Internet of Things (IOT) business has raised a strong interest in assets positioning. While outdoor positioning mostly uses a Global Navigation Satellite System (GNNS) system, those are inoperable in indoor situations. Indoor positioning has been a very active field of study for the last decades and many approaches have been proposed, however those solutions are very often complex to deploy at industrial grade, due to complexity or cost of the hardware or the algorithms. Moreover, deploying those solution often requires a complex and expensive calibration setup such as fingerprinting to establish a radio map of the room. In this study, we focus on Bluetooth Low Energy (BLE) Received Signal Strength Indication (RSSI) positioning using maximum likelihood estimate and Cramer-Ro Lower Bound (CRLB) with the widely used Log-Distance Path Loss (LDPL) model to determine the impact of the calibration setup on the accuracy and precision of the position. Results are then matched with real BLE measurements made in an industrial-like environment.