Using
For Optimisation

One of the features that make Orchestrate unique (in its class) is its use of advanced genetic algorithms for the optimisation process, instead of a rule-based schedule builder. This offers many advantages, not least ease of use.

With most standard capacity planning and scheduling solutions, the user has to determine a variety of rules, through an iterative process, that will deliver the best outcome. However, it can often be difficult and time consuming to determine which actions will have what effect on key performance measures such as inventory levels, WIP, lead time, throughput, manpower, etc., and then from this pre-define the scheduling rules for a particular situation. Also, to customise a general scheduling system for a specialist application, these rules have to be defined very precisely.

Added to this, is the problem that the assumptions used in determining these rules can soon change. For example, variations in product mix, order demand, cycle/process time, downtimes, operator response times, quality checks, rework, set-up/changeovers, etc., all have an impact, and so best rule set for one day, may not be the most appropriate for the next. More pronounced changes as a factory / supply chain evolves with new products, new procedures, or new resources means that existing scheduling rules can just as easily become completely out of date. Essentially, traditional rule-based systems are inherently inflexible, unresponsive to change - something that is now a prime factor in all businesses - and typically require customisation each time the rules change. As a result, this combination of regularly changing business situations and the consuming nature of re-defining rule-sets often leaves companies producing schedules based on a set of obsolete rules.

Whereas, Orchestrate’s optimisation process is based upon business objectives. Rather than going through the rule cycle, users simply specify the required outcome, which might be minimise cost, maximise on time delivery, maximise the efficiency of a particular section of the process, maximise weekly throughput with minimum cost. The state-of-the-art genetic optimisation engine will then automatically take into account current work demand or forecast, current organisational or plant structure, current process constraints and real time disturbances and rapidly work through multiple schedules to determine and then present the planner with the one that best meets the objective.

Not only does using Orchestrate eliminate the time and effort needed to research effective scheduling rules, but its optimisation process can easily be completed within minutes. This means that the system offers a far more flexible and effective way of enabling management to address a variety of business goals and optimising plans on a daily basis is feasible.

Case Studies

Using Orchestrate

Features at a Glance