A project management approach to laboratory resource management

Abstract

The Applied Immunology and Microbiology (AIM) group at Wyeth Vaccine Research carries out immunoassay development, qualification and high throughput testing to support vaccine pre-clinical and clinical trials. Clinical testing workload has increased substantially with the regulatory agencies requiring more stringent criteria for trials, while industry-wide efforts strive to keep costs down, limiting resources. In view of these, AIM responded by initiating an approach labeled as Optimal Working Strategy (OWS), which is designed to enable process understanding and efficiency improvement. The first step of this approach was to establish a project coordination function. Central to this function, it was necessary to build a resource-forecasting model using workload units (defined as time required per unit work); through this exercise, the organization was transformed into a matrix organization - completely aligned with the company's strategy and work is managed by projects. This presentation will explain how the OWS works, and to share the project team's working experience on how a divisional-level resource-forecasting model could be built based on workload units. Furthermore, we will elaborate on the steps undertaken to implement the Optimal Working Strategy to meet the increasing workflow, by applying the PMBOK® Guide , Lean and Six Sigma approaches.

Introduction

Wyeth is one of the world's largest research-driven pharmaceutical and health care products companies. It is a leader in the discovery, development, manufacturing, and marketing of pharmaceuticals, vaccines, biotechnology products and nonprescription medicines that improve the quality of life for people worldwide. Our vaccine R&D effort focuses on producing vaccines against preventable diseases around the world. Our department, Applied Immunology and Microbiology (AIM), is a section within Wyeth Vaccine Research (WVR) responsible for assessing immune responses of our vaccines in human trials. Our scope includes assay development, qualification and high throughput testing under regulated conditions. In this article, we share our strategy for improvement required in our organization. We will discuss the processes and methods that were applied as well as issues that were encountered and resolved.

Case for Action and Vision

Since 2004, demand for our service has increased substantially with the regulatory agencies requiring more stringent criteria for trials, while industry-wide efforts strive to keep costs down, thus, limiting resources. Increased complexity of our vaccines in development and clinical programs require straight adhesion to time lines and the need to provide results in real time. In addition, the department was separated geographically in Rochester and Pearl River, NY. A decision was made to co-locate the two areas and the Rochester branch was moved to Pearl River in May 2004. Harmonization of the business processes of the two branches was required as part of the amalgamation.

In view of these, AIM initiated the Optimal Working Strategy (OWS), an approach designed to enable process harmonization and efficiency improvement. A project coordination core team was formed to manage the strategy in March 2005 with members including the division vice president, sr. director of operations, project coordinator, project analyst and a project administration assistant. A vision was established as follows:

  • AIM is a dynamic, efficient and accountable organization that meets WVR business objectives
  • We have a highly motivated workforce with a high sense of belonging, that is cross-trained and experienced to meet a variety of demands from WVR
  • We are able to adjust resources within the constraints to meet WVR objectives
  • We have accurate metrics and performance measurements in place to meet timelines and goals
  • Our process information is transparent and easily accessible to WVR stakeholders

This is what we wanted to accomplish in three years. Our vision and the situation at hand generated a ‘creative tension’ (Senge, 2004). This tension must be relieved if the vision is to be achieved.

Immediately after the Rochester branch moved to Pearl River, an open communication forum was established. On Wednesday mornings, people were encouraged to participate in this forum. Key personnel in operations were there to address any short-term issues in facility, compliance, process and finance. This forum still exists in order to accommodate the fluctuating issues and concerns of its participants. Another key initiative was the creation of the project coordination function and the development of a resource-forecasting model. This work standardized forecasting of all assays and enabled the department managers to track and monitor changes in demand over time. It provided valuable and credible data that was critical to our strategy development.

Elements Of Optimal Working Strategy (OWS)

As part of the OWS four key areas were identified where results delivered will positively drive us towards the vision:

Harmonize Test Demands

Laboratory testing is downstream of clinical research activities; any changes in workload or timeline could potentially have a major impact on our abilities to meet the demand. Our goal is to harmonize testing demands by leveling our resource allocation over time and by reducing peak activities and increasing trough activities. Testing needs are prioritized according to the company strategy and other common factors. This helps to evenly distribute demand over time.

Increase Internal Throughput Capacity

In order to identify opportunities to improve capacity, we examined the work system as a whole. There are two components that can increase throughput, namely, increase work efficiency and enhancements of facilities. With full support from senior management, new facilities were designed and provided. Three areas were targeted for work efficiency improvement.

  • Research and development of new multiplex assays to replace current single analytical assays
  • Review of current standard operating procedures to shorten processes and time required for material supply
  • Improve process efficiency through introduction of automation

The benefits from improvement could then be measured in terms of increased rate of yield, lead-time reduction, and waste reduction.

Cross Training and Optimize Resource Distribution

People are the most valuable asset of an organization, it is important that we leverage the diversity of individual skills, maximize the use of human resource and promote flexibility among people to move between projects. We began by conducting a talent survey to establish baseline information on people's education, skill, experience and functional areas. Then, we identified, prioritized and quantified the need. Key input to this includes organization strategy, current staffing plan and resource forecasting information. Resources were allocated to high priority projects where need gaps were the greatest. A training curriculum was developed and new team members were cross-trained to meet the changes in responsibilities. Resource gaps that could not be fulfilled with this process were brought to higher-level management; in most cases, recommendations were made regarding how to address the resource gap issues with full justifications supported by data generated by the resource-forecasting model.

Increase External Contract Laboratories Capacity

Services from external contract laboratories provide relief during peak demand periods. It also provides additional time for the internal staff to implement improvement projects. Working with external laboratories in a regulated industry requires formal approaches in the following areas:

  • Qualify laboratories and transfer new assays
  • Negotiation of increased capacity
  • Identify and select new potential contract laboratories
  • Tracking and monitoring external laboratories performance/compliance

Resource Forecasting Methodology – How it works

The resource-forecasting model was designed to systematically capture and translate the work demand into resource measurements that managers could obtain in real-time for planning purposes. Before the OWS could be executed, it was critical to systematically collect the resource forecasting data in a consistent, reliable and defensible way. The data enables us to track and monitor the demand at aggregated as well as project-platform level, and to assess the impact on projected resource requirements. The model provides estimates on number of labor hours and the full time equivalent (FTE) of human resources based on actual demand (number of tests). First we describe the resource-forecasting model in detail followed by an explanation on the use of the resource-forecasting model as part of our demand and resource planning process. There are five key steps in our resource forecasting:

Determine requirements

The first step was to establish effective communication with our clients by appointing one project leader for each priority project. Each project leader is designated as a single point of contact with their respective external clinical teams and other functional areas. Project leaders provide requirements to technology platform leaders who are responsible for carrying out the work as well as convey project related issues internally and externally in order to anticipate changes in demand or timelines.

Quantify demand – type and volume

The scope of projects and associated requirements were translated into test volume estimates with a corresponding time schedule. Work volume is calculated at the lowest level (i.e. tests) because this allows us to assign meaningful workload units to each of the tests in a specific technical platform. From the clinical protocol we obtain the required assays and the total demand. From the clinical development plan, we are able to calculate the flow of specimens per time period and the testing completion date.

Quantify workload

The remaining part of the forecasting model is to estimate the amount of time required to complete the work for each activity. Project leaders are the owners of this task; they provide updates on their estimates as projects evolve over time. Our resource-forecasting model, therefore, evolves around delivery of testing results. The task of figuring out how much time is required for each unit of work is challenging. We want to include all work associated with each activity. To do this, we must have a fundamental understanding of the process and sequence of work needed. Upon completion of assay development, the assay cycle begins from assay qualification to regulated production as shown in the diagram (Exhibit 1). This life cycle can vary depending on individual assay requirements.

Assay Life Cycle

Exhibit 1 – Assay Life Cycle

The production branch of our work is the throughput process where samples are received, processed, and testing results produced in a highly regulated manner. We defined a ‘Work Load Unit (WLU)' as the average effort (time) required to complete a unit of work (test). Assessment of WLU was conducted in technology platform team workshops following the steps below:

  • Identify a typical block of work that represents the normal workload and average capacity. For example, process 300 tests in a week.
  • Breakdown the work into high-level work packages and estimate time required to complete each package. Examples of work packages could be “samples receipt and storage”, “sample preparation” or “review testing results”. The total amount of time per work block is the sum of all the work packages.
  • WLU is calculated through dividing the total time required to complete a typical block of work by the total number of tests completed. WLU of a specific test reflects the amount of time required per unit test. WLU is unique with respect to each assay.

Total workload is calculated by multiplying the number of tests and the corresponding WLU. Note that testing is treated as a repeatable process. WLU can be used repeatedly as long as the work system has not been changed. As efficiency of the process improves, the calculated WLU decreases.

Any assay development and qualification activity is managed as a project and is viewed as a part of the assay life cycle. Workload is captured in a resource loaded work plan, which is developed through a series of workshops incorporating team-building exercises. As part of the project planning work, resource leveling and optimization are often parts of the exercise so as to obtain an accurate account of resource needed at the current efficiency level. The amount of work is then captured as total hours. Additional activities include internal efficiency improvement projects. Their workplans are developed the same way as development and qualification projects. When efficiency of the process improves, the estimated workload unit decrease. Technology platform leaders are responsible for managing improvement projects.

Estimate aggregated workload (in hours) over time

The work required to generate testing results for a project was summed up to the department level. This was done activity-by-activity, assay-by-assay, protocol-by-protocol and project-by-project. It took approximately six months to design, develop and implement the model.

Estimate full-time-equivalent (FTE) workload over time

One way to report the output of resource forecast is Full Time Equivalent or FTE. It measures total workload over a time period of interest and can be harmonized with corporate measurements.

For any given period of time (e.g. month, quarter or year), we define:

FTE = T/S where
T = total workload in hours
S = average scheduled working time available for each person.

This information can be used to evaluate the corresponding staffing level and gaps that exist should be addressed. If the staffing level in one area is significantly less than that of the forecasted resource requirement in FTE then a gap exists and needs to be dealt with.

A Roadmap Towards Building A Project Management Culture

Our approach to drive the Optimal Working Strategy follows:

  • Prioritize and align work with the company strategies
  • Establish the “AIM Project Coordination” function
  • Develop resource forecasting model and organizational metrics based on projects and assay platforms
  • Establish project “Life-cycle Management” process to drive accountability and best practices
  • Promote continuous improvements to streamline demand and resource planning processes
  • Building capability and develop internal/external resources (people, equipment, facility) to meet the changing demand
  • Improve Project Management education and awareness amongst Project/Platform Leaders
  • Promote project-based operations and team building

This strategic approach provides the roadmap for us to drive towards the OWS vision, with utilization of Project Management principles to aid our journey.

Applying PMBOK® Guide , Lean and Six Sigma Processes To Help Drive The Changes

We believe that by practicing the discipline of project management from A Guide to the Project Management Body of Knowledge (PMBOK® Guide) (PMI, 2004), we can greatly enhance our ability to implement the OWS. In addition to that, we have benefited from the use of basic concepts from total quality management such as Six Sigma and LEAN thinking. The building blocks of the resource-forecasting model are interconnected projects or processes. In order to work in a consistent, repeatable and defensible way, we applied the appropriate project management knowledge areas as we move through the five process groups, namely project initiation, planning, execution, closing and “control and monitoring”. For example, scope management during project initiation; time management, risk management and communications management for process steps beyond initiation.

The Six Sigma process emphasizes the cycle of Define, Measure, Analyze, Improve and Control (DMAIC). This process maps well with the conduct of organization effectiveness and project management activities supporting the Optimal Working Strategy. In fact, the DMAIC cycle was repeatedly applied and recognized as a way of continuously improve the quality of our business processes. Exhibit 2 gives a high level mapping. The application of LEAN was very useful at the conceptual level and is a system of high potential to help us drive efficiency improvements for the throughput operations. We are in a “make to order” environment and have now paved the way for LEAN applications by establishing assay platforms and the value of each process. The next opportunity is to work with the platform leaders to drive down wastes in the system as we strive towards process excellence.

Six Sigma DMAIC Process Mapping

Exhibit 2 – Six Sigma DMAIC Process Mapping

Organization Re-alignment And Transition To A New Project-platform Matrix Structure

The management recognized that the organization needed to be re-aligned to effectively handle the tasks at hand. Re-alignment was affected in three areas requiring the following changes:

  • Move from a functional oriented organization towards a strong matrix organization
  • Cross mapping projects with technology platforms
  • Proactively implement the project management approach

Exhibit 3 illustrates how project leaders interface with platform leaders. Since our business is project-driven, this organization structure combines the functional requirements with a strong project focus. Project leaders have full authority to drive and guide requirements with full organization support. Technology platform leaders receive work from the project leaders and are responsible for delivery of results.

Organization Layout After Restructuring

Exhibit 3 – Organization Layout After Restructuring

Application Of Project Life-cycle Management

The department actively pursued OWS under the new organization structure; as a result, many process improvement initiatives have resulted in formal projects. As existing and new projects co-exist in a changing environment, each project goes through its own life cycle. Interdependency among projects created additional complexity for resource and capacity planning. Project life cycle management recognizes different phases and progressive evolving nature of each project and integrates all work at the departmental level. This is particularly appropriate for assays going through stages of development, qualification, ramp-up and running throughputs. We drive these phases through formal governance activities including:

  • Quarterly project review meetings
  • Bi-weekly platform review meetings

For the platform plans to deliver the required results, OWS and project management must be integrated. Successful implementation of OWS in conjunction with project management approach will result in platform plans that are aligned with the best interest of both the corporate strategy and the projects. Metrics should be defined to measure performance and projects should be initiated to drive continuous improvements.

In order to implement the governance activities effectively, we must integrate all planning activities and ensure that resource allocation is prioritized and synchronized with the strategy. Our demand and resource planning process, includes the following key process steps within a review cycle,

  1. Demand forecasting
  2. Project requirements
  3. Capacity planning
  4. Platform-level staffing plan
  5. Full departmental staffing plan

Once the forecasting model is updated, the project leaders are able to specify project requirements to the respective platform leaders. This is communicated formally during the quarterly project review meetings. Platform leaders interface with project leaders to jointly work on capacity planning. Capacity planning at platform level is executed by (re) allocation of FTEs to work assignments. A staffing allocation plan over current and the next quarter (six-month period) is prepared at both platform and project levels. The plan is then summed up to divisional level. The staffing plan was compared with the resource-forecasting model for gap analysis and resource (re) allocation. Exhibit 4 summarizes the cycle of activities described.

Diagram to show cycle of activities

Exhibit 4. Diagram to show cycle of activities.

The implementation of OWS and the accompanying project management approach has resulted in the following benefits, not all are achieved yet organization-wide.

  • Demand driven capacity projections that are constantly aligned with corporate goals
  • Lead time to avoid predictable crisis triggered by change
  • Single point of contact in AIM for each project
  • Process information is integrated and easily accessible to corporate stakeholders
  • Team building within and outside of AIM
  • Motivated and dynamic workforce
  • Consistency and stability in internal structure

This work has resulted in positive changes that are already happening. Looking ahead, we need to standardize the methodologies and accompanying best practices we learned such that our work will consistently result in a high probability of success and high likelihood of sustained performance. Additional effort will be focused on laboratory efficiency improvements, enhancement of the resource-forecasting model, and extension of the resource-forecasting model to link with other planning functions such as: costing and supply chain.

Acknowledgements

This work, and certainly any progress we made in project coordination, could not be accomplished without collaboration and significant contributions from our colleagues including Jessica Zottoli, our project administrative assistant, our functional directors, our project leaders, platform leaders and members and staff who were part of various project teams.

Project Management Institute. (2004) A guide to the project management body of knowledge, Third Edition (PMBOK®) (2004 ed.). Newtown Square, PA: Project Management Institute.

Senge, Peter M., The Fifth Discipline, The Art & Practice of The Learning Organization (1994) [Compact Disc Digital Audio], Bantam Doubleday Dell Audio Publishing, Random House, Inc. New York, NY

This material has been reproduced with the permission of the copyright owner. Unauthorized reproduction of this material is strictly prohibited. For permission to reproduce this material, please contact PMI or any listed author.

© 2006, H. Tsao, J. Tam, K. Shroff and J. Hansen
Originally published as a part of 2006 PMI Global Congress Proceedings – Seattle Washington

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