Development of accident costs estimation model for Hong Kong catering industry

Abstract

Hong Kong has been known as "Gourmet's Paradise" for a long time. However, the number of accidents in catering topped all industries in the last three years in Hong Kong. Accidents are unwanted events because of their unpleasant and harmful consequences to company image, employee's morale, and injury. Accident costs can be divided into direct costs and indirect costs. Examples of direct costs are occupier's liability premium, worker's compensation, and legal representation costs. Indirect costs are hidden costs which are difficult to estimate and quantify. Nevertheless, they must be considered as financial items in a company because it may take the company a huge amount of compensation even in occurrence of one single accident. In this research a company based Accident Cost Estimation Model (ACEM) was developed for estimating, evaluating and predicting accident costs in catering industry. The model contains five different modules viz. safety databank, education, accident cost estimation, accident cost prediction, and accident cost analysis. An approach for estimating indirect accident costs is shown in the model. Direct costs are estimated based on historic data and the related regulation of worker's compensation. For eliminating the chances of miss count and over count, indirect costs are divided into the four categories of people, materials, properties, and work environment. The model is also able to provide prediction of accident costs based on the data validity in the estimation module and government statistics. In the data analysis module, the accident cost ratio, direct accident costs, indirect accident costs, forecasting of accident costs and particular safety and health statistics for any selected period can be shown. It is shown that the comprehensive and systematic accident costs estimation model serves as a powerful management tool for a catering company in estimating, predicting and analyzing accident costs and related statistics.