, and because the hard-coded decision logic is buried in a system, it must be written (and rewritten) for each new platform or channel.
In addition, decision logic is difficult to understand. Because it's lodged in application programming code, business managers often have difficulty saying exactly how decisions are made. Different programmers might have coded layer after layer of policies and other types of rules in various ways. Some companies have tens of thousands of rules coded into their systems, including many that are irrelevant because they're based on market conditions and business requirements that no longer exist. Also, as organizations have moved from proprietary programs and applications to packaged applications from independent software vendors, the range of available decision rules and criteria has shrunk to those that could be "configured" with software system tools and workbenches.
Second, good decision making requires insight, especially into the probability of specific outcomes. Retail banking and other credit-extending companies have used this type of analysis extensively in automated decision systems. These "predictive analytics" are equally valuable -- and still largely unused -- for decision making in other industries. Business managers who want to bring predictive models into their decision processes might be daunted by the complexity of the data and analysis, however. Additionally, there's the impact of analytics deployments on IT resources. Predictive analytics, like decision logic, must be programmed into application code.
Third, although many companies can capture data from front-line systems and have invested heavily in data warehouses to store it, too much time might go by before they draw insights from the data. Most companies, in fact, often operate on stale data, partly because of what must be done to turn the data into a form useful for gaining insight.
Massive investment in business intelligence (BI) and data warehouse technology has undoubtedly helped management understand the impact of their decisions and detect trends in their business. What this technology hasn't done is improve the way employees and information systems that interact with customers make operational, front-line decisions. The purpose of using BI is to put it in the hands of people who can use analytic and business operations skills to understand what it's telling them. No matter how much visualization or smarts are embedded in these tools, they remain focused on knowledge workers who aren't the people making most of the decisions involved in day-to-day operations. These decisions are made by customer service representatives, counter staff, drivers, Web sites, or telephone support staff. [See Sidebar]
Fourth, much money has been spent on customer relationship management (CRM) and other enterprise application technology. Too few CRM implementations have successfully created a unified view of customers, identified their preferences, rewarded them for providing information, and then marketed to them and interacted with them the way they want. Many companies fail to respond to customers in a consistent, focused, targeted way, despite massive investments in CRM. Too many call centers and other groups of front-line workers have been neglected. These agents don't have what they need to help customers solve their problems, too much cross-selling and up-selling are done without sensitivity to customers, and the feedback loop to improve interactions is broken. The move to outsource call centers has only exacerbated this problem.
Current practices in coding, data analysis, data capture, and data management and the priorities represented by enterprise applications have resulted in systems that just aren't smart enough anymore. In an upcoming instalment ("Why Aren't My Systems Smart Enough Already?") we give more details on this problem. But if the current approaches to information systems don't work, what can provide smart enough systems? [See Sidebar]
Decision Management Is Required
Fundamentally, a smart enough system must automate the operational decisions that drive your business. If you identify and automate operational decisions, you can separate them from the rest of your applications so that they can be managed and reused.
Managing decisions isolates the logic behind the decisions, separating it from business processes and the mechanical operations of your applications. Treating decision logic as a manageable enterprise resource means you can reuse it across applications in different operational environments and treat your decisions as a corporate asset.
Managing decisions means applying analytics to make decisions more precise. Using analytics in this way makes it possible to ensure that your decisions are informed by the data you're capturing. Indeed, with experience, you can apply more advanced analytics, take market and economic uncertainties into account, and arrive at optimal decisions.
Managing decisions makes it easier for you to improve decisions over time. You can focus efforts on improving decisions and be certain that improvements will be spread throughout your organization. This focus means your return on investment (ROI) is higher, because any improvements in decisions improve results in all applications that use them, essentially multiplying the value of your investment by the number of applications used.
Managing decisions has a cultural component. By recognizing and separating out operational decisions, you can focus your business thinking and investment on these decisions more easily. You can apply your strategic vision and management approaches to achieve optimal decisions.
So what might an organization that used smart enough systems to run its business look like? How would an organization that managed its decisions act?
Introducing SmartEnough Logistics
SmartEnough Logistics is a glimpse into the immediate future. SmartEnough is a company that ships packages around the world for its business clients and applies smart enough systems to maximize returns and minimize costs.
Customers interact with drivers collecting packages, who can price, up-sell, and cross-sell effectively, thanks to their handheld devices. These devices can record customer preferences and needs, predict whether the service being purchased is more or less than required, match this prediction against the customer's contract and established preferences, recommend cross-sell and up-sell opportunities, and get the price right, given the relevant contracts.
Packages are tracked using RFID and trucks with GPS, so the tracking system knows where each package is (in which truck and at what location). The dispatch system also uses this information to make adjustments. For example, it predicts that a driver's truck won't have the capacity for the second-to-last scheduled pickup because of the volume of orders at that location, so it changes the truck's route dynamically and transmits this information to the anticipated driver, who's used to this kind of just-in-time changes to routes. The historical data from the GPS allows the dispatch system to predict which trucks can make the pickup (by predicting the additional time needed) and select a different truck by balancing this data with each truck's open capacity.
As another example of making just-in-time changes, while a truck is in transit, a customer realizes that one of her packages was shipped with too low a priority. She logs on to the company's Web site to change the shipping priority. The site uses the same decision engine as the driver's handheld device, so it gives her exactly the same information about likely delivery dates, times, and pricing as the driver did, reassuring her that everyone involved knows what they're doing. The customer requests expedited delivery online, and the system responds immediately with available upgrades based on the rules and analytics built into the scheduling system. New pricing is displayed, and the customer accepts; because her relationship with the shipper is established, she doesn't need a credit check or additional credit card.
When the truck arrives at the cross dock, the unloading crew takes each package off the truck. The RFID tags trigger an automated sorting belt that routes them to the correct loading areas for different delivery schedules and destinations. This system routes the changed package differently at this point, given its new information. The manager of the loading area notices this change because he received an automated alert from his activity-monitoring system when a label configuration unusual for the load being assembled was scanned. When he checks, the system can tell him exactly why the routing has been changed.
While the plane carrying the package is in flight, the customer calls the call center from her cell phone on the drive home, worried about her package. The customer service representative (CSR) sees the shipping information, the rerouting and the reason for it, and he has access to the same system for predicting delivery time. This delivery time, of course, now reflects the impact of the rerouting. Despite reassurances from the CSR, the customer is still concerned the package won't arrive on time. The CSR asks the system for other options on the package, and it shows that no additional rush options are available (given the package's current routing). The CSR also sees that an extra notification offer is available for free; this offer is based on the package and its delivery location, and the pricing is based on the customer's status as "concerned" (entered by the CSR) and "long established" (from the system). The customer accepts the offer and goes home to bed.
This package must also go through customs at its delivery location. SmartEnough has another system that applies current rules for the destination (a combination of rules established by the locality and rules from the federal government about exporting the items in the package) and generates the correct customs paperwork. This system ensures that the package won't be held up in customs. Some rules were added just today when new export rules were announced unexpectedly. Fortunately, SmartEnough's system could be updated directly by the legal department as soon as they understood the new rules' implications.
During the flight, bad weather closes an airport on the route, forcing a diversion. This information enters SmartEnough's system directly from the airport's system. Automated routines run in response to see what rerouting options exist for packages on the flight (package by package) and determine that some won't make their scheduled delivery, no matter what. The system immediately notifies these customers of the delay and gives new delivery times. It also makes retention-oriented "we're sorry" offers based on calculated retention risks for those customers and the kind of service they ordered. Packages that can be rerouted are.
Two options are available for the concerned customer's package. The first means it might arrive on time but has the risk of a lengthy delay. The second means it won't be on time but guarantees that it will be only a little late. Given the kind of service ordered, customer preferences, and the package's transit history, the system chooses the first option and informs the customer. At this point, there's time for the customer to change the option if she notices the notification in time (it's now night), but the system has made the best decision it can for now.
When a package is delivered in a foreign country, a third-party delivery company is used. Because SmartEnough makes all its systems available to its extended enterprise, the delivery company's driver has access to the same information and same decisions as the driver of a SmartEnough truck. When the package is delivered -- on time, as it happens -- the third-party delivery staff are notified that acknowledgment of receipt is important for this package, so they double-check with the hotel staff at the delivery location. The system prompts for a name and phone number from the person signing for the package and transmits this information to the customer.
 New regulations from the Basel Committee on Banking Supervision (BCBS) in Basel, Switzerland, aimed to produce uniformity in the way banks and banking regulators approach risk management across national borders.
|Acknowledgement: This material is from the book, Smart (Enough) Systems, by James Taylor and Neil Raden, published by Prentice Hall (June 2007).|
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