Software tools can be used to demonstrate, simulate and model designs. Real time modelling allows clients’ an overview of the proposed operation in the concept design phase enabling the ability to confirm the efficiencies, capacities and improve the resilience of proposed systems.
Simulations predict possible outcomes and scenarios based on real business data. It can help in concept design and in planning to: –
- Improve lead times
- Reduce excess inventory
- Make the most efficient use of available resources
- Address warehousing issues
- Investigate productivity
- Reduce inventory costs
Identification of potential bottlenecks
The material flows and movement of goods within a facility involves many variables which simulation software is able to model in order to predict the possible course of events. The model can help to visualise where bottlenecks may occur and to then model the potential solutions to eliminate them..
The review of Automated System Logic and identification of the issues and proposed solutions.
The simulation and modelling study was commissioned as a result of the underperformance in parts of the automated materials handling system.
Underperformance was manifested for a number of reasons including:
- Late deliveries into customer RDCs
- Orders being delayed
- Incomplete orders being delivered
Problems were also being experienced in the late delivery of raw materials and packaging to Production; causing production delays and shortages that, in turn, had a knock-on adverse effect on the efficiency and timeliness of finished goods warehousing order picking and loading operations.
As output volumes increased and, as the Warehouse was already working 24 hours per day, seven days per week, it was becoming increasingly difficult and more time-consuming to recover from failures when they occurred, and to build in the resilience to cope with future problems.
The initial aim was to examine the plans and details of the technical systems logic and combine this with discussion with the technical user to allow simulation models to be constructed.
A very important part of this procedure and the modelling was to provide a clear understanding of the running of the system. However, due to lack of in depth data on the PLC and control logic, more emphasis was placed on observation and ultimately the understanding gained from the modelling.
Understanding of the operational and management systems was therefore mainly gained by observation, work shop with the client and from reading operational documentation. The observation activities included timing system activities, tracking of product pallets and noting bottlenecks to allow us to construct an “as is” model.
Having identified and mimicked the problem areas, the simulation model was then used to alter parameters, such as where decisions points were, and to vary some of the logic as to where pallets were sent under certain circumstances.
These changes brought about improvements but still there were problems with bottlenecks. The next stage was to modify the layout and add in some by-passes, move profile gauges and adjust some speed settings.
The results of these changes were then noted and all the modifications into either the logic or the physical layout were modelled independently so that the benefits of each could be realised. These results were then passed on to the equipment suppliers for modifications to be made.
The outcome was that the system was made to perform correctly and eliminate the stoppages that were affecting the ability to move raw materials and finished goods to the right place at the right time.
This model depicts a plant that produces corrugated packaging. Typically, they receive blanks from a work in process bank between a corrugator and the converting machines which die cut, print and possibly fold and glue the finished packages. The completed stacks of products are then transported to a finished goods area where the stacks are placed on a pallet, then strapped and sometimes wrapped, depending on customer requirements.
This particular plant originally had an automatic car that collected the stacks from the converters and carried them to the finished goods area. This proposal was modelled using conveyors to do this transfer.
The problem with using cars is that if they are busy, completed stacks may wait at the car pickup point and a queue build back forces the converter to pause production. This is known as blocking and it can greatly reduce the utilisation of the converters, especially those with a high output.
The advantage of conveyors over a car is that while they both carry stacks, the conveyor also provides a buffer storage which can reduce the amount of blocking on the converters.
A further requirement was that stacks from a converter should be released as a pair, and maintained as a pair through the system. This was to allow the fork lifts moving the palletised stacks into the warehouse could most easily stack the pair, thus increasing the utilisation of the fork lift trucks. The model demonstrates this feature.
In practice, the model is configured via an Excel spreadsheet that also contains a schedule for each converter. This permits the customer to experiment with different production schedules and various other factors such as proportion of wrapped pallets and sheet sales (these are stacks that are sold to other converter companies).
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