PROJECT MANAGEMENT CYCLE:PROJECT QUALITY MANAGEMENT

By -
PROJECT QUALITY MANAGEMENT
Processes

Project quality management involves three processes:

1. Quality planning

2. Quality assurance

3. Quality control

The purpose of these processes is to ensure that the project will satisfy the needs for which it was undertaken. These needs are multidimensional—Garvin (1987) suggests that quality has eight dimensions or performance measures:

1. Performance: This dimension refers to the product or service’s primary characteristics, such as the acceleration, cruising speed, and comfort of an automobile or the sound and picture clarity of a TV set. The understanding of performance required by the customer and the design of the service or product to achieve the required performance level are key factors in quality- based competition.

2. Features: This is a secondary aspect of performance—the characteristics that supplement the basic functioning. Garvin (1987) defines features as ‘‘the bells and whistles’’ of the product or service. The flexibility a customer has to select desired options from a large list of such options contributes to the quality of the product or service.

3. Reliability: This performance measure reflects the probability of a product malfunctioning or failing within a specified time period. It reflects on both the cost of maintenance and on downtime of the product.

4. Conformance: This is the degree to which the product or service design and operating char- acteristics meet established standards.

5. Durability: This is a measure of the economic and technical service duration of a product. It relates to the amount of use one can get from a product before it has to be replaced due to technical or economical considerations.

6. Serviceability: This measure reflects the speed, courtesy, competence, and ease of repair should the product fail. The reliability of a product and its serviceability complement each other. A

reliable product that rarely fails, and on those occasions fast and inexpensive service is avail- able, has a lower downtime and better serves its owner.

7. Aesthetics: This is a subjective performance measure related to how the product feels, tastes, looks, or smells. It reflects individual preferences.

8. Perceived quality: This is another subjective measure related to the reputation of product or a service. This reputation may be based on past experience and partial information, but in many cases the customer’s decisions are based on perceived quality because exact information about the other performance measures listed above is not readily available.

Description

Quality planning starts with the definition of standards or performance levels for each of the dimen- sions of quality. Based on the scope of the project, quality policy, standards, and regulations, a quality management plan is developed. The plan describes ‘‘the organizational structure, responsibilities, procedures, processes, and resources needed to implement quality management’’ (ISO 9000), that is, how the project management team will implement its quality policy to achieve the required quality levels. Checklists and metrics or operational definitions are also developed for each performance measure so that actual results and performance can be evaluated against specified requirements.

To provide confidence that the project will achieve the required quality level, a quality assurance process is implemented. By continuously reviewing (or auditing) the actual implementation of the plan developed during quality planning, quality assurance systematically seeks to increase the ef- fectiveness and efficiency of the project and its results.

Actual results are monitored and controlled. This quality-control process provides input to quality assurance as well as a firm basis for acceptance (or rejection) decisions.

Comments

Post a Comment

Popular posts from this blog

DUALITY THEORY:THE ESSENCE OF DUALITY THEORY

By -
Image
THE ESSENCE OF DUALITY THEORY Given our standard form for the primal problem at the left (perhaps after conversion from another form), its dual problem has the form shown to the right. Thus, with the primal problem in maximizatio n form, the dual problem is in minimization form instead. Furthermore, the dual problem uses exactly the same pa r amete r s as the primal problem, but in different locations, as summarized below. 1. The coefficients in the objective function of the primal problem are the right-hand sides of the functional constraints in the dual problem. 2. The right-hand sides of the functional constraints in the primal problem are the coef- ficients in the objective function of the dual problem. 3. The coefficients of a variable in the functional constraints of the primal problem are the coefficients in a functional constraint of the dual problem. To highlight the comparison, now look at these same two problems in matrix notation (as introduced at the beginn...
Read more

NETWORK OPTIMIZATION MODELS:THE MINIMUM SPANNING TREE PROBLEM

By -
Image
THE MINIMUM SPANNING TREE PROBLEM The minimum spanning tree problem bears some similarities to the main version of the shortest-path problem presented in the preceding section. In both cases, an undi r ecte d and connected network is being considered, where the given information includes some mea- sure of the positive length (distance, cost, time, etc.) associated with each link. Both problems also involve choosing a set of links that have the shortest total length among all sets of links that satisfy a certain property. For the shortest-path problem, this property is that the chosen links must provide a path between the origin and the destination. For the minimum spanning tree problem, the required property is that the chosen links must provide a path between ea c h pair of nodes. The minimum spanning tree problem can be summarized as follows: 1. You are given the nodes of a network but not the links. Instead, you are given the po- tential links and the positive length for each ...
Read more

INTEGER PROGRAMMING:THE BRANCH-AND-CUT APPROACH TO SOLVING BIP PROBLEMS

By -
TH E BRANCH-AND-CUT APPROACH TO SOLVING BIP PROBLEMS Integer programming has been an especially exciting area of OR since the mid-1980s be- cause of the dramatic progress being made in its solution methodology. Background To place this progress into perspective, consider the historical background. One big break- through had come in the 1960s and early 1970s with the development and refinement of the branch-and-bound approach. But then the state of the art seemed to hit a plateau. Rel- atively small problems (well under 100 variables) could be solved very efficiently, but even a modest increase in problem size might cause an explosion in computation time be- yond feasible limits. Little progress was being made in overcoming this exponential growth in computation time as the problem size was increased. Many important problems arising in practice could not be solved. Then came the next breakthrough in the mid-1980s, with the introduction of the b r an c h- and-cu t approach to sol...
Read more