Assessing and Optimizing Resource Utilization of a Water Bottling Line Using Simulation

Simulation 1
ASSESSING AND OPTIMIZING RESOURCE UTILIZATION OF A WATER BOTTLING
LINE USING SIMULATION
By (Student’s Name)
Tutor’s Name
Institution
City
Date

Simulation 2
Literature review
2.1 Introduction
Planning a production cycle aims to provide an effective and efficient utilization of
plant resources. In order to meet sales demand by taking into consideration all the
important variables that affect the manufacturing environment. The production planning
process outlines the production targets and the required resource allocations for each
planning period.
There are many factors affecting the production process and it is classified under
two main categories; labor utilization and machine efficiency (S. K. SUBRAMANIAM,
2008). Human capital performance is an important factor that differs based on
capabilities and the volume of the assigned tasks. It has a direct impact on the
production outputs. Moreover, machines efficiency is another crucial factor that is
always monitored as it affects the speed and accuracy of production. Therefore, the
plant management has to highlight and review the maintenance schedules to eleminate
time waste and cost excess (S. K. SUBRAMANIAM, 2008)
Several researchers analyzed the relationship between the facility layout design and
the material handling cost. The layout design is to setup the most effective handling
arrangement as material handling cost is an additional cost to the plant’s operating cost.
The required floor space in any manufacturing plant considers machinery, operation and
maintenance space. According to American Society of Mechanical Engineers, material
handling is the art and science dealing with movement, packaging and storing of

Simulation 3
substances in a form. The equipment utilized in material handling impacts the plant’s
productivity (Syed Asad Ali Naqvi, 2016)
In a related report, Liz long (2017) discussed factors that affect manufacturing cost
like raw materials, packaging, shipping and labor. Liz discussed an example to minimize
the cost implications in a manufacturing process of a pet clothing line. After seeking the
consultant advice to reduce the production cost, the factory change the fabrics, as it
was slippery. The pervious material was difficult to manage on the cutting machines,
required more time to produce and higher than average defect rate. After the fabrics
change, the factory manages to reduce the production cost to 5% less than the previous
operating plan.
The purpose of this chapter is to discuss several studies that focus on the implications
of simulations and control factors in different productions strategies.
2.2 Facility planning
Facility planning is one of the major success factors in any production plants .It
understand the organization’s culture and analyze in-depth the new or exciting
facilities including capabilities, locations, utilizations and conditions to translate goals
into affordable and achievable plans.
A study has been executed by (Rishi H. Singhania, 2012) to replicate a physical
setup of a manufacturing environment in Arena which is a virtual system to simulate
different production scenarios. The model was subjected to three control points and
one variable response. The control points were batch size, target stock and reorder
point to analyze the response of system outputs.

Simulation 4
The first logic in production was the higher the level of goods in inventory the
smoother would be the production flow.
This simulation was applied in Arena system for different batches sizes like 20, 30,
40, 50, 60, 70 and 80. They found that the lower batches in count the higher system
outputs. Therefore, the resource utilization, inventory status and bottle neck was
identified. The simulation of production setup gives a better insight of operations
improvement.
xxxxx discussed in their research paper the flexible manufacturing system
simulation and analysis using Arena to test the system utilization and throughput.
The objective in this study was to evaluate the effect several factors such as arrival
time of demand, number of the automated guided vehicles (AGVs), velocity of the
automated guided vehicles (AGVs) and the distance preferences between two
workstations in system.
Xyz found that system utilization and throughput are both affected by these factors.
Based on the Arena simulation, demand arrival rate and distance are the main
factors affecting both variables.
Another study in motorcycle industry used Arena simulation software conducted by
xxxxxxxx in order to reduce the number of operations in the workstation, improve
productivity and suggesting new ways to achieve the most efficient production line.
The conceptual framework to achieve the optimum results as follows: