Development of a Pull Production Control Method in the Metallurgical Industry
Read this article. It covers how understanding a supply chain can improve internal production processes. Pay particular attention to the section that outlines push versus pull. Can you compare and contrast each system?
Applying the Model for Production Control to the Metallurgical Industry through Simulation
In order to make the decision and to increase the acceptance for future implementations in ETO manufacturers, an agent-based simulation was carried out in order to show the potential of this approach. The goal of this simulation is the comparison of today's common production control logic, the push control logic, with potential pull control logic.
Process Recording and Data Collection
As
explained the methodology the first step was to represent with a
process mapping the material flow of the ETO metallurgical producer and
associating the data to the specific processes and product families. The
important representatives of the individual product families were
depicted in a process mapping tool and then brought into the simulation
model with their respective production routes. For each product family,
the needs of five years were taken to design the base scenario. The
agents that go through the simulation were the individual manufacturing
positions or units with their parameters such as tons, volume,
processing time per machine group, order release date, completion date,
the transport times between machine groups, etc. In addition, these
positions run through the various machine groups along its production.
Each machine group includes a number of machines that manufactures the
same products. The machine groups depend on their shift model, usually
15 or 21 shifts as explained before.
Model Formulation
Later,
the logical differences between the models are to be described. Both
models have the same assumptions as well as the same framework
conditions:
- Time horizon: The tasks are assigned according to their temporal relevance at different planning levels. According to the St. Gallen management model, the strategic planning level has a planning horizon of several years. Based on it, five years were chosen for the simulation of the two models.
- Production mix: Seven different production families with 12 different production routes were considered.
- Finite capacity for all production steps. Production capacity as the sum of the capacities of the machines within a machine group.
- Processing times depend on the product family, on the variant of product family and on the weight of the production unit.
- Same transport times between buffers and machine independently of the product family.
- Infinite transport capacity between machines along the production process.
- Infinite stock capacity before, along, and at the end of the production process.
- Raw and operating materials are always available for the production process.
- Assumed that in 3 weeks a batch for a steel type can be produced.
- Quality problems or rework lead times are not considered.
- Personal planning not considered.
- Depending on the machine group, 15 or 21 shifts per week.
- Production units are the agents that flow along the production process.
- If the load of a bottleneck is too high, the units wait to get a release date. If there are a certain number of units waiting for it, the manufacturer loses the demand that influencing the bottleneck until the number of units falls below the limit.
No. | Difference | Push-Control | Pull-Control Applying TOC/DBR |
---|---|---|---|
1 | Order release | Order release for improving capacity utilization in the first production steps | Regulated order release control based on the system load |
2 | Determination of delivery dates | Same order release for units of the same product family | Adjusted based on the system load |
3 | Sequence planning | First in First out (FIFO) | Global Priority Rule: Priority determined based on time consumption on the delivery date |
Model Programming
![](https://learn.saylor.org/pluginfile.php/5619155/mod_book/chapter/50064/f4.jpeg)
Moreover, the agents within the simulation are the production units with their parameters associated to them as listed below:
- Product family (number);
- Variant within the product family (number);
- Weight (tons);
- Processing lead time per production step (days);
- Days until security buffer (days);
- Transport lead times between all combinations of production steps (days).
- Waiting time until order release is given (days);
- Promised production lead time (days);
- Production lead time (days);
- Transport lead time (days);
- Waiting times along the production process (days);
- Days before or after the promised production lead time (days).
- Demand: production units ordered (units). The value is written in an Excel file in periods of 90 days.
- Production units started: production units released (units). The value is written in an Excel file in periods of 90 days.
- OTD (on-time-delivery): percentage of units produced before the promised delivery date for logistics (%). The value is written in an Excel file in periods of 90 days.
- Production throughput: cumulated production (tons).
- WIP: quantity of units in production process (units).
- Stock before and after production process: quantity of units before and after the production process (units).
Model Testing and Simulating
No. | Adjustable Parameter | Description | Unit |
---|---|---|---|
1 | Demand | Expected value and deviation based on gamma distribution | Units per week |
2 | Maximal load per machine group | Production system load | Days |
3 | Production lead time | Time for production for the next orders | Days |
4 | Security buffer | Time for buffer for the next orders | Days |
5 | Quantity of machines | Number of machines per machine group | Machines |
6 | Shift model | Determines the planned production time | Shifts per week |
7 | Performance factor | Considers availability and performance losses | % |
8 | WIP at time = 0 | Quantity of units in production process at day 0 | Yes/No |
Moreover, the values for the described parameters are to be introduced in the cockpit of the simulation model within the AnyLogic software, with the exception of the shift model that should be introduced in other screen within the model, as depicted in Figure 5:
![](https://learn.saylor.org/pluginfile.php/5619155/mod_book/chapter/50064/f5.jpeg)
![](https://learn.saylor.org/pluginfile.php/5619155/mod_book/chapter/50064/f6.jpeg)