Abstract:
With the increase of international trade activities through sea and resulting exponential growth of number of ships calling on coastal nations’ ports, ship repair is becoming an increasingly attractive opportunity for littoral countries. Being a coastal nation, there were rapid growth of number of local shipyards in Bangladesh for past couple of years. This factor put CDDL faced with tremendous competition in ship repair arena in the national market. Besides, docking of ships and ship repairing work, are, by nature labor intensive. Labour cost contributes significantly to total repair cost. Besides acquiring market information, CDDL must estimate labor cost accurately to give competitive quotations in order to obtain ship repair orders. Lower labour cost value allow shipyards and ship owners to get higher productivity and lower final invoice respectively.
Forecasting estimated labor cost will allow CDDL to stay competitive among the ship repair
industries. In this project paper, attempt has been made to identify the number of those independent variables that influence ship repairing labour (dependent variable) and their inter-relationship. Since labor cost for ship repair can be expressed as a function of ship’s age, deadweight, displacement, type of ship and various repair works, so a multiple linear regression model is developed to construct a labor cost estimation model. From 2002 to 2019, ship repairing labour (man-days) related information for 43 sets for fishing vessel, 30 sets for oil tanker, 51 sets for multipurpose cargo ship, 40 sets for warship, 11 sets for dredger/barge and 15 sets for tugboat of various ages, sizes and types were collected from data storage of CDDL to construct models for each ship group. Regression coefficients are found out by applying “Method of Least Squares” in regression analysis. CDDL can use this mathematical model as a guiding tool to forecast labour cost estimates more realistically for ships to be under repair.
Description:
This thesis is submitted to the Department of Industrial Engineering and Management, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Industrial Engineering and Management, December 2019.
Cataloged from PDF Version of Thesis.
Includes bibliographical references (pages 84-88).