The document proposes a modified version of the Manufacturing Cost Deployment (MCD) method called Project Cost Deployment (PCD) for analyzing engineer-to-order (ETO) production systems. The PCD introduces two key modifications: 1) replacing the concept of production stations with manual assembly macro-activities, and 2) introducing a new structure for classifying and analyzing losses specific to manual assembly tasks. The validity of the PCD approach is demonstrated through a real-world industrial application to a train wagon manufacturer. The results show that PCD can identify hidden losses, quantify wastes economically, and estimate the impacts of potential lean improvements in terms of efficiency and effectiveness.
1. See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/327106278
Lean manufacturing tool in engineer-to-order environment: Project cost
deployment
Article in International Journal of Production Research · August 2018
DOI: 10.1080/00207543.2018.1508905
CITATIONS
25
READS
6,434
4 authors:
Some of the authors of this publication are also working on these related projects:
Advanced tools for lean manufacturing and Industry 4.0 View project
Special Issue - "Supply Chain Design and Management in the Industry 4.0 Era" View project
Marcello Braglia
Università di Pisa
136 PUBLICATIONS 4,890 CITATIONS
SEE PROFILE
Marco Frosolini
Università di Pisa
48 PUBLICATIONS 1,514 CITATIONS
SEE PROFILE
Mosè Gallo
University of Naples Federico II
60 PUBLICATIONS 536 CITATIONS
SEE PROFILE
Leonardo Marrazzini
Università di Pisa
26 PUBLICATIONS 127 CITATIONS
SEE PROFILE
All content following this page was uploaded by Leonardo Marrazzini on 27 March 2019.
The user has requested enhancement of the downloaded file.
2. Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=tprs20
International Journal of Production Research
ISSN: 0020-7543 (Print) 1366-588X (Online) Journal homepage: https://www.tandfonline.com/loi/tprs20
Lean manufacturing tool in engineer-to-order
environment: Project cost deployment
Marcello Braglia, Marco Frosolini, Mosè Gallo & Leonardo Marrazzini
To cite this article: Marcello Braglia, Marco Frosolini, Mosè Gallo & Leonardo Marrazzini (2019)
Lean manufacturing tool in engineer-to-order environment: Project cost deployment, International
Journal of Production Research, 57:6, 1825-1839, DOI: 10.1080/00207543.2018.1508905
To link to this article: https://doi.org/10.1080/00207543.2018.1508905
4. For the above reasons, the development of new tools and methods able to support the implementation of lean principles in
ETO environment represents an important potential field of research. Nevertheless, the literature in the field is limited if com-
pared to that concerning mass production or batch type systems (Birkie and Trucco 2016). Despite the widespread and
ongoing research on lean manufacturing, there is a dearth of evidence addressing the peculiarities of implementation in man-
ufacturing ETO contexts.
If we limit our attention to the conventional initial step of lean implementation, that is the analysis of wastes and losses of
a certain process, it is possible to identify three generally used tools:
. Overall Equipment Effectiveness (OEE);
. Value Stream Mapping (VSM);
. Manufacturing cost deployment (MCD).
OEE represents the most known measurement tool for effectiveness, both in Lean and Total Productive Maintenance
(TPM) implementation processes (Hansen 2002; Stamatis 2010). In brief, along with the measure of effectiveness, it
gives an interesting and explanatory interpretation of the efficacy of the adopted actions and countermeasures. OEE can
be adapted to ETO contexts without adaptations and/or extensions. However, it is important to pinpoint its scarce relevance
in the ETO environments. In fact, in ETO companies, nearly all the production of the different components is outsourced, and
only the (manual) assembly of the final product is performed internally. Sometimes a small internal machining workshop is
mainly devoted to the production of a few special parts.
VSM is a commonly used tool in lean manufacturing. It is a comprehensive analysis and visualisation tool, used to illus-
trate the main processes and their operations, together with lead times, buffers, and information flows (Rother and Shook
2003). Probably, VSM is the lean tool that received the most attention, both in terms of evolution and adaptation, for its
use in ETO environments. The works of Khaswala and Irani (2001), Braglia, Carmignani, and Zammori (2006) and Matt
(2014) represent possible variants or integrations of the original VSM technique that has been developed after lean appli-
cations in different environments and ETO contexts.
In order to increase visibility, a better cost management granularity must be obtained. MCD is a systematic procedure that
clarifies the structure and nature of costs associated with various production losses (or ‘wastes’ in lean lingo) of the manu-
facturing process (Yamashina and Kubo 2002). It allows both a punctual efficiency analysis of the single station and a coher-
ent evaluation of the whole production flow. Therefore, MCD permits to develop a cost-reduction program, selecting
improvement projects that eliminate the root causes of problems. MCD represents the fundamental pillar of World Class
Manufacturing (WCM), a structured lean approach developed for the automotive industries (Silva et al. 2013).
Although a cost evaluation of the wastes represents an appealing information for the lean analyst, MCD is not widely
used in practice and the corresponding literature is rather limited (Chakravorty 2012; Silva et al. 2013). Moreover, to the
best authors’ knowledge, no papers have been published so far that deal with cost reduction in ETO contexts by means
of the MCD methodology. Nevertheless, the adoption of a technique able to investigate the impact of losses on the
overall project costs would be extremely valuable for all ETO environments.
Hence, the goal of this work is to propose a new modified and adapted MCD framework to best fit the features and the
requirements of typical ETO environments. Named Project cost deployment (PCD), the proposed method is mainly devoted
to the cost deployment analysis in ETO companies characterised by complex medium/large final products with:
. different customisation levels,
. low production volumes,
. extreme variability of production mix and in the associated production flows,
. high production lead times, ranging from weeks to months,
. high (often excessive) number of tasks,
. high cycle times,
. work performed mainly on fixed place manual assembly stations.
Examples of these production systems are, for instance, the companies operating in the machinery-building industry, con-
structions processes, shipbuilding, aerospace, railway equipment (e.g. trains, metros, etc.).
In order to conform to the typical ETO working environment, the new version of PCD will introduce two substantial and
innovative modifications with respect to the original model proposed by Yamashina and Kubo (2002):
1. a new structure for the classification of losses, specifically conceived and developed to analyse the losses within the
manual assembly macro-activities, rather than the original structure derived from standard OEE;
2. the replacement of the analysis sheets for the production/assembly cells with the assembly macro-activities, usually
performed on fixed place stations.
1826 M. Braglia et al.
5. Another important contribution of this framework is that it considers lean principles and some resilience engineering
factors (Azadeh et al. 2017) to improve both performance and sustainability. Resilience engineering (RE) is a new
concept which seeks safety and performance improvement in organisations (Bevilacqua, Ciarapica, and De Sanctis
2017). According to De Sanctis, Meré, and Ciarapica (2018), it helps organisations to make their system much more reliable
and sustainable in case of any disturbance. In particular, PCD covers two basilar RE concepts: (i) it tries to preclude losses,
failures, and damages, and (ii) it can react in a structured way after these phenomena took place. More specifically, being a
performance monitoring system, the PCD can be considered as a tool to increase the control and supervision of the system’s
performance and operational status in search of possible weak signals.
The remainder of this paper is organised as follows. The second section provides the background for the proposed meth-
odological approach. In the third section, the project cost deployment approach is fully presented. In order to show the oper-
ating principles and potential results of this novel approach, a real industrial implementation concerning a manufacturer of
train wagons via manual assembly lines is commented in the fourth section using a case study. Finally, the fifth section is
devoted to conclusions and proposals for future possible developments.
2. An overview of manufacturing cost deployment
The MCD methodology was originally introduced by Yamashina and Kubo (2002) as an effective tool to develop cost-
reduction programs. The main aim of MCD is to establish:
. the impact of the different losses on the manufacturing cost of a product;
. a priority of projects to reduce waste and losses in accordance with the priorities derived from an analysis of costs/
benefits.
Yamashina and Kubo (2002) developed a precise and well-structured procedure to achieve this aim, which is rigorously
supported using five matrices (Figure 1):
. The A-matrix categorises and quantifies wastes and losses in all the relevant production processes;
. The B-matrix shows the causal-resultant relationships among the losses;
. The C-matrix presents the total cost assigned to each causal loss;
. The D-matrix shows the expected cost savings for each improvement proposed project, combining data from losses
and associated costs;
. The E-matrix presents the investment efficiency associated with each improvement project.
Figure 1. Logic route of manufacturing cost deployment.
International Journal of Production Research 1827
6. Later, this methodology has been constantly refined through benchmarking with companies and its current seven-step
implementation procedure (Figure 2) is fully integrated into the WCM model, at Fiat Group Automobiles Production
Systems (FAPS), as a systematic way to sustain manufacturing cost reduction (Massone 2007).
WCM is a major philosophy focusing primarily on production, with a level of excellence throughout the logistics and
productive cycles, in reference to the methodologies applied and the performance achieved by the best companies world-
wide, mostly based on the concepts of Total Quality (TQC), Total Productive Maintenance (TPM), Total Industrial Engin-
eering (TIE) and Just in Time (JIT) (Midor 2012; De Felice, Petrillo, and Monfreda 2013). Its implementation involves the
entire factory organisation through the application of ten technical-managerial pillars (Massone 2007), starting from health
and safety, involving quality system, maintenance system, workplace organisation, logistics, and environment. Hence, being
quality and cost saving among its ‘grand strategies’, cost deployment is the main and the most peculiar pillar of the WCM
model, opportunely named ‘Cost Deployment’ pillar (Chiarini and Vagnoni 2015). In fact, it is transversal to all the other
WCM pillars and represents the necessary causal link between the identification of improvement actions on the targeted
areas and the evaluation of the results achieved through the implementation of specific pillars (Petrillo, De Felice, and Zom-
parelli 2017).
Independently of its many adaptations and modifications, MCD is a method that innovates systems management and
control of production activities focusing on the concept of loss and its economical evaluation, forcing the management –
besides finding the causes of waste and loss – to measure them properly from an economical point of view.
3. Project cost deployment
In order to fully exploit the potential of MCD framework in ETO settings, some modifications to its original formulation have
to be made. In the current section, these modifications have been reported and appropriately commented while presenting the
proposed methodology.
As cited above, the manufacturing cost is the portion of costs on which the PCD may act with adequately identified
improvement actions. A necessary condition for an effective and long-lasting reduction of this kind of cost is the capability
to identify and to analyse all the wastes and losses encountered along the manufacturing process. With reference to this issue,
the proposed PCD methodology differs substantially from the work of Yamashina and Kubo (2002).
3.1. The PCD losses classification structure
As shown in Figure 3, the PCD introduces a new structure for the classification of losses. In general, within all ETO com-
panies, a ‘waste’ is represented by the amount of time that is lost in unproductive activities. Therefore, the gap between the
standard time, in which a task is processed under optimal operating conditions, and the effective time can be viewed as the
consequence of multiple causes of inefficiency. These, in turn, progressively increase the time necessary to complete a
Figure 2. Seven-step roadmap of manufacturing cost deployment.
1828 M. Braglia et al.
7. manufacturing task. In other words, due to planned and unplanned stops, only a portion of the working time is effectively
used for manufacturing.
In addition, there is a gap between the losses due to inefficiencies that are external to the manufacturing system, and the
losses due to inefficiencies directly ascribable to the manufacturing system. In this way, it is possible to evaluate the portion
of time lost during a manufacturing activity. Internal losses can be further divided into losses that are internal to the man-
ufacturing systems and those that are internal, but that can be directly ascribable to a specific task. This classification is
aimed at identifying the key areas for prioritising the improvement activities.
PCD evaluates the amount of time that is lost in unproductive activities, but it does not directly impute the increase in the
duration of a task to the skill of the operator. With reference to this potential issue, the authors suggest a future study to over-
come the lack of an evaluation of workers skills. However, manual assembly lines, or, more generally, manual flow lines, are
used in high-production situations where the work to be performed can be divided into small tasks. By giving each worker a
limited set of tasks to do repeatedly, the worker is able to perform them more quickly and more consistently changing his skill
class.
3.2. The decomposition of the manufacturing process
Once a classification of the losses has been appropriately identified, it is fundamental to investigate in which stage losses
occur within the production system. PDC introduces a further modification that distinguishes itself from the traditional
MCD. This modification is a consequence of how production activities are arranged in a typical ETO manufacturing
system. In particular, it consists in the punctual substitution, within the analysis sheets used to record losses, of all the stations
within the manufacturing/assembly cells crossed by the product with the manual assembly macro-activities, usually per-
formed in a fixed place layout.
Typically, the best available instruments to estimate macro-activities, in terms of duration, resource requirements, and
budget are those of Project Management (PM). In this context, a large number of PM frameworks, methodologies and
Figure 3. The Project cost deployment losses classification structure.
International Journal of Production Research 1829
8. approaches have been developed over the past few decades. Among them, the most popular are presented and suggested by
the ‘Project Management Body of Knowledge’ (PMBOK), edited by the Project Management Institute (see, for instance,
Rose 2013). In particular, once the Work Breakdown Structure (WBS) has been defined, the estimate of the macro-activity
duration is made subjectively by the Project Manager on the basis of experience gathered on previous similar projects, taking
into account any exceptions and peculiarities of the new context. WBS also provides the necessary framework for detailed
cost and resources estimating and control.
It is noteworthy that aggregating tasks in macro-activities dramatically improves the planning process. Usually, it is good
to aggregate tasks within shifts or, in certain contexts, within a few days. Even with such limitations, it is not uncommon to
generate too many activities. In such cases, it could be useful to proceed further with the aggregation process, still avoiding to
add more than 10 macro-activities. With respect to the depth of the levels into which decomposing the manufacturing
process, the constraints are represented by potential issues in managing inventories and spaces. Therefore, the authors rec-
ommend a maximum of two levels only for those items characterised by of high cost or high risk.
To resume, the authors suggest the following decomposition structure:
. level zero: identification of the production item or the assembly process under analysis;
. first level: splitting of process steps in macro-activities, where all elements of each activity are estimated in terms of
resource requirements, budget and duration, linked by dependencies, and scheduled;
. second level: splitting of macro-activities into elementary tasks only for items that are characterised by high cost or
risk.
An example of the partial decomposition of the assembly process of a train wagon is presented in Table 1.
3.3. The methodology: the PCD five matrices
Applying PCD requires the use of some matrices that support a stepwise implementation of the methodology. The A-matrix
(Figure 4) shows the total magnitude of losses and wastes, for each category and production processes, within a predefined
time window. The loss types are given on the vertical axis, while the horizontal axis represents the macro-activities (first-level
decomposition) or the elementary operations (second-level decomposition) of a specific process step (level zero decompo-
sition). Each loss is ranked by means of symbols (A, B or C) or colours (red, yellow, or green) based on its significance in
terms of frequency of occurrence. In particular, red colour (or symbol A) refers to very important losses, yellow colour (or
symbol B) to important ones, and green colour (or symbol C) to minimal ones. These marks represent the priorities for loss
reduction. A square empty means no need for loss reduction.
Hence, the purpose of the A-matrix is to show what losses and where they occur. Each identified loss should be inves-
tigated in order to document its potential impact on other processes. This is to increase the understanding of the processes, but
primarily to calculate the real cost of the loss. Cost deployment goes deeper: it does not stop after identifying losses, as it
happens in the traditional way of managing manufacturing, but it also tries to ascertain the causes of such losses. For
example, a wrong technical documentation may originate the operator error.
Table 1. Example of the decomposition of the assembly process of a train wagon.
Level zero
decomposition First-level decomposition Second-level decomposition
Assembly of a train
wagon
Installation of the access door Door threshold installation
Installation of the door opening side
Installation of upper guide (vertical loads)
Installation of door leaf
Installation of top guide (horizontal loads)
Installation of door movement mechanism
External cover installation
External dripper installation
Mobile step installation
Heating ventilation and air conditioning
(HVAC)
Installation of the toilet Toilet module installation
Installation of internal cover
Connection of water, air, drainage, and toilet drains with toilet
module
Installation of the fire system
1830 M. Braglia et al.
9. The B-matrix, shown in Figure 5, is used to clarify the cause-effect relationship of losses identified in matrix A. This
matrix places causal losses and the locations of their occurrence on the vertical axis and the resultant/consequent losses
and their locations of occurrence on the horizontal axis. The mark (x), a Boolean value indicating the presence of correlation,
Figure 4. The A-Matrix.
Figure 5. The B-Matrix.
International Journal of Production Research 1831
10. is entered to indicate which causal loss is related to which resultant loss and each location where the loss occurs within the
assembly process. With respect to the possibility of reducing or eliminating it, a resulting loss cannot be correctly managed if
it is not linked to a corresponding causal loss. Besides, a causal loss may exist within different macro-activities.
Again, take for example the operator error. It can be defined as a causal loss if it involves a reworking, while it can be
defined as resulting loss if it occurs after a design documentation mistake. Hence, it is vital to analyse the whole process,
including all the potential causal losses of all the resulting losses within the linked activities.
By using A and B matrices, it is possible to clarify how and where losses occur and the cause–effect relationships among
them. However, to reduce manufacturing costs systematically it is necessary to further clarify how each loss increases the
manufacturing cost.
The C-matrix (Figure 6) is adopted to convert casual losses into the corresponding manufacturing costs. This matrix
places causal losses on the vertical axis, reporting in which macro-activities casual losses occur, and the cost factors on
the horizontal axis. Cljk represents the cost due to loss l, that originated within macro-activity j and resulted in a cost
factor k. To properly apply the methodology, it is essential that the manufacturing costs, increased by resultant losses, are
increased by their respective causal loss. Therefore, each casual loss (l) is opportunely increased by the cost of the resultant
losses (Crl) directly attributable to the loss l, involved in the macro-activity j.
In repetitive production environments, it is rather simple to define and to measure standard indicators or to use analytical
relations to convert the losses into manufacturing costs (Son 1991; Chiadamrong 2003). On the contrary, in the ETO pro-
duction systems, characterised by medium/large complex final products, with hundreds or even thousands elementary
tasks, high cycle times and, prevalently, manual fixed place assembly stations, it can be too difficult to derive a standard
cost-effective methodology for the assignment of manufacturing costs. In this sense, the literature is very sparse. Emad
and Mangin (2002) propose an approach for assessing productivity by using an analytical cost model and indicators
widely used to measure the site productivity. Ioannou, Angus, and Brennan (2018) report the development of a set of para-
metric models for capital expenditure, operational expenditure, and levelized cost of energy as a function of a set of global
variables for offshore wind farms.
Unfortunately, all the formulas used for the cost-model valorisation are deeply specific to their case studies. In addition,
sophisticated cost models risk to oversize the simplicity of the PCD analysis. Hence, the authors suggest classifying losses
into two different groups, namely direct and indirect cost losses. Examples of direct costs are direct labour, direct materials,
and transports. Examples of indirect costs are production supervision salaries, quality control costs, and depreciation.
Figure 6. The C-Matrix.
1832 M. Braglia et al.
11. The direct costs, that can be effectively related to a specific task, can be proportionally measured in terms of time loss
during manufacturing. This concept becomes more troublesome and critical when determining the indirect cost. Indirect
costs cannot be ascribed to a specific project but are relative to the functioning of the whole business unit. If the projects
are internal and represent a limited amount of the overall investments, it may be useless to consider the indirect costs.
On the contrary, if they are relevant enough, the management has to decide whether include them and how to ascribe the
increase of indirect costs to the various losses.
In order to address the losses identified and appropriately quantified, it is necessary to put in place one or more improve-
ment activities implementing specific techniques or launching real improvement projects.
The lack of structured project selection methods leads to lost opportunities, sub-optimisation, and inefficient resource
allocation (Kornfeld and Kara 2011). Companies can use different methods to select and prioritise improvement activities
or projects. Not surprisingly, companies that use objective prioritisation methods report a higher success rate for improve-
ment projects compared to those companies that exclusively use subjective methods (Kirkham et al. 2014). Hence, a well-
designed project selection method should offer a structured improvement process. That is what PCD does. In particular, the
D-matrix (Figure 7) clarifies what (lean) improvement techniques or projects should be implemented for each loss in each
stage of the production system, as well as what kind of knowledge is required to reduce the losses themselves. A clear
explanation of the various available lean improvement tools and techniques is presented in the recent framework of
Zahraee (2016).
Here, the question that must be addressed is what loss should be attacked and solved first. In order to define the improve-
ment priorities, the ICE method (see Equation (1)), as suggested by the WCM, can be a valid method to use the most relevant
causal losses. This method effectively and appropriately priories the causal losses identified by the C-matrix, evaluating their
impacts, costs, and feasibility according to the following expression:
ICE = I · C · E (1)
where:
. the impact factor (I) expresses qualitatively, on a 1–5 scale, the economic impact of the loss;
. the cost factor (C) expresses, on a 1–5 scale, the economic weight of costs that should be sustained to improve the
system by removing or reducing the loss;
. the easiness factor (E) represents, on the same scale, the simplicity, in terms of resources and time, of the actions that
are necessary to reduce/eliminate the loss.
Therefore, the ICE index qualitatively expresses the degree at which the loss may be attacked, on a scale ranging from 1
to 125.
After identifying the appropriate methods to reduce the most significant losses within the various processes, it is necess-
ary to evaluate an economic balance between the implementation cost of the new method and the benefit deriving from it.
Finally, building the E-Matrix (Figure 8) which is based on the costs/benefits balance it is possible to decide which actions to
start first.
Figure 7. The D-Matrix.
International Journal of Production Research 1833
12. 4. Case study
In this section, the developed methodology is applied to an industrial case, which refers to a railway company that assembles
train wagons, to demonstrate the effectiveness and usefulness of the methodology previously defined. The main customers of
the company are train corporations all over the world. Operating in a very competitive scenario, the company needs to con-
sider some of the highly innovative technical features of trains, in terms of frequency and speed of the service, and number of
passengers. Consequently, the company has been asked for remarkable products from a technical point of view, basically in
terms of service reliability and economic stability. To this extent, the PCD approach has been recognised by the company top
management as a valid method and tool to identify the hidden losses and to quantify the wastes from an economical point of
view. In addition, PCD sets the focus on areas where the greatest casual losses are placed, providing opportunities for greater
efficiency and effectiveness in reducing and eliminating them.
The first phase of the PCD study was the identification of the manufacturing activities involved in the assembly process.
Authors identified nine major macro-activities, all completed on fixed place stations. Macro-activities involve both the instal-
lation of seats, windows, luggage areas and other components and the realisation of complete furniture sets. Figure 9 reports
the length, the ubication and the order of the macro-activities following the assembly sequence scheduled by the Gantt chart.
4.1. Losses identification with work sampling methodology
The second phase of the study was the identification of the losses in all task activities. In order to obtain regular data on all
losses, evaluating how people spent their working time, the well-known Work Sampling methodology was applied (Pape
1988). It is important to acknowledge that there are other methods available for determining how people spend their
working time. Continual observation is frequently used, where people are shadowed for a period of time and their work
Figure 9. Length and ubication of the macro-activities performed.
Figure 8. The E-Matrix.
1834 M. Braglia et al.
13. tasks noted. Although it can yield highly detailed data, such observation is extremely time-consuming, thereby greatly
restricting the number of people that can be examined at any one time. Furthermore, though stop-watch time study is
very useful for repetitive operations, it cannot measure accurately long and irregular work cycles.
On the contrary, Work Sampling is particularly useful in the analysis of non-repetitive or irregularly occurring activities
and represents a suitable method to identify the hidden losses that account for most of the time of an assembly activity (see,
for instance, the recent framework of Hajikazemi, Andersen, and Langlo 2017). It essentially records the tasks that people
perform at many randomly occurring sample points. Once data have been collected from a sufficiently large number of
sample points, it is then possible to estimate the working/not working time, which indicates how the time is effectively allo-
cated to the various activities.
4.2. The PCD in the railway company
The Work Sampling evaluations were done exactly every five working days from 6.45 in the morning when the work started
until 4.00 in the afternoon when the work finished. All data were collected into a module designed with the aim of being:
. Representative. Every observation includes the date in which the observation was made, and which value or not-
value work activity has been performed.
. Random. The observer guaranteed, using a random generation system, the absolute randomness of the moment of
the observations.
. Instantaneous. The observer captured the situation in an instant and univocal way, to avoid interpreting what he
observed.
Once timing losses evaluation was completed, the five PCD matrixes were built, following the step-procedure reported in
the methodology section:
1) quantify losses (A-Matrix);
2) establish cause-and-effect relationships (B-Matrix);
3) assign costs to losses (C-Matrix);
4) identify improvement activities/projects (D-Matrix);
5) identify implementation costs and total cost–benefit ratio (E-Matrix).
The A-Matrix (Figure 10) shows the identified losses and wastes and where they occur. Since the assembly of a wagon is
divided among nine macro-activities in the assembly, the columns in A- Matrix have been divided into the same macro-
activities. In this matrix, each loss is ranked by means of three colours (green, yellow, and red), based on its frequency
of occurrence. If a loss impacted for at least 10% of the duration of the macro-activity, the square is coloured red; if the
impact was at least 5%, the colour of the square is yellow while if it was less than 5% the colour is green. Finally, if a
square is empty, there was no impact at all.
Figure 10. Case study A-matrix.
International Journal of Production Research 1835
14. After this step, it was possible to build the B-matrix and then the C-matrix which, respectively, allowed to study how a
loss can influence the rest of the production process, linking resultant losses to the respective causal losses. Finally, it was
possible to transform each loss into a cost.
As shown in Figure 11, an example of casual loss involved the installation of the furniture panels in the lower compartment
(macro-activity E) was the ‘technical documentation mistake’. In particular, an incorrect quote caused interference during the
fitting of the passenger seats (macro-activity F). The technical documentation mistake can be defined as causal loss because it
involved an operator mistake in the macro-activity E. The mistake has been later corrected during macro-activity F. Obviously,
this increase in time in both the activities, that represents the true resultant loss, is due to the original documentation mistake.
To properly apply the PCD methodology, it is essential that the manufacturing costs, increased by resultant losses, are
increased by their respective causal loss. Following this feature, it was possible to evaluate the total amount of the manu-
facturing cost lost due to the casual loss.
The D-Matrix (Figure 12) utilises the loss stratification as a starting point to define the activities and the improvement
projects and to set priority level. It represents the foundation of the PCD analyses and gives a first glance at the prioritisation
of measures and projects applicable to reduce losses and gain efficiency.
As shown in Figure 12, to prevent the previous technical document mistake, standardized work sheet was selected as a
useful tool for creating standard work conditions (including the time to complete each activity) with the aim of carefully
specifying the exact procedure for performing each task. By means of standard and well-structured procedures, the
Figure 12. Case study D-Matrix.
Figure 11. An example of casual – resultant losses.
1836 M. Braglia et al.
15. process becomes more organised and improvement opportunities become immediately apparent. In addition, another lean
method, as the well-known 5S, was selected for housekeeping tools, parts and other objects and make sure that they are
in known, optimum locations, to prevent another casual loss: ‘searching for material/equipment due to disorder’.
Finally, the E-Matrix was built. Figure 13 reports some lines of the E-Matrix. E-matrix was prepared to keep track of the
several (lean) improvements activities and projects started during the analysis. Every used tool or technique contained infor-
mation about:
. the type of loss that is attacked and the related macro-activity;
. the yearly recovered losses;
. the activity/project cost.
Based on a costs/benefits analysis, it was possible to properly rank the improvement activities.
4.3. Results
The validation process of the PCD methodology was done within the company with the certification by the administration
and control function. The evaluation period covers an entire year. During this period 15 railway carriages have been
assembled. Based on previous similar projects, it was estimated that proceeding with the implementation of the current meth-
odology, the expected savings are about 8% of the total costs to assembly the 15 trains. The savings have been estimated on
the basis of the potential losses identified by the PCD (about 10% of the total assembly costs), and the impact of potential
(lean) improvement activities and projects (about 2% of the total assembly costs).
In addition, the constant refinement of the ability to identify new losses can lead to a further increase in the number of
losses identified, as well as the improvement of the data collection system must allow the definition and application of for-
mulas able to translate, with increasing precision, losses in costs. For this reason, the cost deployment process does not stop
after the first analysis is completed but starts again to further investigate costs, trying to individuate ulterior hidden wastes
and losses. The controller could check periodically the status of every project undertaken, validating, and certifying the
associated savings. In this way, the cost deployment approach can reduce the manufacturing cost in a rational manner
and achieve great cumulative effects during the whole period.
5. Conclusions
In this paper, a modified version of the Manufacturing cost deployment (MCD) is proposed to assess how much of the man-
ufacturing cost of a project is due to losses and for setting priorities in choosing the improvement projects, based on a benefit-
loss analysis and on the economic return calculation.
The framework, named Project cost deployment (PCD), improves both the resiliency and the performance of manufac-
turing processes. In particular, it can be effectively used in ETO production systems thanks to the introduction of two sub-
stantial and innovative changes. The first consists in replacing the station concept with the manual assembly macro-activity
of the finished product. The second variation defines a new structure for the classification of losses, specifically designed to
analyse the inefficiencies within the manual assembly macro-activities.
To systematically support the implementation of the approach, five matrices were proposed: (i) the A-Matrix identifies
and quantifies the losses, (ii) the B-Matrix clarifies cause-and-effect relationships, (iii) the C-Matrix connects losses and
Figure 13. Case study E-Matrix.
International Journal of Production Research 1837
16. manufacturing costs, (iv) the D-Matrix connects causal losses and improvement techniques and (v) the E-Matrix identifies
benefit values and establishes the cost-reduction program.
The validity of the approach is confirmed by an industrial application, included in this article. The results obtained
demonstrate that the PCD allows the analyst to identify the hidden losses and to quantify the wastes from an economical
point of view. In addition, PCD sets the focus on areas where the greatest casual losses are placed, providing opportunities
for greater efficiency and effectiveness in reducing and eliminating them. It also facilitates the selection of (lean) improve-
ment activities and projects to be activated to remove or to correct the causes of such losses, allowing an economical evalu-
ation of costs and benefits. Finally, due to its structured step-by-step features, its implementation in interconnected electronic
worksheets is simple and immediate assuring the ease of use for the user.
As cited in the methodology section, a potential issue of this work is the difficulty to derive a standard cost function for
the assignment of manufacturing costs in an ETO production system. However, PCD identifies cost elements which should
be included in the analysis of complex systems and it proposes a structured framework to estimate them. In addition, PCD
determines the sources of losses and their impact they on production, but it does not measure the time loss due to the operator
skill. In particular, when employees are not properly skilled, work performance (impact on the variability, stoppages pro-
duction, quality defaults) deteriorates and introduces a bias in the time evaluation. In order to better investigate this potential
issue, the authors propose a future study of a structured metric to overcome the lack of an evaluation of workers skills.
Starting from the present work, several future research directions can be identified. They are briefly sketched as follows:
. the perimeter of the cost deployment should include everything that contributes to determine the cost of the transform-
ation of a product. The analysis should be carried out on the manufacturing process, which generally covers about 90%
of production costs, and on the support processes as well, such as, among others, logistics, human resources, and quality.
. as the digitisation of manufacturing continues, following the pervasive Industry 4.0 paradigm, the authors believe that
PCD, thanks to the strongly structured and procedurised approach, could be fully integrated into business software and,
therefore, become easier to apply and maintain. In particular, companies that are already using cutting-edge technology
are likely to gain a further competitive advantage because they can guarantee high-quality input data to the model. In
addition, cross-departmental integration permits direct communication with manufacturing systems, thereby allowing
problems to be solved and corrective decisions to be activated in a timely fashion from an economical point of view.
Disclosure statement
No potential conflict of interest was reported by the authors.
ORCID
Marcello Braglia http://orcid.org/0000-0002-1415-6140
Marco Frosolini http://orcid.org/0000-0002-1958-6297
Mosè Gallo http://orcid.org/0000-0002-9023-0752
Leonardo Marrazzini http://orcid.org/0000-0002-6793-7813
References
Al-Sudairi, A. A. 2007. “Evaluating the Effect of Construction Process Characteristics to the Applicability of Lean Principles.”
Construction Innovation 7 (1): 99–121. doi:10.1108/14714170710721322.
Azadeh, A., S. Elahi, M. H. Farahani, and B. Nasirian. 2017. “A Genetic Algorithm-Taguchi Based Approach to Inventory Routing
Problem of a Single Perishable Product with Transshipment.” Computers & Industrial Engineering 104: 124–133. doi:10.1016/
j.eswa.2017.05.012.
Bevilacqua, M., F. E. Ciarapica, and I. De Sanctis. 2017. “Lean Practices Implementation and Their Relationships with Operational
Responsiveness and Company Performance: An Italian Study.” International Journal of Production Research 55 (3): 769–794.
doi:10.1080/00207543.2016.1211346.
Birkie, S. E., and P. Trucco. 2016. “Understanding Dynamism and Complexity Factors in Engineer-to-order and Their Influence on Lean
Implementation Strategy.” Production Planning & Control 27 (5): 345–359. doi:10.1080/09537287.2015.1127446.
Braglia, M., G. Carmignani, and F. Zammori. 2006. “A New Value Stream Mapping Approach for Complex Production Systems.”
International Journal of Production Research 44 (18/19): 3929–3952. doi:10.1080/00207540600690545.
Chakravorty, S. S. 2012. “Prioritizing Improvement Projects: Benefit & Effort (B&E) Analysis.” Quality Management Journal 19 (1): 24–
33. doi:10.1080/10686967.2012.11918525.
Chiadamrong, N. 2003. “The Development of an Economic Quality Cost Model.” TQM & Business Excellence 14 (9): 999–1014. doi:10.
1080/1478336032000090914.
1838 M. Braglia et al.
17. Chiarini, A., and E. Vagnoni. 2015. “World-class Manufacturing by Fiat. Comparison with Toyota Production System from a Strategic
Management, Management Accounting, Operations Management and Performance Measurement Dimension.” International
Journal of Production Research 53 (2): 590–606. doi:10.1080/00207543.2014.958596.
De Felice, F., A. Petrillo, and S. Monfreda. 2013. “Improving Operations Performance with World Class Manufacturing Technique: A Case
in Automotive Industry.” In Operations Management, edited by Schiraldi. InTech. doi:10.5772/54450.
De Sanctis, I., J. O. Meré, and F. E. Ciarapica. 2018. “Resilience for Lean Organisational Network.” International Journal of Production
Research. doi:10.1080/00207543.2018.1457810.
Emad, A. A., and J. C. Mangin. 2002. “A New Cost Control Model and Indicators to Measure Productivity on Building Sites.”
Construction Innovation 2 (2): 83–101.
Hajikazemi, S., B. Andersen, and J. A. Langlo. 2017. “Analyzing Electrical Installation Labor Productivity Through Work Sampling.”
International Journal of Productivity and Performance Management 66 (4): 539–553. doi:10.1108/IJPPM-06-2016-0122.
Hansen, R. C. 2002. Overall Equipment Effectiveness: A Powerful Production/Maintenance Tool for Increased Profits. New York:
Industrial Press Inc.
Ioannou, A., A. Angus, and F. Brennan. 2018. “Parametric CAPEX, OPEX, and LCOE Expressions for Offshore Wind Farms Based on
Global Deployment Parameters.” Energy Sources, Part B: Economics, Planning and Policy 13 (5): 281–290. doi:10.1080/
15567249.2018.1461150.
Khaswala, Z. N., and S. A. Irani. 2001. “Value Network Mapping (VNM): Visualization and Analysis of Multiple Flows in Value Stream
Maps.” Proceedings of the Lean Management Solutions Conference, St. Louis, September 10–11.
Kirkham, L., J. A. Garza-Reyes, V. Kumar, and J. Antony. 2014. “Prioritisation of Operations Improvement Projects in the European
Manufacturing Industry.” International Journal of Production Research 52 (18): 5323–5345. doi:10.1080/00207543.2014.903345.
Kornfeld, B. J., and S. Kara. 2011. “Project Portfolio Selection in Continuous Improvement.” International Journal of Operations and
Production Management 31 (10): 1071–1088. doi:10.1108/01443571111172435.
Lane, G. 2007. Made-to-order Lean: Excelling in a High-Mix, Low-volume Environment. New York, NY: Productivity Press.
Massone, L. 2007. “Fiat Group Automobiles Production System: Manual do WCM, Wold Class Manufacturing: Towards Excellence Class
Safety, Quality, Productivity and Delivery.” Edited by Fiat, Brazil.
Matt, D. T. 2014. “Adaptation of the Value Stream Mapping Approach to the Design of Lean Engineer-to-order Production Systems: A
Case Study.” Journal of Manufacturing Technology Management 25 (3): 334–350. doi:10.1108/JMTM-05-2012-0054.
Matt, D. T., and E. Rauch. 2014. “Implementing Lean in Engineer-to-order Manufacturing: Experiences from a ETO Manufacturer.”
In Handbook of Research on Design and Management of Lean Production Systems, edited by V. Modrák, and P. Semančo,
148–172. Hershey, PA: IGI Global.
Midor, K. 2012. “World Class Manufacturing – Characteristics and Implementation in an Automotive Enterprise.” Scientific Journals 32
(104): 42–47.
Mostafa, S., J. Dumrak, and H. Soltan. 2013. “A Framework for Lean Manufacturing Implementation.” Production & Manufacturing
Research 1 (1): 44–64. doi:10.1080/21693277.2013.862159.
Pape, E. S. 1988. “Work Sampling.” In The Job Analysis Handbook for Business, Industry, and Government, edited by S. Gael, 518–535.
New York: John Wiley & Sons.
Petrillo, A., F. De Felice, and F. Zomparelli. 2017. “Performance Measurement for World-class Manufacturing: A Model for the Italian
Automotive Industry.” Total Quality Management and Business Excellence. doi:10.1080/14783363.2017.1408402.
Romero, D., and Z. Chavez. 2011. “Use of Value Mapping Tools for Manufacturing Systems Redesign.” Proceedings of the World
Congress on Engineering, London, July 6–8.
Rose, K. H. 2013. “A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Fifth Edition.” Project Management
Journal 44. doi:10.1002/pmj.21345.
Rother, M., and J. Shook. 2003. Learning to see: Value Stream Mapping to Create Value and Eliminate Muda. Cambridge: Lean Enterprise
Institute.
Silva, L. C. S., J. L. Kovaleski, S. Gaia, M. Garcia, and P. P. Andrade Júnior. 2013. “Cost Deployment Tool for Technological Innovation of
World Class Manufacturing.” Journal of Transportation Technologies 3: 17–23. doi:10.4236/jtts.2013.31002.
Son, Y. K. 1991. “A Cost Estimation Model for Advanced Manufacturing Systems.” International Journal of Production Research 29 (3):
442–1991. doi:10.1080/00207549108930081.
Stamatis,D.H.2010.TheOEEPrimer:UnderstandingOverallEquipmentEffectiveness,Reliability,andMaintainability.NewYork:CRCPress.
Womack, J., and D. Jones. 2003. Lean Thinking: Banish Waste and Create Wealth in Your Corporation. London: Free Press.
Worley, J. M., and T. L. Doolen. 2006. “The Role of Communication and Management Support in a Lean Manufacturing Implementation.”
Management Decision 44 (2): 228–245. doi:10.1108/00251740610650210.
Yamashina, H., and T. Kubo. 2002. “Manufacturing Cost Deployment.” International Journal of Production Research 40 (16): 4077–
4091. doi:10.1080/00207540210157178.
Yang, L. R. 2013. “Key Practices, Manufacturing Capability and Attainment of Manufacturing Goals: The Perspective of Project/Engineer-
to-order Manufacturing.” International Journal of Project Management 31 (1): 109–125. doi:10.1016/j.ijproman.2012.03.005.
Zahraee, S. M. 2016. “A Survey on Lean Manufacturing Implementation in a Selected Manufacturing Industry in Iran.” International
Journal of Lean Six Sigma 7 (2): 136–148. doi:10.1108/IJLSS-03-2015-0010.
International Journal of Production Research 1839
View publication stats
View publication stats
