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Book ChapterDOI

How to Enable Multiple Skill Learning in a SLA Constrained Service System

TLDR
A model for on-job training which extends the business process for IT service delivery and provides insights into what kind of planning and dispatch policies should be practiced for achieving the desired goals of multi-skill learning and SLA success.
Abstract
In a knowledge based service system like IT services, the requirements of skills to service customer requests keep changing with time. The service workers are expected to learn the required skills very quickly and become productive. Due to high attrition rate and demand, service workers are given basic class room training and then rest of the training is carried out on-job. When a service worker learns multiple skills simultaneously, learning slows down due to factors like forgetting and interference. At the same time, the organization needs to meet service level agreements (SLA). We have developed a model for on-job training which extends the business process for IT service delivery. The key idea is to model learning, forgetting and interference in service time estimation to get realistic service times. Accurate estimation of service time taken by a service worker to resolve the service tickets helps in resource allocation and planning decisions for achieving the desired objectives of upskilling and SLA success. The simulation of execution of the augmented business process provides insights into what kind of planning and dispatch policies should be practiced for achieving the desired goals of multi-skill learning and SLA success.

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References
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Journal ArticleDOI

A model for interference and forgetting

TL;DR: In this article, a new model for interference and forgetting is presented based on the Raaijmakers and Shiffrin search of associative memory (SAM) theory for retrieval from long-term memory.
Journal ArticleDOI

Organizational Learning and Personnel Turnover

TL;DR: In this article, the impact of personnel turnover on an organization's ability to learn, and hence on its ultimate performance, is explored for organizations with different structures and different tasks, where a model of organizational decision making is presented where: 1 the organization is faced with a continuous sequence of similar but not identical problems; each problem is so complex that no one person has access to all of the information nor the skill to comprehend all of information necessary to make the decision.
Journal ArticleDOI

Job Matching and On-the-Job Training

TL;DR: In this paper, the matching process constitutes a key feature of the on-the-job training model presented in this article and tested with a unique data set containing extensive information concerning on the job training, employer search, wages and wage and productivity growth.
Journal ArticleDOI

Production breaks and the learning curve: The forgetting phenomenon

TL;DR: In this paper, the forgetting slope is shown to be mathematically dependent on the following factors: 1) the learning slope, 2) the quantity produced to date, and 3) the minimum break at which total forgetting occurs.
Proceedings ArticleDOI

Reinforcement learning from simultaneous human and MDP reward

TL;DR: A novel algorithm is introduced that shares the same spirit as tamer+rl but learns simultaneously from both reward sources, enabling the human feedback to come at any time during the reinforcement learning process.
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