Increasing Productivity of Manufacturing

There are many legacy techniques still being applied to the design of large organizations and capital intensive installations such as factories. Often these are driven by tradition or accounting practice rather than customer focus or operational effectiveness. For example it's not uncommon to find guidelines requiring the highest possible utilization of the most expensive piece of equipment, such as a welding robot for example. This is meant to amortize the cost of the capital expenditure associated with the robot driving down its why per-unit cost. But with accounting strategies such as this can actually have an overall negative effect on the bottom-line. For example this robot, running continually, will increase the material supply cost upstream of its location and may far exceed the downstream operation's input capacity. This creates a great deal of work-in-progress inventory, drives material handling and storing costs up as half completed pieces are shoulder on the factory, and may even require a floor plan expansion. Robot output has increased, but factory output has stayed the same while factory costs have increased. Not a good scenario.

Real-world operations at several levels of complexity: multiple production lines, competition for resources, hidden costs, and so on. Traditional process redesign and often simplify a complex process but usually falls far short of optimizing it. There are simply too many interacting variables and ripple effects. A suggested improvement may appear elegant on paper but have significant unintended consequences buried deep within. We face this situation every day.

We tackle this challenge via simulation modeling. Following a data gathering and assessment stage, we design a simulation model that will give the experimenter the scope to consider all planned modifications, assess their effectiveness, and make any necessary corrections prior to the start of implementation.

Typical manufacturing problems pose to us are: Manufacturing Expansion, Increasing Output, Reducing Production Cycle Time, Improving Equipment Utilization, Materials Analysis and Volume Optimization.