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Decision support system and method to optimize fulfillment for service delivery via rotation Disclosure Number: IPCOM000210342D
Publication Date: 2011-Aug-30
Document File: 5 page(s) / 110K

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The Prior Art Database


This article talks about a Decision Support System (DSS) to assist Resource & Capacity Planning Managers (RCM) in a large IT Services delivery organization to make organizationally optimal decisions for job fulfillement, via rotation of resources to more demanding roles backed up with another suitable resource. In a service delivery organization projects vary from weeks to months and require mix of skills, it is very crucial to make appropriate assignments so as to fulfill maximum demand and keep high utilization of resources. There is generally a generic supply demand mismatch during fulfillment process which can be due to multiple reasons such as skill mismatch even though resources are on bench, unavailability of a resource, organizational barriers etc. Organizations employ several conventional methods such as hiring, cross training and some non conventional methods such as rotation to reduce this supply demand gap. Rotation is a systematic movement of an employee from her currently assigned job to another within the company. Rotated employee will be active in their current assignment and hence usually will be backfilled by a suitable practitioner. The proposed method in this article will help identify the candidates eligible for rotation and give optimal fulfillment along with backfill for rotation canditates.

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Decision support system and method to optimize fulfillment for service delivery via rotation

Job rotation is a systematic movement of employees from job to job within a company. A number of published articles[1, 3, 4, 5] review Rotation policy followed by various organizations and have come to a conclusion that job rotation is very important for career development. In [3], they concentrate on three theories of job rotation, while in [1], it is shown that rotation is a form of career development that is more common for employees in early career than for those in later career.

In general, they talks about:
• Factors affecting Rotation.
• Advantages of Rotating candidates from employee as well as employers point of view.
• Risks involved with Rotation.

An important result shown in [2] is that not all benefits of job rotation necessarily accrue to all types of firms. Similarly, not all workers will receive the same utility benefits from rotation, making them less willing to accept it in return for a possible wage cut. The emergence of rotation in equilibrium, therefore, requires a matching of firms and workers such that they enjoy mutual benefits from the practice. So there is a need for an adequate model to enforce rotation quantitatively, taking all the factors into account.

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Figure 1: Process of Rotation

2. Method and Approach


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Today, there is no known methodology for identifying rotation candidates and computing optimal assignments for jobs with rotation candidates and their backfill. What we are proposing is a system which will :
• Identify candidates across organization based on various metrics such as current experience, career aspirations, current cost of project, backfill options available, and current bench composition
etc. using a decision tree on an ongoing basis.
• Identify critical high value demand likely to suffer due to lack of skilled resources using a

predictive model and augment those with more experienced rotation candidates.

• Rotation and backfill resource allocation recommendations generated using a mathematical

programming approach to optimize global metrics such a bench and overdue.

• Provide What-if analysis capability to assess project cost and project metrics such as delivery schedule etc. if recommended candidates is rotated out. This feature is important for business for

project managers (PM) to decide which resource can be allowed to rotate. (feature for PM)

• Interactive user interface for showing fulfillment with both backfill and rotation candidates in a user friendly format.
• Provide a Utility function to be optimized for globally optimal recommendations for wider pool of supply resources.

System gathers all the data for demand and supply attributes and then builds the model as shown in Figure 2. Edge is placed between supply and demand if both of them are compatible, i.e. they can be allocated to each other. Rotation...