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Abstract number: 020-0068
Abstract title: Measuring Agility Index Using System Flexibility and Response
Authors’ information:
Dr. Emad S. Abouel Nasr
Faculty of Engineering, Industrial Eng. Dept., King Saud University, Riyadh, KSA
And Mechanical Eng. Dept., Helwan University, Cairo, Egypt
E-mail: [email protected]
Tel.: +966569958202
Prof. Mohammed I. Osman
Mechanical Eng. Dept., Helwan University, Cairo, Egypt
E-mail: [email protected]
Tel.: +20190005832
Eng. SOHA R. ELATTY
Mechanical Eng. Dept., Helwan University, Cairo, Egypt
E-mail: [email protected]
Tel.: +20168845214
POMS 22nd Annual Conference
Reno, Nevada, U.S.A.
April 29 to May 2, 2011
Measuring Agility Index Using System Flexibility and Response
Emad S. Abouel Nasr1, Mohammed I. Osman
2 , and SOHA R. ELATTY
3
1Faculty of Engineering, Industrial Eng. Dept., King Saud University, Riyadh, KSA
2,3Mechanical Eng. Dept., Helwan University, Cairo, Egypt
Abstract
The purpose of this paper is to measure system agility and to propose an agility index
which is used to show how agile the system is. The agility index is measured
according to the operational prospective and its level is concerned with two
dimensions. The first dimension is the flexibility concerning three types; volume
flexibility, variety flexibility, and delivery flexibility. The second is the response of
the scheduling process applied in the system concerning the schedule stability and the
frequency of rescheduling. A simulation model is used to measure agility dimensions
and to demonstrate how the level of disruption and initial system conditions affect
agility level.
Keywords: agile system, agility index, flexibility, response
1. Introduction
For over the past 20 years, companies have been seeking to improve their competitiveness to
market challenges. Competing on multiple dimensions of cost, quality, delivery time, and
product variety requires efficient operations that are tailored to the specific needs of a firm's
customers. These conditions require a responsive new manufacturing approach that enables
the quick launch of a batch of another product model, rapid adjustment of the manufacturing
system capacity to market demands, rapid integration of new functions and process
technologies into existing systems, and easy adaptation to changed quantities of products.
Manufacturing competitiveness has moved from the “era of mass production” to the “era of
agility”. Manufacturing agility is the ability to respond to changes, and create new windows
of opportunities in a turbulent market environment driven by individualizing customer
requirements, cost effectively, rapidly and continuously. Agility is a concept that combines
the organization, people and technology into an integrated and coordinated whole. It also
represents how easy the system responds to changes and how flexible is this system. More
agile systems require higher initial investment costs but once a highly agile system is
acquired, reconfiguring it to produce different types of products can take place with either
less time or less cost (Daghestani, 1998).
Agile system definitions are considered diverse and not considering the manufacturing
system only, but the whole organization. Gunasekeran and Yusuf (2002) described agile
system as the ability of the system to thrive in a competitive environment of continuous and
unanticipated change and to respond quickly.
There are four principles that characterize the agile system. These principles are Customer
enriching and collaboration, co-operating to enhance competitiveness, mastering change and
uncertainty, leveraging people and information (Groover, 2008).
Measuring agility is important to identify the effectiveness of the applied production
strategy and identifies less agile areas in the enterprise and thus it can plan for
improvements. Moreover, measurement of agility gives enterprise an indication for its
competitiveness and readiness for changes in the market so that the enterprise can stay
competitive in the market. Measuring agility should focus on specific agility types from
which overall agility measures will be derived from.
The system under study is flowshop containing parallel unrelated machines such that new
capabilities are added and may not be related to old ones. Three types of disturbances were
studied. A hybrid rescheduling policies which combines event driven scheduling and
continuous scheduling, are used for rescheduling. In case of arriving new job or when the
quantity of a job that already finished or under process changes, the rescheduling process
will take place when the number of new arriving jobs reach certain level which will be
determined by the level of capacity required and available. Whenever urgent job arrive and
when a breakdown occur a rescheduling process will be executed. Agility index are chosen
to measure system agility level. Two dimensions which are flexibility and the response of
the schedule are used in agility measurement. The total agility is then measured which will
used to determine how effective is the scheduling system. A simulation model is used to
demonstrate how the initial system conditions and level of disruption affects agility.
2. Literature Review
By studying the previous studies, it was observed differences in the dimensions that are used
in measuring agility which cause lack of universal matrix. Majority of methods rely on data
gathered by long questionnaires answered by the personals working in the organization.
These methods gives limited information about the industrial system as it is mainly
concerned about whole organization as management, quality and personal. Moreover most
of the previous studies focused on four agility infrastructures when measuring agility which
are production infrastructures, market infrastructure, people infrastructure, and information
infrastructure.
Due to difficulty to measure and quantify the agility of the system, most work done on
agility measurement depends on giving scores for the whole firm attributes. Most
measurement systems concentrate on operational measures of the system; however, many of
the manufacturing strategies are based on structural properties of the system architecture,
technology resources, and system control policies.
Tsourveloudis and Philis (1998) presented a methodology which aims at providing a way for
measurement the agility, which is then derived, implemented and tested in simulation
environment. They suggested a knowledge-based methodology for the measurement of
manufacturing flexibility. Nine different flexibility types are measured, while the overall
flexibility is given as the combined effect of these types. The result showed that the required
flexibility must be consistent with current flexibility capabilities of a manufacturing system
and market requirements to avoid over or under flexibility investment.
Siegner et al. (1999) provided an innovative effort to provide a solid framework for
determining and measuring enterprise agility in terms of a list of quantitatively defined
parameters. The proposed measurement framework is direct, adaptive, holistic and
knowledge-based. The agility parameters are grouped into production, market, people and
information infrastructures, all contributing to the overall agility measurement. The results
of such a study is useful in determining how much agility is needed and to what extent it
will affect the profitability of the enterprise.
Bessant (2001) studied agility in small to medium sized enterprises and a four-dimension
(agile strategy, agile processes, agile linkages, and agile people) were considered. Each
dimension has been subdivided into four sub-categories. This is then employed as a
framework for an audit in a company. Examples are given of the use of this subjective
strategic level audit in two companies. He suggested that it is necessary to explore the
different agile configurations and develop frameworks for facilitating strategic decision-
makers in identifying the particular configuration necessary for their sector or product.
Bititci et al. (2001) utilized the analytical hierarchy process to measure performance. These
measurement models of operational measures adhere to a decision theoretical framework
such that they identify many factors, elicit quantification from the decision maker
(management), and then aggregate the factors into an overall score. It was obtained that
decision theoretical approaches do not directly measure whether systems are flexible, agile,
etc. because they measure past outcomes and cannot say to what degree a system posses a
structural property.
Hoek (2001) discussed supply chain agility and developed measuring industrial views on
five factors that bear on this agility. He asserted that “agility is all about customer
responsiveness and mastering market turbulence” and that it needs extra capabilities beyond
those of standard lean manufacturing. He went on to suggest a series of features of agility
and potential measures. The main result of the audit was that of the five factors that have
been studied, customer sensitivity was the major concern.
Sanchezy and Nagly (2001) made a survey for the literature review that considered about
agility. In this survey recent work in agile manufacturing systems was reviewed and 73
papers were analyzed. They suggested classification scheme with nine major research areas:
(i) product and manufacturing systems design; (ii) process planning; (iii) production
planning, scheduling and control; (iv) facilities design and location; (v) material handling
and storage systems; (vi) information systems; (vii) supply chain; (viii) human factors; (ix)
business practices and processes. They highlighted that the information systems area was the
research topic where the most amount of work has been performed. In addition, they
observed that from 1995 until now a consistent number of papers about agile manufacturing
systems have been published every year.
Giachetti et al. (2003) presented a measurement framework to analyze measures of
structural properties of the agile enterprise system. The analysis revealed undesirable
properties of some measures, mainly because they are developed without any formal basis.
The measurement framework provided a consistent basis for specifying and using measures,
which will empower system designers to better incorporate desirable structural properties to
align system design with enterprise strategy.
Pujawan (2004) presented a case study of schedule nervousness based on field observations
in a shoe manufacturing company in Indonesia. A model to quantify nervousness is also
presented. This study provides an insight that schedule nervousness is an important issue in
practice and efforts to quantify nervousness, as well as to understand how it occurred, are
necessary in order to reduce nervousness.
Chandna (2008) presented a fuzzy logic, knowledge-based framework for the assessment of
manufacturing agility. In order to calculate the overall agility of an enterprise, a set of
quantitatively agility parameters is proposed and grouped into production, market, people
and information infrastructures, all contributing to the overall agility measurement. The
simulation integrates the modeling of agility infrastructures, simulation of an enterprise
through its infrastructures, real-life data, and a virtual reality based interface. The proposed
framework provides successive aggregation of the agility levels as they are expressed
through the known agility types and can be easily implemented within a virtual reality based
simulation testbed. Also, it helps in management planning and execution tools. This involves
the use of techniques as manufacturing resource planning, real-time manufacturing
execution systems, production planning configurations, and real-time threaded scheduling
through fuzzy Logic approach.
Erande and Verma (2008) presented a comprehensive agility measurement tool which
measures agility on the scale of 1-5; 1 being least agile and 5 being highly agile. This tool
captures agility using 10 agility enablers and thus also points out areas lacking agility. Use
of Analytic Hierarchy process gives flexibility to this tool and also solves the problem of
changing priorities of agility enablers from one enterprise to another. It was concluded that
the most important factors affect the agility are human resource management and uses
training of employees, attrition rate and percent increase in yearly profit to measure human
resource agility and visionary leadership.
Lomas et al. (2008) explored the way in which agility across the product development
process may be measured using a previously defined measure of agility: Key Agility Index.
It is a fact that very few companies keep accurate records of project timings and the delays
caused by unpredictable events. The classification of unexpected events for two case studies
is explored, based on a previously defined classification system of trivial; minor; major and
fatal events. The case studies show how empirical qualitative data regarding project timings
and unexpected events can be gathered through expert interview and can be used with the
Key Agility Index to provide a realistic and practical measure of agility.
3. Measuring Agility
Since agility is not a concept that is applied to the production only but it is a concept that is
applied to every department in the organization such as the people, management,
information technology, customers, suppliers, quality and production system therefore
defining and measuring agility is considered difficult to specify as agility is
multidimensional and each study may concern about different dimensions. Agility describes
the ability of the system to adopt changes. The manufacturing system will be more agile if it
can handle a wider range of changes such as more variety of products or changes in
production rates in effective manner in order to response to customer or market
requirements.
The agility is concerned about two concepts, the flexibility of the system and the response to
the changes .In this paper, agility is measured according to the operations only to determine
how flexibility and scheduling response will affect the system agility. The following
dimensions are calculated then the agility is given according to the results of the below
calculations. Four types of disturbances are studied and agility is measured in terms of
flexibility and response. Table 1 shows the different dimensions that will be used in
calculation of agility index. Agility index is then calculated. A simulation model is used to
determine how the amount of disturbance and also how the initial size and conditions of the
system affects the agility level
Table 1. Agility Measurement Matrix
3.1 Flexibility Calculations
Volume Flexibility
The volume flexibility is the ability of the system to handle the change in a wide range of
production volume that the system can produce. This can be determined by the
percentage of the total new capacity that can be handled by the system to produce amount
q of job i to the average capacity available at time t.
(1)
Where = volume flexibility, quantity of job i., = average capacity available at
time t
Variety Flexibility
It represents the range of various products that the system can produce. This can be
expressed by the percentage of the number of new jobs that enter the system and
produced per unit time to the total number of jobs per same unit time.
(2)
Where = variety flexibility, = number of new jobs that enter the system and
produced per unit time, = total number of jobs per same unit time.
Delivery Flexibility
This indicates the ability of the system to react effectively with the delivery of urgent
jobs. This can be determined by calculating the percentage of the number of urgent jobs
that meets the due date to the total number of jobs in the system.
(3)
Where = delivery flexibility, JU = number of urgent jobs, = total number of job
per same unit time.
3.2 Response Calculations
Schedule Stability
It is the measure of nervousness of the schedule, the average of deviation between the
revised and initial schedule divided by the rescheduling frequency. This indicates how
stable is the original schedule and how the system reacts with the disruptions without so
much changes and effort.
Nervousness = r (4)
Where ti = the new start time of job i and t'i = the start time of job i in the initial schedule,
Fr = Total Rescheduling frequency.
The Schedule stability is calculated as = 1 – (nervousness/maximum completion time)
Rescheduling Frequency
As the rescheduling policy proposed depends on the machine breakdowns rates and the
arrival of new jobs, this identifies the number of changes that a schedule undergoes. The less
rescheduling frequency occurs, the more agile the system is. The total rescheduling
frequency is the sum of the rescheduling frequency due to volume change, rescheduling
frequency due to product change, rescheduling frequency due to urgent jobs and
rescheduling frequency due to machine breakdown.
(5)
In case of arriving new jobs, a complete rescheduling policy is used to schedule the jobs on
the machines. The jobs being considered are those jobs that were scheduled but not have
been processed and those which arrived since the last rescheduling time. The rescheduling
process will occur every time period depending on the arrival rate and capacity of the
system. The rescheduling will occur when the new capacity needed on any stage is larger
than or equal the available capacity of the same stage.
The rescheduling frequency will be as below
(6)
Where θ t new job arrival capacity rate, and CR = average remaining capacity
Change in volume is another type of disturbance that agile system has to react with
effectively. Demanded quantities of the existed jobs may be changed while executing the
initial schedule. The quantity of job is increased by a specific amount. This job may not be
processed yet or under process. In this case, the schedule will not be completely changed.
An additional capacity will be needed to process the new job. In case that the job is already
processed, the new quantity will be considered as a new job and it will be added when
complete rescheduling takes place.
The rescheduling frequency will be as follows
(7)
Where = rate of arriving capacity, T = total time unit.
In case of arriving urgent jobs, a partial rescheduling policy is used to schedule the jobs on
the machines. The urgent job is added to the machine with available capacity that can
process this job. The jobs that were scheduled but not have been processed are postponed
and shifted according the due date of the urgent job.
The frequency of rescheduling due to urgent job arriving.
Tu (8)
Where Tu is the number of urgent jobs.
Machine breakdown is common in all manufacturing systems. This type of disruption affects
greatly the execution of the schedule. In order to increase the stability of the schedule,
partial rescheduling is considered in case of machine breakdown. In case of machine failure,
the capacity of the system is reduced as a result of not using the capacity of the failed
machine. In this case, the jobs on the failed machine in the old schedule are reassigned to
other machines that can perform the same jobs without rescheduling the whole jobs. The
jobs that will be rescheduled are the jobs that will be affected with the changes in the
schedule. In this case, the jobs that can be processed on the repaired machine are
rescheduled on the repaired machine and the alternative machines and any changes in the
other operations for the same jobs on the different machine in the schedule will be changed
accordingly. The frequency of rescheduling due to machine failure will be
(9)
Where T = total time unit, = mean time between failure, = mean time
between repair.
3.3 Calculation of Agility Index
Agility index is the main goal of the research. The agility is the effort done by the system to
compensate the disruption that the system subject to. The factors that affect agility as stated
above are the flexibility and response (rescheduling frequency and schedule stability). The
flexibility affects agility greatly as the flexibility increase, the agility increase. Also, as the
stability increases the agility level increases too. The agility index will be calculated as the
stability factor multiply by average flexibility and rate of rescheduling frequency.
AI= (10)
4. Results and Discussion
The simulation model is set to run for 336 hours = 2 working weeks. The results facilitate
recognizing the following:
1- Calculating the agility index to demonstrate the level of existing agility level.
2- Demonstrate the relationship between initial system size and agility level
3- Demonstrate the relationship between different amount of disturbance and agility level.
4.1 Results of the simulation demonstrating the relationship between initial system size
and agility level
The effect of batch size, capacity and capability on agility dimensions is illustrated.
Experiments were executed, with low capability flexibility; the time to start producing new
product is 240 hours and the percent of available capability 60 %; and low capability
flexibility; the time to start producing new product 140 hours and the percent of available
capability 90 %; high capacity; 3 parallel machine; low capacity; 2 parallel machines, batch
size; apply batch size strategy, no batch size strategy. Table 2, shows the results of
experiments for different initial system parameters on different agility dimensions. The
experiments show that high system capacity increases the volume and variety flexibility, and
high machine flexibility increases the variety flexibility, and that the use of batch size
strategy affects the flexibility.
Table 2. The results of experiments for initial system parameters on different agility factors.
Initial System
Properties
Number of parallel Machine
2 parallel machines 3 parallel machines
Batch Batch
Batch Size Strategy No Batching Batch Size Strategy No Batching
Initial
Machine
Flexibility
High
Volume flexibility= 0.043
Variety flexibility=0.75
Delivery flexibility= 0.25
nervousness =14.7
Volume flexibility=0.0115
Variety flexibility= 0.6771
Delivery flexibility=0.132
nervousness =24.6
Volume flexibility= 0.054
Variety flexibility= 0.7178
Delivery flexibility= 0.066
nervousness =14.7
Volume flexibility= 0.0736
Variety flexibility=0.287
Delivery flexibility=0.113
nervousness =24.6
Low
Volume flexibility= 0.043
Variety flexibility= 0.3707
Delivery flexibility= 0.132
nervousness =14.7
Volume flexibility= 0.0115
Variety flexibility=0.2143
Delivery flexibility: 0.766
nervousness =24.67
Volume flexibility= 0.054
Variety flexibility= 0.6222
Delivery flexibility= 0.066
nervousness =14.7
Volume flexibility= 0.0736
Variety flexibility=0.1837
Delivery flexibility= 0.113
nervousness =24.6
Agility index is calculated by the above flexibility and response results. As shown in table 3,
the agility index is calculated in different system characteristics. The results show the effect
of the initial system size on agility levels.
Table 3. The results of experiments for initial system parameters on agility index
4.2 Results Demonstrating The Effect of The Degree of Disturbance on The Agility
Factors
For the final results, the effect of the degree of disturbance on the agility factors is illustrated
in Table 4. The experiment studied the agility factors in different product change; the
percentage of new product changed; and showed that when the new product types increased,
the volume flexibility, delivery flexibility stays the same whereas the variety flexibility
decreased as shown in Figure 1. Moreover the frequency and schedule nervousness stay the
same as shown in Figure 2.
Initial System
Properties
Number of parallel Machine
2 parallel machines 3 parallel machines
Batch Size
Strategy No Batching
Batch Size
Strategy No Batching
Initial
Machine
Flexibility
High AI=0.33158 AI=0.24717
AI=0.139009
AI=0.272411
Low
AI=0.173403 AI=0.29865 AI=0.23571 AI=0.111537
Table 4. The effect of degree of disturbance on different agility factors
Degree of
disturbance Volume
flexibility
Variety
flexibility
Delivery
flexibility
Frequency
of
reschedule
Schedule
Nervousness
Agility
Index
Product
change
20% 1.23 0.5634 0.023 59 5.74
0.417785
40%
1.23 0.4629 0.023 59 5.74
0.394669
60% 1.23 0.3052 0.023 59 5.74 0.358469
80% 1.23 0.141 0.023 59 5.74 0.320694
Volume
change
48hrs 1.23 0.053 0.0017 19 6.5
0.094887
96hrs 1.15 0.1024 0.0017 7 7.0206
0.03409
144hrs 1.21 0.0886 0.0017 14 7.238
0.070621
192hrs 1.59 0.022 0.0017 3 7.05
0.018781
240hrs
1.095 0.0718 0.0017 6 4.87
0.027235
Urgent
Jobs
5 %
1.24 0.55 0.023 59 5.74 0.417003
10 %
1.26 0.65 0.112 77 6.03 0.606557
20%
1.014 0.7064 0.1224 78 5.87 0.559986
30%
0.7286 0.638 0.2691 79 5.59 0.503677
40%
0.8067 0.6313 0.423 83 6.15 0.601647
Figure 1. The effect of product change on the flexibility level
Figure 2 The effect of product change on the Frequency and schedule nervousness
Also, in case that the volume changed; the arrival rate changed and the change in volume
consequently changed; delivery flexibility stays the same but the volume and variety
flexibility shows different behaviors' as shown in Figure 3. Frequency of rescheduling
decreased first then increased and the schedule nervousness increased then decreased again,
this is illustrated in Figure 4.
Figure 3. The effect of volume change on the flexibility level
Figure 4. The effect of volume change on the Rescheduling frequency and schedule
nervousness
The delivery flexibility increased while the volume and variety flexibility varies as shown in
Figure 5 when the percentage of urgent jobs increased. Moreover, the rescheduling
frequency increased while the schedule nervousness doesn’t show big changes as illustrated
in Figure 6.
Figure 5. The effect of urgent jobs on flexibility level
Figure 6. The effect of urgent jobs on the Rescheduling frequency & schedule nervousness
The maximum value of the agility index was obtained when the new types of products are
20 % of the total number of jobs, and was lower at 80 %. Also, the maximum values of the
agility index were obtained when the change in volume occurs every 48 hours and 144
hours, and lower value was obtained when the change in volume occurs every 194 hours.
Moreover, the maximum values of the agility index were obtained when the percentage of
the urgent jobs was 10 % and 40 % from the total number of new jobs, and was the lowest in
value at 5%. Figures 7, 8 and 9 show the effects of the level of change in product types, the
rate of capacity change, and the number of urgent jobs on agility index.
Figure 7. The effect of product change on agility
Figure 8. The effect of volume change on agility
Figure 9. The effect of urgent jobs on agility
5. Conclusions
As was indicated before, the objective of this research is to propose a measurement for
agility level in manufacturing systems. With the aid of simulation experiments, results have
been obtained. Experiments were conducted, with low - high capability flexibility, low- high
capacity, apply batch size strategy- no batch size strategy. It was concluded that high system
capacity increases the volume and variety flexibility, high machine flexibility increases the
variety flexibility, and that the use of batch size strategy affects the flexibility. The best level
for Agility Index was obtained in two parallel production lines with high flexibility and
applied batch size strategy.
Experiments were conducted to study the agility factors in different system changes. When
conducting experiments for the effect of disturbance on agility level it was obtained that
when the new product types increased, the volume flexibility, delivery flexibility stays the
same whereas the variety flexibility decreased. Moreover the frequency and schedule
nervousness stayed the same as. Also, in case that the volume changed delivery flexibility
stays the same but the volume and variety flexibility shows different behaviors. Frequency
of rescheduling decreased first then increased and the schedule nervousness increased then
decreased again. The highest agility levels where with low product change, high volume
change and 10 % urgent jobs. It was concluded that at certain level of disruption the system
can react effectively with the disruption and if the level of disruptions exceeds certain level.
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