Job Planning and Scheduling in ISOM Systems

In a busy make-to-order manufacturing enterprise, scheduling jobs and making sure that the needed materials are available in time, using manual processes or even an ERP system, can feel like a continual process of fighting fires. ISOM systems can however relieve operations managers and their staff of much of this burden.

When an intelligent agent process (IAP) creates a work order or imports it from an ERP system, the IAP can work backwards from the planned shipment or installation date to when it is necessary to order material and to start each operation in the planned work order route. This is typically done using “standard” times stored in the ISOM database.

It is important to realize, however, that in most make-to-order manufacturing operations, these dates and times are only approximations which can be disrupted by many factors, such as supply-chain issues, and depend on many factors such as the product, the quantity being made, the equipment being used and who is doing the work.

The algorithm planning purchasing and production, however, can improve its estimates by including factors such as people’s working schedule as well as the setup and cleanup time for each operation. They can be improved further by using IAPs to learn the times required for each operation and how it can vary with product, length of run, machine used, etc. based on real operational data collected in the ISOM’s history tracking database.

These are still, however, estimates. In a make-to-order operation, the schedules produced by the planning systems are only an approximation, which have to be supplemented by time-consuming daily planning meetings. Even then, the production schedule devised at the start of shift planning meeting is often “destroyed” by unexpected events.

As a result, ISOM systems also use dynamic scheduling algorithms, which continuously adjust the schedule of who works on what and using which equipment, using the times and dates produced by the planning algorithms as desired outcomes and not fixed plans.

These scheduling algorithms can be as simple as recommending the next task to work on for production workers based on planned delivery date. They can also take account the availability of raw materials, machines, and people’s skills and training.

As a result, these ISOM algorithms can eliminate the need for most daily planning meetings and on-going impromptu meetings for adjustments to schedules during the day. They can also eliminate employees “cherry picking” easier tasks to work on and they can make sure, as far as possible, that resources are efficiently used to get customer orders shipped on time.

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