U
NIVERSITÀ DI
P
ISA
Dottorato di Ricerca in Ingegneria Industriale
Curriculum in Ingegneria Meccanica
Ciclo XXXII
Development and implementation of
Lean Manufacturing tools and methods for the
manufacturing efficiency in
Engineer-to-Order production environments
Author
Leonardo MARRAZZINI
Supervisors
Prof. Marcello BRAGLIA Prof. Roberto GABBRIELLI Prof. Marco FROSOLINI
Coordinator of the PhD Program
Prof. Giovanni Mengali
“If you do not believe in yourself, no one will do it for you.”
List of Publications
This Thesis discusses the work and the main results obtained along my Ph.D. course, from November 2016 to October 2019, and carried out at the Department of Civil and Industrial Engineering of the University of Pisa. The contents of this Thesis may also be found in the following journal papers:
[J1] BRAGLIA, M., GABBRIELLI, R., MARRAZZINI, L. (Accepted). Rolling Kanban: a new visual tool to schedule family batch manufacturing processes with kanban. International Journal of Production Research. DOI:10.1080/00207543.2019.1639224.
[J2] BRAGLIA, M., CASTELLANO, D., GABBRIELLI, R., MARRAZZINI, L. 2020. Energy Cost Deployment (ECD): a novel Lean approach to tackle energy losses. Journal of Cleaner
Production 246. DOI:10.1016/j.jclepro.2019.119056.
[J3] BRAGLIA, M., GABBRIELLI, R., MARRAZZINI, L. 2019. Overall Task Effectiveness: a new Lean performance indicator in engineer-to-order environment. International Journal of Productivity and Performance Management 68 (2), 407-422. DOI:10.1108/IJPPM-05-2018-0192
[J4] BRAGLIA, M., FROSOLINI, M., GALLO, M., MARRAZZINI, L. 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. [J5] BRAGLIA, M., DI DONATO, L., GABBRIELLI, R., MARRAZZINI, L. 2018. The house of
safety: A novel method for risk assessment including human misbehaviour. Safety Science
110 Part A, 249–264. DOI:10.1016/j.ssci.2018.08.015.
in the following conference paper:
[C1] BRAGLIA, M., FROSOLINI, M., GABBRIELLI, R., MARRAZZINI, L., PADELLINI, L. 2019. An ensemble-learning model for failure rate prediction. Proceeding of the International Conference on Industry 4.0 and Smart Manufacturing, Cosenza, Italy, 20-22 November.
and in the following journal papers under review:
[J1] BRAGLIA, M., DALLASEGA, P., MARRAZZINI, L. Overall Construction Productivity: a new Lean Metric to identify and analyse losses in Engineer-to-Order Construction Supply Chains. Production Planning & Control.
[J2] BRAGLIA, M., GABBRIELLI, R., MARRAZZINI, L. A revised PFMEA approach for reliable design of assembly activities. International Journal of Reliability and Safety.
[J3] BRAGLIA, M., GABBRIELLI, R., MARRAZZINI, L. Risk Failure Deployment: a novel integrated method for FMEA to prioritize corrective actions. Quality and Reliability Engineering International.
manufacturing industries. It must be noted that, in these industries, there are numerous researches illustrating success in the implementation of Lean manufacturing. In the last few years, in recognition of the success of the Lean manufacturing approach in the manufacturing sector, the amount of papers implementing Lean manufacturing outside the repetitive production environment, especially in Engineer-to-Order (ETO) manufacturing companies, has increased. However, while lean principles can be applied in any industry, in an ETO environment the implementation of methods and tools must be adapted. Only conceiving different new methods and tools it is possible to think to overcome one of the major constraints to unfold the full potential of Lean manufacturing in non-repetitive manufacturing environment.
The research activities presented in this Thesis are inserted in this context. With the aim of extending the toolbox available to all practitioners in the ETO manufacturing companies, the research activity carried out focused on the development and implementation of Lean manufacturing tools and methods for the manufacturing efficiency in ETO production environments.
This Thesis is concretized as follows. The first five chapters present research activities directly ascribable to ETO production environment. After exploiting the peculiarities of Manufacturing cost deployment (MCD), which is the fundamental pillar of World Class Manufacturing (WCM), the evaluation model has been extended to the ‘world’ of risk analysis and reliability, as well as to that of energy diagnosis in the process industry. Finally, other two chapters report additional research activities outstanding the Lean-ETO context.
Still the interest in implementing Lean in non-repetitive manufacturing environment is growing, future work may be thus devoted to further extending the research here presented.
Contents
Abstract 1
Preface 7
CHAPTER 1 12
1. Introduction 12
2. An overview of Manufacturing Cost Deployment 15
3. Project Cost Deployment 17
3.1 The PCD losses classification structure 17
3.2 The decomposition of the manufacturing process 18
3.3 The methodology: the PCD five matrices 20
4. Case study 24
4.1 Losses identification with Work Sampling methodology 25
4.2 The PCD in the railway company 26
4.3 Results 29
5. Conclusions 29
References 31
CHAPTER 2 34
1. Introduction 34
2. Overall Task Effectiveness (OTE) 37
2.1 Typical losses in a ETO environment 37
2.2 A novel classification of manual assembly losses 38
2.3 The new Lean metric 39
2.4 The losses time evaluation 42
3. Case study 42
3.1 Losses identification with Work Sampling methodology 43
3.3 Results 46
4. Conclusions 49
References 51
CHAPTER 3 54
1. Introduction 54
2. State of the Art 55
3. Framework and OCP metric 58
3.1 Context 58
3.2 Framework to classify ETO construction losses 59
3.3 Overall Construction Productivity 61
4. Case study 63
4.1 General description 63
4.2 Application of the approach 64
4.3 Results 65
5. Discussion 71
6. Conclusions and outlook 71
References 73
CHAPTER 4 77
1. Introduction 77
2. Kanban system vs set-up times 79
3. The Rolling Kanban board 83
3.1. The structure of the Kanban Board 84
3.2 The Rolling Kanban: principles and management of the board 85
4. Industrial implementation and associated limits of Rolling Kanban 90
5. A modified Rolling Kanban board 94
7. Conclusions and suggestions for future work 98 References 101 CHAPTER 5 104 1. Introduction 104 2. Methodology 107 2.1 General Procedure 107
2.2 Steps of the analysis PFMEA 108
2.3 Definition of the Design Job Element Sheets 109
3. Case Study 112
4. Conclusions 120
References 122
CHAPTER 6 124
1. Introduction 124
2. Risk Failure Deployment (RFD) 126
2.1 The methodology: the RFD matrices 127
2.1.1 The RFD A-Matrix 127 2.1.2 The RFD B-Matrix 128 2.1.3 The RFD ICE-Matrix 129 3. Case study 133 4. Conclusions 142 References 144 CHAPTER 7 146 1. Introduction 146
2. System boundaries, factory decomposition, and classification of energy losses 148
3.2. The B-Matrix: defining cause-effect relationships between losses 153
3.3. The C-Matrix: quantifying energy losses and costs 154
3.4. The D-Matrix: identifying improvement techniques to tackle losses 157
3.5. The E-Matrix: selecting improvement projects to tackle losses 159
4. Case study 161
4.1. Description of the tissue paper mill 161
4.2. Application of ECD method to the tissue paper mill 163
4.2.1. The A-Matrix case study: classifying energy losses within the tissue paper mill 163
4.2.2. The B-Matrix case study: identifying cause-effect relationships between losses 163
4.2.3. The C-Matrix case study: quantifying energy losses and costs 163
4.2.4. The D-Matrix case study: identifying improvement techniques to tackle losses 164
4.2.5. The E-Matrix case study: selecting improvement projects to tackle losses 164
5. Conclusions 170
References 172
CHAPTER 8 174
1. Introduction 174
2. Safety Function Deployment methodology to analyse the risks of human behaviour 174
2.1 Step 1 of the safety function deployment 176
2.1.2 Room 2: risks in machine areas 179
2.1.3 Room 3: Relationship Matrix between behaviour and risks 179
2.1.4 Room 4: behaviour incidence 180
2.1.5 Room 6: correlation matrix between risks 180
2.1.6 Rooms 7 and 8: Risk Priority Number 180
2.2 Step 2 of SFD 183
2.2.1 Room 1: Risks 185
2.2.2 Room 2: Safeguards 185
2.2.3 Room 3: Relationship Matrix between risks and safeguards 185
2.2.4 Room 6: Correlation Matrix of safeguards 185
2.2.5 Rooms 7-8: Effectiveness of the safeguards 186
3. Case study 190
3.1 General description of the machine 190
3.3 Application of SFD 193
3.3.1 The first step of SFD 193
3.3.2 The second step of SFD 194
4. Conclusions and future remarks 197
Acknowledgements 198
References 199
CHAPTER 9 200
1. Introduction 200
2. Basic concepts of ensemble modelling 200
3. Case study 201
3.1. Brief overview of the refinery plant 201
3.2. Dataset description 202
3.3. Ensemble model for failures analysis 203
4. Conclusions 210
Acknowledgments 211
References 211
Preface
Lean manufacturing represents a successful strategic paradigm in the industrial production context. Nowadays, at the managerial level there is unanimous consensus in crediting the advantages that its application may foster, in terms of widespread benefits and performance, design and management improvements.
Lean manufacturing is a multidimensional approach that includes a set of “standard” tools (e.g., Kanban, Overall Equipment Effectiveness (OEE), Value Stream Mapping (VSM), etc.) and methods (e.g., Total Productive Maintenance (TPM), Single Minute Exchange of Die (SMED), 5-WHYs, etc.) that Lean thinkers use to achieve improvements in delivery, quality, and cost by eliminating wastes (Figure 1). While a Lean project is carried out, these methods and tools are applied in an integrated manner and progressively applied over time, following a step by step implementation process.
Figure 1. An example of Lean manufacturing tools and methods.
Traditionally, Lean manufacturing has been the focus of low-mix high-volume repetitive manufacturing industries as automotive and electronics. It must be noted that, in these industries, there are numerous researches illustrating success in the implementation of Lean manufacturing. Unfortunately, while in the automotive or in affine/related contexts the application of Lean methods is common, for the companies belonging to the so-called project manufacturing or Engineer-to-Order (ETO) manufacturing category, these tools and methods usually do not fit or have to be limited in use for lean improvements in simple processes. Because of the low repeat frequency of similar or equal products and the high variance of manufacturing processes, the implementation of Lean manufacturing systems is quite challenging.
In the last few years, in recognition of the success of the Lean manufacturing approach in the manufacturing sector, and its potential applications in planning, design, and assembly phases, the
amount of papers implementing Lean outside the repetitive production environment has increased. Recent studies show that there are some lean concepts applied in ETO environment, such as “elimination of wastes” and “just in time deliveries”. However, while lean principles can be applied in any industry, in an ETO environment the implementation of methods and tools must be adapted. Only with these new different methods and tools it is possible to think to overcome one of the major constraints to unfold the full potential of Lean in non-repetitive manufacturing environment.
For the above reasons, the development of new tools and methods able to support the implementation of lean principles in ETO environment can be considered a “more and more” important potential field of research. In particular, the present discussion will be mainly focused on to the conventional first operational step of analyzing the losses of a process implementing a Lean project: “Identify and deliver value to the customer: eliminate anything that does not add value”.
Owing to these considerations, the objective of this Thesis is to fill this gap by proposing a novel Toolset with suitable Lean tools and methods for ETO manufacturing companies. Specifically, the main aim of this Thesis is thus to provide a significant contribution to extending the toolbox available to all practitioners in the ETO manufacturing companies.
This Thesis is mainly devoted to ETO companies characterized by medium/large complex final products mainly performed on fixed-place manual assembly station with: (i) different customisation levels, (ii) low production volumes, (iii) extreme variability of production mix and in the associated production flows, (iv) high production lead times, ranging from weeks to months, (v) high (often excessive) number of tasks, and (vi) high cycle times.
Examples of these production systems are the companies operating in the machinery-building industry, constructions processes, shipbuilding, aerospace, railway equipment (e.g., trains, metros, etc.).
The reminder of the Thesis is sketched in Figure 2 and organised as follows:
• The first five chapters present research activities studied during the Ph.D. program directly ascribable to the ETO production environment.
• After exploiting the peculiarities of Project Cost Deployment in Chapter 1, the evaluation model has been extended, using appropriate modifications, to the ‘world’ of risk analysis and reliability (Chapter 6), as well as to that of energy diagnosis in the process industry (Chapter 7).
• Chapter 8 and Chapter 9 report additional research activities outstanding the Lean-ETO context.
It is important to highlight here how each research activity has been presented in a separate chapter and can be read independently from the others without jeopardize its understanding. Specifically, each chapter is the result of a scientific publication which has been accepted or submitted in an international journal. That is, the reader is not forced to follow rigorously the chapter order to understand this Thesis.
Now, before proceeding with the body of the Thesis, a summary description of each chapter is reported:
• Chapter 1 proposes a modified version of the Manufacturing Cost Deployment method to analyze ETO manufacturing companies. The novel Lean method, named Project Cost Deployment, evaluates how much of the manufacturing cost of a project is due to losses and for setting priorities in choosing the (lean) improvement projects, based on a benefit-loss analysis. It can be effectively used in ETO environments thanks to the introduction of two substantial 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 analyze the inefficiencies within the manual assembly macro-activities. The results discussed in this chapter have been published in [J4].
• Chapter 2 proposes a novel Lean metric, named Overall Task Effectiveness, which can help analyst (i) to define target task times, and (ii) to identify the hidden losses that account for most of the recorded time of a manual assembly activities, estimating the impacts of potential corrective actions in terms of both efficiency and effectiveness. Specifically, starting from the classification structure of losses reported in Chapter 1, a novel set of Lean metrics, inspired from the well-known Overall Equipment Effectiveness, is developed to evaluate the effectiveness of a manual assembly task. The results discussed in this chapter have been published in [J3].
• Chapter 3 reports an extension of performance indicators developed in Chapter 2 in order to maximize the production efficiency in the construction of buildings. Construction projects are often unique, time limited and require a high degree of customization by involving a significant number of ETO components. Usually, ETO construction supply chains are composed by a fabrication part off-site and the installation part on-site where different supply chains have to be merged. Generally, many problems are identified during construction on-site and often in a late phase when recovering actions cannot be applied anymore. As a result, construction projects frequently face delays and budget overruns. Owing to these considerations, Chapter 3 presents (i) a framework able to identify the losses and causes of ETO construction supply chains, and (ii) a novel Lean metric, named Overall Construction Productivity, supporting the quantification of the overall impact of losses and the implementation of improvement actions. The results discussed in this chapter have been submitted for publication in [J6].
• Chapter 4 presents the Rolling Kanban, a visual planning methodology based on the production of product-families and variants where: (i) set-up times are reduced between the products of the same family, and (ii) relevant times (dozens of minutes if not even hours) must be considered for changeover between products of different families. In addition, the cyclic production sequence
between different product families cannot be maintained. Rolling Kanban allows kanban management even in "hostile" production environments, i.e., characterized by high and variable set-up times, as commonly happens in Make-to-Order manufacturing process, typical of ETO supply. The results discussed in this chapter have been published in [J1].
• Chapter 5 presents a novel Process Failure Mode and Effect Analysis (PFMEA) approach for the reliable design of assembly activities in order to prevent product defects due to errors during assembly of complex products, mainly performed on fixed-place (manual assembly) stations. PFMEA is approached as an integrated method that, in addition to implement recommended actions, supports the design of worksheets, equipment and layout of the assembly lines of complex systems, early in the design phase of the product. As a result, the innovative Design Job Element Sheets, which report work instructions to the operator for assembly cycles, are defined before the design of the production and assembly process. The results discussed in this chapter have been submitted for publication in [J7].
• Chapter 6 reports a novel integrated tool for Failure Mode and Effect Analysis (FMEA), opportunely named Risk Failure Deployment, which is able to evaluate the most critical failure modes and to provide analyst with a practical and step-by-step guidance by selecting the most effective corrective actions for removal/mitigation process of root causes is fully presented. The results discussed in this chapter have been submitted for publication in [J8].
• Chapter 7 presents a novel Lean method, called Energy Cost Deployment, whose objectives are to classify, analyse, and eliminate energy losses within a factory. It uses five matrices to identify the causal factor of losses related to energy, focusing on areas with the greatest causal losses, and providing opportunities for greater efficiency and effectiveness in reducing and eliminating them. The results discussed in this chapter have been published in [J2].
• Chapter 8 reports the House of Safety which, due to its similarity to “house of quality”, makes it possible to manage the risks related to the man-machinery interaction, in order (i) to connect the worker’s misconducts to the risks that are present in the machinery, and (ii) to quantify their effects. Furthermore, the aim is also to evaluate the effectiveness of new generation protection devices as augmented reality systems compared to conventional risk management tools such as PPE barriers, proximity sensors. The results discussed in this chapter have been published in [J5].
• Chapter 9 reports an ensemble-learning model exploiting the advances in big data analytics to
estimate the failure rate of equipment subject to different operating conditions. At the same time, the method makes it possible identifying the most important working parameters affecting the failure rate. An industrial application is considered to show the potentialities and the effectiveness of the proposed method. The results discussed in this chapter have been published in [C1].
CHAPTER
1
Lean manufacturing tool in Engineer-To-Order
environment: Project Cost Deployment
1. IntroductionLean manufacturing represents a successful paradigm in the industrial production context. Nowadays, at the managerial level there is unanimous consensus in crediting the advantages that its application may foster, in terms of widespread benefits and performance, design and management improvements. Lean manufacturing includes a set of principles (Womack and Jones, 2003) that Lean thinkers use to achieve improvements in productivity, quality, and lead-time by the application of certain tools (e.g., Overall Equipment Effectiveness, Value Stream Mapping, Kanban system, etc.) and methods (e.g., Single Minute Exchange of Die, Total Productive Maintenance, 5S, etc.) that have been defined and optimised during the last decades. While a Lean project is carried out, these methods and tools are progressively applied over time, following a step by step implementation process. A clear example of such process is reported, for instance, in the well-structured framework proposed by Mostafa et al. (2013).
Womack and Jones (2003) stated that lean principles can be applied in any industry. Unfortunately, the analysis of the results of many improvement programs clearly demonstrates that Lean manufacturing implementations have not succeeded universally and that several different variables may affect an industrial Lean project (Worley and Doolen, 2006). One of these variables is the use of the incorrect Lean tools and methods for the given industrial environment. In fact, as reported by Lane (2007), it is strictly necessary to avoid using the wrong Lean tools and methods for a specific production context: in the best case they give no benefit, but in general they may worsen the overall situation.
It is worth noting that the “standard” Lean tools and methods have been developed in and applied to repetitive production environments, such as series low-mix high-volume productions. In other words, they can be ascribed almost entirely to the Toyota Production System (TPS) and have been used widely within the automotive industry or in affine/related contexts. Therefore, these tools and methods in general do not fit for companies belonging to the so-called project manufacturing or Engineer-to-Order (ETO) manufacturing category (Romero and Chávez 2011). In the recent past, these tools had limited application to lean improvements in simple processes (Matt, 2014; Al-Sudairi,
2007). Because of the low repetition frequency of similar or equal products and the high variance of manufacturing processes, the implementation of Lean manufacturing systems is quite challenging. Industrial experiences from several case studies illustrate that the suitability of certain Lean tools, such as Value Stream Mapping or Kanban is very limited (Matt and Rauch, 2014). While lean principles still apply, the implementation methods and tools must be adapted to the specific ETO environments and alternative methods embraced (Lane, 2007; Yang, 2013). Only with these new approaches and instruments it is possible to overcome the major constraints and to unfold the full potential of Lean in non-repetitive manufacturing environments.
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 compared to that concerning mass production or batch type systems (Birkie and Trucco, 2016). Despite the widespread and ongoing research on Lean manufacturing, there is dearth of evidence addressing the peculiarities of implementation in manufacturing ETO contexts.
Limiting 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 few special parts.
VSM is a commonly used tool in Lean manufacturing. It is a comprehensive analysis and visualization tool, used to illustrate 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 most attention, both in terms of evolution and adaptation, for its use in ETO environments. The papers of Khaswala and Irani (2001), Braglia et al. (2006), Matt (2014) represent possible variants or integrations of the original VSM technique, that have been developed after lean applications 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 to various production losses (or “wastes” in lean lingo) of the manufacturing process (Yamashina and Kubo, 2002). It allows both a punctual efficiency analysis of the single station and a coherent evaluation of the whole production flow. Therefore, MCD permits to develop a cost reduction program, selecting improvement projects that eliminates 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 of our 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 characterized 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 the companies operating in the machinery-building industry, constructions 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.
The remainder of this chapter is organized as follows. The section 2 provides the background for the proposed methodological approach. In section 3 the Project Cost Deployment approach is fully presented. In order to show the operating 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 section 4 using a case study. Finally, section 5 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 categorizes 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;
Figure 1. Logic route of Manufacturing Cost Deployment.
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).
Figure 2. Seven step roadmap of Manufacturing Cost Deployment.
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 worldwide, mostly based on the concepts of Total Quality (TQC), Total Productive Maintenance (TPM), Total Industrial Engineering (TIE) and Just in Time (JIT) (De
application of ten technical-managerial pillars (Massone, 2007), starting from health and safety, involving quality system, maintenance system, workplace organization, 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 et al., 2017).
Independently of its many adaptations and modifications, MCD is a method that innovate 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 analyze 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 Yamashima 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 companies, 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 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 manufacturing 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.
Figure 3. The Project Cost Deployment losses classification structure. 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 distinguish 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 performed 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 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, considering 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 in order to avoid adding more than ten 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, it is recommended a maximum of two levels only for those items characterized by of high cost or high risk.
So, in order to resume, it could be suggested 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 characterized by high cost or risk.
An example of the partial decomposition of the assembly process of a train wagon is presented in Table 1.
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
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 decomposition). 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 investigated in order to document its potential impact over 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.
Figure 4. The A-Matrix.
The B-Matrix, shown in Figure 5, is used to clarify the cause‐effect relationship of losses identified with A-Matrix. 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
Loss category Loss type … … …
Loss type 1
Loss type 2 Very important losses
Loss type 3 Important losses
… Minimal losses
Loss type n No losses
Loss type 1 … Loss type n Loss type 1 Loss type 2 Loss type 3 Loss type 4 … Loss type n Loss category n Process Step Loss category 1 Loss category 2 Macroactivity 1 Macroactivity n
mark (x), boolean value indicating the presence of correlation, 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 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.
Figure 5. The B-Matrix.
The C-Matrix (Figure 6) is adopted to convert causal losses into the corresponding manufacturing costs. This matrix places causal losses on the vertical axis, reporting in which macro-activities causal
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,
…
… …
Where Mac 1 Mac 2 Mac 1 Mac n
Macro-activity 1 X … … X Macro-activity n Macro-activity 1 X … … Macro-activity n X … … … X Macro-activity 1 X … … Macro-activity n Resultant Loss
Loss category 1 Loss category n Loss type 2 Loss type 1
Loss type 1 Loss type 2 Loss type 1 Loss category 2 Loss category 1 Causal Loss
it is essential that the manufacturing costs, increased by resultant losses, are increased by their respective causal loss. Therefore, each causal 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 (Chiadamrong, 2003; Son, 1991). On the contrary, in the ETO production systems, characterized 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 et al. (2018) report the development of a set of parametric 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, it is possible to classify losses into two different groups, namely direct and indirect cost losses. Examples of direct costs are direct labor, direct materials, and transports. Examples of indirect costs are production supervision salaries, quality control costs, and depreciation.
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.
Figure 6. The C-Matrix.
In order to address the losses identified and appropriately quantified, it is necessary to put in place one or more improvement activities implementing specific techniques or launching real improvement projects.
The lack of structured project selection methods leads to lost opportunities, sub-optimization, and inefficient resource allocation (Kornfeld and Kara, 2011). Companies can use different methods to select and prioritize improvement activities or projects. Not surprisingly, companies that use objective prioritization methods report a higher success rate for improvement 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 improvement 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, estimating their impacts, costs, and feasibility according to the following expression:
Cost 1 … Cost n Cost 1 Cost K
Macro-activity 1 … … Macro-activity n Macro-activity 1 … Macro-activity n … Macroactivity j … … Macro-activity 1 … … Macro-activity n Total cost Loss type 2 Loss type l Loss category 1 Loss type 1
Loss category n Loss type 1
Cost
Direct cost Indirect cost
Cost resulting losses
Casual Loss
𝐶𝑟𝑙 𝐶𝑙𝑗𝑘 𝐶𝑟𝑙 𝐶𝑙𝑗𝑘
ICE = I ∙ C ∙ E (1) where:
• the impact factor (I) expresses qualitatively, on a 1 to 5 scale, the economic impact of the loss; • the cost factor (C) expresses, on a 1 to 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.
Figure 7. The D-Matrix.
After identifying the appropriate methods to reduce the most significant losses within the various processes, it is necessary 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 8. The E-Matrix. 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 consider some of the highly innovative technical features of trains, in terms of frequency and speed of the service, and
S M E D T o ta l P ro d u c ti v e M a in te n a n c e ( T P M ) C o n ti n o u s I m p ro v e m e n t K a iz e n 5S P o k a -Y o k e S ta n d a rd iz e d W o rk C h a rt V is u a l C o n tr o l J u s t-In -S e q u e n c e ( J IS ) Im p a c t (h ig h :5 ; lo w :1 ) C o s t (h ig h :1 ; l o w :5 ) E a s y n e s s (h ig h :5 ; l o w :1 ) IC E ICE
Loss category Loss type Process activity Loss (€/time period)
Lean Tools & Techniques
Loss category Process activity Loss type Initial cost loss Improvment technique/project Yearly recovered cost (€) Cost to achieve saving (€) Estimated saving (€)
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 recognized 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 causal 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. Nine major macro-activities have been identified, all completed on fixed place stations. Macro-activities involve both the installation of seats, windows, luggage areas and other components and the realization 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.
Figure 9. Length and ubication of the macro-activities performed. 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 tasks noted.
Id A B C D E F G H I 68,75 63 Description
Installation of the control cabin
Installation of the passenger seats in the middle front compartment Installation of the front access door
Installation of the windows in the upper compartment Installation of the forniture panels in the lower compartment Installation of the passenger seats in the middle rear compartment
Installation of the rear access door
Installation of passenger seats in the middle rear compartment Installation of the forniture panels in the luggage area
Lenght (h) 48,25 132,32 67,5 256,5 53,25 54,5 66 A B C D E F G H I
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 time of an assembly activity (see, for instance, the recent framework of Hajikazemi et al., 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 allocated 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 of 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 colored 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.
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 causal 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 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.
Figure 11. An example of causal-resultant losses.
Loss category Loss type Mac A Mac B Mac C Mac D Mac E Mac F Mac G Mac H Mac I
Non-scheduled personal break Significant Impact
Strikes Middle Impact
Failure of the material handling Low Impact
Additional moving between working spots No Impact
Searching for material /equipment due to the disorder Waiting because of lack of coordination
Waiting because of lack of operator involved in the handling Waiting because of lack of material
Waiting because of lack of equipment Correction of defects/previous incorrect operations,
Rework of components as too much time has passed since the time planned for assembly Design mistake
Technical documentation mistake Inadequate equipment Inadequate tool Not planned control activity Operator error Supplier mistake
Process Step
Time losses due to Performance Losses
(PLT) Order Down Time
(ODT) Not Working Time
(NWT)
Standby Time (SBT)
…
Where … Mac E … … … Mac F …
… Macro-activity E X … Macro-activity E X … Resultant Loss
Time losses due to Performance Losses (PLT) Correction of previous
incorrect operations Operator error
Casual Loss
Time losses due to Performance Losses (PLT) Technical documentation mistake Operator error
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 manufacturing cost lost due to the causal loss.
The D-Matrix (Figure 12) utilizes 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 prioritization 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 process becomes more organized 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 causal loss: “searching for material/equipment due to disorder”.
Figure 12. Case study D-Matrix.
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 information about:
• the type of loss that are attacked and the related macro-activity; • the yearly recovered losses;
• the activity/project cost.
Then, it was possible to properly rank the improvement activities.
S M E D To ta l P ro d u c ti v e M a in te n a n c e ( TP M ) C o n ti n o u s I m p ro v e m e n t K a iz e n 5S P o k a -Y o k e S ta n d a rd iz e d W o rk C h a rt V is u a l C o n tr o l J u s t-In -S e q u e n c e ( J IS ) Im p a c t (h ig h :5 ; lo w :1 ) C o s t (h ig h :1 ; l o w :5 ) E a s y n e s s (h ig h :5 ; l o w :1 ) IC E Mac B € 1.254,40 X 5 3 3 45 Mac C € 1.517,60 X 3 3 2 18 Mac E € 162,40 X 1 3 3 9 Mac F € 756,00 X 3 3 4 36 Mac H € 274,40 X 2 3 2 12 Mac C € 1.191,68 X 5 4 4 80 Mac E € 1.441,72 X 1 4 4 16 Mac F € 154,28 X 3 4 3 36 Mac H € 718,20 X 3 4 4 48 ICE
Loss category Loss type Process
macro-activity Loss (€/time period)
Lean Tools & Techniques
Time losses due to Performance Losses (PLT)
Technical documentation mistake € 3.505,88
Standby Time (SBT)
Searching for material /equipment
Figure 13. Case study E-Matrix. 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 methodology, 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 formulas 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 is proposed to assess how much of the manufacturing 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, can be effectively used in ETO production systems thanks to the introduction of two substantial 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 analyze the inefficiencies within the manual assembly macro-activities.
Loss category Process
macro-activity Loss type
Initial cost loss Improvment technique/project Yearly recovered cost (€) Cost to achieve saving (€) Estimated saving (€)
Mac B Searching for material /equipment due to the disorder € 1,254.40 5S € 37,632.00 € 4,000.00 € 33,632.00 Mac C Searching for material /equipment due to the disorder € 1,517.60 5S € 45,528.00 € 4,000.00 € 41,528.00 Mac E Searching for material /equipment due to the disorder € 162.40 5S € 4,872.00 € 4,000.00 € 872.00 Mac F Searching for material /equipment due to the disorder € 756.00 5S € 22,680.00 € 4,000.00 € 18,680.00 Mac H Searching for material /equipment due to the disorder € 274.40 5S € 8,232.00 € 4,000.00 € 4,232.00 Mac C Technical documentation mistake € 1,191.68 Standardized Work Sheet € 35,750.40 € 2,000.00 € 33,750.40 Mac E Technical documentation mistake € 1,441.72 Standardized Work Sheet € 43,251.60 € 2,000.00 € 41,251.60 Mac F Technical documentation mistake € 154.28 Standardized Work Sheet € 4,628.40 € 2,000.00 € 2,628.40 Mac H Technical documentation mistake € 718.20 Standardized Work Sheet € 21,546.00 € 2,000.00 € 19,546.00 Total: € 7,470.68 € 224,120.40 € 28,000.00 € 196,120.40 Standby Time (SBT) Time losses due to Performance Losses (PLT)
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 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 causal losses are placed, providing opportunities for greater efficiency and effectiveness in reducing and eliminating them. It also facilitates the selection of (lean) improvement activities and projects to be activated to remove or to correct the causes of such losses, allowing an economical evaluation 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 ETO production environments. 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 production, quality defaults) deteriorates and introduces a bias in the time evaluation. In order to better investigate this potential issue, a future study of a structured metric, able to overcome the lack of an evaluation of workers skills, can be suggested.
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