Visibility in the global supply chain is almost a prerequisite for managing the complex web of product and information flows. The capacity to reconfigure resources globally can start with a simple trend analysis of the key metrics across different markets and product categories. But this beginning should be expanded to a capacity for rapid response to changes in either external market demand or internal process capabilities available at a given point in time.

For example, U.K.-based Aviva plc, the largest insurance company in Europe, is architecting its global customer service processes to constantly search for innovation and efficiency gains to deliver value to its customers. It is common for customer support call centers to use technologies to route calls to appropriate agents (agents with specific skills and temperament based on customer needs) within an office. Aviva’s insurance underwriting and claims business processes are designed to dynamically leverage the appropriate competencies from its global service centers, ranging from Australia to the Philippines, India, Europe, and Canada. Aviva’s focus is on enhancing the consumer’s experience (N = 1) by dynamically routing customer service requests to different parts of the world to provide the best service for that customer without compromising the cost of that service. This requires a capacity for realtime matching of customer profiles with agent skill profiles on a global basis.

The capacity to reconfigure resources globally can start with a simple trend analysis of the key metrics across different markets and product categories.

In its global customer support processes, Aviva worked with its partners, including a business process outsourcing (BPO) firm called 24/7 Customer in India, to capture metrics in every subtask of the entire customer engagement process to better understand its customers. The process adopted by 24/7 Customer is visible with performance metrics such as customer satisfaction and time to resolve the problem. Outcome measures such as cross-sale and transaction completion are tracked in real time for each call. In order to accomplish this dynamic routing, Aviva must have visibility to the type of customer, loads, and the quality of agents and their skills in various locations. In an article published in the Economic Times in India in 2006, Richard Harvey, Aviva Group CEO, says, “Because we take a lot of care to measure customer satisfaction on a completely arm’s-length basis, we can demonstrate that our customer satisfaction from India is as strong as or even stronger than the United Kingdom.”

An additional benefit of this transparency in its global processes is that it enables Aviva to constantly monitor the best-in-class process execution across its global centers and disseminate that knowledge to other centers. John Ainley, HR director at Aviva, admits that the company is building a culture within the organization to promote competition in process performance across its global centers, to prepare its employees to emerge out of the “not-invented-here” syndrome and to accept process innovations from other centers. This leads to continuous improvement across all centers. Aviva has certainly taken a lead in reconfiguring global resources to create customer value in the insurance industry. But it is not alone.

Aviva worked with its partners to capture metrics in every subtask of the entire customer engagement process to better understand its customers. The process is visible with performance metrics such as customer satisfaction and time to resolve the problem. Outcome measures such as cross-sale and transaction completion are tracked in real time for each call.

A visit to the Chennai (India) office of the Dallas-based Perot Systems reveals a new level of visibility in its processes and a capability to predict and reconfigure resources for its global clients. The business process service unit of Perot Systems provides backoffice support to a number of hospitals and health insurance clients. Its Chennai center has developed a customized technology platform that integrates operations, HR, and finance business processes in a single portal. The Chennai team has disaggregated every process assigned to them and carefully identified both the skill requirements and performance metrics around each task. For example, each claim can be broken into subtasks. Each subtask requires a specific skill. One can identify the performance metrics appropriate for each subtask. Such a detailed understanding of the business process (granularity) is a key ingredient in their success.

Granularity is as important as visibility. Granularity allows managers to examine in depth the process steps, as well as the appropriate skills needed to perform them. The training modules required for each task at Perot Systems processes are digitized so that individual agents can take a set of e-learning courses at a time convenient to them. As the back-office business processes for large health insurance clients are executed in its Chennai office, the integrated platform automatically tracks the performance of every process step by every agent in every work shift. The best and worst performance levels across the organization are derived in real time through live data. Performance goals for each agent are redefined periodically with an analytical engine to enable continuous improvements in their processes and hence value for their global client. The same analytical engine also computes profitability for every client at the end of each shift.

The business process service unit of Perot Systems provides backoffice support to a number of hospitals and health insurance clients. Its Chennai center has developed a customized technology platform that integrates operations, HR, and finance business processes in a single portal.

Anurag Jain, vice president for business process services at Perot Systems in Dallas, states that this integrated platform in the company’s India office allows it to assess the performance of its employees in a direct and transparent way by which individual employees are presented with their performance in a task as compared to the mean, best, and worst 10 percent of performers in that task within the organization. It is not surprising that this BPO unit of Perot Systems has bagged several awards. And Mr. Jain has now been promoted to the position of India head at Perot Systems, which means he is leading the overall consulting, applications, insurance, and BPO units in India. Perot Systems has also extended into a new business service that helps engineering services firms apply lean manufacturing concepts to their operations.

While this may be viewed as an invasion of privacy, the reality is that firms are beginning to operate at a new level of visibility to individual performance, a performance that is measured and compared with others in the organization. In a high-performance organization, there may be no place to hide for the employees, agents, or their managers. Vardhman Jain, heading the offshore BPO Chennai center of Perot Systems, claims that its primary motivation was to create a transparent culture in which there is constant peer pressure to perform as well as incentive to improve processes.

A majority of its process improvement effort emanates from its own agents, akin to a Toyota production system. He adds that the company immediately spots development areas of employees who are unable to perform at expected levels and assigns training modules that specifically improve performance in targeted areas.

This same transparency in the company’s processes and analytics also enables it to accurately measure the cost incurred for each global client, and hence, the related client profitability. Performances of individuals or the profitability associated with a customer are not exercises performed periodically; rather they are performed continually. The company’s platform provides instant profitability of each client as it executes its processes. The analytical model can also predict future run rates of revenue based on demand patterns. This creates a capacity for Perot Systems to know profitability levels of potential engagements. This level of granularity and the capacity to execute the engagements allows Perot to submit proposals of great accuracy.

In addition to business process visibility and metrics- and measurements-based decisions in daily management, Nirvana has further integrated analytics-driven insights into its decision processes to build a capacity for dynamic resource reconfiguration.

Large IT systems vendors in India, such as Infosys and TCS, have developed capabilities to constantly monitor the demand and resources needed for new IT services in their global markets. These firms recruit about 25,000 people annually, and their business models demand that they train these new recruits rapidly. These firms manage around 3,000 projects on site and offshore globally. They need to build capabilities to track latent demand for expertise in specific IT tools such as J2EE (Java to Enterprise Edition) or technology such as RFIDs and use these insights to manage their talent supply chain. Their annual training budget exceeds half a billion dollars. They need to understand resource needs and performance at the project level and profitability and experience at the customer level. Their challenge is to anticipate global demand for services, recruit and train for the right skills rapidly, and deploy resources to the right projects for the right clients globally to maximize long-term profitability. This is an analytical problem akin to a quantitative assignment problem familiar to operations researchers.

Nirvana, an emerging BPO company in Bangalore that serves global financial services clients in customer support and other back-office processes, is yet another example of a company’s unique applications of analytics and process discipline to constantly improve its understanding of customers and deliver value through global resource leverage. In addition to business process visibility and metrics- and measurements-based decisions in daily management, Nirvana has further integrated analytics-driven insights into its decision processes to build a capacity for dynamic resource reconfiguration. For example, while typical BPO organizations record at most 10 to 15 percent of the customer calls for customer support from India, Nirvana records 100 percent of the customer calls. This enables Nirvana to build a real-time customer profile based on both transaction data and keywords searched from customer conversations recorded digitally and mined for insights. In addition, Nirvana’s IT infrastructure also tracks the voice amplitude of each customer during the service call to sense the customer’s frame of mind or temper. For example, the voice of a male customer calling from Dallas is tracked and compared to the typical voice profile from similar callers. The variation in a customer’s voice amplitude is tracked in real time to be used as one of the inputs to build real-time customer insights and alter the company’s services appropriately, if needed. For example, Nirvana’s analytics engine based on data from multiple sources (transaction data, voice recordings, and keywords used by customers) has helped a large U.S. financial institution predict propensity to switch to a competitor at an individual customer level. This information has enabled the company to proactively alter its services to some of the high-risk customers and reduce its customer churn rate by
15 percent.

In this partnership with online retailers, 24/7 Customer experimented with analytics to crack the science of determining the right filters to apply in inviting customers to chat and at the same time matching the appropriate resources (that is, agents) for a given customer to enhance overall customer experience.

Similarly, consider the collaboration between the multi-billion dollar online retailer Overstock.com in the United States and 24/7 Customer in India. Virtual stores and sales chat agents are common in online retail sites because they try to enhance customer experience through either automated or human support “online chats” with customers. Unlike physical stores, online retailers, such as Amazon.com, eBay, or Overstock.com, have millions of visitors every day, and the majority of these visitors have no intention to buy and can easily switch to other shopping sites at the click of a mouse. Hence these retailers look for analytics to identify the right customers to engage in chat.

In this partnership with online retailers, 24/7 Customer experimented with analytics to crack the science of determining the right filters to apply in inviting customers to chat and at the same time matching the appropriate resources (that is, agents) for a given customer to enhance overall customer experience. First, the process of selecting customers and assigning agents is made visible to the U.S.-based retailer, and performance outcomes are transparent. Second, for individual customers who are invited to chat, the past data about those customers and their current requests or queries are combined to identify the appropriate agent to be assigned to that chat, illustrating real-time reconfiguration of resources.

The performance of agents, in terms of closing sales and overall customer experience and loyalty, is constantly assessed as feedback inputs to this analytics engine. The goal here is not to optimize product-agent selling output but to develop a real-time analytics engine that uses data from multiple sources to assess agents based on a set of customer, product, and experience attributes to determine the best available agent to talk to a given hot-lead customer. This process has also improved the performance of some agents by over 60 percent because it matches the right agents (based on their strengths and knowledge in specific product and customer categories) with the right customers. Now, if the company extends this by allowing customers to define profiles of the agents it would like to chat with, we will be moving closer to anticipation of demand and resource needs and cocreation of value.

In order to perform analytics for insights, we need to focus on the visibility, granularity, accuracy, and timeliness of data. Visibility to the processes is a necessary first step.

It must be obvious that in order to perform analytics for insights, we need to focus on the visibility, granularity, accuracy, and timeliness of data. Visibility to the processes is a necessary first step. The premium paid by large businesses for logistics services offered by UPS or FedEx is not for mere visibility. These businesses are also paying for accuracy, timeliness, and the ability to reroute the businesses’ packages based on their current needs—that is, the capacity to reconfigure resources. Dave Barnes, senior vice president and CIO at UPS, states that his company has undertaken several time and motion studies to continuously optimize every step in the package delivery processes. These studies have revealed methods for loading the trucks in better ways through new heuristics and analytical methods such as training their drivers to fasten their seatbelt with their left hand while turning the ignition key with their other hand. Package routing information is constantly tracked and planned for each delivery truck, allowing for any changes in the routes if required either by the customer or by other interferences such as traffic or weather.

The examples of Li & Fung, UPS, the Department of Defense, 24/7 Customer, Perot Systems, and Nirvana illustrate increasing sophistication in the means available to create visibility and transparency to business processes. These examples also highlight the learning capability to reconfigure resources in real time, continuously improving the skill base of employees such that consumer needs and employee skills can be matched, and finally, building a personalization component in activities that appear simple and commonplace, such as delivery of parcels. These advances call for the integration of analytics with explicit business processes defined with fine granularity. Such integration demands extreme levels of training and intense measurement of both people and business processes. These systems are measurement intensive, and they prosper with the capacity for real-time feedback and corrective actions.

R = G needs to be appropriately configured to serve N = 1. The building blocks of analytic capabilities for R = G are depicted in Figure 3.2. It should now be obvious that visibility to processes and data within global supply chains (R = G) is crucial for building the multiple layers of capabilities that are critical for dynamic reconfiguration of resources. This visibility also helps managers anticipate consumer behaviors such that they can add or subtract appropriate resources to the whole supply network. In this process, we will also be able to get new insights—be it for operational improvement as in the case of UPS or for strategic redirection and course correction as in the case of the DOD supply chains that require integration of three distinct supply chains into one.

by C.K. Prahalad and M.S. Krishnan, Via The New Age of Innovation: Driving Cocreated Value Through Global Networks (2008)

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