eLoyalty Enhances Its Field Service And Logistics Services

eLoyalty defines strategies, outlines tactics, manages technology implementations, and provides ongoing support for its clients. It has 14 practices that focus on specific aspects of CRM such as Marketing, Internet, and Operations. Its Field Service and Logistics practice focuses on effective utilization of the field service staff; the goal is to improve customer satisfaction while simultaneously making the best use of field service resources. Knowledge management resource selections and dispatch optimization are the primary technologies eLoyalty employs to ensure that service representatives have the right skills and resources at the right place at the right time.

ServicePower is a dispatch optimization vendor headquartered in England. Its main product is ServicePower 2000. Dispatch optimization includes the following functions:

* Scheduling service appointments

* Mapping service appointments and required service skills with the proper available field service representatives

* Optimizing the service schedule to ensure field service resources are not being wasted

In the new partnership between the two companies, eLoyalty will provide implementation and ongoing support services for ServicePower software, in addition to creating field service strategy and tactics. eLoyalty told TEC that increasing customer satisfaction through enhancing field service (and customer service and support in general) is a fundamental, yet somewhat neglected aspect of CRM. eLoyalty believes that demand for CRM software will increase as organizations move from a product focus to a customer focus. eLoyalty also believes that demand for field service and logistics components of CRM will increase as organizations realize these can be just as valuable as such popular components as call center management and campaign management. The partnership with ServicePower signifies eLoyalty's commitment to provide Field Service and Logistics services as this market grows.

Market Impact

eLoyalty primarily competes against the Big Four consulting firms. These firms have partnered with CRM vendors and built a workforce capable of implementing CRM products. eLoyalty has partnerships with large CRM vendors including Siebel, Vantive, E.piphany, and Kana. Each of these vendors has also partnered with at least one of the Big Four. This creates a challenge for eLoyalty because the Big Four have existing relationships with a large percentage of the potential CRM market. Smaller consulting firms find it very difficult to win contracts when they compete against Big Four firms that have already done work for the client.

eLoyalty's biggest competitive advantage is its workforce. Unlike many of the consultants at a Big Four firm eLoyalty only hires seasoned veterans out of industry. As a result it has a staff that brings more personal experience to a project than what might be expected from the Big Four. eLoyalty is also completely focused on CRM. Thus they may convince potential clients that their understanding of CRM is deeper than what may be found in a large firm that hires recent graduates and implements nearly every type of business application.

Although eLoyalty's stock has suffered the plight of many technology firms (losing over 74% of its value since mid February) eLoyalty is profitable and revenues are growing. Their 3Q00 revenue was $46.8 million, up 11.4% from the previous quarter and up 42% from 3Q99. Earnings per share for 3Q00 were $0.03.

Supply Chain Management: Morphing the Functional Scope of Service Parts

There are many requirements involved in the supply chain management (SCM) of service and

replacement parts that make the process different from traditional, "new parts" SCM (see Part

One). As a result, some specialist SCM solutions have been developed to address these

challenges. Some might resemble conventional SCM solutions, but feature different approaches.

The requirements of service and replacement parts SCM solutions also vary given the wide range

of members that exist across multi-node supply chains. Each of these members can be grouped into

a few major solution functional categories.

Part Two of the Lucrative but Risky "Aftermarket" Business: Service and Replacement Parts SCM

series.

Service and replacement parts resource management, which is the main focus of this article,

consists of a variety of solutions that are comparable to supply chain planning (SCP) components

in conventional SCM suites. Service and replacement parts management has inventory optimization

at its core that determines the best way to stock inventory across the supply chain to maximize

service levels while minimizing investment. In other words, the basic goal is to maintain the

optimal placement of resources, including parts, tools, and service technicians, across service

regions to meet service level agreement (SLA) commitments at the lowest possible cost.

These spare parts planning systems provide the means to define and implement a spare parts

inventory strategy that meets enterprise objectives. In other words, they tend to help

enterprises understand the relationship between a customer service target level and the value of

the inventory required to support it. To that end, they combine forecasting with replenishment

logic to determine the optimal level and mix of parts to carry at each stocking tier, given

certain capital investment targets and customer service level goals. Unlike finished goods,

where nearly 100 percent customer service levels are desirable, here only certain classes of

spare parts need to be available all the time, at all supply chain nodes.

Spare parts planning systems might also improve user productivity, since by automating the basic

forecasting and replenishment process, planners and inventory managers can focus on exceptions

and more-strategic planning activities, such as how to handle expensive, slow-moving items or

how to use substitute parts to reduce costs or obsolescence.

Achieving this goal requires a mix of tools. These range from strategic tools identifying demand

profiles, service objectives, and the best way to position resources to meet demand, to tactical

tools determining what orders need to be placed to meet strategic objectives. Such goals include

managing the risk inherent in allocations and transships; repair or new purchase orders; new

product introductions (NPI) or discontinuations; and the replenishment and redeployment

decisions.

Tactical refinements of inventory optimization entail setting minimum and maximum inventory

levels, which recognizing stochastic, changing demand and lead-time. The algorithms required to

provide this support are significantly different from those found in conventional, new parts

production SCM, and justify the use of focused, point solutions, including dynamic programming,

simulation, mixed integer optimization, etc. In the case of inventory optimization, two parts

may be present:

1. Multi-echelon optimization determines optimal stocking levels of an item at a particular

location, based on the item's possible investment levels. In this case, an echelon is the level

of supply chain nodes, or disintermediation. For example, a supply chain with two independent

factory warehouses and nine wholesale warehouses delivering product to 350 retail stores is a

supply chain with three echelons between the factory and the end customer. One echelon consists

of the two independent factory warehouses, the other echelon consists of the nine wholesale

warehouses, and the third echelon consists of the 350 retail stores. Each echelon adds operating

expenses, holds inventory, adds to the cycle time, and expects to make a profit.
2. Multi-item optimization determines the optimal allocation of inventory investment across

items in a product group.

Even fundamental concepts like customer service level are different in the service and

replacement parts milieu. Namely, in new parts production, the customer service level

(synonymous with customer service ratio, fill rate, order-fill ratio, and percent of fill) is a

measure of the delivery performance of finished goods, usually expressed as a percentage. In a

make-to-stock (MTS) company, this percentage usually represents the number of items or dollars

(on one or more customer orders) that were shipped on schedule for a specific time period,

compared with the total that were supposed to be shipped in that time period. Likewise, in a

make-to-order (MTO) company, the customer service level is usually a comparison between the

number of jobs or dollars shipped in a given time period and the number of jobs or dollars that

were supposed to be shipped in the same period. Yet, in the service and replacement parts world,

with a high level of unpredictability, how can one forecast the dollar amount of service or

repair parts that were supposed to be shipped during a particular period?

Thus, given the random nature of service and breakdown events, it is clear that demand

uncertainty (which can be measured by the standard deviation, mean absolute deviation [MAD], or

variance of forecast errors) cannot be eliminated through traditional forecasting methods.

Hence, trade-offs must be evaluated on the basis of captured future risk assessments; estimates

of demand probability distribution, relevant to specific customer products; and locations at

future points in time. The decisions made across the planning horizon thus constitutes an

exercise in risk management