bucket and shovel in the sand

When we began to plan our software platform, OPTANO production, and the ideas for its implementation became more specific, we knew that we wanted to offer more than “just” an optimum solution to a planning problem. We also wanted to find solutions which didn’t even exist (but could). We wanted to allow more scope for planning. We created this scope with scenarios.

What-if scenarios enable the user to test what could happen if this or that were to change. Imagine a sandbox in which you can play and experiment as you wish. The data is the sand with which you can build the castles and waterways and tunnels or even just a sandpie … in other words: you can change everything (or also just little things) and see what happens. You can simply experiment without having to worry about the consequences. The good thing  is that it has absolutely no effect on the result that is currently being applied. If you’re happy with the new result and want to apply it, then go ahead! Just substitute the plan you’ve been using so far.  And if the result turns out to be a disaster, no worries! You can delete the scenario and it’s as if nothing had ever happened.

This possibility has two enormous advantages: On the one hand, by experimenting you can understand connections so much better. What would happen if a particular machine broke down? Simply deactivate the machine and see what happens. Furthermore, if you can experiment without having to consider the consequences, you can take a shot in the dark and test the consequences of investments. Scenarios offer you the opportunity to try everything  – no limits, no consequences – apart from the fact that you can find solutions which you hadn’t reckoned with beforehand.

The (data) basis for scenarios

In order to create these flexible scenarios, the data which the calculations are based on must be divided into master data and scenario-dependent data. This division is more content than technical-related : All data which is considered  stable or is to be used for reference purposes belongs in the master data (e.g. sites, machines and customers). It forms the stable data basis of production planning and flows into the scenarios as a basis – however, only as a copy. In this way they can be processed without their actual dataset being changed. In scenarios you can change whatever you want, the master data is still available and is not affected. The scenario-dependent data contains data which is subject to fluctuations: orders, capacities and so on. In the scenarios, the data can simply be changed, the consequences resulting thereof can be analyzed and stored separately.

What can you do with scenarios?

Let’s take a look at a couple of questions which can be answered with the aid of scenarios and use production planning as an example. As you know, you can differentiate between general and detailed planning.  With general planning, an interval and a resource is set up for all work procedures and then we determine whether the capacities are sufficient or if there is an overload. However, a sequence for the work procedures hasn’t been established yet. This task is reserved for detailed planning. Therefore, both application areas provide different evaluation possibilities:

Scenarios in general planning:

The following questions may be of interest in the case of general planning:

  •  Would we fail to meet deadlines; for instance, if station 1 broke down?
  •  Would orders be delayed if we scheduled in a new order at short notice? If so, which ones would be affected?
  •  What would be the earliest delivery date if material that we needed for an order were blocked?

Scenarios in detailed planning

The following questions may be considered in the case of detailed planning:

  • The volume or the production deadline for an order has changed. What impact does this have on the existing production plan and which changes must/can I make?
  •  A machine has broken down  all of a sudden. Which changes can we make to compensate for this as best as possible?
  •   How will deadlines and set-up times change if we schedule in a new shift at station 1 at short notice?

Alongside all the routine questions during production planning, scenarios can be a great help, even when making major decisions, for instance in the case of large investments. You can generate orders for a longer interval and examine how the production plans may change if the machine park does, too. It is also possible to test the changes in the production plans if the demand  for a product or product group , a market or customer or  site changes. Expanding into new markets and changes in demand become plannable and enable you to make better decisions.

 How can you compare scenarios?

Once production plans have been created for various start data, it is interesting to compare the scenarios with one another to determine which variants are the more feasible. Here, defined key figures ,i.e. Key Performance Indicators (KPIs) can be useful. The total processing times or individual cycle times can be established and compared as a KPI. Delays can also be a good indicator for the quality of a production plan. These (and other) values can be presented in a diagram and compared, thus quickly highlighting the consequences of the changes. Scenario- comparative pivot charts can also be helpful in production planning. In this way you get a good overview of the differences between two scenarios. With one click, a scenario is defined as a basic reference and the charts and tables show absolute and relative deviations .

We are absolutely convinced of the possibilities that scenarios have to offer! This is  why they are also a component of the OPTANO platform. Alongside production planning they are also available for other problems.

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