Job shop scheduling problem (JSSP) has remained a challenge both for the practitioners and the researchers. A JSSP consists of multiple number of machines (m) and jobs (n).As the number of jobs increases, the complexity of the problem increases exponentially and it becomes difficult to schedule manually. Many papers in the literature discuss heuristic and metaheuristic solutions to solve Job shop scheduling problems. But there is no ease of use for practitioners who rely on their experience to schedule jobs in ad hoc sessions resulting in inefficient allocation of jobs and machines. In this paper, a job shop scheduling problem under static and dynamic conditions is solved using heuristic approaches using python programming with an MS Excel user interface. For a supplier of automotive parts with a set of jobs and machines, priority dispatching rules, viz., Shortest Processing Time (SPT), Earliest Due Date (EDD), First-In First-Out (FIFO), Critical Ratio (CR) and Slack Per Remaining Operation (S/RO) are evaluated. The obtained performance metrics such as makespan, and tardiness are compared between the heuristics to select an optimal schedule by the job shop. The user inputs the jobs, machines, start and due dates through the MS Excel interface and obtains faster, practically usable results. This reduces the time taken for job scheduling and helps in making faster productivity-based decisions to maximize resource utilization and the total time to produce the product. Integrating Python at the backend and Excel at the front end will encourage many MSMEs to perform optimized scheduling using heuristics thereby reducing the throughput time.