Assembly line balancing can be loosely defined as the process of optimizing an assembly line with regard to certain factors. Configuring an assembly line is a complicated process, and optimizing that system is an important part of many manufacturing business models. Maintaining and operating one is often quite costly, as well. The main focus of balancing is usually to optimize existing or planned assembly lines to minimize costs and maximize gains.
For instance, a car company might want to alter its assembly line layout in order to speed production. The company might consider the number of work stations a manufactured item must pass before it is complete and the time required at each point. Of course, each stage of this process requires a certain length of time, and the allotted time to finish a process, the number of workers, or the resource demand may also be considered, based on the specific manufacturing requirements.
The possible results of an assembly line balancing process might be maximized efficiency, minimized time to finish a process, or minimized number of work stations necessary within a certain time frame. Each manufacturing process might be quite different from another, so a company balancing unique workloads must work within the constraints and restrictions affecting its specific assembly line.
To optimize very specific operations, balancing an assembly line might require different methods, some of which include equations and algorithms concerning specific aspects of the manufacturing process. Complex manufacturing processes, such as making automobiles in large quantities, can be broken down into smaller parts, such as individual task times or the resource demands for each machine. This might be especially helpful in manufacturing processes that require the consideration of many variables, such as customized vehicles. Assembly line balancing can also guide decision-making based on the multitude of variables that can affect the manufacturing process.
Many times, this process might be used as support in decision making by offering many different models and types of data. For instance, the manager of a car manufacturer might analyze his or her operation based on the concepts of assembly line balancing using many different variables, and then make a decision based on that analysis. While this might provide the best response to an optimization effort based on one set of variables, the final decision may rest on multiple mathematical perspectives of the same problem.