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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.19 No.2 pp.412-425
DOI : https://doi.org/10.7232/iems.2020.19.2.412

A Novel Qualitative-Quantitative Cause-and-Effect Tool for Analysis, Presentation and Decision-Support

Abdul Kareem Abdul Jawwad*
Industrial Engineering Department, University of Jordan, Amman 11942, Jordan
*Corresponding Author, E-mail: akjawwad@ju.edu.jo
November 19, 2019 May 18, 2020 June 4, 2020

ABSTRACT


Several CEA tools are available to the analyst. Usually an analytical tool, such as the cause and effect diagram is applied for the investigation phase while a visual display tool, such as Pareto and pie charts, is used for presentation and decision support. A thorough look into these tools reveals the presence of fundamental limitations such as: (i) the particular tool is only efficient in the investigative stage but not in decision support, (ii) no qualitative and/or quantitative relationships between causes and effects can be obtained or (iii) the tool cannot accommodate multi-level CEA analyses. A new tool called the Drain-Pipe-Diagram (DPD) has been developed to overcome these limitations and enable easy decision making. The principle of the DPD derives from the working nature of a typical drainpipe network where the main collection-pipe resembles the “main effect” whereas higher-level feeding pipes resemble causes and subcauses. In addition, color coding and diametrical ratios were used to signify importance and share, respectively. The DPD was assessed against other CAE tools using a practical case study and proved to be superior to all compared CEA tools in both diagnostic and presentation abilities. Furthermore, the DPD was successfully applied to nontechnical areas including environmental applications.



초록


    1. INTRODUCTION

    Cause-and-effect analysis (CEA) is a general term used to describe a host of methodologies and techniques used for systematically identifying the causes of a, usually undesired, occurrence such as product non-conformance to quality standards. Sometimes, the term CEA is also used interchangeably with the term root-causeanalysis (RCA). Intuitively, however, RCA is concerned with finding the “single” most basic reason for an undesirable condition or problem, i.e., the root cause (Andersen and Fagerhaug, 2006). Identifying and eliminating the intrinsic root cause is of great importance in problemsolving and preventing the recurrence of such problems in future (Dew, 1991;Yuniarto, 2012). By contrast, CEA encompasses both single-cause (one dimensional) and multi-cause (multi dimensional) analysis (Katsakiori et al., 2009;Graves et al., 2010). Nevertheless, the same tools and techniques are usually used in either case. Many distinctive CEA tools were developed over time depending on the nature of the particular problem being investigated (quality problems, accident investigations, process improvement, etc.). Furthermore, different tools were classified according to the main process being followed in identifying the root causes (brainstorming, process mapping, logic analysis, etc.) (Yuniarto, 2012;Lee et al., 2018). Addressing all available CEA tools is beyond the scope and interest of the present paper and, therefore, discussion will be limited to comparing major CEA tools mainly applied in solving quality problems, process performance and comparable problems.

    Amongst the most widely applied tools in these areas is the famous cause-and-effect diagram (also known as Fishbone and Ishikawa diagram) introduced by Professor Kaoru Ishikawa in 1943 (Ishikawa, 1976). The fishbone diagram, Figure 1 (van Aartsengel and Kurtoglu, 2013), represents a model of suggestive presentation for, mainly qualitative, correlations between an event (effect) and its causes. The particular structure provided by the diagram helps to trace problems, or any effects of interest, to their root cause(s). The original fishbone diagram lacks the ability of providing any quantitative correlations between the effects and their causes and it also does not offer any scale of importance among the different causes (Park et al., 2011;Yuniarto, 2012). The probabilistic causeand- effect diagram is an improved version where percentage weights are written beside each branch (bone) to indicate its relative importance in relation to producing the effect under consideration (Kalinowski et al., 2008). Another enhancement suggested by (Kalinowski et al., 2011) is the use of thicker lines and/or darker shades for the more prominent causes. Despite these enhancements the fishbone diagram is still considered, and mainly used, as a qualitative analysis tool (Doggett, 2004;Yuniarto, 2012).

    The 5 whys methodology is a simple and common CEA technique originally developed by Sakichi Toyoda for the Toyota Industries Corporation (Ohno, 1988). The merit of this method is to “dig down” to the root of the problem by answering a five-level why string or five consecutive whys as to the cause of the problem under consideration. Each why takes the investigators one level deep. The number 5 is a rule of thumb where in reality one might need to ask either larger or a fewer number of whys. This method has been, however, criticized for being prone to termination at shallower levels than the root causes really are (Katsakiori et al., 2009;Yuniarto, 2012;Serrat, 2017;Lee et al., 2018); needless to say the method does not offer any quantitative correlations between causes and effects.

    Process mapping is technique that utilizes visual display in process related CEA.

    A process map is basically a diagram of a work flow which enables the user to examine the process and determine points of vulnerability or inefficiency and bench mark against best practices (Graves et al., 2010). This process is also frequently used as a proactive analysis tool for new projects/products and would be coupled with failure mode and effect analysis (FMEA); a technique for anticipating possible failure modes within a certain process / product design with possible consequences in an attempt to proactively prevent such events

    Another commonly used tool that utilizes visual display for analyzing quality problems is the color-codematrix, Figure 2, which is basically a matrix or a table where the effects (or lower-level causes) are listed on the top row while the causes (or higher-level causes) are listed on the far left column. A color is assigned to the cell that marks the intersection of an effect’s column with a cause’s row based on the nature of the relationship between the cause and the effect. If a particular cause is considered to have a strong contribution to that effect (or lower-level cause) it would be given a red color indicating a highly important cause. A less important contribution would be assigned an orange, yellow or green color indicating relatively important, medium-level importance or not important, respectively. It is worth mentioning that the color-code-matrix may either be used as a diagnostic and /or presentation and decision-support tool.

    A more recent development includes a set of causal mapping techniques based on the original TRIZ technique (Terninko et al., 1998;Litvin et al., 2012) (TRIZ is a Russian abbreviation of Teoria Resheniya Izobretatelskikh Zadatch which means Theory of Inventive Problem Solving). The basic methodology of this technique falls under hierarchical causal process mapping where the main problem (referred to as the phenomenon) being investigated is mapped or linked to the processes falling at the lower level through their respective physical, chemical and mathematical correlations. Each process then becomes a phenomenon to the lower level processes and so on. The process was further enhanced through a series of research studies (Karnaukhov, 2006;Lee, 2016, 2017, 2019;Lee et al., 2018) leading to a category of techniques know as cause-effect-chain-analysis (CECA). These techniques are said to be powerful in revealing “hidden” causes through mapping the chain of events and states leading to the target problem but also have been reported difficult to use especially for the non experienced or specialized analyst (Lee et al., 2018). Some special purpose RCA such as the Fault tree are used mainly in accident and similar investigations where the effect (event being investigated) is linked to foreseeable causes by a hierarchical of logic using a combination of logic gates such as the AND, OR, NOR, etc. (Ericson, 1999).

    Both analysis and presentation tools are needed during the course of any CEA for the diagnostic and decision- making stages, respectively. The methodologies and techniques presented so far are primarily analytical tools which are normally limited to the diagnostic phase of the CEA. A complete study, however, requires that proper courses of actions being taken in order to rectify the current situation and to prevent the occurrence of such, and similar, problems in future. This would entail the incorporation of a decision-making aid in the analysis process. The decision-making itself could be based on written recommendations, for example when the results from analysis phase are qualitative, and/or by using some of the common visual presentation aids, such as the pie and Pareto charts, when the results involve quantitative or numerical values. Both of these chart types and their variations are well known to the research and practitioners community and will not be re-explained here (interested readers may consult other references such as Hart and Hart (1989) and Montgomery (2012)).

    The primary role of the decision-making aid is to enable decision makers to pursue the right course of action without having to go through the full analysis or, in other words, redo the analysis themselves. This is especially true in instances where the decision maker is either not fully acquainted with the technical analysis or being under time-limitation pressures (which is mostly the case with senior management staff) and where very limited or no time maybe allowed for extended explanations or further investigations.

    A fair review of even the most commonly applied diagnostic CEA tools would reveal the presence of some inherent and rather fundamental limitations within these tools. Yuniarto (2012) compared some 24 RCA/CEA methodologies (including the tools discussed in this paper) and concluded that all these methodologies share some common limitations including: (i) The lack of problemstructuring feature, that presents a comprehensive understanding, as to why causes underlying a problem occur in the first place; (ii) The lack of a system’s perspective and inability of observing the interrelatedness among the causes of a problem and (iii) negligence of ‘soft’ issues as significant causes of the problem and just aiming at the ‘hard’ factors, which has impaired their ability to capture the whole picture of the problem.

    Yuniarto (2012) advocates the fact that, with a shortage of those holistic features, presently available RCA / CEA tools may not be competent in solving problems in complex systems.

    Other studies (Lee et al., 2018;Katsakiori et al., 2009;Graves et al., 2010) have put forward some criteria as a basis for preference amongst RCA/CEA tools. The main features required for a potent analytical tool may be stated as follows:

    • (i) Quantitative;

    • (ii) Simple and reliable;

    • (iii) Practical and consequential;

    • (iv) Multi-dimensional (multi-cause analysis)

    • (v) Provision of visual display;

    • (vi) Integration of solving directions (Decisionsupport features)

    Results observed by these three studies have revealed the majority of the presently available RCA/CEA tools to either lack some of these features and/or have an ill inclusion of such features.

    As to the decision-making and presentation tools; the strengths and limitations will be discussed in light of the results of case study presented in subsequent sections.

    In light of the above, the main problem tackled in this article is “the lack of simple CEA tool(s) and charts that can be used for qualitative and quantitative analysis for both diagnostic and presentation purposes while serving the ultimate goal of simplifying the decision-making process.”

    The main objective of this study is, therefore, to create a new CEA tool in order to help complete the inadequacies found in currently available CEA tools. Based on the discussion above the new tool should have some desired features and characteristics including the following:

    • • Simple and practical

    • • Useful in handling both qualitative and quantitative analysis

    • •Capable of supporting decision making

    • •Capable of dealing with multi-level CEA with ease and without losing its presentation and decisionmaking support features

    • •Suitable for both analysis and presentation purposes

    • •Compatible with other quality tools and policies such as continuous improvement

    • •Easy to use and interpret

    2. METHODOLOGY

    The methodology adopted in this study included the application of the previously mentioned tools to an actual multi-level CEA case study and then present the new tool developed in this study and apply it to the same case study. This methodology was adopted in order to help present the new tool from a practical rather than a purely theoretical perspective.

    2.1 Present Case Study - Metallic Extrusion Process

    The present case study was selected merely out of convenience for being within the author's main specialty (Materials and Manufacturing Engineering). The metallic extrusion process, Figure 3 (Kalpakjian and Schmid, 2007), is basically a hot forming process where a preheated billet (usually round) placed in a heated chamber or container is pushed by a ram and forced through a die opening. The shape of the final product (known as the extrudate) would be the same as that of the die opening. After being extruded, mechanically and thermally treated, the profiles are given either an anodizing treatment or electro-plated for a final coloring finish. A number of defects are common to extruded profile surfaces. These may either result from the extrusion process (referred to as surface defects), the anodizing or the electro-plating (hereafter referred to as painting) steps.

    This study has concentrated on surface defects in extruded aluminum profiles. The CEA for this particular problem was carried out through a three-fold process: (i) detailed literature review of surface defects in extruded aluminum (Arif et al., 2002;Qamar et al., 2004;Chondronasios, 2015;Chondronasios et al., 2016;Zhu 2010); (ii) a special survey that has been designed and distributed on industrial extruders and (iii) interviews held with technical and managerial staff members of some of these industrial extruders. The collective purpose of these three approaches is to obtain as much qualitative and quantitative data, on the relationships between surface defects and their causes, as possible. Details of the literature review are beyond the scope of this article as they represent either technical details and/or being relevant to the particular experimental / industrial setup being investigated. Results from literature review together with information obtained through the questionnaire and personal interviews were complied in a table format and formed the major input to the present CEA. Readers interested in more technical details may refer to references listed at the end of this paper.

    3. RESULTS AND DISCUSSION

    3.1 Application of Available Analysis Tools

    This section will apply currently available analysis tools to the present case study.

    3.1.1 Qualitative CEA using the Fishbone (Ishikawa) Diagram

    The fishbone diagram for this problem is shown in Figure 4. Figure 4 relates the main categories of surface defects (tearing, streaking, die-lines, blistering, piping and internal cracking) to their main causes (level 1 causes) and their sub-causes (level 2 causes). As can be seen in Figure 4 the fishbone diagram is a qualitative diagnostic tool that displays possible cause-and-effect relationships, but it does not present any information as to the relative importance of a particular cause nor does it provide a means for any relevant decision-making process regarding this analysis. For example, the diagram does not tell a particular company where to start in order to eliminate these defects in its products. In addition if a third level is added the diagram would look extremely crowded and difficult to interpret and use.

    3.1.2 Quantitative CEA

    The overall percentages of extrusion defects of one contacted company are shown in Table 1, while quantitative data on surface defects and their causes are shown in Table 2. In order to make things easier to understand it might be necessary to explain some of the data in Tables 1 and 2 as follows. For example 70% of the company rejects were a result of the presence of surface defects on its profiles. 36% of these defects were of the tearing-type among which 60% were caused by high friction problems. Abbreviations were chosen for main causes in order to facilitate their insertion in the different illustrations to come as full description of causes would occupy significant portion of any chart’s area and may lead to unnecessary overlapping of chart labels.

    The Pie chart. The percentages of main surface defects are shown in the form of a pie chart in Figure 5. This chart is mostly suitable for a single level analysis and would be extremely difficult to employ in higher level analysis. A two-level pie chart for the present case would be an extremely crowded chart as each contributing cause would be contained in a sub “slice” within the main sectors of major surface defects, in which case the chart would become too difficult to interpret and comprehend.

    A common solution to this problem is normally to build separate “lower-level” charts for sub-causes. An example is shown in Figure 6 for the tearing defect where it is shown that high frictional condition has the major share in promoting this kind of defect. This solution may be appropriate for small number of sub-causes but if the number of causes is relatively large this is expected to lead to loss of traceability while having to follow a series of different charts. It is needless to say that not only traceability is lost but also hierarchical interdependence would become extremely difficult to comprehend in this case and that decision making becomes extremely difficult.

    Pareto chart. Figure 7 shows the Pareto chart for the present problem where the most (and less) important defects maybe discerned easily. A two-level Pareto chart maybe obtained where “main causes” or factors are divided in two or more causes (or factors) as shown in Figure 8. There are two fundamental limitations within this type of chart; (i) inability to represent data in the form of percentages, as the sum of percentages of causes for any defect would add up to 100% as shown in Figure 8. This feature would make these charts inferior in regards to their adequacy in bringing out major problems to be tackled. (ii) as in the case of the pie chart the Pareto chart becomes really crowded and interpretation becomes difficult if the number of second (or higher) level causes is relatively large; needless to say that insertion of a third level would be impossible.

    Color-code matrix. The color code matrix for the present problem is shown in Table 3 where the ranges for each color code have been conveniently chosen as shown in Table 4. As discussed earlier this tool is effective only in single-level CEA due to presentation limitations and also would only be appropriate for general analysis where decision making is not, particularly, a sensitive issue. This comes from the fact that causes (or contributing factors) having close contributions may be grouped into different importance level while, on the other hand, factors having relatively distant contributions may be grouped in a single importance range depending on the particular selection criteria of importance-range values. In the present case incipient melting (IM) has a contribution of 40% within tearing defects (the most critical main defect) and has been ranked of medium criticality (yellow color code); falling within the same importance range as the presence of trapped air (PTA) which contributes 28% of the blistering (least critical) defect. As such the color-code matrix is more of a one-to-one relational chart rather than a system relational mapping tool.

    3.2 The new tool - Drain-Pipe-Diagram (DPD)

    From the discussion above it can be seen that some of the tools and charts used in CEA are only suitable for the diagnostic part of the CEA while others are limited in one way or another in dealing with complex quantitative and/or multi-level analyses (e.g. effects, causes, sub causes, sub-sub causes, etc.). Some of these tools lose their presentation and decision-making support power when more than one level analysis is being considered. A number of preferable features of any new tool were listed in the introduction section. Having these characteristics in mind a new tool called the Drain-Pipe-Diagram (DPD) was developed.

    As the name implies the design and development of this tool was devised from the simple working nature of a typical drainpipe system where the main (lowest level) pipe, representing the overall effect or main problem, is fed through a network of higher level pipes (first level causes) and so on. This feature helps understand, and present, hierarchical interrelationships between different levels of causes and effects. The quantitative nature of this chart is derived from the analogy to an actual drainpipe system where the quantity delivered to any lowerlevel pipe is the same as that passing through all higherlevel pipes feeding into that particular pipe. This system is simple to understand, apply, present and interpret.

    DPD defined. A qualitative-quantitative analytical tool and visualization chart used for multi-level CEA problems. The DPD may be constructed and drawn with ease using several readily available basic software programs.

    3.2.1 Constructing a DPD

    The following steps represent an easy way to construct a DPD:

    1. Create the base cylinder (main pipe) representing the overall problem or main effect. Any convenient diameter maybe chosen for this pipe depending on chart size and presentation needs. This overall diameter is assigned a 100% value and simply represents “ALL” types and/or amounts of defects (or any phenomena of interest). This level is given the base or zero (ground) level.

    2. Create first level pipes representing the share of each relevant cause contributing to the main effect. These pipes, as in the case of the main pipe, are constructed vertically with their diameters taken as percentages from the main-pipe diameter equal to their shares / percentages in causing the main effect.

    For cause No. n (Causen) the pipe diameter (Dn) is calculated as follows:

    Dn = Causen percentage * pipe diameter of immediate lower level cause (or effect) (D)

    Where D = D1 + D2 + D3 + D4 +....... + Dn; for each level

    In actual piping works the ratio would be based on the square of the diameters rather than the diameter itself (i.e., cross sectional area), but as this is a visualization tool it would be much easier for the viewer to estimate the share of each cause if the ratio is based on the diameter.

    • 3. Connect the first level pipes to the main pipe using lines (connector pipes) of any convenient size. Arrowheads may or may not be used as the DPD default flow direction is downwards. Figure 9 shows a DPD for a main effect with four causes having 10%, 20%, 30%, and 40% shares for cause 1, 2, 3 and 4 respectively.

    • 4. Assign colors to the different pipes starting with red for the largest pipe followed by orange, yellow and last green. If the analyst believes more than one cause deserve the high criticality status then these extra causes (feed pipes) would also be assigned a red color. Use the same color for both feed pipes and connection pipes. The base pipe can be given any color as this is not expected to have any implications on the analysis or final decisions.

    • 5. Continue to create higher levels, using the same procedure, according to the particular problem being considered until all the levels have been included in the diagram.

    • 6. Locate the “HOT PATH.” This is the path that includes the red-colored feed and connection pipes and reflects the path that contains the most important / critical causes and where any improvement process should start.

    3.2.2 Application of the DPD Tool to the Present Problem

    The procedure outlined above is explained below using the present problem of surface defects in metallic extrusion.

    1. The main pipe or base cylinder has been constructed with diameter of 100mm (before scaling), Figure 10, representing the overall number of “all” defects reported by the company to be present in its products.

    2. The first level contains pipes that represent the several sub-categories of defects such as anodizing, painting and surface defects. Diameter calculations for these pipes are shown in Table 5 while first level DPD construction is shown in Figure 11.

    3. Red, yellow and green colors are assigned to the surface, anodizing and painting defects, respectively, representing their share, and hence, criticality, as shown in Figure 12.

    4. Continue to create levels until all levels have been completed. Examples of second level diameter calculations are presented in Table 6 while the completed second level analysis is shown in Figure 13

    5. Third-level calculations were carried out in the same way and resulted in the complete DPD as shown in Figure 14.

    The hot path can be easily identified in this diagram. It is obvious that any improvement effort should start with treating surface defects within which the high friction (HF) and incipient melting (IM) are to be given primary attention. Secondly, the die-line problems should be given special attention.

    The present DPD easily presents the problem with its multi-level analysis. It is obvious that the hot path concept adopted in this diagram is also an important feature in supporting quick and accurate decision-making process.

    3.2.3 Support of Continuous Improvement Cycle by the DPD System

    The DPD system can be easily applied to support the continuous improvement cycle (identify, plan, execute, review) which is a very important concept in total quality management as well as in other managerial and strategic tools including six-sigma, just in time and lean manufacturing. As mentioned above the improvement starts by following the hot path (identify and plan) and eliminating the causes along this path (execute). After this "primary" hot path has been eliminated (by rectifying the causes associated with this path) the DPD takes a new shape with a new hot path outlining the course of action for further improvement (review). This compatibility with the continuous improvement cycle stems from the fact that the DPD uses percentages rather than numbers, i. e., the basic level will always be of a 100% value regardless of the number it represents. In other words, as long as a single defect is still present there will be a hot path tracing this defect back to its root cause(s).

    Figure 15 shows the new appearance of the DPD after eliminating the primary hot path in the previous diagram, Figure 14, with a new hot path showing the die lines problem to be the one that needs immediate course of action.

    The fact that there will always be a hot path indicates the DPD to be rather a dynamic chart that is very effective in supporting continuous improvement concept. The hot path will disappear only when there are “ZERO” defects. This is the essence of continuous improvement from a quality management perspective.

    4. APPLICATIONS OF THE DPD TO NON-TECHNICAL PROBLEMS

    As mentioned previously in the introduction section the CEA studies are not limited to technical engineering problems but could be applied to any phenomena of interest to researchers involving contribution of a set of causes (and sub-causes) to a certain end effect of interest. As an example the new DPD tool has been successfully applied to a major problem in the environmental and ecological area, i.e., the problem of greenhouse emissions.

    Figure 16 presents a DPD for the issue of global greenhouse gas emissions. The data used to build this diagram were compiled from different sources (Global greenhouse emissions 2015, US national greenhouse emissions 2015, China carbon emissions 2015). This DPD clearly and accurately presents the problem of global greenhouse emissions in a simple way that is easy to interpret and use, especially in highlighting the “imme-diate action” locations through its hot path feature. For instance, it is very obvious in the diagram that immediate attention should be given to China’s manufacturing and construction sectors and the US electricity and transportation sectors for notable reductions in these emissions. One might argue that this implication may very well be known to people involved in this area. While that is true the idea here is to present the power and applicability of the DPD tool in presenting CEA problems and facilitating the decision making process. Furthermore, such data with this multilevel nature and hierarchical interaction may be extremely difficult (if possible at all) to be presented, both qualitatively and quantitatively, in any other type of diagram. It is worth mentioning that, in this DPD, one pipe was left uncolored and represents the share of carbon dioxide coming from all other countries, not mentioned here, in which case it would be difficult to indicate in any single class of criticality and/or policy of actions.

    Finally, Table 7 presents a summary comparison between the DPD and some common CEA/RCA tools. From Table 7 it can be seen that the capabilities of the DPD surpass all the compared CEA/RCA tools especially for being a quantitative multi-level, multi-cause tool for both analysis and presentation. It should be noted that, in Table 7, dynamic means that the particular tool can reflect current improvements and reveal the next most critical cause(s) for further improvement actions.

    5. CONCLUSIONS

    A new cause and effect analysis (CEA) tool was developed in this study in response to the several limitations inherent within currently available investigative and presentation tools. The new tool (called the Drain-Pipe- Diagram (DPD)) derives its principle from the working nature of an ordinary drainpipe system where the main collection pipe represents the end effect, or phenomenon to be studied, while the feeding pipes represent its causes and their sub-causes. The DPD has proved to be superior to all other CEA tools in handling multi-level CEAs and in its diagnostic and decision-making support abilities. The DPD also proved to be a powerful presentation tool that is simple to construct, use and interpret. The DPD was successfully applied to both technical and nontechnical CEA studies and is believed to be able to provide analysts and researchers with an efficient platform for investigation, presentation and decision-making support. The DPD was also shown to be very compatible with quality management strategies such as the continuous improvement cycle.

    ACKNOWLEDGMENT

    The Author would like to express sincere gratitude to all companies and personnel who provided valuable data and information used in this study. Special thanks go for The Arab Aluminum Company (ARAL) and Universal Metal Extrusion Company (UMEX) in Jordan. Special thanks are due to undergraduate students (Saja, Arwa, Hadeel and Majd) who helped in data collection and preparing some of the diagrams in this study.

    Figure

    IEMS-19-2-412_F1.gif

    The cause and effect (fishbone) diagram (van Aartsengel and Kurtoglu, 2013).

    IEMS-19-2-412_F2.gif

    Illustration of the color-code matrix structure.

    IEMS-19-2-412_F3.gif

    A schematic illustration of the metallic extrusion process (Kalpakjian and Schmid, 2007).

    IEMS-19-2-412_F4.gif

    Fishbone diagram for surface defects in metallic extrusion.

    IEMS-19-2-412_F5.gif

    A Pie chart of main surface defects.

    IEMS-19-2-412_F6.gif

    Second level pie chart applied to the tearing defect.

    IEMS-19-2-412_F7.gif

    Pareto chart for main surface defects.

    IEMS-19-2-412_F8.gif

    Two-level percentage Pareto chart for surface defects.

    IEMS-19-2-412_F9.gif

    Diameter calculation in DPD with a base pipe diameter of 100 mm (before scaling).

    IEMS-19-2-412_F10.gif

    Main pipe (base cylinder) representing a 100% value.

    IEMS-19-2-412_F11.gif

    DPD First level construction.

    IEMS-19-2-412_F12.gif

    Color coding of first level in DPD.

    IEMS-19-2-412_F13.gif

    Second level analysis in DPD.

    IEMS-19-2-412_F14.gif

    Complete DPD with four levels CEA

    IEMS-19-2-412_F15.gif

    New DPD after eliminating the primary hot path in the previous DPD.

    IEMS-19-2-412_F16.gif

    DPD for the problem of global greenhouse emissions.

    Table

    Overall percentage of extrusion defects

    Percentage of surface defects and their causes

    Color-code matrix for Surface defects and main causes

    Criticality ranges for the color code matrix

    Diameter calculations for first level pipes

    Second level diameter calculations for DPD

    Comparison of the DPD to some common CEA/RCA tools

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