Cost performance analysis of small scale automotive maintenance firms in Calabar metropolis. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Nwankwojike, Bethrand N.; Inah, Oliver I.; Osinachi, Onwuka S.; Abam, Fidelis I.
• Editors: Pham, Duc
• Is Part Of: Cogent engineering. Volume 4:Issue 1(2017)
• Publication Date: 2017-01-01
• Subjects: small scale -- automotive -- maintenance -- corrective -- preventive -- cost -- time -- overrun productivity -- estimation -- performance; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2017.1369233
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: The paper presents the evaluation of cost-time performance analysis of small scale automotive maintenance firms in Calabar metropolis. The objective of the study is to ascertain the impacts of cost-time variation on the productivity performance of SMS auto maintenance firms. Primary data from seventy different auto-maintenance projects executed by Automobile, Heavy duty, Generator and Panel beater auto firms between February to October 2016 were sampled using direct observation. Analyses of data was performed using Microsoft Excel 2013 and Eviews 9.7 software. Descriptive results revealed poor cost estimation, maintenance duration, labour cost increase, maintenance type, industry size and poor scheduling which ranged between 69.31 ≥ 64.31 ≥ 63.75 ≥ 61.67 ≥ 60.97 ≥ 59.31 respectively as major factors causing cost-time overrun in SMS auto maintenance firms. Average cost and time overrun were specified at 82.8 and 62.8%, thus resulting in a cumulative cost-time performance factor of 6.4, 41.3 and 93.2%, 30.5% as productivity lost rate from the four sectors. Furthermore, regression models formulated include (i) Model I and II–Cost and Time overrun with maintenance type, industry size, and estimated cost (iii) Model III–Actual performance factor with cost overrun rate, and time overrun rate. The output of the regression Models I, II and III shows a predictive efficiency of 42, 43% (i.e. a fair correlation) and 74% (high correlation) respectively as correlation coefficient ( R 2 ) at p < 0.05. Hence, the study recommends that scheduling and proper cost estimation during negotiation stage should be considered critically, to avoid unnecessary variation in cost and time at maintenance completion.