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An assembly line consisting of workstations arranged in sequences is high-efficiency method used in the manufacturing of products in high volumes for example microcomputers and automobile parts. During the design of assembly lines, the line has to be "balanced" so as to maintain a uniform flow. A balance in the stages of production is maintained so as to guarantee smooth material flow and this is done by ensuring production is equalized at all times throughout the entire stages of production, therefore, reducing idle time at every individual production stage. According to Becker and Scholl (2006), line balancing has an aim of minimizing cycle time and a number of workstations while optimizing the grouping of elements at work (Becker & Scholl, 2006).

Operations have constituted an increased labor cost for many manufacturing companies in the recent years and for that Design for Assembly (DFA) has ended up receiving a lot of attention. DFA entails the designing of a product using the least number of parts and designing the other remaining parts so as to guarantee ease during assembly. The product design phase usually determines the assembly cost because it is then that the total number of parts needed for the product is determined and choices are made concerning the necessary components for assembly. Once the choice has been made, nothing much can be done to bring about influence to the cost of assembly.

This project seeks to achieve the following objectives:

  1. To get a clear understanding of production line, assembly design and algorithms used in line balancing.
  2. To understand parameters used in the design of an assembly line.
  3. To analyze both existing and proposed assembly lines.
  4. To assist in the optimization of time taken for production on an assembly line.

The project's major concern is with the effectiveness of line balancing which is made possible by placing the sequential work activities in groups of workstations so that the idle time can be at its minimum. The major target is to make an exploration of a company which is fit for this project and from there learn and gather the necessary data from the systems which are in place and from there propose an optimum system in regards to balance delay, idle time, line efficiency and cycle time. From the findings of this research, a recommendation based on the plan of implementation of the proposed system is stated.

Manufacturing systems as a phrase were used as early as 1812 by Owen, a utopian socialist and therefore it is not new. It was at this time that the phrase meant a series of inventions created during the Great Britain's industrial revolutions almost 210 years ago. In the early twentieth century, manufacturing system got a whole new emphasis in terms of manufacturing and management Sivasankaran and Shahabudeen (2014). Currently, this term signifies a large systematic view of the manufacturing process. According to Akpinar and Baykaso?lu (2014), a manufacturing system basically entails the conversion of raw material into finished products where its functions are overseen by management systems which ensure control and planning. What should be noted is that, from a wider perspective, manufacturing or rather a production plays a major role in the construction of knowledge systems, security systems and international structural power (Scholl, Boysen & Fliedner, 2013).

Line assembly varies depending on the production strategy and is based on the product variants that is whether the product is a different variant or single variant. Due to this variation, the assembly line is split into three categories: multi-model line, mixed-model line and single-model line.

Problem Statement

According to Battaia and Dolgui (2013), a single model line functions in the manufacturing of a product which does not have any variants. The tasks in all stations are constant for all the task-pieces, and the final product of the assembly line is the same. When it comes to the multi-model assembly line, the product variants are manufactured in batches and the operations vary for the diverse variants which need a setup for the change of another equipment for a tool. So as to reduce the cost incurred during setup, the products are produced in batches (Hamta, Ghomi, Jolia & Shirazi, 2013).

According to Akp?Nar, Bayhan and Baykasoglu (2013), the mixed-model line usually has to cope with products which have diverse variants. Here, operations are the same for different variants and therefore it does not have any constraints in terms of resources needed for assembly. However, they might have different times of operations based on the different variants.

According to Fischer and Ruhland (2013), assembly line balancing is the procedure in which work is distributed to every assembly station in the line evenly such that each workstation has the same amount of work at a given time. What is very significant is that with the balanced workload for each operator at every workstation, the idle time is reduced over the task's time and this means that the station's unused time is decreased. With a well-balanced workstation, very many advantages are guaranteed in terms of reduction of wastes and they include the idleness of workers, the necessity of having the operator, faulty stock and products changed. A well-balanced workstation also allows a given company to reduce the prices of their final products because of the reduction in the unit' production cost (Ozceylan, Eren, Turan & Tolga, 2014).

This chapter includes the research paper's methodology and in more details, this is the part where the research strategy, methods of data collection, research approach and research methods are outlined by the author.

Applied research was used for this research paper where existing literature was used. Several pieces of previous academic research paper dealing with the balancing of assembly lines are present and therefore, the proposed research in this research paper took the form of a new research but still focused on subjects from existing research.

So as to satisfy the dissertation's objectives, a qualitative approach was done mainly because it is appropriate for small samples while also ensuring that the results are quantifiable and measurable. This qualitative research study will use secondary data which is obtained from published and unpublished data. Published data used in this research is derived from government publications, trade and technical journals report from various businesses and institutions, public records and publications by Jack Automotive Company.

The author will use the data which was collected by somebody else in the past and it has an advantage because they are not expensive. It is important that by using secondary data the author will save a lot of time because the resources are easily available on the internet. Lastly, Secondary data will see to it that the author will base this research project on a large scope of data.

Research Objectives

There is hope that the selected secondary data will successfully represent a diverse and broad segment of the Jack Automotive Company's assembly line operations. There is also hope that the secondary data will offer insights which are diverse and ultimately add depth to results analysis

Step 1: Understanding Takt Time

So as to ensure that line balancing proceeds, it is important to understand the rate of consumer demands plus the time required to complete a particular manufacturing process. Lean provides a good metrics for this in the form of a takt time which according to Fleszar (2017), is the throughput rate at which consumers can expect the delivery of certain products or services.

Takt time=Available time to work/ number of units requested by the consumer

Step 2: Develop a Process top to Down Chart

So as to guarantee a balanced assembly line, it is very important to understand the existing relationship and the sequencing between the diverse activities in a particular manufacturing process. This goes further to provide a method in which sequencing relationship is identified between the activities in the process (Vala & Pereira, 2014).

Step 3: Understand the Total time Needed for the Activities in a Specific Manufacturing Process.

To be considered effective and efficient, line balancing has to take the effort needed to perform many necessary activities in a manufacturing process. According to Andrew and Dhillon (2014), this can be done using motion and time study for the processes which are conducted in a given manufacturing process.

Step 4: Calculate the Total number of Workstations Needed.

 According to walker (2017), the takt time and the cycle time which is calculated for the "as-is" process gives an opportunity to get the total number of workstations which are needed for a particular manufacturing process.

Workstations= (cycle time* L)/ takt time

L is the multiplying factor which is empirical for process inefficiency representation.

Step 5: Identify the Areas which are Affected by Inefficiencies

The takt time and the cycle time which is calculated for the "as-is" process gives an opportunity of identifying two critical aspects of the manufacturing process. The two aspects include identification of the longest bar in the graph which indicates the maximum throughput rate and the zones of inefficiencies in regards to idle time and possible instances of work in progress (Liu, Kawamoto, Nishiyama & Kadowaki, 2014).  

In this chapter, the findings of the new Jack Automotive Company line are identified. These findings come in form of different ways in the automotive manufacturing ergonomic complexity assessment, assembly complexity assessment, operator utilization, balance loss, productivity, and layout. The assessment of assembly ergonomic and assembly complexity undergo evaluation in every individual task (Nagahban & Smith, 2014).

This is one of the most important factors which play an important role in the determination of productivity of the line. What is essential for this is to keep the balance loss at its minimum so as to guarantee an increase from the line's productivity. With each individual cycle time starting from workstation 90 to workstation 400 tabled at 38.9 to 38 seconds, the balance loss was clearly under 7% (Khanna, 2015).

Scope of the Project

The layout of Jack Automotive Company is set in the available space which is given. The significant notion of the line layout gives the necessary space for the material flow, material handling, and enough space for the workstation operators and access to fork-lifts so that goods and services can be received from other departments and production lines (Fleischmann & Wagner, 2015).

This research project had a major objective of meeting customer demands and to be able to achieve that, Jack Automotive Company's cycle time should be less than 40 seconds compared to the takt time. After assembly line balancing, the company achieved 38 seconds which is well below the 40 seconds (Stephen & Meyers, 2013). A 25-minute lunch and interval break is given to the employees and therefore the assembly line does not have any planned downtime. Given that Jack Automotive Company gives an 8 percent fatigue time to the workstation operators, the assembly line is most likely operating at an efficiency of 92 percent.

The most critical objective of this project was to get rid of ergonomic risks present in the plant's production line. So as to get rid of this risks, new and better standards regarding ergonomics are implemented in Jack Automotive Company's production line with regards to the set ergonomic standards. Based on the ergonomic risk assessment, balancing will ensure that Jack Automotive Company has a negligible ergonomic risk level (Lambert & Gupta, 2016).

The major objective of this assessment for this research project is to provide feedback and guidelines for product designers and designers of process lines in regards to the development and design of suitable assembly processes and low assembly complexity products. This complexity assessment is performed based on the criteria discussed in Chapter Three's Assembly complexity. This has undergone evaluation for 40 work tasks as finalized.  Out of the 40 task, 21 tasks are at low complexity levels, 4 are rather low, and 10 are moderate, 4 are rather high and none is considered as the high level of assembly complexity (Colledani et al., 2014).


The proposed Jack Automotive Company's assembly line in Australia has provided an essential answer while also giving the best possibility of assembling the parts of an automotive.

Here is a list of conclusions achieved from the entire research project:

  1. The assembly line can meet the current demand of 160 automotive per shift. Additionally, the assembly line can produce more than the rate of demand.
  2. There is a flexibility of 8 percent in each individual workstation in regards to balanced cycle time ad this is Jack Automotive Company's strategy. In addition, station 350 and 450 underwent balancing with high operator idle times. This, therefore, gives space for the tasks of each automotive model which is to be added to these stations in the future.
  3. Jack Automotive Company's assembly line layout is designed in accordance with the available floor space in the department concerned with assembly.
  4. The solution is met by much better standards in regards to the ergonomic and assembly complexity related problems.

According to the Jack Automotive Company's team, the solutions which are implemented on the quality and ergonomic issues is a very strong way in which there is a potential for the new projects which are planned for the future together with the existing assembly line. The outcome results are satisfying to the company which the research is based (Seidgar, Kiani & Fazlollahtabar, 2014).

Because most of the required specifications are met, the end result has satisfied the project's goal. However, due to the limited resources and time, some of the areas need further improvement.

The existing condition of ergonomic risk standards has undergone a lot of improvement but still, there is more room for improvement. The proposed line has only one medium ergonomic risk level in its entire operation. The risk for the Jack Automotive Company is the retention of power tools. The problem concerned with excessive vibration from the tools end up causing damage to the hands and to fix this, the company has suggested the use of dampening materials to reduce vibrations and prevent workstation operators from developing hand-arm vibration syndrome.

Literature Review

The first operation task at Jack Automotive Company is to fix the body to the frame ad this is done in workstation 98 of B12 line and it is a very complicated procedure in terms of assembly line complexity. The reason for this is that accuracy of the highest order is required to precisely fix the two parts and therefore it takes a very long time. To help with this the Design for Manufacturability has suggested the use of high-precision robotic welding arms to handle the fixing.


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Akp?Nar, S., Bayhan, G. M., & Baykasoglu, A. (2013). Hybridizing ant colony optimization via genetic algorithm for mixed-model assembly line balancing problem with sequence dependent setup times between tasks. Applied Soft Computing, 13(1), 574-589.

Andrews, J. G., Singh, S., Ye, Q., Lin, X., & Dhillon, H. S. (2014). An overview of load balancing in HetNets: Old myths and open problems. IEEE Wireless Communications, 21(2), 18-25.

Battaïa, O., & Dolgui, A. (2013). A taxonomy of line balancing problems and their solutionapproaches. International Journal of Production Economics, 142(2), 259-277.

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Fischer, T., & Ruhland, J. (2013, November). Scalable Planning in the Semantic Web--A Smart Factory Assembly Line Balancing Example. In Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on (Vol. 1, pp. 221-226). IEEE.

Fleischmann, B., Meyr, H., & Wagner, M. (2015). Advanced planning. In Supply chain management and advanced planning(pp. 71-95). Springer Berlin Heidelberg.

Fleszar, K. (2017). A new MILP model for the accessibility windows assembly line balancing problem level 2 (AWALBP-L2). European Journal of Operational Research, 259(1), 169- 174.

Hamta, N., Ghomi, S. F., Jolai, F., & Shirazi, M. A. (2013). A hybrid PSO algorithm for a multi- objective assembly line balancing problem with flexible operation times, sequence- dependent setup times and learning effect. International Journal of Production Economics, 141(1), 99-111.

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Özceylan, E., Paksoy, T., & Bekta?, T. (2014). Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing. Transportation research part E: logistics and transportation review, 61, 142- 164.

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Sivasankaran, P., & Shahabudeen, P. (2014). Literature review of assembly line balancing problems. The International Journal of Advanced Manufacturing Technology, 73(9-12), 1665-1694.

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