Planning with iterations, an example

This example explains how item master plans are simulated when iterations are used to determine the consequences of material and/or capacity constraints for the total planning.

Overview

A certain chair is manufactured from a leather seat and a frame (both are critical components). The frame is manufactured from two metal pipes (which are critical components).

The leather seat is also used (as a critical component) for the production of a certain stool.

In the planning, items with the following item codes are used:

CHAIR
FRAME
METAL PIPE
LEATHER SEAT
STOOL

The frame, the metal pipe, and the leather seat are defined as constraints.

Initial situation

In this example, a single plan period is considered.

The following assumptions apply:

The projected inventory of the previous plan period is zero for each item.
The lead-time offsets involved are zero.
The inventory plan for each item is zero.
The planning of CHAIR has priority over the planning of STOOL (see also Workload control, to compute planning priorities.

A previous simulation (or an initial infinite planning run) resulted in the following master plan data for CHAIR, FRAME, METAL PIPE, and LEATHER SEAT:

A	Demand (forecast)
B	Production plan
C	Projected inventory
D	Dependent demand

Note The dependent demand for LEATHER SEAT also includes the demand originating from the production plan of STOOL (50).

Simulating the master plans

Suppose that the actual demand for CHAIR turns out to be 60, thus surpassing the demand forecast. Moreover, the resource where METAL PIPE is produced is overloaded, so that actually only 80 pipes can be produced.

Suppose further that the item master plans in question are simulated while considering material and capacity constraints, and using one iteration.

First, a normal planning pass (in order of increasing phase number: first the end item, then the component) is performed.

Next, an iteration is carried out, consisting of:

A reverse planning pass (in order of decreasing phase number: first the component, then the end item).
A normal planning pass (in order of increasing phase number: first the end item, then the component).

The following table shows the results of the simulation for CHAIR, FRAME, and METAL PIPE. The first column contains the old master plan values. The second column contains the result of the first (normal) planning pass, that is, before the iteration; the third column shows the result of the (first) iteration.

	Old	New
		Before iteration	After iteration
CHAIR
Demand (forecast)	50	50	50
Demand (actual)	0	60	60
Production plan	50	60	40
Projected inventory	0	0	-20

FRAME
Dependent demand	50	60	40
Production plan	50	60	40
Projected inventory	0	0	0

METAL PIPE
Dependent demand	100	120	80
Production plan	100	80	80
Projected inventory	0	-40	0

Remarks:

If the number of iterations used is greater than zero, material constraints are ignored during the first planning pass (before the first iteration), and are only considered during the iteration(s).
In the new simulation, the actual demand for CHAIR is used instead of the demand forecast (because the actual demand is higher).
During the first part of the iteration (the reverse pass: from component to end item), the constraint on the availability of METAL PIPE is passed on to FRAME and to CHAIR.
During the second part of the iteration (the normal pass: from end item to component), the reduction of the production plan for CHAIR is passed on as a reduced dependent demand for its components (including LEATHER SEAT).

The reduction of the demand for LEATHER SEAT could imply that the production plan for STOOL (another item using LEATHER SEAT as a component) can be increased. Moreover, the resource capacity no longer needed for the production of CHAIR can now be used for the production of another item. In both cases, another iteration will be necessary to reallocate the components and/or capacity involved.

Especially when there are a lot of interdepencies between items (competing for common components and/or a common resource), the use of multiple iterations can be necessary to fully optimize the planning. In the Generate Master Planning (cprmp1202m000) session, you can specify the maximum number of iterations used for the simulation run.