Report on Manufacturing Process, FlexSIM, and Production Efficiency

This report analyzes a manufacturing process, focusing on the use of FlexSIM software for simulation and optimization. The report explores the integration of both physical and logical enablers, including computer-aided design and adaptable scheduling, to enhance production efficiency. It highlights the capabilities of FlexSIM in simulating various production scenarios and its role in process planning and resource allocation. The report also discusses the benefits of flexible manufacturing systems and hybrid production processes, as well as the limitations of FlexSIM, such as the lack of real-world considerations and challenges in conveyor logic. Several references are included, which provide the basis for the analysis of the manufacturing process.

MANUFACTURING PROCESS 1
Manufacturing process
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Enablers
Physical (Hard) Enablers
Computer aided design is also used to enhance flexibility in production by making the
design and generating prototypes that are then analyzed through simulation and adjustments
made using the FlexSIM software application. The software package has standard objects in its
library with pre-built logic as well as task executions that imitate resources and operations in real
world manufacturing operations. The objects are programmed and defined into classes with logic
tools for building, hence little or no computer code can be used to accurately model the system.
The drag and drop controls are easy to use and prototyping enables production processes and
scheduled to made to attain maximum efficiency. Every section had a unique code assigned to it
and feedback monitored through created labels for each type of item. With FlexSIM, each and
every process can be simulated, analyzed, and adjustments made for higher efficiencies
Logical (Soft) Enablers
Logical features including adaptable and flexible scheduling and process plans, along
with group technology and hybrid manufacturing approach were incorporated in the production
scheduling system. Standard tasks are automated, while tasks that can be highly variable, such as
inspection and testing of the finished product is done manually using human labor (Jha, 2012).
Grouping technology alone is insufficient, so the process was enhanced using adaptable and
flexible process plans so that different item sizes or materials can be produced using the same
overall process plan. Using parallel process plans, especially the initial steps, enabled flexible
routing and scheduling that optimizes resources and minimizes wastages (Beier et al, 2017). The
hybrid production process will ensure capacity can be scaled up and down easily. The processes
are also flexible, as shown by Fig II in which several tasks are undertaken concurrently, to enable
flexible processes, some can be done after or before others, without affecting quality or output
(‘International Conference on Hybrid Artificial Intelligence Systems’, 2009).
FlexSIM
The software package has the great advantage of enabling simulation: this helps the
production engineer/ designer have different perspectives on the best production processes and

MANUFACTURING PROCESS 3
approaches, before making a decision on the one to use. The entire system can be simulated and
then analyzed quickly, even for complex logic, to generate the most efficient production
scheduling and process plans. The software helps improve understanding of how the production
system will operate, lessen the costs and time associated with experimentation with a real
system, and minimize risk of making errors when handling proposed or actual systems. With the
software, a give process can be tested and evaluated without the risk of making fatal errors.
However, the software still has limitations; simulations do not take into consideration real
life issues such as the location of the facility, and issues such as demand and required time to
deliver products. Further, creating conveyor logic is extremely difficult with the FlexSIM
process flow functionality. It also lacks initial 3 dimensional mapping

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References
Beier, J., Herrmann, C., Kara, S., Sangwan, K. S., & Technische Universitä t Braunschweig.
(2017). Simulation approach towards energy flexible manufacturing systems. Cham: Springer.
International Conference on Hybrid Artificial Intelligence Systems. (2009). Hybrid artificial
intelligence systems: 4th international conference, HAIS 2009, Salamanca, Spain, June 10-12,
2009 : proceedings. Berlin: Springer.
Jha, N. K. (2012). Handbook of flexible manufacturing systems. San Diego: Academic Press.