Modelling Trade Credit and Sales Growth in Industrial Cluster: Implications for Business Strategies in Logistics and Supply Chain Management

This study focused on the empirical relationship of trade credit and sales growth of different sectorial industrial economy by employing Seemingly Unrelated Regression Model (SUR) for each industry group. It constructed a panel data, over a 10-year time span, for four industrial clusters i.e. textile; Food and Sugar; automobile, trailers and auto-parts; and fuel and energy. The final sample of Textile economic group includes 153 units with 1530 observations; Food and Sugar includes 54 units with 540 observations; automobile, trailers & auto parts comprise 22 possessing units with 220 observations; and Fuel and Energy group having 19 non-financial firms with 190 observations. The outcome of the study shows mix results between trade credit calculated in terms of account receivables and accounts payable, and sales growth. It also shows a mix results between capital structure, financial leverage & profitability, and sales growth among different studied industries. The non-financial firms may increase their sales growth by granting more trade credit to their customers. Since, the trade credit has a positive impact in studied industrial clusters so it has a major implication for the supply chain and logistics management. The research proves that trade credit is directly proportional to the size of cargo flow required for the supply chain. The logistics component in supply chain for these firms can be better managed through a digitalized system, which would be good for overall effectiveness of strategies and actions at managerial level.