Advanced Planning and Scheduling (APS) systems support forward-looking planning across several stages of the value chain, from the supplier to the customer – or even beyond. To this end, they determine dependencies between processes and their impact on scheduling and capacity requirements. The planning and scheduling of processes is supported by visualisations, rules and optimisation procedures.

If there are several bottlenecks that are already being utilised around the clock and cannot be substituted, capacity becomes a hard constraint. If the order volume is also very high or the production structure is very complex – e.g. if several operations of an order are processed at the same bottleneck – optimisation or automation, see also Business Intelligence, proves to be a welcome support. Most APS systems focus on this optimisation and skip the basics of interactive planning completely or partially.

How optimal is optimisation?

Mathematically speaking, optimisation always delivers the best result, but only in accordance with the defined objectives and boundary conditions. However, for the core tasks of APS systems – production planning and scheduling – it is not possible to carry out mathematical optimisation due to the combinatorial possibilities of sequencing. Instead, mathematical heuristics are used to find the best possible solution in the foreseeable future. These heuristics are usually also referred to as optimisation. An example of this is palletising or block heuristics in intralogistics.

Optimisers for simplified, period-related rough-cut planning are excluded from the following consideration, as these allow other optimisation algorithms due to their simplified task definition. However, these simplifications mean that many production constraints cannot be mapped and defects such as uncontrolled overlaps in processing and/or uncontrolled expansions in throughput time occur.

It is difficult to compare optimisers, as the optimisation tasks vary depending on the data model (i.e. the APS system), company and order situation. What applies to all optimisers, however, is that every optimiser has its limits and – contrary to what the name suggests – does not generally deliver the best economic result in practice.

APS: The limits of optimisation

The extreme case for the use of optimisation is that the optimiser of the APS system controls production and replaces the planner. In practice, this only works in exceptional cases. As helpful and time-saving as optimisation can be, attempts to bypass the planner are problematic. In many cases, these approaches simply fail due to poor results. In other cases – if the potential for improvement compared to the previous planning is large enough – the result falls short of the possibilities.

The most common shortcomings of an optimisation result are unnecessarily high delays to the customer, high turnaround times and inconsistent planning results. This results in a shift of effort from planning to master data maintenance and problem solving at the execution level. The latter is problematic because it establishes an informal parallel organisation that increasingly ignores the planning specifications.

There are four main reasons for the shortcomings in the optimisation results:

1. Poor master data quality
2. Poor starting point for optimisation
3. Flexibility of the organisation is not sufficiently taken into account
4. Insufficient information content

As a result, the optimisation result lags behind the planner’s planning, at least as far as these points are concerned. Attempts to map the complexity of reality using systems technology have proven to be a mistake. Even a moderate increase in the level of detail often means that APS systems are no longer operable due to their complexity – which in turn leads to other errors.

Conclusion: In order to utilise the economic potential of planning, it is essential that planners are offered sufficient analysis and intervention options. If these interactive intervention options are missing, neither the frequent shortcomings of unnecessary delays can be remedied, nor are there any options for reacting to orders with special priority. If companies rely solely on the inadequate optimisation provided by the APS system, another problematic aspect becomes apparent: the question of responsibility for the plan. If the planner can no longer plan, who is responsible for low delivery reliability and high stock levels?

For more information on this topic, see What requirements an APS system must fulfil – Part I and What requirements an APS system must fulfil – Part II.

Image source: © Wassermann AG