As planning is an activity that occurs prior to control in manufacturing
environments, the majority of research papers found in the literature
discuss production planning rather than its control mechanism. However,
it is obvious that planning without control is not effective. In this
respect, it is important to integrate both planning and control activities
into a unique program. This implies that production control must be carried
out based on an earlier plan. Otherwise, the performance of the production
system e.g., on-time and on-budget delivery to customer cannot be measured
accurately. Actual records from a production planning horizon can be also
used for planning the resulting production.
Analytical models, e.g., Linear Programming (LP) techniques, may generate
infeasible solutions for practical problems due to their ignorance of
some facts during modeling. The literature indicates that hybrid analytical-simulation
analysis can be efficiently performed (Byrne and Bakir, 1999). Therefore,
optimization techniques such as LP models are unable to consider some
operational criteria in a machine-shop such as machine order visit as
proposed and modeled by Byrne and Bakir (1999), Kim and Kim (2001) and
finally Byrne and Hossain (2005). In the approaches advocated by the above
authors, the initial production plans have been generated by applying
LP formulations and then the results found by LP are taken as input to
a simulation model in order to adjust capacity for producing each of the
products. Simulation analysis is stopped whenever the total output is
feasible in accordance with capacity constraints and operational criteria
available in the shop. On the other hand, some research papers were found
to be focused mainly on shop floor control concepts in order to control
manufacturing processes and production environments. Monch (2007) described
benchmarking efforts for production control in complex manufacturing environments
where large numbers of products, sequence dependent set up times, mixtures
of different process types and internal-external disturbances were included.
Monch suggested software for production control and discussed limitations
of proposed software in different application areas. Monostori et al.
(2007) summarized the main challenges and issues associated with customized
mass production control. They applied both traditional discrete event
simulation and agent based approaches and tested the effectiveness of
their proposed approach using experimental data from industry.
Lia and Liu (2006) proposed a production system in two stages (including
upstream and downstream) where significant setup times at the upstream
levels were considered. A threshold production control system was also
employed in order to minimize total work in process mainstreaming at the
required downstream level. Finally a Markov model was constructed and
numerical optimization performed using a simple algorithm.
Csajia et al. (2006) presented an adaptive scheduling system which
performs in a market based production control system thorough a triple
level learning mechanism. Numerical function approximator, reinforcement learning system and simulated
annealing algorithm were thus considered in lower, medium and higher levels
respectively. They also examined time and space complexity of the solution
using experimental investigations.
Al-Tahata and Mukattash (2006) designed production control schemes for
Kanban based Just-in-Time (JIT) environments. For this reason, the Kanban
system was formulated as a queuing model and a new approach was discussed
for analyzing it. Also numerical examples for determining parameters of
system were provided.
Dassisti and Galantucci (2005) proposed a commercial use of object oriented
discrete event simulator called pseudo fuzzy discrete event simulation
where the fuzzy operator was used as a simulator embedded with stochastic
function in order to facilitate an online production control. Their approach
was evaluated through an industrial benchmark.
Gharbi and Kenné (2005) addressed a production and maintenance
control problem where multiple-machine manufacturing systems were considered.
For both identical and non identical manufacturing systems a two level
hierarchical control model was developed. The results obtained extended
the previously conducted investigations to address the non identical types
that had not been considered accordingly. Sensitivity analysis for robustness
and preventive maintenance were thus focused on large scaled manufacturing
Kenne and Gharbi (2004) focused on manufacturing systems where machine
maintenance and constant demand rate were assumed in order to minimize
discounted inventory holding costs and backlogging costs over the planning
horizon. It is pointed out that production and machine repair rates had
to be considered as decision variables. Finally they showed that hedging
point policy is optimal.
Barker (2001) focused on implementation of time based value adding strategy
and pull-type block control in an electrical manufacturing company and
took numerous corrective actions in order to improve value adding processes.
Kogan and Lou (2002) analyzed a manufacturing environment with tandem
machines where the system periodically stopped for maintenance. The objective
was to minimize total inventory and backlogging expenses which was demonstrated
using a numerical example and it was found that in order to present optimal
production policy only restricting machines needed to be considered.
EARNED VALUE MANAGEMENT SYSTEM (EVMS)
Earned value method: Earned Value (EV) is a method for managing
projects in such a way to incorporate scope and integrate it with both
project time and cost points of view. Thus, planned value (PV)-sometimes
called as the Budgeted Cost for Work Scheduled (BCWS)-can be calculated
based on distribution of activity budget on its own duration. Thus, planned
value represents total expenditures to be spent versus time. Summation
of planned value at the end of project is called Budget at Completion
(BAC), which is usually considered for project performance measurement.
It is obvious that BAC is usually less than contract price. That is why,
some of cost components such as overhead and some other expenditure cannot
be explicitly distributed over time. On the other hand, actual cost (AC)-also
called Actual Costs of Work Performed (ACWP)-of work performed must be
totally calculated up to project data date. The amount of EV-also referred
to as the Budgeted Cost of Work Performed (BCWP)-can be measured by progress
achieved for each activity. It is thus calculated by multiplying the progress
of each activity on its assigned budget.
PV, AC and EV, at each data date (or report date) can be compared and
the corresponding cost performance index (CPI) and schedule performance
index (SPI) can be easily calculated as follows (Project Management Body
of Knowledge, 2004):
Generic model of EV method is presented in Fig. 1.
||Generic model of EV method
EV analysis: literature survey: The EV method is extended mainly
into two areas. The first category of research is related to working on
EV developments or extension in EV metrics or principle. The second type
of research area attempts to address applications of EV management system
in both organizations and projects. Therefore, the focus is on how to
implement EV efficiently. However, some of research articles described
both points of view.
As an important research work on EVA and forecasting features, Vandevoorde
and Vanhoucke (2006) not only concentrated on traditional EV metrics,
but also developed earned schedule performance indicators namely SV(t)
and SPI(t). Their proposed approach was also able to yield forecast of
total project duration. Their developed formula was compared with three
available methods in the literature based on testing three real life projects
in several situations. Although they claimed superiority for their proposed
approach, they speculated that depending on every situation, e.g., project
manager`s knowledge and the formation of project management team, other
methods may also be useful.
Vitner et al. (2006) applied a data envelopment analysis (DEA)
for performance evaluation in a multi project environment where each project
was defined uniquely. They integrated EV management system (EVMS) with
multi denominational control system (MPCS). They also provided a new approach
in order to reduce the number of inputs and outputs in their developed
approach to achieve better results in multi project environments. However,
they claimed that it was for the first time that DEA was being applied
in project environment as it had been previously only used in organizations
e.g., hospital, banking etc.
Moslehi et al. (2004) presented an integrated web based time and
cost control system for construction projects which mapped Work Breakdown
Structure (WBS) into an object oriented model to enable generating EV
reports at control objects and resource levels. Moreover, in order to
analyze project variance, a set of resource performance indicators was
used. Their system also assisted to share data within the World Wide Web.
Stratton (2007) discussed applying earned schedule analysis in order
to estimate completion date. Firstly, he presented commonly used EV technique
including schedule performance index (SPI) and then discussed that SPI
(t) can be estimated based on earned schedule divided on actual time where
earned schedule can be calculated based on mapping EV amount on time (horizontal)
As it is evident from the above literature survey, PPP and project management
areas are extensively discussed. However no related research could be
found where both earned value analysis and production planning concept
were used simultaneously in order to control the production status in
manufacturing environment. As only related work, PPPs were solved by applying
project scheduling techniques by Markus et al. (2003). Moreover,
they solved common PPPs by project scheduling approach and further they
discussed about its application in material and capacity requirements
To the best of our knowledge, there is neither any closely related research
that proposes a project management technique for production control especially
during manufacturing processes. It is also worth noting that regarding multi
product-multi period PPPs no specific control mechanism has been published in
APPLYING EV MANAGEMENT SYSTEMS AS CONTROL MECHANISM IN MANUFACTURING
Problem statement: The MPMP problem under capacity constraint and
machine order visit was initially proposed by Byrne and Bakir (1999) and
was followed by Kim and Kim (2001) and Byrne and Hossain (2005), accordingly.
The problem consists of multiple products that are to be delivered at
multiple periods. Customer demand at each period for each product is assumed
to be known and deterministic. There are several machine centers considered
whose processing times and machine order visit (sequence to be met) for
each product are individually pre-specified. Moreover, the cost coefficient
for each product at each period in terms of units of production, inventory
holding costs and shortage costs are known. The problem in this paper
is also considered under both capacity constraints in machine centers
and material balancing.
The objective is to control production rates at each period for each
product in order to provide on time-on budget delivery performance for
the customer. In this regard, both completion time and cost (budget) must
be taken into consideration simultaneously. The approach used in this
paper incorporates production control during the implementation of production
phase and can therefore have at least the following advantages:
||Identify the production
status by comparing planned and actual production amount and provide
report for each time as required
progress achieved and compare with planned progress
production status during manufacturing processes from both time and
cost points of view
a simultaneous schedule and cost performance index based on achieved
the gap generated and determine the important results incorporating
quick decision making for the managers
a forecast for both time and cost aspects and raise alarm in the case
of over budget/over schedule before finishing the production process
Proposed approach: The approach used in this study is initiated
using a hybrid of analytical modeling and simulation analysis applied
for MPMP problems as proposed by Byrne and Bakir (1999). That is why the
solution is completely feasible resulting from adjusting overloaded capacity.
Since the problem must be controlled, it certainly has to be converted
to a project management network e.g., activity on node (AON) and consequently
a Gantt chart which will yield a detailed time schedule based on results
published by Byrne and Bakir (1999) or work presented by Byrne and Hossain
Thus, the resulting time schedule can be expressed under EV analysis
incorporating a simultaneous cost/time control mechanism. At this stage
the control period (i.e., how often control actions have to be performed)
and the control level (e.g., activity) must be clarified. It must be pointed
that in this paper, activity level is consider to be controlled periodically.
Thus, each process that must be achieved on a machine for producing a
specific product at each period is considered as an activity to be controlled.
Cost of each activity must be calculated considering all relevant items
and distributed on its own activity accordingly. The cumulative amount
called budget at completion (BAC) has to be maintained. BAC will be also
used for EV calculation by multiplying BAC by the percentage of progress
resulting from progress in production. Finally by comparing, actual costs-associated
with activity-, PV and EV, the corresponding indexes can be found to provide
a forecasting based on current achieved performance. Thus, this process
must be repeated for each control period and at the end of each one, corrective
actions in case of bad EV metrics have to be investigated. The stopping
condition will occur when the last control period (I) appears. It is obvious
that it can be extended until delivery to the customer has been made.
The corrective actions may include revision of production plan, injection
of new budget or even time/cost trade off in case of being over completion
time or customer due date. Finally all related data have to be gathered
for subsequent projects. Clearly, a well organized database management
system would be helpful in controlling actions. The proposed approach
can be found in detail as shown in Fig. 2.
framework for MPMP problems
The example presented in this paper was initially proposed by Byrne and
Bakir (1999) and also Byrne and Hossain (2005). The case consists of a
three period, three products PPP to be proceeded through four machine
centers, each including one machine and one input buffer. The capacity
constraint for each machine is equal to 2400 min per week. Cost components
and coefficient for each product, at each period are given in Table
1. Also customer demand, processing times and process routines are
given in Table 2-4.
Production time schedule: Based on a feasible production planning
approach, the production rate of each product, at each period on corresponding
machine center is considered as an activity. Then based of flowchart presented
in Fig. 2, after definition of precedence and resource
assignment, the production time schedule can be observed as given in Fig.
Planned value: Firstly, planned value of each task including production
costs, shortage and lost sale must be calculated. Then the planned value
of each activity must be distributed on its own duration. It is thus expected
to achieve planned value of each production planning period (e.g., each
day) by calculating cumulative amount of activities to be done on a specific
date. Method of estimation usually can be considered using normal distribution
function. That is why it is common that at the start of project, progress
rate of an activity is low and then it will increase up until the middle
of its own duration accordingly and then it will decrease until finishing
the activity. It is thus expected to apply a normal distribution curve.
However, it is possible to try other types of probability distribution
functions e.g., log normal, exponential, etc. It is obvious that budget
at completion can also be achieved using cumulative amount of planned
value of each day.
EV: As a simple calculation method, EV can be calculated based
on progress achieved in shop floor multiplied by planned value. Progress
(P) achieved for each activity can be calculated based on the following
actual production rate
planned production rate
of activities associated with progress calculation or activities involved
Indeed, in order to calculate progress percentage, total produced products
must be divided by total planned production based on time schedule. The
progress can be easily calculated for each production planning period
e.g., day, week etc.
Actual costs: The actual costs of work performed can be determined
for each activity and therefore the total expenditures at the end of production
planning period can be thus calculated. It is clear that only the expenditure
that had been used in planned value calculations can be further used during
actual costs calculations. Hereby, the expenditures must be allocated
based on cost codes assigned at the start of project to its own category.
EV measurement: As it is clear in Fig. 4, planned
value, EV and actual costs have been drawn versus time. Total amount of
EV is less than total actual costs and total actual costs is less than
planned value. This means that the manufacturing process is both over
schedule and budget. Thus, additional budget and time are required for
finishing pre-determined production amounts to be delivered to the customer.
The results have been prepared based on current date which is the 10th
day from production start time. The values 0.5 and 0.7 have been achieved
for SPI and CPI respectively. These are strong indices that on time on
budget delivery to the customer cannot occur since CPI and SPI are less
than 1. Hereby, it is necessary to take some preventive actions in order
to control any poor performance. It is obvious that in subsequent periods
the trend of progress by EV metrics can be traced accordingly.
regard, bottlenecks must be identified and prevented from reoccurrence
in the upcoming processes. By this reasoning, a cause and effect diagram
can also be elaborated in order to identify the root causes of issues
happened in the shop floor to analyze them for future. Also, other strategies
can be proposed by the managers involved, e.g., using overtime for production
The horizontal axis shows days and the vertical one represents the total
Forecasting new budget/time to be delivered to the customer: Since
the EV method represents schedule and cost performance indices based on
the achieved progress, it is also possible to present a forecast for on
time and specially budget required at completion. It is pointed out that
forecasting results will be updated periodically just at the end of each
control period. This helps the manufacturer to monitor progress trend
during manufacturing processes and demonstrate output or achieved results
Based on the planned value method (Anbari, 2003) planned value rate for
each week is equal to BAC/PD, where BAC and PD indicate budget at completion
and planned duration. Therefore, the planned value rate is almost 12500.
In other words, schedule variance arises due to the difference between
EV and planned value. By dividing the schedule variance by planned value
rate (50,000/12500) a four weeks slippage can appear due to obtained performance.
This implies that the actual achievement in comparison with the initial
planned delivery performance will reach the customer with 4 weeks delay.
In order to forecast estimate at completion cost (EAC) the following
formula can be efficiently used (Al-Tabtabai and Diekmann, 1992):
In this case, almost 515000 will be estimated as the amount required
at completion. The manufacturer in this case must thus focus on weakness
in order to make corrective actions otherwise profit margins will decrease.
It is also possible to apply other forecasting formulae based on manufacturing
strategy and performance (Al-Tabtabai and Diekmann, 1992; Anbari, 2003).
CONCLUSION REMARK AND FURTHER RESEARCH
This study not only addressed a control mechanism during implementation
of manufacturing process, but also provided a forecasting in each period
of manufacturing control based on pervious performance achieved in production
The approach can be efficiently used in manufacturing processes where
a manufacturer intends to ensure there is enough time/cost in order to
achieve on time-on budget delivery performance to the customer.
In case of bad EV metrics, it is desired to apply time- cost trade off
models in order to meet delivery due date. However, in this case, integration
of those models embedded with production environments is planned for further
The authors wish to express their acknowledgments to Iran National Science
Foundation (INSF) for supporting this research financially (Grant No.