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Advanced planning and scheduling software differs from traditional planning and scheduling in that it is not bound to a strict mathematical formula, but rather can tradeoff between a number of factors to find the best solution from many alternatives. That ability to weight multiple factors and find an overall best solution is called optimization.
Supply chain planning, production planning and production scheduling are complex processes that require the planner to constantly consider the interactions between many different factors. For example, freight costs can be reduced by shipping larger quantities less often, but that requires maintaining larger inventories at the receiving end, which increases cost there. Additional inventory also requires more storage space, equipment and people.
Optimization via advanced planning and scheduling software
Arguably all supply chain planning and scheduling decisions are complex and require tradeoffs. There is a range of alternatives, and the planner must sort out all the related costs, as well as the potential impact of the decision on availability and shortages; capacity and capabilities of producers, transportation links and cash flow; and many more interactions and dependencies.
Some transportation modes, like rail compared to truck, are less costly. But rail is slower, and it often requires transfer to a truck for the initial or final move from source to depot or depot to destination. Air is fastest of all, but is much more expensive.
Straightforward mathematical algorithms, such as those used by traditional ERP planning and scheduling, are limited to standards and assumptions, as well as predefined logic. They are unable to weight the relative merits of multiple factors as outlined above, so they are of limited usefulness in anything other than stable, controlled environments where fixed rules are sufficient and judgment is not required.
The optimization available through advanced planning and scheduling software changes the game by making such judgments based on a set of rules and relative values that the user company assigns when setting up the optimization software. The system can then solve for minimum or maximum value for many, many different combinations of the identified factors.
In a simple example, traditional plant scheduling calculations use standard lead times, wait times and processing times to map out a production schedule for each identified job or work order. Each schedule is calculated independently, with no consideration for other work order schedules. As a result, the system might schedule two, three or even four or more jobs for the same machine at the same time, when that machine might only be able to do one job at a time. Additional software, like capacity requirements planning, can provide visibility into this problem, but the planner must resolve the issue manually.
An optimization-based, so-called advanced scheduler or finite-capacity scheduler can determine the earliest possible and latest possible schedule for each job at each work center, and then try different combinations and schedules until it finds the best fit overall. That best fit may be the lowest overall cost, highest work center utilization, least amount of overtime, highest percentage of on-time completions, highest on-time percentage for preferred customers or products, fewest delays across the board, or some other measure of maximum value or minimum cost or penalty, as defined during the setup.
The end result is a more realistic plan or schedule that is more likely to come to fruition. It is also more likely to provide better information for customers, including more realistic lead times and promised completion dates, and can offer better resource utilization, lower costs and better customer service.
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