Measuring the performance of assembly processes …...Measuring the performance of assembly...

International Association for Management of Technology

IAMOT 2010 Proceedings

Johannes Hinckeldeyn; Dennis Kubera; Nils Altfeld; Jochen Kreutzfeldt 1

Measuring the performance of assembly processes using throughput curves

MEASURING THE PERFORMANCE OF ASSEMBLY PROCESSES USING THROUGHPUT

CURVES

JOHANNES HINCKELDEYN*

Hamburg University of Applied Science

Department Mechanical Engineering and Production

Berliner Tor 21, 20099 Hamburg, Germany

Email: [email protected]

Dennis Kubera





Nils Altfeld





Jochen Kreutzfeldt





*corresponding author

Abstract: The performance in many manufacturing companies is limited through bottlenecks in both production and assem-

bly divisions. For this reason, bottlenecks are often the starting point of improvement initiatives. While there has been much

work done on bottlenecks that appear in production systems, bottlenecks in assembly processes are often overlooked. Due to

their location at the end of the manufacturing process however, bottlenecks in assembly processes have an impact on the

throughput of the entire system. To achieve optimal assembly performance, a coordinated and concerted consolidation of ma-

terial flows into the assembly is vital. The presented work is completed as part of the research project "DePlaVis" that is lo-

cated at the University of Applied Sciences in Hamburg. The starting point of its development is the throughput curve, which

identifies and evaluates bottlenecks in the manufacturing environment. To create the new assembly throughput curve, the

most influential factors affecting the assembly process are sought and two factors are identified – the workload and material

availability. The relationship between these two factors is mapped and a throughput curve was determined for an assembly

station. In order to verify the findings, an event-discrete simulation was programmed and analyzed. The simulation that is

conducted supported the developed model. It produces similar results for the throughput and thus for the performance. The

possible applications of the assembly throughput curve lies principally in the planning and control of orders for assembly

processes. Three planning cases and six operating cases have been identified. Each of these basic cases can be extracted and

displayed using the operating curve. Furthermore, it is also possible to derive information from the curve that can be used to

improve performance and efficiency.

Keywords: bottleneck management, throughput curve, assembly, performance measurement

mailto:[email protected]








Introduction

The overall performance of production processes is determined from the assembly performance. Nearly

all manufacturing processes involve some kind of assembly operation (Hopp & Spearman 2000, p. 315).

Assembly is defined as all activities, which are necessary to build products together from several parts

or components (VDI 1990, p.2). The assembly process is usually located in the end of the overall pro-

duction process chain. Therefore the delivery performance is strongly depending on the performance of

the assembly process. For the assembly of products, all necessary parts and components have to be

available and sufficient capacity has to be provided for the operation. If only one factor is missing, the

respective order cannot be processed. The order decoupling point has also to be considered, because it

affects the point in the production where the customer requirements are taken into account (Dekkers

2006, p. 4014). In the case of engineer to order, make to order or assemble to order products, the cus-

tomer decoupling point is located in advance to the assembly process and the availability of the right

material plays an important role. Additionally, variations and uncertainties in the upstream functions, for

example the delayed supply of components, can cause bigger variations in the downstream processes.

This phenomenon is known as the Bullwhip Effect (Lee et.al. 1997, p. 93). Bottlenecks in the upstream

process steps are threatening the outcome of the whole process and can constrain the organization from

fulfilling customer due dates and lowers the logistical performance of the whole production system

(Wiendahl 2002, p.3). Apart from mass production processes, assembly is cost intensive and cause

therefore up to 70 percent of the overall production costs (Lotter & Wiendahl 2006, p.6). Assembly

processes are usually costly, because of the high investment volume into automation (Boysen et.al.

2007, p. 676). In an increasing high-tech world, robotic systems offer good perspectives for the automa-

tion of assembly activities. Rampersad (1995) discusses some of the problems that thwart the wide-

spread development and application of such systems. However, improvement measures have to be un-

dertaken by assembly workers. For the implementation of these measures, the shop floor workers have

to be convinced and inspired. Visual approaches have a good acceptance in practice (Eppler & Mengis

2009, p.50) and a graphical solution for linking capacity and material availability data to assembly per-

formance and planning quality information is preferred.

Research Project DePlaVis – Development of a visual bottleneck approach

The research project DePlaVis was introduced to develop a visual bottleneck management solution for

production systems. The project is founded by the German Federal Ministry of Education and Research.

The consortium of the project consists of three mechanical engineering companies, two software houses

and two Universities. The bottleneck management approach of DePlaVis is based upon the theory of lo-

gistic operation curves (Nyhuis & Wiendahl 2003). In contrast to the original approach, the research

team uses the throughput curve, which is able to visualize and assess bottlenecks in the production sys-

tem (Schultheiss & Kreutzfeldt 2009). This solution draws the causal relation of particular bottlenecks

and the overall system performance. With the developed software demonstrator on hand, the throughput

curve can be computed and directly communicated on the shop floor to discuss countermeasure or im-

provement initiatives. The investigation in the three engineering companies underpinned the need for a

bottleneck management solution, which is easy to handle and understand in practice. Today the

throughput curve is restricted to capacity bottlenecks. However the material supply plays a big role in

assembly processes and the availability has to be considered, when bottlenecks are computed and eva-

luated.

Objective and structure of the paper

For the application of throughput curves in assembly processes, it is therefore important to integrate the

material availability into the bottleneck management concept. The objective of this paper is to demon-

strate and verify a possible visualization for bottleneck management in assembly processes to under-





stand and communicate bottleneck problems on the shop floor. The paper is structured as follows: In the

next chapter, planning and control approaches of assembly processes under consideration of the theory

of logistic operating curves are discussed. Thereafter, the approach for bottleneck management with

throughput curves is outlined. Based on the appearing gaps a possible solution is developed with diffe-

rentiation of nine original cases of planning and operation. This throughput curve is verified and dis-

cussed with an event-discrete simulation model. At the end of the paper, possible areas for further re-

search and next steps are identified for the planning and control bottlenecks in assembly processes.

Planning and Control of Assembly Processes

Assembly planning and control is a well addressed topic in literature. Addressed topics are planning and

scheduling approaches, bioanalogical solutions, heuristics, mathematical models and assembly line bal-

ancing. The last issue seeks to cover the problem, how to configure the planning of assembly lines. It

can be divided into Simple Assembly Line Balancing (SALB) and General Assembly Line Balancing

(GALB) (Boysen et.al. 2007, p. 675). SALB comprise assigning task to assembly lines under simplified

assumptions, whereas GALP tries to integrate more problem parameters, like the assembly line layout.

Another problem to assembly line balancing is the consideration of hybrid flow shops, which has a het-

erogeneous structure in the layout of the respective assembly lines (Ribas et al 2010, p.1439).

Many of the assembly planning and scheduling approaches are bottleneck based and several solutions

for dispatching rules are developed. For example Salegna and Park (1994, p.91) suggest a solution,

which considers load smoothing in the order release process. Further Rajendran and Alicke (2007, p.89)

have developed a set of dispatching rules for static flow shops with bottleneck machines. They consider

this problem using three measures of performance - minimization of total flow time of jobs, the minimi-

zation of the sum of earliness and tardiness of jobs, and the minimization of total tardiness of jobs. The

findings of their experimental investigation have been measured against conventional dispatching rules.

Another way for planning and scheduling is the application of bioanalogical approaches, like insect al-

gorithms from ants and wasps (Cicirello & Smith 2001, p.1; Cicirello & Smith 2004, p.237). The advan-

tage of these solutions is the robustness against unpredictable and dynamic changes. Song et al. (2007,

p.569) expand biological algorithms on the field of optimization called ant colony optimization (ACO).

The authors formulate the bottleneck station scheduling problem, and then apply ant colony optimiza-

tion (ACO) to solve it metaheuristically.

Heuristics are an often preferred solution for bottleneck-based planning and scheduling in assembly

processes. Chen and Chen (2009) have developed a bottleneck based heuristic to solve flexible flow line

problems that contain a bottleneck stage. The objective of the heuristic is to minimize makespan. Com-

putational results show that it significantly outperforms all the well-known heuristics. Furthermore, this

method can also be applied to solve other scheduling problems such as job shop problems with bottle-

neck stages. An approach, which expands heuristic to the whole supply chains is proposed by Kung and

Chern (2009, p.2513). It is called the Heuristic Factory Planning Algorithm (HFPA). After identifying

the bottleneck work centre, the HFPA sorts the jobs using a series of criteria before the final planning

step. One method to improve planning heuristics are Petri Nets (Chauvet et al. 2003, p.150). The appli-

cation of such enhanced heuristics has been simulated under restricted conditions with positive results.

For more exact solution, mathematical models for scheduling and control of assembly processes are de-

veloped. Aggarwal et al (1986, p.11) have developed a mathematical method for solving assembly line

problems for jobs that are dependant. The paper considers two cases for reducing the makespan. Manu-

facturing equipment on the factory floor is typically unreliable and the buffers are finite. Therefore pro-

duction systems are stochastic and nonlinear. Ching et al. (2008, p.911) have developed a practical ma-





thematical method for analysis and improvement of assembly systems with non-exponential machines.

The bottleneck is identified based on the frequencies of machine blockages and starvations.

In the discussed literature many bottleneck management approaches for assembly processes can be

found. However none of the presented solutions is able to visualize the interrelation between input pa-

rameters of an assembly line, the respective bottleneck station and the throughput of the system. The

development of an easily understandable tool would be very helpful to enhance the communication of

problems and countermeasures on the shop floor.

Bottleneck Management with Throughput Curves

The first approach to describe logistical systems also graphically was the funnel model, (Bechte 1984;

Wiendahl 1992). This analogy describes a work system, like a work station or even a whole factory. The

fill level of the funnel depicts the work load of the system. The width of the funnel opening, through

which the production orders flow, equates to the capacity. The relationship between these parameters

can be found of the throughput diagram, the first visual impression of the logistical parameters of a

work system. Input and output are depicted over time. Based on this analogy, the Theory of Logistic

Operating Curves was developed (Nyhuis 1991, 2006, 2007) (Wiendahl and Nyhuis 2003). In a deduc-

tive-empirical approach, the performance and the throughput time of a work system are considered as

dependent variables, which are related to the order backlog as independent variable. A C-Norm function

provides the mathematical basis for the computation of the logistical curves. As this approach is work-

ing with average values, the curve is only suitable for static analysis of logistical system. The applica-

tion of logistic operation lies therefore more in the strategic positioning of logistical systems and not in

the operative control. Wiendahl and Hegenscheidt (2002) use an operating curve to describe the utiliza-

tion of assembly lines as a function of the operational availability. Therewith the buffer sizes between

the individual workstations can be determined. However the curve for assembly processes operates also

with average values and its application lies more in the strategic area for decisions in assembly lines, es-

pecially in the dimensioning of buffer sizes between assembly stations. An operative bottleneck man-

agement is not considered within this approach.

The throughput curve as a bottleneck management instrument is developed by Kreutzfeld (1995). It

enables a bottleneck management with a visual approach linked to an approximation algorithm. To

represent the relationship between the input variables (e.g. load, work plans and capacity) and output va-

riables (e.g. performance, utilization and inventory) of work systems, the throughput curve was selected

as the basis for the identification and evaluation of bottlenecks. To apply it, three assumptions in an

analogy to electrical systems are made (Kreutzfeldt 2007):

1. A bottleneck restricts the flow of production orders in a similar manner that a resistor limits the flow

of electrical current in an electrical network. This similarity can be assumed, if discrete order flows

are considered as continuous flows. The probability that an order is processed depends on the ratio

of the workload to the capacity at the work station with the greatest workload. This work station is

termed the throughput limiter. The throughput limiter is defined as the work station with the greatest

ratio of work load to capacity.

2. Thus, a parallel can be drawn between the continuous flow of orders through a production system

and an electrical current as it flows through an electrical network. All orders that move through the

same limiter become a continuous flow of orders. This flow contains the workload of all orders on

all work stations in a period.

3. In this way, just as an electrical network can be described by electric currents and resistors, a pro-

duction network can be modeled based on bottlenecks and flows of production orders.





To better understand the calculation of the throughput curve, an example for a throughput curve is

drawn in Figure 1. The pictured throughput curve represents the performance of a single order stream.

The order stream is determined by the bottleneck work station. All orders, which go through this work

station, are summarized as one order stream. This follows the principle of Goldratt (1994), who states

that the bottleneck work station controls the performance of a system. In this example, the workload of

all orders within the order stream is summarized up to a work content of 120 hours. Due to the capacity

constraint of the bottleneck, the order stream is only able to transform 60 hours of the workload into

throughput and is shown as an angle bisector in the first half of the throughput curve. The remaining

workload of 60 hours builds inventory in front of the bottleneck and is called throughput potential. The

visual representation is a horizontal line in the second half of the throughput curve. The effect of the

bottleneck onto other work stations within the order stream can be estimated by differentiation of the

throughput into direct and indirect throughput. The overall throughput of the whole order stream is 60

hours. The amount of work processed on work station in front or behind the bottleneck is drawn as a

vertical line on the end of the throughput curve and called indirect throughput. The indirect throughput

in this example is 40 hours. The direct throughput is shown as a free area between the bottom of the dia-

gram and the beginning indirect throughput line. It equals the work processed directly on the bottleneck

workstation. The relation of direct and indirect throughput is very important to assess the effect of bot-

tlenecks on other workstations within the order stream. As bigger the amount of indirect throughput to

direct throughput as bigger is the effect of the bottleneck onto other work stations.

Figure 1: Example of a throughput curve according to Kreutzfeldt (1995).

The diagram shows visually two bottleneck parameters. First, the throughput potential shows the effect

of a bottleneck on the inventory within an order stream. In case of an optimization measure to decrease

inventory within the system, the bottlenecks with a long horizontal line should be addressed. Second,

the proportion of indirect and direct throughput indicates the effect of the bottleneck on other worksta-

tion. This means as a converse argument, that a capacity enlargement at this station promises a large ef-

fect of the overall system performance. In case of a productivity optimization of the whole system, the

bottlenecks with the biggest proportion of indirect and direct throughput should be addressed first.

This approach provides and easy to understand and communicate solution for bottleneck problems in the

manufacturing environment. However there are only capacity bottlenecks considered at the moment.

This a restriction for assembly processes, where the availability of material plays an important role as

well as the availability of capacity. To show the optimization potential in assembly processes the





throughput curve should be enhanced with a material dimension to use this visualization and communi-

cation tool in this part of a production system as well. The evaluation of bottlenecks gives the opportuni-

ty to prioritize bottlenecks regarding their optimization potential. This is very important if resources for

the bottleneck relief are scarce. The communication tool shows quickly this optimization potential and

so it is possible to take countermeasures, where they unfold the largest effect. This is important for

companies to ensure a sustainable bottleneck management.

´

Throughput Curves for Assembly Processes

The application of throughput curves in the manufacturing environment (Schultheiss & Kreutzfeldt

2009) marks the starting point for an expansion to assembly processes, which are particularly interesting

for bottleneck investigation. The assembly process, as the next step in the value chain, is depending on

the supply with parts and components, as well as the availability of capacity. The assembly is mostly the

last step in the production process. Problems in the manufacturing process have therefore a direct impact

on the performance of assembly processes, if required materials are not available. Delays from previous

processes are summarizing to an overall scheduling delay of the order until the last required component

has arrived. This is called the Forrester effect (Lee et.al. 1997) and usually responsible for further prob-

lems in the assembly process. An early prediction of these arising problems in manufacturing and their

effect on the assembly would make it possible to take well-timed countermeasures. Examples are capac-

ity shortages or rising inventories. The objective to adopt throughput curves for assembly processes is to

identify and visualize bottlenecks, which are the basis for improvement measures in manufacturing and

assembly. Figure 2 shows the system, which has to be realized for control and permanent improvement

of assembly processes.

Figure 2: System architecture of an optimized assembly process.

The bottleneck management software DePlaVis is able to identify and evaluate bottlenecks based upon

planned workload and production master data. The relationship between the input of an assembly

process and the expected performance is drawn in an operating curve, so called throughput curve. This

graphic allows the assembly planer to take all necessary countermeasures to break the bottlenecks and

increase the assembly performance with optimized planning parameters. However the throughput curves

are currently only dealing with capacity bottlenecks. For a successful application of throughput curves

to assembly processes, the availability of material has to be integrated as an additional input factor.

bottleneck

management

DePlaVis

assembly

planning system

evaluated

bottleneck

visualisation

Material

availability

supply chain

process

production

master data

process

parameters

throughput

workload

parameters

assembly process

Legend 2:

Material Flow

Information Flow





Adaptation of a bottleneck management solution for assembly processes

According to Lotter and Wiendahl (2006, p.370) the performance of assembly processes is depending

on four areas of influence:

Organization

work stations

parts and components

system

An analysis of available datasets in existing ERP systems1 shows that the four areas of influence can be

linked to the datasets within the system. These datasets are further linked to the input parameters of the

throughput curve to ensure an automated processing. The relationship between areas of influence, ERP

datasets and input parameters of the throughput curve are shown in table 1:

Table 1: Relationship of input parameters and areas of influence in assembly.

Area of influence ERP data sets (examples) Input Parameters for Assem-

bly Throughput Curves

Work station Workload Workload

Cycle time

Machine breakdown rate Production master data

Organization Capacity

Assembly operations

System Assembly structure

Assembly elasticity

Parts and components Availability Material

Quality

The workload in a certain period has impact on the performance of an assembly process. The assembly

processes have to provide sufficient free capacity available to perform the assembly order. The amount

of free capacity in a certain period is computed from the shift plan of the company. In the case of ma-

chine use, the machine breakdown rate also has to be considered. The capacity is computed for single

workstations and aggregated for larger planning purposes, for example whole factories. The first para-

meter to be considered is the workload (W) for processing assembly orders. The workload is usually

computed under consideration of work plans, planned cycle times and amount of products to be assem-

bled. To process the planned workload, the work system has to provide sufficient free capacity. The re-

lationship of capacity and workload can be described in three cases:

1. sufficient capacity (capacity = workload)

2. excess capacity (capacity > workload)

3. insufficient capacity (capacity < workload)

Case 1. and case 2. are uncritical for assembly planning, because all planned orders can be processed

from a capacity perspective. Insufficient capacity (case 3) however leads to a capacity bottleneck and

increasing stock. Such a bottleneck can be hold responsible for tardiness of customer orders.

A further important input parameter is the availability of material and components, which are assembled

to fulfill the customer order (Jünemann et.al. 1989). Every part must be available in the respective

1 The applied ERP system in the research project DePlaVis was PSIPENTA (www.psipenta.de).

http://www.psipenta.de/





amount and quality to perform the assembly operation. The operation can be made impossible just by

the absence of a single component and can therefore lead to a delay of the whole customer order with ef-

fects to all following process steps. For that reason, the input parameter material availability (MA) is in-

troduced to incorporate the material dimension in the assembly throughput curve.

The planned assembly throughput marks the starting point for the computation of MA. This parameter is

derived from the respective bill of material of the planned customer orders. The amount of material for

each component is necessary to process all planned orders within a period and is called material demand

MDx. For the computation of MA, it is important to distinguish in ERP datasets between self-made

components, which are manufactured by the company itself, subcontracted components, which are man-

ufactured by outside suppliers, and components on stock, which are stored in the inventory. Availability

of self-made components can be estimated with the original throughput curve for manufacturing

processes (Schultheiss & Kreutzfeldt 2009). The availability of subcontracted components and compo-

nents in stock are found in the datasets of the ERP system. Based on this raw data, the value of MAx is

computed by comparison of MDx with the expected material supply MSx of the component:

[1]

Hence, MAx is non-dimensional and shows the relative value of the demanded parts and components,

expected to be available. Three states of MA can be distinguished:

1. material sufficiency (MA = 1)

2. material shortage (MA < 1)

3. material oversupply (MA > 1)

Like in the capacity situation, case 1. and 3. are uncritical for the processing of assembly orders. But it

must be mentioned that a material oversupply can cause inefficiencies, for example higher storage costs.

A shortage of material however threatens the fulfillment of customer requirements. The new throughput

curve is developed based on the two dimensions workload W and material availability MA. An example

of the assembly throughput curve is shown in figure 3. To achieve a clear and easily understandable di-

agram, the abscissa standard mMA of the diagram should be equaled to the ordinate standard, where mW

counts for one unit:

mMA = mW * Cmax [2]

This standardization ensures an ideal assembly throughput curve with tan(φ) = 1 or φ = 45°.





estimated operating point under consideration of system constraints

Legend 3:

workload [hours]

MA1

Cmax

W

material availibility [dimensionless]MA

W

capacity [hours]C

φ

Figure 3: Assembly throughput curve with operating point.

The x-axis shows MA as the independent variable, because the material availability is related to the per-

formance of the manufacturing process of the company itself and of supplying subcontractors. The point

MA = 1 is particularly marked, as there is every material available to planned process the customer or-

ders. This means in addition, that the material supply meets the material demand (MDx=MSx). The pa-

rameter workload (W) is shown on the y-axis. The maximum capacity Cmax has to be here particularly

marked, because exceeding workload will lead to a capacity bottleneck. Therefore, the optimal operating

point of the assembly system is the intersection point of MA =1 and W = Cmax and marked as a cross.

This point indicates the highest possible performance from the assembly system with consideration of

all system constraints. In this example all orders can be processed, because there is enough capacity

available and the material supply is sufficient. Additionally, no idle capacity or material oversupply is

wasted and the system works in the most efficient state. Based on this operation point, the throughput

curve can be drawn. As MA is the independent variable, a decrease will subsequently lead to a lower re-

quired capacity. On the other hand, an increase of MA (more material available as necessary) will have

no effect on the assembly performance, since more orders cannot be processed due to capacity restric-

tions. The assembly process and the respective manufacturing and supply chain process is optimal ba-

lanced. Hence, the assembly throughput curve can also assess the quality of planning between the as-

sembly and the supplying processes. Nine possible cases and respective assembly throughput curves can

be derived based on the three situations for each state of MA and W. These nine cases are displayed in

the following figure 4.





W

MA

W

MA MA MA

MA MA MA

MA MA MA

= Cmax < Cmax > Cmax

= 1

< 1

> 1

1 1 1

111

111

CmaxCmax Cmax

CmaxCmaxCmax

Cmax Cmax Cmax

1 2 3

4 5 6

7 8 9

W W

W W W

W W W

MO material over supply

MC material constraint

operating curve

Legend 4:

IC


CC

MC

IC

MC

CC

MC

MO

MO

IC

MO

CC

IC idle capacity

CC capacity constraint

Figure 4: Nine possible cases for assembly throughput curves.

The cases 1, 2 and 3 are typical for planning. In this stadium, the planner assumes the full availability of

the activity-scheduled parts and components. Parameter MA contains therefore the value 1. This meets

the assumption that the demand of material can be fully satisfied by inventory, self-manufacturing or

subcontractor. The planned workload Wplan is derived from the planned amount of goods to be assem-

bled multiplied with the process time per unit according to the working plan. The value of the workload

differs from the parameter Cmax, if the planned capacity is not well synchronized with the material

supply. The resulting point is marked with a cross. Case 2 lefts idle capacity (IC), as there is not enough

planned workload for the capacity supply. Case 3 on the other hand shows a capacity constraint (CC),

because the planned workload exceeds the capacity supply. Set-up time is neglected in all cases, but can

be easily added and would lead to a vertical adjustment of the curve according to the value of the set-up

time. Following improvement measures could be recommended:

- Case 1: Optimal planning. No action is required.

- Case 2: There is idle capacity created on the assembly station. It would be possible to reduce the ca-

pacity to streamline the assembly process or to dispatch more orders.





- Case 3: A capacity bottleneck is going to appear. The capacity supply should be increased to process

all the assembly orders or the material availability can be reduced to avoid the build of inventory.

For the cases 4 to 9, the real material availability has to be computed. It is especially important to con-

sider the material constraint, if necessary. That means the material with the least availability in the bill

of materials for the respective assembly must be identified. Therefore the material availability of every

component MAx is computed. Afterwards the minimum MAx is selected, which represents the material

shortage and therefore the assembly bottleneck. This value can be equalized to MA on the x-axis and is

the basis for further computation steps of the assembly throughput curve.

The cases 4 and 5 are only restricted by the material constraints (MC) and the capacity demand does not

exceed the capacity supply. The function to calculate the operating point is therefore only depending on

MA. The operating point shows additionally some idle capacity (IC), because of the undersupply with

material. Measures for performance improvement for the several cases could be as follows:

- Case 4: An increase of material availability makes no sense without a synchronized increase of ca-

pacity, due to fully loaded capacity.

- Case 5: An increase of material availability could be useful, because there is idle capacity left to

process more orders.

The cases 7 and 8 however are not restricted by any parameter. Both cases show a material oversupply

(MO) compared to the planned scenario. Due to the operating point below the maximum capacity, it is

possible to process more orders than required. Measures for performance improvement for the several

cases could be:

- Case 7: This case has a planning issue, because there is more material available than for planned or-

ders, but with enough capacity. It should be checked, if all assembled orders can be sold or used.

- Case 8: This case can be compared to case 7, but with idle capacity. It would be possible to plan

more orders for assembly processing. Anyhow there is also a planning issue.

In the next step the capacity supply must also be considered. Despite the constraint of material availabil-

ity, the capacity can limit the assembly performance in addition. It is therefore the question, what the

real bottleneck is. A capacity bottleneck is created, if the workload exceeds the capacity supply. This

happens in case 6 and 9. There might a material restriction anyhow, like in case 6. Nevertheless the op-

erating point must be corrected twice in these cases and the function of the operating point is depending

on MA and Cmax. Measures for performance improvement for case 6 and 9 could be as follows:

- Case 6: The capacity bottleneck restricts the assembly from processing all the planned orders, as

well as the material constraint. It has to be investigated, what the dominating constraint is and then

careful countermeasures can be undertaken.

- Case 9: Capacity demand and material availability exceed the system capacities. Nevertheless, the

capacity shortage restricts the assembly from achieving more throughput. A capacity increase would

help to process more assembly orders.

The assembly throughput curves are able to visualize bottleneck parameters from planning and reality.

The planning and scheduling of assembly processes can be supported as well as the indication of opti-

mization potential. Especially the quality of planning can be assessed with the curve. The solution will

be verified in the next section.





Verification through an event-discrete simulation model

The assembly throughput curve is verified with an event-discrete simulation model in two simulation

scenarios. The simulation model contains three work systems (WSi). These include two manufacturing

systems (WS1 and WS2), one assembly system (WS3). Furthermore there are two sources and one sink.

The sources are raw material suppliers to the manufacturing systems and supply continuously all raw

material demanded. The two manufacturing systems produce the two different parts P1 and P2. Both

parts are matched in the assembly system into product P3. It is important to notice, that one unit of P1

and two units of P2 are needed to assemble one unit of P3. A practical example could be the assembly of

two different gearwheels and one shaft. This ratio between P1 and P2 is called joining relationship and

can be found in the bill of material. The considered period is 60 minutes which equals also the maxi-

mum capacity Cmax of every manufacturing system and the assembly system.

Table 2: Simulation parameter for scenario 1.

i WSi Work system parameters Work plan parameters

Description Capacity Cmax

[min]

Product Pi Joining relationship

[P1 : P2]

Cycle time t(Pi)

[min/unit]

1 WS1 Manufacturing 1 60 P1 n/a 0.25


3 WS3 Assembler 60 P3 1:2 2

First, it is assumed to manufacture one unit of P1 on WS1 and P2 on WS2 within a period of 0.25 minutes

each. The assembly process on WS3 takes 2 minutes per unit of P3. It is further assumed to manufacture

only one kind of component within the considered period on each manufacturing system. The planned

workload is computed as multiplication of planned production volume PVi and the respective cycle time

t(Pi):

Wplan(WSi) = PVplan(Pi) * t(Pi) [5]

The planned production volume PVplan(Pi), the computed workload Wplan(WSi) and the outcomes of sce-

nario 1 can be found in table 3:

Table 3: Production plan and output of simulation model for scenario 1.

Product

Pi

Production plan Output

PVplan [units] Wplan [min] [units] [min]

P1 120 30 120 30

P2 240 60 239 59,75

P3 120 240 29 58

It is evident, that the manufacturing systems are nearly able to fulfill the production plan as given. Ma-

terial is sufficiently available. Therefore parameter MA can be rounded to 1. The system constrain in

this process is the capacity of the assembly system (WS3), which can only provide a maximum capacity

(Cmax) of 60 minutes. This is nearly 25 percent of the demanded capacity of the assembly process. The

resulting assembly throughput curve of WS3 corresponds to case 3. Figure 5 shows the adjusted assem-





bly throughput curve. It is important to notice, that for displaying reasons, the upper part of the diagram

is abbreviated.

Legend 5:

MA1

60

W

240

0,24

operating curve


CC capacity constraint

CC

Figure 5: Assembly throughput curve of scenario 1.

The objective of the simulation is to investigate the influence of the material availability on the assem-

bly system performance. Therefore parameter MA is successively reduced by increasing the cycle time

t(P1) from 15 seconds to 130 seconds. All other parameters of the simulation model remained constant-

ly. Table 4 displays the new parameters for scenario 2 of the simulation:

Table 4: Simulation parameter for scenario 2.

i WSi Work system parameters Work plan parameters

Description Capacity Cmax

[min]

Product Pi Joining relationship

[P1 : P2]

Cycle time t(Pi)

[min/unit]



3 WS3 Assembler 60 P3 1:2 2

Under the different conditions of scenario 2, the production plan changed as well. The new production

plan and the outcomes are shown in table 5:





Table 5: Production plan and output of simulation model for scenario 1.

Product

Pi

Production plan Output

PVplan [units] Wplan [min] [units] [min]

P1 120 269 27 58.5

P2 240 60 239 59,75

P3 120 240 26 52

The outcomes show that, despite constant capacity and cycle time of WS3, the throughput of the assem-

bly process decreases. The bottleneck shifts from the assembly capacity to the availability of component

P1. This can be traced back to the capacity of WS1, which is not sufficient to process the necessary

amount of P1 to ensure full load of the assembly process. The respective assembly throughput curve in

figure 6 shows an operating point below the maximum capacity of Cmax(WS3) and represents case 5. The

system constraint is the availability of component P1 with the lowest value of MA (MA (P1) 0.22).

Legend 6:

MA1

60

W

240

0,22

52

MC material constraint

operating curve


IC idle capacity

MC

IC

Figure 6: Assembly throughput curve of scenario 2.

The utilized capacity, which is needed for assembling all components of P1, is below the maximum ca-

pacity Cmax of the assembly process. It is therefore proved, that the bottleneck has shifted from the as-

sembly capacity to the availability of P1.





Discussion

The outcomes of the simulation show in comparison an identical behavior to the proposed assembly

throughput curve. The parameters are easily extracted from the diagram and can directly used for com-

munication. It is therefore easy to discuss and plan improvement measures on the shop floor and with

the production manager. A possible solution could be, not to release as much manufacturing orders as

possible to achieve a high utilization of the manufacturing system. As stated from Goldratt (1990), the

system has to be aligned on the overall system constraint, no matter if manufacturing or assembly. Such

a measure would prevent this system before generating inventory within its production process, which

would subsequently lead to longer and varied lead times and decreasing throughput.

However, this approach has currently several limits, which lead to further research. The described as-

sembly throughput curve deals with stochastic probabilities and relies therefore on sufficient large num-

bers of parts and orders. This is mostly the case in serial or mass production and in contrast to make to

order or engineer to order products, which are usually unique. These production processes have to deal

with small numbers or even event discrete factors. This is especially important for the assembly, where

the availability of certain components has an enormous impact on the successful completion of an as-

sembly order.

A further limit is the lacking integration of time in the computation of the assembly throughput curve.

The moment of arrival of certain parts is currently not considered. This plays an inferior role in the

analysis of serial production process. However, this is different to make to order or engineer to order

production processes, where the temporary lack of a certain component can hinder the progress of the

whole assembly. Further it is not possible to analyse different periods at the moment. It is therefore not

possible to identify dynamic bottleneck behavior, like shifting bottlenecks, without the repeated compu-

tation and manual comparison of different diagrams.

Concluding remarks

The assembly throughput curve is a useful tool for the identification of assembly bottlenecks and the re-

spective optimization potentials. It is possible to evaluate the quality of assembly and production plan-

ning against the estimated situation in the assembly. This offers production managers the opportunity to

take countermeasures against bottlenecks. A first possible improvement activity could be the alignment

of assembly planning. It is also worth to think about activities for manufacturing or purchasing to ensure

a material supply according to the expected demand in the assembly process. All these parameters can

be easily visualized and discussed in the diagram of an assembly throughput curve. This approach is al-

so a good communication tool to transfer identified optimization potential directly onto the shop floor

without the analysis of data and transformation into understandable Power Point slides.

All the described research work is carried out within the research project DePlaVis. The next steps are

the investigation of communication behavior and how effective this tool is applied in practice and the

estimation of the respective training effort to apply this visualization tool. It is therefore planned to test

the assembly throughput curve in three different companies with a distinctive assembly process.

As these companies are working in the engineer to order environment, the boundaries of the approach

have also to be investigated. It is likely, that the assembly throughput curve is only applicable in conti-

nuous processes, like in the series production. To adopt the approach in make to order or engineer to or-

der environments, event discrete effects have to be considering in the computation of the assembly per-

formance.





Acknowledgements

The authors would like to thank Harburg Freudenberger Maschinenbau, Voith Turbo BHS Getriebe,

General Electric Sensing and Inspection Technologies, PSIPENTA GmbH and Berghof System e.K. for

providing a funding contribution towards the research carried out in the DePlaVis project.

References

Aggarwal, V., Tikekar, V.G. & Hsu, L., 1986. Bottleneck assignment problems under categorization.

Computers & Operations Research, 13(1), 11-26.

Bechte, W., 1984. Steuerung der Durchlaufzeit durch belastungsorientierte Auftragsfreigabe bei Werk-

stattfertigung,

Boysen, N., Fliedner, M. & Scholl, A., 2007. A classification of assembly line balancing problems. Eu-

ropean Journal of Operational Research, 183(2), 674-693.

Chauvet, F., Herrmann, J. & Proth, J., 2003. Optimization of cyclic production systems: a heuristic ap-

proach. IEEE Transactions on Robotics and Automation, 19(1), 150-154.

Chen, C. & Chen, C., 2009. A bottleneck-based heuristic for minimizing makespan in a flexible flow

line with unrelated parallel machines. Computers & Operations Research, 36(11), 3073-3081.

Ching, S., Meerkov, S.M. & Zhang, L., 2008. Assembly systems with non-exponential machines:

Throughput and bottlenecks. Nonlinear Analysis: Theory, Methods & Applications, 69(3), 911-

917.

Cicirello, V.A. & Smith, S.F., 2004. Wasp-like agents for distributed factory coordination. Autonomous

Agents and Multi-agent systems, 8(3), 237-266.

Cicirello, V. & Smith, S., 2001. Insect Societies and Manufacturing.

Dekkers, R., 2006. Engineering management and the Order Entry Point. International Journal of Pro-

duction Research, 44, 4011-4025.

Eppler, M.J., 2009. Wie Entscheider und Experten reden lernen. Harvard-Business-Manager, 31(4), 50-

58.

Goldratt, E., 1994. Theory of Constraints 1. Aufl., Gower Publishing.

Goldratt, E. & Cox, J., 2004. The goal: A process of ongoing improvement 3. Aufl., Great Barringtion:

The North River Press.

Hopp, W. & Spearman, M., 2000. Factory Physics 2. Aufl., Singapore: McGraw-Hill Book Co.

Jünemann, R. u. a., 1989. Materialfluß und Logistik, Springer.

Kreutzfeldt, J., 1995. Planen mit Bearbeitungsalternativen in der Teilefertigung. Hannover.

Kreutzfeldt, J., 2007. DEPLAVIS - Durchsatzsteigerung im Anlagenbau und –betrieb durch engpassori-

entierte Planung und Visualisierung, Hamburg: Hochschule für Angewandte Wissenschaften Hamburg.





Kung, L. & Chern, C., 2009. Heuristic factory planning algorithm for advanced planning and schedul-

ing. Computers & Operations Research, 36(9), 2513-2530.

Lee, H.L., Padmanabhan, V. & Whang, S., 1997. The bullwhip effect in supply chains. Sloan manage-

ment review, 38, 93-102.

Lotter, B. & Wiendahl, H., 2006. Montage in der industriellen Produktion: Optimierte Abläufe, ratio-

nelle Automatisierung 1. Aufl., Springer, Berlin.

Nyhuis, P., 2008. Beiträge zu einer Theorie der Logistik 1. Aufl., Springer, Berlin.

Nyhuis, P. u. a., 2005. Applying Simulation and Analytical Models for Logistic Performance Prediction.

CIRP Annals - Manufacturing Technology, 54(1), 417-422.

Nyhuis, P. & Wiendahl, H., 2003. Logistische Kennlinien: Grundlagen, Werkzeuge und Anwendungen,

Rajendran, C. & Alicke, K., 2007. Dispatching in flowshops with bottleneck machines. Computers &

Industrial Engineering, 52(1), 89-106.

Rampersad, H.K., 1995. State of the art in robotic assembly. Industrial Robot: An International Journal,

22(2), 10 - 13.

Ribas, I., Leisten, R. & Framiñan, J.M., 2010. Review and classification of hybrid flow shop scheduling

problems from a production system and a solutions procedure perspective. Computers & Opera-

tions Research, 37(8), 1439-1454.

Salegna, G.J. & Park, P.S., 1996. Workload smoothing in a bottleneck job shop. International Journal

of Operations & Production Management, 16(1), 91 - 110.

Schultheiss, J. & Kreutzfeldt, J., 2009. Performance improvement in production systems through prac-

tice-oriented bottleneck management. In Proceedings of the 4th European Conference on Tech-

nology Management. Glasgow, S. 1 - 20.

Song, Y. u. a., 2007. Bottleneck station scheduling in semiconductor assembly and test manufacturing

using ant colony optimization. IEEE Transactions on Automation Science and Engineering, 4(4), 569.

VDI, 1990. 2860: Montage-und Handhabungstechnik; Handhabungsfunktionen, Handhabungseinrich-

tungen; Begriffe, Definitionen, Symbole. Düsseldorf: VDI-Verlag.

Wiendahl, H. & Hegenscheidt, M., 2002. Bottleneck Analysis of Assembly Lines with Characteristic

Curves. CIRP Annals - Manufacturing Technology, 51(1), 15-19.

Wiendahl, H., 2002. Erfolgsfaktor Logistikqualität 1. Aufl., Berlin Heidelberg New York: Springer Ver-

lag.

Wiendahl, H., 1997. Fertigungsregelung - Logistische Beherrschung von Fertigungsabläufen auf der

Basis des Trichtermodells 1. Aufl., München Wien: Carl Hanser Verlag.

Wiendahl, H. & Nyhuis, P., 1998. Engpaßorientierte Logistikanalyse: Methoden zur kurzfristigen Leis-

tungssteigerung in Produktionsprozessen, München: TCW Transfer-Centrum.

Measuring the performance of assembly processes …...Measuring the performance of assembly...

Documents

Transcript of Measuring the performance of assembly processes …...Measuring the performance of assembly...