Suppose that you want to optimize an automotive assembly plant. In your plant the automotive frame and whole components arrive at the plant and the task is to assemble the complete automobile. Each task needs a robot $($Arithmetic and Logic Unit, ALU$)$ and a human supervisor $($Control Unit$)$ to complete.The human supervisor can manage multiple robots if they are all performing the same task in parallel.For simplicity, let us assume there are five steps to assembling an automobile:A$.$ add engine in frame;B$.$ add seats;C$.$ assemble wheel subsystem;D$.$ assemble steering;E$.$ assemble braking system.for simplicity, assume that these can be done in any order and can be done at the same time, except that $($step B$)$ the assembly of the seats cannot happen at the same time as $($step D$)$ the assembly of the steering system, and $($step E$)$ assembly of the brakes has to come after $($step C$)$ the assembly of the wheels. Also assume that the time for all steps is the same, except that the time to assemble the braking system $($step E$)$ is two times the time required for each of the other steps.Design automotive assembly strategies based on SISD, SIMD, and MIMD. Analyze your design and discuss the performance gain of your system over a simple monolitic assembly system in terms of efficiency, latency, and throughput. Efficiency measures how many cars can be assembled per robot or per supervisor. Latency measures how long it takes for a car to be assembled. Throughput measures how many cars can be assembled per unit of time.