Capacity planning under uncertainty is one of the crucial points as it relates on the investment in a company. This research is based on case company in a multinational hard disk drive company in Malaysia. This research is extended on the previous research by Chong and Asih (2015) which proposed some scenarios of capacity planning under demand uncertainty towards the number of required testers. These scenarios impact on the investment on expansion planning in order to meet customer demand. Therefore, this research is proposed to develop CVP analysis for multi products to evaluate how many units or dollars must be earned to break-even for capacity planning under demand uncertainty. The result shows scenario 9 has the highest number of products and dollars to break–even because this scenario has high protection level to handle demand uncertainty. In addition, compared to other products, Product B has the lowest number to break-even. It is because this product has the lowest customer demand and the longest testing durations. On the other hand, Product T has the highest number to break-even as it has the highest demand and the lowest testing durations. For managerial insight, this research could assist decision maker in analyzing the different scenarios for capacity planning under demand uncertainty.  Capacity planning under uncertainty is one of the crucial points as it relates on the investment in a company. This research is based on case company in a multinational hard disk drive company in Malaysia. This research is extended on the previous research by Chong and Asih (2015) which proposed some scenarios of capacity planning under demand uncertainty towards the number of required testers. These scenarios impact on the investment on expansion planning in order to meet customer demand. Therefore, this research is proposed to develop CVP analysis for multi products to evaluate how many units or dollars must be earned to break-even for capacity planning under demand uncertainty. The result shows scenario 9 has the highest number of products and dollars to break–even because this scenario has high protection level to handle demand uncertainty. In addition, compared to other products, Product B has the lowest number to break-even. It is because this product has the lowest customer demand and the longest testing durations. On the other hand, Product T has the highest number to break-even as it has the highest demand and the lowest testing durations. For managerial insight, this research could assist decision maker in analyzing the different scenarios for capacity planning under demand uncertainty.