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Online File W11.1 What Services Do Customers Need?
Insights on online customer services: ◗ Customer preferences. Customers tend not to do much self-service in terms of getting information from companies (e.g., only 19% use FAQs), so they require attention. As more companies offer online self-service, though, this situation is changing. When contacting companies for information, customers use e-mail more than the telephone (71% versus 51%). ◗ Types of service. Four types of service exist, based on where the customer is in the purchase experience: during shopping (search products, compare, find product attributes); during buying (questions on warranties, billing, receipt, payment); after placing the order (checking status in processing and in shipping); and after receiving the item (checking return procedures, how to use the item). ◗ Problem resolution. Customers expect quick resolutions to problems, and expect problems to be resolved to their satisfaction. Therefore, easy returns and order tracking are desirable. ◗ Shipping options. Several shipping options are usually needed to make customers happy. ◗ Fraud protection. Customers need to make sure that sellers or others are not going to cheat them (Chapters 9 and 14). ◗ Order status and updates. Customers want to have some way to check on the status of their order, which involves tracking either by phone or online. These services are highly desired, including order notification and a clear return policy. ◗ Developing customer relationships. This includes building trust, providing security, and ensuring privacy protection (see Chapter 4). ◗ Agent profiling. The process of matching service agents directly with the needs and personalities of customers is a win-win situation for businesses, customers, and employees.

Online File W11.2 The Bullwhip Effect
The bullwhip effect refers to erratic shifts in orders up and down supply chains (see en.wikipedia.org/wiki/Bullwhip_effect). This effect was initially observed by Procter & Gamble (P&G) with its disposable diapers in offline retail stores. Although actual sales in stores were fairly stable and predictable, orders from distributors had wild swings, creating production and inventory problems for P&G and their suppliers. An investigation revealed that distributors’ orders were fluctuating because of poor demand forecasts, price fluctuations, order batching, and rationing within the supply chain. All of this resulted in unnecessary inventories in various places along the supply chain, fluctuations in P&G orders to its suppliers, and the flow of inaccurate information. Distorted or late information can lead to tremendous inefficiencies, excessive inventories, poor customer service, lost revenues, ineffective shipments, and missed production schedules. The bullwhip effect is not unique to P&G. Firms from HP in the computer industry to Bristol-Myers Squibb in the pharmaceutical field have experienced a similar phenomenon. Basically, even slight demand uncertainties and variabilities become magnified when viewed through the eyes of managers at each link in the supply chain. If each distinct entity makes ordering and inventory decisions with an eye to its own interest above those of the chain, stockpiling may be occurring simultaneously at as many as seven or eight different places along the supply chain as assurance against shortages. Such stockpiling can lead to as many as 100 days of inventory waiting “just in case.” Companies may avoid the “sting of the bullwhip” if they take steps to share information along the supply chain. Such information sharing is implemented and facilitated by EDI, extranets, and collaborative technologies.

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ONLINE FILE W11.3

Application Case

HOW DELL FULFILLS CUSTOMER REPAIR ORDERS
One of Dell’s success factors is its superb logistics and order fulfillment systems. Customer orders, which are received mostly online, are automatically transferred to the production area, where configuration determines which components and parts are needed to create the customized computer that the customer wants. Once configuration is complete, the problem becomes how to get all the needed components so that a computer can be ready for shipment the next day. As part of the solution, Dell created a network of dedicated suppliers for just-in-time deliveries, as well as a sophisticated computerized global network of components and parts inventories. The global network is also used for product services (e.g., repairs, upgrades, remanufacturing, etc.). Let’s examine how Dell provides service when a computer that is in the customer’s possession needs to be repaired. Dell is trying to achieve for repairs, upgrades, and other services the next-day shipment that it uses for new computers. For repair activities, Dell needs parts and subassemblies to be delivered to hundreds of repair stations, worldwide, from internal warehouses or external vendors. The search for the parts and their delivery must be done very quickly. To facilitate this search for parts, Dell is using an online intelligent inventory optimization system from LPA software (now clickcommerce.com). The system can reconcile the demand for parts with the action needed (e.g., repair, upgrade, transfer, or remanufacture). For example, the system allows Dell to factor the yield on reusable parts into its supply projection. This allows Dell to use repairable parts to compress time and reduce costs, enabling a team of about 10 employees to successfully process more than 6,000 service orders every day. The online system generates timely information about demand forecast, the cost of needed inventory, and “days of supply of inventory.” It compares actual with forecasted demand. This enables Dell to communicate critical information to external and internal customers, reducing order fulfillment delays. Producing or acquiring the required parts through component substitution, upgrades, and engineering change orders must be effective in order to provide superb customer service at a low inventory cost. The system also provides an online standard body of knowledge about parts and planning strategies.

Questions
1. What portions of order fulfillment does this process improve? 2. Enter dell.com and find information about how Dell conducts repair (warranty) customer service. 3. Relate this case to the discussion of “returns” in this chapter. 4. What competitive advantage is provided by this Dell system?

REFERENCES FOR ONLINE FILE W11.3 dell.com (accessed March 2009). Xelus, Inc. “Case Study: Dell.” 1999. xelus.com/industries/ cs_dell.html (no longer available online).

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ONLINE FILE W11.4

Application Case

GROCERY SUPERMARKET KEEPS IT FRESH—WOOLWORTHS OF AUSTRALIA
Dealing with early movers of pure e-tailing is a major problem for established retailing. How is a well-established major supermarket to respond? With huge investments in brick-and-mortar stores, Woolworths of Australia found itself dealing with just this question. Three major players dominate the grocery market in Australia: Coles Myers, Woolworths, and Franklins. These three companies control some 80 percent of the marketplace. Franklins, which is owned by a company in Hong Kong, takes a low-cost, minimum-service approach. The others, both Australian-based, provide a full range of products, including fresh foods and prepared meals. Woolworths’ initial approach was to set up a standard website offering a limited range of goods, but excluding perishable items. The delivery service was initially available only in areas near the company’s major supermarkets. Woolworths felt it had to respond to the newly emerging approaches from online entrepreneurs. If those organizations were allowed to take over a sizable segment of the market, it could be difficult to recover it. It was not long before management realized that this was not an effective approach. Woolworths’ staff had to walk the aisles, fill the baskets, pack the goods, and deliver them. For an organization that had optimized its supply chain in order to cut costs, here was a sudden explosion in costs. When gross margins are only 10 percent, and net margins around 4 percent, it is very easy to become unprofitable. Furthermore, Woolworths has established its place in public perception as “the fresh food people,” with fruits and vegetables, freshly baked breads, meats, and prepared meals being promoted heavily. If home shopping ignores these, Woolworths is avoiding its strengths. Woolworths’ Homeshop, the second-generation home shopping site (woolworths.com.au), was designed with freshness in mind, and all the fresh foods are available for delivery. Deliveries are arranged from major regional supermarkets, rather than from every local store. There is an AU$50 minimum order, and a 7.5 percent surcharge for home delivery, as well as an AU$6 delivery charge. This helps in recovering the additional costs, but an average order, around AU$200, still returns little profit. New users can register only if deliveries are possible to their postal address. On first use of the system, the customer is guided to find the products that they want with suggestions from the list of best-selling items. Alternatively, the customer can browse for items by category or search by keyword. Items are accumulated in the “shopping trolley” (cart). The first order is entered into a master list for future orders, as are subsequent orders. When the customer has selected the required items, they select “checkout;” at that point, the total value is computed and the customer confirms the shopping list. Payment is made only at the time of delivery using a mobile (cellular) electronic funds transfer (EFT) POS terminal, and either a credit card or a debit card. In this way, precise charges can be made based on weight of meat or fish, as well as allowing for out-of-stock items. The customer has to set the delivery time and day. If the customer is not home to accept the delivery, additional charges will be applied. Additional services that are available include dietary advice, recipes, and recording of preferred food items.
Source: Jordan, E.“Grocery Supermarket Keeps It Fresh: Woolworths of Australia.” Professor, Macquarie Graduate School of Management, Australia, August 2000, revised June 2011. Used with permission.

Questions
1. Describe the driver of the online initiative. 2. Describe the difficulties of moving online. 3. Find the status of online service today at woolworths.com.au.

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Online File W11.5 Order Fulfillment at GroceryWorks

EXHIBIT W11.5.1
1 Each customer order is placed 6.5 to 9 hours ahead of delivery time. 2 Suppliers pick goods off their own shelves and package them for pickup, with orders sorted by customer and placed in coded bags. 3 GroceryWorks’ vans pick up the goods from suppliers. 4 Fresh goods from suppliers are sent along a conveyor belt; dry goods are picked from GroceryWorks’ warehouse shelves. 5 GroceryWorks’ vans head to customers’ homes, stopping by suppliers on their return trip to the local warehouse to pick up the next round of customer orders. Frozen foods vendor 2 3 Produce vendor Meat vendor “Home meal” vendor GroceryWorks’ trucks pick up the next batch of fresh goods from vendors after finishing delivery to customers’ homes.

1

RECEIVING Conveyor belt 4 5 Customers’ homes Picking zones Dry goods LOADING Video store

Dry cleaner

Source: From T. Steinert-Threkeld (January 31, 2000). Originally published in Interactive Week, www.xplane.com. Reprinted by permission.

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ONLINE FILE W11.6

Application Case

HOW WALMART USES EC IN ITS SUPPLY CHAIN
Walmart Stores, Inc., is the world’s largest public corporation by revenue and the largest private employer in the world (about 2.1 million employees in 2008). Also in 2008, the company operated about 4,000 stores in the United States (discount, supercenters, neighborhood markets, and Sam’s Clubs) as well as more than 2,200 stores in other countries, mostly in Mexico, Canada, Brazil, and the United Kingdom. Its revenue exceeded $400 billion, with net income of about $15 billion. For further details, see en.wikipedia.org/wiki/ Walmart and walmartfacts.com. A major determinant of the success of Walmart is its IT and EC-driven supply chain. Walmart’s Supply Chain Walmart pioneered the world’s most efficient technologydriven supply chain. Let’s look at some of its components and innovations. Walmart invited its major suppliers to codevelop profitable supply chain partnerships. These partnerships are intended to amplify product flow efficiency and, in turn, Walmart’s profitability. A case in point is Walmart’s supplier relationship with P&G, a major supplier of consumer products. This relationship enables interoperation between the companies’ systems at transactional, operational, and strategic levels. Since 1988, the relationship has evolved to yield tremendous value to both companies, and their mutual business has grown manifold. Examples of intercompany innovations are vendor-managed inventory (VMI), CPFR, and RFID. Let’s look closer at Walmart and some of its supply chain–related initiatives. Inventory Management Inventory management is done at the corporate and individual store levels. In both cases, computerized systems facilitate proper inventory levels and reordering of goods. Stores manage their inventories and order goods as needed instead of the company using a centralized control. By networking with suppliers, a quick replenishment order could be placed via Walmart’s own satellite communication system. This way, suppliers can quickly deliver the goods directly to the store concerned or to the nearest distribution center. The suppliers are able to reduce costs and prices due to better coordination. Walmart invested $4 billion into a retail link collaboration system. About 20,000 suppliers use the retail link system to monitor the sales of their goods at individual stores and accordingly replenish inventory. The system has been upgraded several times with Web-enabled technologies. Walmart also uses advanced EC-based communication and processing systems, and it has extensive disaster recovery plans, enabling the company to track goods and inventory levels when disaster strikes. This ensures uninterrupted service to Walmart customers, suppliers, and partners. With its major suppliers, Walmart has VMI agreements. Managing Distribution Centers and Forklift Management Walmart uses hundreds of distribution centers worldwide. Goods are transported to these centers from suppliers and then stored. When needed, goods are reorganized in trucks and delivered to the stores. Walmart uses a computerized warehouse management system (WMS) to track and manage the flow of goods through its distribution centers. This system manages not only the forklifts within the distribution center, but also Walmart’s fleet of trucks. Wireless Industrial Vehicle Management System Forklifts and other industrial vehicles are the workhorses of material handling within the distribution centers and thus are critical factors in facility productivity. In each center, Walmart installs a comprehensive wireless Vehicle Management System (VMS). The major capabilities of this system (from I.D. Systems, Inc., id-systems.com) are listed here and organized by productivity and safety features: Productivity Features ◗ A two-way text messaging system that enables management to divert material-handling resources effectively and quickly to the point of activity where they are needed the most. ◗ Software that displays a graphical facility map, which enables not only near real-time visibility of vehicle/operator location and status, but also the ability to play back the trail of a vehicle movement over any slice of time. The system also helps to locate vehicles in real time. ◗ Unique data on peak vehicle utilization that enables optimal computerized fleet “right sizing.” It also helps work assignments and communication, especially in response to unexpected changes and needs. Safety Features ◗ Electronic safety checklist system for identifying and responding to vehicles’ problems. ◗ Access authorization to drive certain vehicles by trained drivers only. ◗ Impact sensing that provides a broad choice of automated management responses, from alerting a supervisor with visual or audible alarms, to generating a warning icon on a graphical software display of the facility, to sending an e-mail or text message to management. (continued)

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ONLINE FILE W11.6

(continued)
Walmart and RFID Adoption One of Walmart’s major initiatives in the supply chain area is pioneering the use of RFID. In the first week of April 2004, Walmart launched its first live test of RFID tracking technology. Using one distribution center and seven stores, 21 products from participating vendors were used in the pilot test. Walmart set a January 2005 target for its top 100 suppliers to place RFID tags on cases and pallets destined for Walmart stores. The system expanded to all major suppliers during 2006 through 2009, especially in the B2B Sam’s Club stores. It improves flow along the supply chain, reduces theft, increases sales, reduces inventory costs (by eliminating overstocking), and provides visibility and accuracy throughout Walmart’s supply chain. To encourage more suppliers to cooperate, in January 2008 Walmart started to charge $2 per case or pallet not tagged (see Hayes-Weier 2008). In addition to requiring RFID tags from its suppliers, Walmart is installing the technology internally. According to Scherago (2006), more than 2,000 Walmart stores were RFID-enabled with gate readers and handhelds at loading docks, facility entrances, stock rooms, and sales floors by the end of 2006. According to Songini (2007), the emphasis now is on the use of RFID in stores rather than in distribution hubs. The RFID initiative is an integral part of improving the company’s supply chain (Scherago 2006). RFID along with a new EDI improves collaboration with the suppliers and helps reduce inventories. Companies that conformed early to Walmart’s RFID mandate enjoy benefits, too. For example, Daisy Brand, the manufacturer of sour cream and cottage cheese, started shipping RFID-tagged cases and pallets to Walmart in the fall of 2004. Daisy says its investment in RFID has been a boon, helping it better manage the flow of its perishable products through Walmart stores and ensuring that marketing promotions proceed as planned (Hayes-Weier 2008). The next step in Walmart’s pilot is to mark each individual item of large goods with a tag. This plan raises a possible privacy issue: What if the tags are not removed from the products? People fear that they will be tracked after leaving the store. Walmart can also use RFID for many other applications. For example, it could attach tags to shoppers’ children, so when they are lost in the megastore, they could be tracked in seconds. Conclusion Walmart’s competitiveness and its future success depend on EC and IT’s ability to deliver applications and systems that are agile and easy to adopt to changing market conditions, especially along the supply chain. Special attention needs to be paid to global operation and transportation. It is still difficult to find items in stores due to the lack of Walmart (continued)

◗ Automatic reporting and prioritization of emergency repair issues that are identified on electronic safety checklists, where operator responses are flagged by severity of the vehicle condition. ◗ Wireless, remote lock-out of vehicles that are unsafe or in need of repair. For further details, see the Walmart case at id-systems.com. Warehouse Management System A warehouse management system (WMS) is a key part of the supply chain that primarily aims to control the movement and storage of material within a warehouse and process the associated transactions including receiving, shipping, and in-warehouse picking. The system also optimizes stock levels based on real-time information about the usage of parts and materials. Warehouse management systems often utilize information technologies, such as bar code scanners, mobile computers, Wi-Fi, and RFID to efficiently monitor the flow of products. Once data has been collected, there is either a batch synchronization with, or real-time wireless transmission to, a central database. The database can then provide useful reports about the status of goods in the warehouse. Warehouse management systems can be stand-alone systems, or modules in an ERP system (e.g., at SAP and Oracle) or in a supply chain management suite. The role and capabilities of WMS are ever-expanding. Many vendors provide WMS software (e.g., see qssi-wms.com). For a comprehensive coverage of WMS, see Piasecki (2006). Fleet and Transportation Management Several thousands of company-owned trucks move goods from the distribution centers to stores. Walmart uses several EC and IT tools for managing the trucks. These include a decision support system (DSS) for optimal scheduling, dispatching, and matching of drivers with vehicles; a computerized system for efficient purchasing and use of gasoline; a computerized preventive maintenance management system for efficient maintenance and repairs procedures; and a system that helps maximize the size of truck necessary for any given shipment. The company is experimenting with the use of a wireless GPS/GIS system for finding the trucks’ locations at any given time. Decisions about cross-docking are computerized. Crossdocking involves the elimination of the distribution center and instead uses a direct delivery to the customer after picking and sorting the goods from the suppliers. This is possible only if the suppliers ensure delivery within a specified time frame. Going Green Walmart is spending $500 million a year to increase fuel efficiency in Walmart’s truck fleet by 25 percent over the next 3 years and plans to double it within 10 years.

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ONLINE FILE W11.6

(continued) Questions
1. Why is Walmart concentrating on supply chain projects? 2. Walmart mandates RFID tags from all its large suppliers. Why are some suppliers not in compliance? 3. Investigate the options for international customers on the Walmart website. 4. Compare walmart.com with target.com, costco.com, kmart.com, and other direct competitors. Write a report. 5. Envision how transaction processing systems (TPSs) are used in Walmart stores. Go to Walmart and pay with a check. How has EC improved the old way of paying with checks?

associates, as well as to check prices due to poor labeling in some cases. The future use of RFID can help the company overcome many of these problems. Walmart is using EC in many other applications. For example, the company has more than 30 million shoppers each day, which generates 800 million transactions (each item you buy adds one transaction regarding inventory levels and sale volume). Walmart operates a huge data warehouse and uses business intelligence (BI) for reporting and analysis purposes. Finally, Walmart introduces more and more innovations. To increase the efficiency of money flow and customer service, Walmart has introduced a smart network (Birchall 2008).

REFERENCES FOR ONLINE FILE W11.6
Birchall, J. “Walmart to Deploy ‘Smart’ Shop Network.” Financial Times, September 4, 2008. Hayes-Weier, M. “Sam’s Club Suppliers Required to Use Tags or Face $2 Fee.” InformationWeek, January 21, 2008. Piasecki, D. “Warehouse Management Systems (WMS).” InventoryOps.com, July 19, 2006. inventoryops.com/ warehouse_management_systems.htm (accessed March 2009). Scherago, D. “Wal-Smart.” Retail Technology Quarterly ( January 2006). Songini, M. L. “Walmart Shifts RFID Plans.” Computerworld, February 26, 2007.

Online File W11.7 Players and Challenges in B2B Order Fulfillment

Players
Shippers (sellers) Receivers (buyers) Carriers Third-party logistics providers (3 PLs) Warehouse companies Vertical e-marketplaces Transportation e-marketplaces Logistics software application vendors

Challenges
Mix of channels, choice of logistics partners, go solo or use aggregation, what to outsource, integration of strategic, tactical, and operational decisions Solo and/or consortia buy sites, supply chain collaboration, total delivered costs, when to buy Self-service websites, links to vertical transportation e-marketplaces, institutional drag Cooperation from carriers, breadth of modes/services, IT resources, customer acquisition Location, operational intensity, capital investment, mode of automation, choice of builders Where is the “ship-it” button? Who’s behind it? What services are offered? Moving beyond spot transactions to ASPs and value-added services, neutrality versus alignment, market mechanisms (e.g., bidding) Comprehensive solutions, e-marketplace involvement, strategic partnerships, integration with existing software

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Online File W11.8 The CPFR Process
As part of a pilot project, Wagner-Lambert (WL), now a Pfizer company, shared strategic plans, performance data, and market insight with Walmart. The company realized that it could benefit from Walmart’s market knowledge, just as Walmart could benefit from WL’s product knowledge. In CPFR, trading partners collaborate on making demand forecasts. Using CPFR, WL increased its products’ shelf-fill rate (the extent to which a store’s shelves are fully stocked) from 87 percent to 98 percent, earning the company about $8 million a year in additional sales. When implementing a CPFR process, the collaborators agree on a standard process, shown in Exhibit W11.8.1. The process ends with an order forecast. CPFR provides a standard framework for collaborative planning. Retailers and vendors determine the “rules of engagement,” such as how often and at what level information will be provided. Typically, they share greater amounts of more detailed information, such as promotion schedules and item point-of-sale history, and use store-level expectations as the basis for all forecasts. The idea is to improve demand forecasting for all of the partners in the supply chain and then communicate forecasts using information-sharing applications (already developed by technology companies such as Oracle and JDA systems). For the retailer, collaborative forecasting means fewer out-of-stocks and resultant lost sales and less stored inventory. For the manufacturer, collaborative forecasting means fewer expedited shipments, optimal inventory level, and optimally sized production runs. Besides working together to develop production plans and forecasts for stock replenishment, suppliers and retailers also coordinate the related logistics activities (such as shipment or warehousing) using a common language standard and new information methodologies. The CPFR strategy has been driven by Walmart and various benchmarking partners. After a successful pilot between Walmart and Warner-Lambert involving Listerine products, a VICS (Voluntary Interindustry Commerce Standards) subcommittee was established to develop the proposed CPFR standard for the participating retailing industries (Walmart’s suppliers).

EXHIBIT W11.8.1 The CPFR Process
Company decides on participating suppliers Agreement on scope of collaboration Selection of supporting software (e.g., from JDA Software)

Develop jointly the forecasts, resolve forecasts’ exceptions

Determine specific project (e.g., demand forecast, logistics forecast)

Examine the value chain

Use result to make inventory and scheduling decision

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Online File W11.9 Intelligent Agents and Their Role in E-Commerce
As various chapters in the text have demonstrated, intelligent or software agents have come to play an increasingly important role in EC—providing assistance with Web searches, helping consumers comparison shop, making shopping recommendations, matching buyers to sellers, monitoring activities, and automatically notifying users of recent events (e.g., new job openings). This section is provided for those readers who want to learn a little more about the general features and operation of software and intelligent agents in a networked world such as the Web.

Definitions and Basic Concepts
There are several definitions of intelligent agents. Definition An intelligent agent (IA) is an autonomous entity that perceives its environment via sensors, and acts upon that environment by directing its activity toward achieving a goal(s) (i.e., acting rationally) using its actuators. The process is illustrated in Exhibit W11.9.1. Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex: A thermostat, for example, is an intelligent agent, as is a human being, as is a community of human beings working together toward a goal. Our attention here is directed to computer-based software agents. Examples of IA elements include: ◗ ◗ ◗ ◗ intelligent agent (IA) An autonomous entity that perceives its environment via sensors, and acts upon that environment directing its activity toward achieving a goal(s) (i.e., acting rationally) using its actuators.

Sensors. Eyes, nose, camera, sonar, laser range finder, search engine Percepts. Electronic signals, noise level, temperature level, e-mail volume Actuators. Limbs (artificial, real), digits, electronic commands Actions. Move an arm (real, artificial), activate electronic command, move, close, or open switch

Types of Agents Some definitions of intelligent agents emphasize their autonomy, and so prefer the term autonomous intelligent agents. Still others consider goal-directed behavior as the essence of intelligence and so prefer a term borrowed from economics, rational agent.

EXHIBIT W11.9.1

A Simple Intelligent Agent
Perceptions Sensors

Agent What is the environment like now?

Environment

Condition-action (if/then) rules (based on a goal)

Action to be taken

Action System Actuators

Action

(continued)

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Online File W11.9 (continued)
Intelligent agents in artificial intelligence are closely related to agents in economics, and versions of the intelligent agent paradigm are studied in cognitive science, ethics, and the philosophy of practical reason, as well as in many interdisciplinary sociocognitive modeling and computer social simulations. Software Agents Intelligent agents are also closely related to software agents, which are autonomous software programs that carry out tasks on behalf of users. In computer science, the term intelligent agent may be used to refer to a software agent that has some intelligence, regardless of whether it is or is not a rational agent. For example, autonomous programs used for operator assistance or data mining (sometimes referred to as bots) are also called intelligent agents. The two terms are often confused and used interchangeably. Note that most EC agents are software agents, but several have some intelligence. Following are the major types of software agents: software agents Autonomous software programs that carry out tasks on behalf of users.

◗ Simple reflex agents. Simple reflex agents act only on the basis of the current precept. The agent’s function is based on the condition-action rule: if condition, then action. ◗ Model-based reflex agents. Model-based agents can handle partially observable environments. Its current state is stored inside the agent, maintaining some kind of structure that describes the part of the world that cannot be seen. This behavior requires information on how the environment behaves and works. ◗ Goal-based agents. Goal-based agents are model-based agents that store information regarding situations that are desirable. This allows the agent a way to choose among multiple possibilities, selecting the one that reaches a goal state. ◗ Utility-based agents. Goal-based agents distinguish only between goal states and nongoal states. It is possible to define a measure of how desirable a particular state is. This measure can be obtained through the use of a utility function (or value function), which maps a state to a measure of the utility of the state. Besides these essential traits, a software agent may also possess additional traits such as adaptability, mobility, sociability, and personality. Typically, these latter traits are found in more advanced research prototypes. In this section, we will consider the essential traits first.

The Essential Traits of Software Agents
The following are the major traits of software agents. Autonomy Autonomous software agents can perform certain tasks automatically according to the rules and inference mechanisms given by the designer. As Maes (1995) points out, regular computer programs respond only to direct manipulation. In contrast, a software agent senses its environment and acts autonomously upon it. A software agent can initiate communication, monitor events, and perform tasks without the direct intervention of humans or others. For more, see Greenwald, et al. (2003). Autonomy implies that an agent takes initiative and exercises control over its own actions (Huhns and Buell 2002) and thus displays the following characteristics: ◗ Goal orientation. Accepts high-level requests indicating what a human wants, and is responsible for deciding how and where to satisfy the requests. These are referred to by Hess, et al. (2000) as homeostatic goal(s). ◗ Collaboration. Does not blindly obey commands but can modify requests, ask clarification questions, or even refuse to satisfy certain requests. ◗ Flexibility. Actions are not scripted; the agent is able to dynamically choose which actions to invoke, and in what sequence, in response to the state of its external environment. ◗ Self-starting. Unlike standard programs directly invoked by a user, an agent can sense changes in its environment and decide when to act. Autonomous agents can be resident or mobile (see Zhang, et al. 2004). Temporal Continuity A software agent is a program to which a user assigns a goal or task. The idea is that once a task or goal has been delegated, it is up to the agent to work tirelessly in pursuit of that goal. Unlike regular computer programs (continued)

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Online File W11.9 (continued) that terminate when processing is complete, an agent continues to run—either actively in the foreground or sleeping in the background—monitoring system events that trigger its actions. You can think of this attribute as “set and forget.” Reactivity A software agent responds in a timely fashion to changes in its environment. This characteristic is crucial for delegation and automation. The general principle on which software agents operate is “When X happens, do Y,” where X is some system or network event that the agent continually monitors (Gilbert 1997). Goal Driven A software agent does more than simply respond to changes in its environment. An agent can accept high-level requests specifying the goals of a human user (or another agent) and decide how and where to satisfy the requests. In some cases, an agent can modify the goals or establish goals of its own.

Other Common Traits
Some software agents also possess other common traits. Communication (Interactivity) Many agents are designed to interact with other agents, humans, or software programs. This is a critical ability in view of the narrow repertoire of any given agent. Instead of making a single agent conduct several tasks, additional agents can be created to handle undelegated tasks. Thus, communication is necessary in these instances. Agents communicate by following certain communication languages and standards, such as Agent Communication Language (ACL) and Knowledge Query and Manipulation Language (KQML) (see en.wikipedia.org/wiki/Agent_Communication_Language and en.wikipedia.org/wiki/Knowledge_Query_and_Manipulation_Language). Intelligence and Learning Currently, the majority of agents are not truly intelligent because they cannot learn; only some agents can learn. This learning goes beyond mere rule-based reasoning, because the agent is expected to use learning to behave autonomously. Although many in the artificial intelligence (AI) community argue that few people want agents who learn by “spying” on their users, the ability to learn often begins with the ability to observe users and to predict their behavior. One of the most common examples of learning agents is the wizards found in many commercial software programs (e.g., in Microsoft Office applications). These wizards offer hints to the user based on patterns the program detects in the user’s activities. Some of the newer Internet search engines boast intelligent resident agents agents that can learn from previous requests the user has made. Software agents that stay For a comprehensive discussion of these and additional characteristics, see Gudwin and Queiroz (2006). in the computer or system and perform their tasks. Mobile Agents Agents can be classified into two major categories: resident and mobile. Resident agents stay in the computer or system and perform their tasks there. For instance, many of the wizards in software programs are designed to carry out very specific tasks while a person is using his or her computer. Mobile agents, however, move to other systems, performing tasks there. A mobile agent can transport itself across different system architectures and platforms. EC agents are mobile. For applications in EC and m-commerce, see Wan (2006). Mobility. Mobility refers to the degree to which the agents themselves travel over the network. Some agents are very mobile; others are not. Mobile agents can move from one website to another and send data to and retrieve data from the user, who can focus on other tasks in the meantime. This can be very helpful to a user. For example, if a user wants to continuously monitor an electronic auction that takes a few days, the user essentially would have to be online continuously for days. Software applications that automatically watch auctions and stocks are readily available, alerting users when relevant changes are being made. Recommendation agents can improve performance by monitoring a user’s behavior after they provide the user with a recommendation (i.e., whether the recommendations are accepted or not). mobile agents Software agents that move to other systems, performing tasks there. A mobile agent can transport itself across different system architectures and platforms. mobility The degree to which the agents themselves travel over the network. Some agents are very mobile; others are not. (continued)

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Online File W11.9 (continued)
Learning Agents Software agents are called learning agents if they have the capacity to adapt or modify their behavior—that is, to learn. Simple software agents, such as e-mail agents, lack this capacity. If a simple software agent has any intelligence at all, it is found in the subroutine or methods that the agent uses to perform its tasks. Learning agents can act as assistants to humans. A learning agent can modify its behavior in four ways:

learning agents Software agents that have the capacity to adapt or modify their behavior— that is, to learn.

1. “Look over the shoulder” of the user. An agent can continually monitor the user’s interactions with the computer. By keeping track of the user’s actions over an extended period of time, the agent can discern regularities or recurrent patterns and offer to correct or automate these patterns. 2. Provide direct and indirect user feedback. The user can provide the agent with negative feedback either in a direct or an indirect fashion. Directly, the user can tell the agent not to repeat a particular action. Indirectly, the user can neglect the advice offered by an agent and take a different course of action. 3. Learn from examples given by the user. The user can train the agent by providing it with hypothetical examples of events and actions that indicate how the agent should behave in similar situations. 4. Ask the agents of other users. If an agent encounters a situation for which it has no recommended plan of action, it can ask other agents what actions they would recommend for that situation. An examples of commercial personal learning assistants is Cybelle (see agentland.com).

Multiagent Systems
Agents can communicate, cooperate, and negotiate with other agents. The basic idea of multiagent systems is that it is easy to build an agent that has a small amount of specialized knowledge and then group several agents to create a system where each agent is assigned to a simple subtask. However, in executing complex tasks that require much knowledge, it frequently is necessary to employ several software agents in one application. These agents need to share their knowledge, otherwise the results of applying this knowledge together may fail (see en.wikipedia.org/wiki/Multiagent_system). Multiagent Systems at Work multiagent systems With multiagent systems (MASs), no single designer stands behind all the agents. Each agent in the system may be working toward different goals, even contradictory ones. Agents either (MASs) compete or cooperate. For example, a customer may want to place a long-distance call. Once this Computer systems in which information is known, agents representing the carriers submit bids simultaneously. The bids are there is no single designer collected, and the best bid wins. In a complex system, the customer’s agent may take the process who stands behind all one step further by showing all bidders the offers, allowing them to rebid or negotiate. the agents; each agent in A complex task is broken into subtasks, each of which is assigned to an agent that works on the system can be working its task independently of others and is supported by a knowledge base. Acquiring and interpreting information is done by knowledge processing agents that use deductive and inductive methods, as toward different, even contradictory, goals. well as computations. The data are defined, interpreted, and sent to the coordinator, who transfers whatever is relevant to a specific user’s inquiry or need to the user interface. If no existing knowledge is available to answer an inquiry, knowledge creating and collecting agents of various types are triggered. Of the many topics related to MASs, ones that are related to EC are negotiation, coordination, collaboration, communities of agents, and agent networking. Example: Multiagent in E-Commerce. Consider a situation in which agents cooperate to arrange for a person’s summer vacation in Hawaii. The person’s agent notifies sellers’ agents about the potential traveler’s needs for a hotel, plane tickets, and a rental car; the sellers’ agents submit bids. The person’s agent collects the bids and tries to get lower rebids. The sellers’ agents can use rules for a negotiation. Related to negotiation is intermediation (see Bohte and La Poutre 2006). Applications of Software and Intelligent Agents in E-Commerce In addition to applications cited in the various chapters, or to supplement the descriptions there, we provide a comprehensive list. ◗ Mundane personal activity. In a fast-paced society, time-strapped people need new ways to minimize the time spent on routine personal tasks, such as shopping for groceries or travel planning, so that they can devote more time to professional and leisure activities. An agent can help in several tasks. ◗ Search and retrieval. Shoppers need to find information and then compare and analyze it. It is not possible to directly manipulate a distributed database system containing millions of data objects for such activities. Users will have (continued)

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Online File W11.9 (continued) to delegate the tasks of searching and cost comparison to agents. Such agents perform the tedious, time-consuming, and repetitive tasks of searching databases, retrieving and filtering information, and delivering results back to the user. Repetitive office activity. There is a pressing need to automate tasks performed by administrative and clerical personnel in functions such as online sales, desk purchasing, or customer support in order to reduce labor costs and increase office productivity. Labor costs were estimated to be as much as 60 percent of the total cost of information delivery in EC. Decision support. Increased support for tasks performed by knowledge workers, especially in the decision-making arena, is needed. Timely and knowledgeable decisions made by EC professionals greatly increase their effectiveness and the success of their businesses in the marketplace. Domain experts. It is advisable to model costly expertise and make it widely available. Expert software agents could model real-world agents such as EC consultants, EC system developers, EC site translators, EC lawyers, and so forth. Data mining. Finding patterns and relationships in data, including Web data, can be done by data mining agents, even in real time. This is especially important in market research and personalization. For a discussion, see en.wikipedia. org/data_mining. Web and text mining. Web mining—the analysis of Web data—can be facilitated by agents that can analyze large volumes of data very rapidly. The results can be used to improve Internet advertising and customer service. For additional applications in e-commerce, see Turban, et al. (2011) and Chapter 13.

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General Resources About Intelligent Agents
The following are some of the best general resources on software agents: ◗ One of the best places to start is the University of Maryland’s website on intelligent agents (agents.umbc.edu). Start with Agents 101 at agents.umbc.edu/introduction. The site has downloadable papers and reports and an extensive bibliography with abstracts (see “Publication and Presentation”). ◗ BotSpot (botspot.com) has comprehensive information about e-commerce agents and other agents. See also internet.com. ◗ MIT Media Lab (search for media projects at media.mit.edu) provides a list of agent projects and much more. ◗ The American Association of Artificial Intelligence provides comprehensive information about agents at aaai.org. ◗ The Computer Information Center in the United Kingdom provides a comprehensive knowledge base about intelligent agents at compinfo.co.uk/ai/intelligent_agents.htm. ◗ Comprehensive knowledge bases about agents are available at agent.org and 123-bots.com. ◗ Carnegie Mellon University has several agent-related programs (search for software agents at cs.cmu.edu/~softagents). ◗ IBM has several agent-development projects (research.ibm.com/iagents and alphaworks.ibm.com). ◗ Stanford University has several research teams developing agent technology (search for Knowledge Systems Laboratory at stanford.edu). ◗ Agentland.com is another “must” place to visit. It contains an up-to-date list of dozens of agents classified into e-commerce and entertainment. Some of the agents and development tools can be downloaded. ◗ The Computer Information Center (compinfo-center.com) facilitates collaboration and technology transfer about agent development. ◗ The University of Michigan has several agent development projects (eecs.umich.edu). An extensive list of resources also is available at ai.eecs.umich.edu. ◗ The National Research Council of Canada (nrc.ca) provides an artificial intelligence subject index for agents. ◗ Botknowledge.com provides considerable information about all types of bots. ◗ The Xerox Palo Alto Research Center (parc.com) provides information on software agents in general and on multiagent systems in particular. In addition to references, articles, and application cases, you can find a list of leading vendors, some with customers’ success stories. Related intelligent systems are covered as well. ◗ Microsoft employs dozens of agents (or “wizards”) in most of its software products. For details, see Microsoft’s SMS Operations Guide (e-books.bassq.nl/Microsoft/SCCM_&_SMS/Microsoft_Systems_Management_Server_2003_Operations_ Guide.pdf). With the Microsoft Agent set of software services, developers can easily enhance the user interface of their applications and Web pages with interactive personalities in the form of animated characters. These characters can move freely within the computer display, speak aloud (and display text on screen), and even listen for spoken voice commands (see msagentring.org). You can download Microsoft Agent at microsoft.com/products/msagent. (continued)

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Periodicals and Magazines
The following periodicals and magazines often contain feature articles on agents and agent-related technologies: ◗ ◗ ◗ ◗ ◗ AI Magazine Journal of Artificial Intelligence Research Annals of Mathematics and AI Expert Systems IEEE Intelligent Systems

Online File W11.9 • Review Questions
1. Define intelligent agents. 2. List and describe the major components of an intelligent agent. 3. Define a software agent. 4. Describe a mobile agent. 5. Define a learning agent. 6. Define multiagents and describe some of their applications.

REFERENCES FOR ONLINE FILE W11.9
Bohte, S. M., and H. La Poutre. “Emergent Intelligence in Competitive Multi-Agent Systems.” ERCIM News, January 2006. Gilbert, D. “Intelligent Agents: The Right Information at the Right Time.” White paper, IBM, May 1997. citeseer.nj. nec.com/context/1105800/0 (no longer available online). Greenwald, A., N. R. Jennings, and P. Stone (Eds.). “Agents and Markets.” Special issue, IEEE Intelligent Systems, November–December 2003. Gudwin, R., and J. Queiroz. Semiotics and Intelligent Systems Development. Hershey, PA: The Idea Group, 2006. Hess, T. J., L. P. Rees, and T. R. Rakes. “Using Autonomous Software Agents to Create the Next Generation DSS.” Decision Sciences, 31, no. 1 ( July 2000). Huhns, M. N., and C. A. Buell. “Trusted Autonomy.” IEEE Internet Computing (May–July 2002). Maes, P. “Artificial Intelligence Meets Entertainment: Life-like Autonomous Agents.” Communications of the ACM (November 1995). Turban, E., et al. Decision Support and Business Intelligence Systems. Upper Saddle River, NJ: Prentice Hall, 2011. Wan, Y. “Comparison-Shopping Agents and Online Small Business.” In M. Khosrow-Pour (Ed.), Encyclopedia of E-Commerce, E-Government, and Mobile Commerce. Hershey, PA: Idea Group Reference, 2006. Zhang, N., O. Shi, M. Merabti, and R. Askwith. “Autonomous Mobile Agent Based Fair Exchange.” Computer Networks (December 20, 2004).