A Multi-Vendor Service Model

Multi-vendor service models continue to evolve in order to address changing customer and market needs, such as the increasing need for asset utilization data that is vendor independent.

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Using Data to Drive Asset Procurement, Redeployment and Disposition

Lab managers, aided by the recent economic climate, have seen their roles expand beyond science in order to ensure that the lab is a strategic entity that runs like a business. They are charged with implementing strategic decisions to drive lab productivity and manage costs, particularly as they relate to the lab’s major capital assets: laboratory instruments.

The greater interest in capital assets correlates with a greater interest by procurement in how lab managers justify decisions regarding assets. Quantifiable data is key, and that data increasingly needs to be insightful. It’s not only how many instruments you have but also how they are being utilized and keeping track of service, the compliance program and overall cost.

When weighing redeployment, disposition and procurement questions, the answers are not always obvious given the potential scenarios below:

• The HPLC asset purchased seven years ago may no longer be cost-effective to maintain when compared with one bought two years ago. Would it be better used in a lower-throughput research lab? Or should it be disposed of via an auction or dumpster?

• One lab is utilizing an asset 80 percent of the time while another asset is being utilized only 15 percent of the time. Can the second asset be put to better use elsewhere in the lab? Or at another corporate site?

• A lab needs high-throughput forensic analysis. Its current instrument is meeting demand requirements, but a new instrument has come on the market that offers improved sample handling and temperature stabilization. Would the increase in throughput provide differentiation opportunities or allow additional work to be secured to justify purchase of the newer model?

• A new procurement decision is under consideration for a given technology. How does vendor A compare to vendors B and C across a variety of different criteria, including service cost, uptime and cost of operation?

Answering these questions requires data—as does making decisions about redeployment and disposition of assets—as mergers and acquisitions, budget tightening and shifting agendas alter the landscape. Yet, at the same time, many labs do not even have an accurate inventory of instruments.

Asset data and data mining are new endeavors for many labs and for good reason: Instrument data is typically inconsistent, non-standardized and stored in multiple data sources, making it difficult to mine in order to provide business insight.

Historically, asset management has been an ad hoc process in labs, and the data reflects it. Until recently, many labs deployed an OEM model for service, turning to the manufacturer of each instrument to provide the necessary work. The process, typically fragmented and disconnected, not surprisingly created a data stream with the same characteristics. Data is housed in multiple non-integrated systems and provides limited insight, frequently limited to:

• Financial data, such as purchase price and depreciation
• Service history
• Service costs
• Service contracts
• Consumables costs
• Compliance status

With data non-standardized and siloed asset by asset, vendor by vendor, it is difficult to pull together and analyze.

Without cohesive data, analysis of assets and costs is a rudimentary survey of service contracts, repair activities, instrument value and cost to maintain an instrument in good working order. Most likely the data will be inconsistent from OEM to OEM and from site to site and will be filled with gaps. Furthermore, the information will be accurate only for the point in time when the review takes place. Rather than ongoing and strategic, the asset management process is episodic and reactive.

Implementation of a systematic approach requires designing workflows to implement data collection and then monitoring the system. These activities place a burden on scarce resources and siphon manpower away from scientific needs in the lab. It is no surprise, then, that administrative data collection is relegated to a lower priority or not done at all.

Coincidentally, as the economic downturn forced companies to reassess budgets across the board, many turned to comprehensive multi-vendor service models. The concept combines the best practices of insurance-based service models coupled with significant internal investment in training and certifying engineers on a variety of instruments from a variety of manufacturers to provide a next-generation service model.

Rather than chasing a multitude of vendors, the customer needs to make only one call to the multi-vendor service provider to initiate or even facilitate the entire service process. As instrument quantities or service requirements aggregate, dedicated service personnel are assigned to specific customer locations.


Figure 1. Service Performance: OEM vs. OnSite Service Model

This next-generation service model increases instrument uptime, improves service response time and frees scientists to concentrate on their core competencies, all the while curbing and managing costs. A typical on-site service model saves 10 percent to 15 percent on service costs in the first year.http://en.wikipedia.org/wiki/Original_equipment_manufactureroem

The model produces significant response time improvements over the traditional OEM model, as seen in Figure 1.

Interestingly, the initial draw for customers to this service model was cost savings, although the advanced comprehensive service model improved productivity and reduced administrative costs. Now the multi-vendor service model is producing another key value driver: standardized data that is vendor independent. Instead of siloed information about each brand of an HPLC asset, as an example, a customer can now see data about all HPLCs throughout the laboratory. For companies with multiple sites, the view broadens to encompass all instruments at other sites or even around the globe (see Figure 2).

The multi-vendor service model provides standardized, continuous data and, in the most advanced versions, easy access to the metrics. With such a program, the information is available via online dashboards housed in a secure portal 24 hours a day and seven days a week. No longer burdened with chasing multiple vendors and with standardized, vendor-independent data, lab managers employing the multi-vendor model now also have the data to justify new capital asset requests, identify when to decommission assets, and determine where to shift resources as workloads and agendas shift.

Seeing the cohesive, standardized data that is obtainable within a quality multi-vendor service model has provided a stimulus in these labs. Taking the data one step further, such a model can provide another crucial tier in the data stream: asset utilization for each instrument, regardless of technology or manufacturer.


Figure 2. Global Collection of Asset Data Drive Decisions

Utilization information coupled with operational service and financial metrics improves decision-making capabilities. With this type of information, the lab manager can justify capital requests using quantifiable data about instrument utilization. Through a partnership with a multi-vendor service provider, the lab manager can pinpoint the time when assets become too costly to maintain and need to be decommissioned or auctioned; or the lab manager uses the data to justify redeploying an underused HPLC to another branch or lab to distribute workload.

When the time comes for shifting assets, a comprehensive multi-vendor service provider will have resources at hand to simplify redeployment, either within the site or to other sites, including internationally.

Such a service provider should also be able to help with disposition of assets, including increasing market value by providing checkout certification and warranties to assure potential buyers that the instrument is in good working order and includes all necessary parts, thus maximizing the value of the instrument. These certification programs function much like certified pre-owned programs in the automobile industry. Offering assurances and security regarding the quality of the used instrumentation increases its auction value.

Typical services that increase instrument value include:

• Asset inspection and certification
• Used instrument warrantiesM
• Decommissioning
• Cleaning and sales preparation
• Relocation and storage

Multi-vendor service models continue to be in strong demand in the marketplace. They continue to evolve in order to address changing customer and market needs, such as the increasing need for asset utilization data that is vendor independent. Asset utilization and asset operational data are seen as the next opportunities to unlock savings and productivity throughout a lab, a site or the entire enterprise, and will be key factors in the asset procurement, redeployment or disposition decisions in the future.

Categories: Business Management

Published In

Q is for Quality Magazine Issue Cover
Q is for Quality

Published: May 1, 2010

Cover Story

Q is for Quality

Despite the vigilance of federal, state and local regulators and of accreditation organizations that evaluate and certify laboratories, the development and maintenance of quality in laboratories are constant concerns.