Application of root cause analysis in improvement of product quality and productivity

Mahto, Dalgobind; Kumar, Anjani Article Application of root cause analysis in improvement of product quality and productivity Journal of Industrial Engineering and Management (JIEM) Provided in Cooperation with: The School of Industrial, Aerospace and Audiovisual Engineering of Terrassa (ESEIAAT), Universitat Politècnica de Catalunya (UPC) Suggested Citation: Mahto, Dalgobind; Kumar, Anjani (2008) : Application of root cause analysis in improvement of product quality and productivity, Journal of Industrial Engineering and Management (JIEM), ISSN 2013-0953, OmniaScience, Barcelona, Vol. 1, Iss. 2, pp. 16-53, https://doi.org/10.3926/jiem.v1n2.p16-53 This Version is available at: https://hdl.handle.net/10419/188371 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/3.0/ doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 16 D. Mahto; A. Kumar Application of root cause analysis in improvement of product quality and productivity Dalgobind Mahto; Anjani Kumar National Institute of Technology (INDIA) [email protected]; [email protected] Received July 2008 Accepted December 2008 Abstract : Root-cause identification for quality and productivity related problems are key issues for manufacturing processes. It has been a very challenging engineering problem particularly in a multistage manufacturing, where maximum number of processes and activities are performed. However, it may also be implemented with ease in each and every individual set up and activities in any manufacturing process. In this paper, root-cause identification methodology has been adopted to eliminate the dimensional defects in cutting operation in CNC oxy flame cutting machine and a rejection has been reduced from 11.87% to 1.92% on an average. A detailed experimental study has illustrated the effectiveness of the proposed methodology. Keywords: root cause analysis, cause and effect diagram, interrelationship diagram and current reality tree 1. Introduction In Root Cause Analysis (RCA) is the process of identifying causal factors using a structured approach with techniques designed to provide a focus for identifying and resolving problems. Tools that assist groups or individuals in identifying the root causes of problems are known as root cause analysis tools. Every equipment failure happens for a number of reasons. There is a definite progression of actions and consequences that lead to a failure. Root Cause Analysis is a step-by-step method doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 17 D. Mahto; A. Kumar that leads to the discovery of faults or root cause. An RCA investigation traces the cause and effect trail from the end failure back to the root cause. It is much like a detective solving a crime. To meet up the high changing market demands along with high quality at comparable prices, one shall have to identify quickly the root causes of quality related problems by reviewing an event, with the goals of determining what has happened, why it has happened and what can be done to reduce the likelihood of recurrence. 2. Objective and outline of the study There are varieties of problems related to product quality and productivity in industries due to varying degrees of abnormality and inefficiency which ultimately causes rejection. Root-cause identification for quality-related problems is a key and necessary step in the operations of manufacturing processes, especially in highthroughput automated processes. This is predominantly true for the multistage manufacturing processes, which is defined as a process that produces the products under multiple setups. The quality information flow of the product in a multistage manufacturing system and the interaction between the process faults and the product quality characteristics are very complicated. In multistage process, the identification of process root cause is also not simple. It has been observed that the implementation of Root Cause Analysis in a particular single individual setup has simplified the problem. A case study was done for an industry which was in doldrums condition. The abrupt shutdowns and breakdowns (5.19% of annual sales), frequent customer complaints (367pa), line balancing delay (27%), material scarcity or unavailability of matching material (58 days pa), rejection (3.03% of sales) and various other key success factors were not up to the mark. Therefore, the Root Cause Analysis was undertaken to improve the plant situation. But, the study was confined to the CNC Oxy Flame Cutting Machine. In this paper, the identification of the problem has been simplified taking into consideration a particular stage of manufacturing. It has been observed that RCA can also be implemented in each and every individual set up of manufacturing to improve product quality and productivity. doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 18 D. Mahto; A. Kumar 3. Literature review Wilson et al. (1993) have defined the Root Cause Analysis as an analytic tool that can be used to perform a comprehensive, system-based review of critical incidents. It includes the identification of the root and contributory factors, determination of risk reduction strategies, and development of action plans along with measurement strategies to evaluate the effectiveness of the plans. Canadian Root Cause Analysis Framework (2005) says that root cause analysis is an important component of a thorough understanding of “what happened”. The team begins by reviewing an “initial understanding” of the event and identifying unanswered questions and information gaps. The information-gathering process includes interviews with staff, who were directly and indirectly involved, examination of the physical environment where the event and other relevant processes took place, and observation of usual work processes. This information is synthesized into a “final understanding”, which is then used by the team to begin the “why” portion of the analysis. Similarly, to solve a problem, one must first recognize and understand what is causing the problem. This is the essence of root cause analysis. According to Wilson et al. (1993) a root cause is the most basic reason for an undesirable condition or problem. If the real cause of the problem is not identified, then one is merely addressing the symptoms and the problem will continue to exist. Dew (1991) and Sproull (2001) state that identifying and eliminating root causes of any problem is of utmost importance. Root cause analysis is the process of identifying causal factors using a structured approach with techniques designed to provide a focus for identifying and resolving problems. Tools that assist groups and individuals in identifying the root causes of problems are known as root cause analysis tools. According to Duggett (2004) several root cause analysis tools have emerged from the literature as generic standards for identifying root causes. Some of them are the Why Why Analysis, Multi Vari Analysis, Cause-and-Effect Diagram (CED), the Interrelationship Diagram (ID), and the Current Reality Tree (CRT). He has added that Why Why analysis is the most simplistic root cause analysis tool where as current reality tree is used for possible failures of a system and it is commonly doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 19 D. Mahto; A. Kumar used in the design stages of a project and works well to identify causal relationships. There is no shortage of information available about these tools. The literatures confirmed that these tools do, in fact, have the capacity to find the root causes with varying degrees of accuracy, efficiency, and quality. DOE Guideline Root Cause Analysis Guidance Document February (1992) says that immediately after the occurrence identification, it is important to begin the data collection phase of the root cause process using these tools to ensure that data are not lost. The data should be collected even during an occurrence without compromising with safety or recovery. The information that should be collected consists of conditions before, during, and after the occurrence; personnel involvement (including actions taken); environmental factors; and other information having relevance to the condition or problem. For serious cases, photographing the area of the occurrence from several views may be useful in analysis. Every effort should be made to preserve physical evidence such as failed components, ruptured gaskets, burned leads, blown fuses, spilled fluids, and partially completed work orders and procedures. This should be done despite operational pressures to restore equipment to service. Occurrence participants and other knowledgeable individuals should be identified. Anderson and Fagerhaug (2000) have simplified the root cause analysis. They provide a comprehensive study about the theory and application of metrics in root cause analysis. It emphasizes the difficulty in achieving process capability in software domain and is cautious about SPC implementation. They mention that the use of control charts can be helpful for an organization especially as a supplementary tool to quality engineering models such as defect models and reliability models. However, it is not possible to provide control as in manufacturing since the parameters being charted are usually in-process measures instead of representing the final product quality. The final product quality can only be measured at the end of a project as opposed to the production in manufacturing industry, so that on-time control on processes becomes impossible. They also underline the necessity of maturity for achieving process stability in development of product quality and productivity. Finally, they bring a relaxed understanding by stating that the processes can be regarded in control when the project meets inprocess targets and achieves end-product quality and productivity improvement goals. doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 20 D. Mahto; A. Kumar Arcaro (1997) has presented various tools for identifying root causes. He describes that RCA techniques are constrained within domain and give a detailed tutorial by supporting theoretical knowledge with practical experiences. He states that all RCA techniques may not be applicable for all processes. Brown (1994) has used the root cause technique to analyze the assembly of commercial aircraft. He has concluded that it is the most effective tool to eliminate the causes in most vital assemblies like aircraft, where utmost safety and reliability is needed. Brassard (1996), and Brassard and Ritter (1994) have put their emphasis on continuous improvement and effective planning. They have pointed out that Root Cause analyzing tools give management to think ahead about failures and plan accordingly. They emphasize that process improvement models implicitly direct companies to implement RCA as a crucial step for project level process control and organizational level process improvement purposes. Quantitative Process Management requires establishing goals for the performance of the project's defined process, taking measurements of the process performance, analyzing these measurements, and making adjustments to maintain process performance within acceptable limits. Cox and Spencer (1998) have advocated that RCA tools effectively give solution to handle constraints and arrive at an appropriate decision. Like Cox and Spencer (1998), Dettmer (1997) has also used root cause analysis on management of constraints. He presents one of the earliest studies on the debate of applying Root Cause Analysis to processes. A proper management decision is necessary to succeed the RCA tools and methods in a particular environment. Lepore and Cohen (1999), Moran et al. (1990), Robson (1993) and Scheinkopf (1999) move ahead that when change is needed, then think root cause analyzing, identifying and eliminating. The foundations of their studies are pioneering one as they question an accepted practice for root cause analysis and the results of the example studies are encouraging. However, the studies are far from being practical one as they include too many parameters and assumptions. Smith (2000) has explained that Root Cause Tools can resolve conflicting strategies, policies, and measures. The perception is that one tool is as good as another tool. While the literature was quite complete on each tool as a stand-alone doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 21 D. Mahto; A. Kumar application and their relationship with other problem solving methods. There are very few literatures available on the comparative study of various root cause analysis tools and methods. The study on three tools namely Cause-and-Effect Diagram (CED), the Interrelationship Diagram (ID), and the Current Reality Tree (CRT) is deficient on how these three tools directly compare to each other. In fact, there are only two studies that compared them and the comparisons were qualitative. Likewise, Fredendall et al. (2002) have also compared the CED and the CRT using previously published examples of their separate effectiveness. While Pasquarella et al. (1997) compared CED, ID and CRT on Equipment/Material Problem, Procedure Problem, Personnel Error, Design Problem, Training Deficiency, Management Problem and External Phenomena using a one-group post-test design with qualitative responses. There is little published research that quantitatively measures and compares the Why Why Analysis, Multi Vari Analysis, Cause-and-Effect Diagram (CED), the Interrelationship Diagram (ID), and the Current Reality Tree (CRT). Geno (2007) has presented some insight into the comparison of common root cause analysis tools and methods. He indicates that there are some comparative differences between tool and method of a RCA. He has added that tools are included along with methods because tools are often touted and used as a fullblown root cause analysis. 4. Basic terminologies in root cause analysis  Facility: Facility may be defined as any equipment, structure, system, process, or activity that fulfills a specific purpose. Some of the examples include production or processing plants, accelerators, storage areas, fusion research devices, nuclear reactors, coal conversion plants, magneto hydrodynamics experiments, windmills, radioactive waste, disposal systems, testing and research laboratories, transportation activities, and accommodations for analytical examinations of irradiated and unpredicted components.  Condition: It may be defined as a state, whether or not resulting from an event, that may have adverse safety, health, quality assurance, security, doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 22 D. Mahto; A. Kumar operational, or environmental implications. A rendition is usually programmatic in nature; for example, an (existing) error in analysis or calculation, an anomaly associated with (resulting from) design or performance, or an item indicating weaknesses in the management process are all conditions.  Root Cause: The cause that, if corrected, would prevent recurrence of this and similar occurrences. The root cause does not apply to this occurrence only, but has generic implications to a broad group of possible occurrences, and it is the most fundamental aspect of the cause that can logically be identified and corrected. There may be a series of causes that can be identified, one leading to another. This series should be pursued until the fundamental, correctable cause has been identified. For example, in the case of a leak, the root cause could be management, not its maintenance, which ensures that it is effectively managed and controlled. This cause could have led to the use of improper seal material or missed preventive maintenance on a component, which ultimately led to the leak. In the case of a system misalignment, the root cause could be a problem in the training program, leading to a situation in which operators are not fully familiar with control room procedures and are willing to accept excessive distractions.  Causal Factor: A condition or an event that results in an effect (anything that shapes or influences the outcome). This may be anything from noise in an instrument channel, a pipe break, an operator error, or a weakness or deficiency in management or administration. In the context of DOE there are seven major causal factor categories. These major categories are: o Equipment/Material Problem o Procedure Problem o Personnel Error o Design Problem o Training Deficiency o Management Problem doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 23 D. Mahto; A. Kumar o External Phenomenon 5. Root cause analysis tools and techniques Many Root Cause Analysis Tools have emerged from the literature as generic standards for identifying root causes. They are the Cause-and-Effect Diagram (CED), the Interrelationship Diagram (ID), and the Current Reality Tree (CRT), Why Why Analysis, Multi Vari Analysis. Ample of information is available about these tools, in open literature (See References). 5.1 Causes-and-effect diagram (CED) This diagram, also called Ishikawa or Fishbone Diagram, is used to associate multiple possible causes with a single effect. The diagram is constructed to identify and organize the possible causes for a particular single effect. Causes in Cause and Effect Diagram are frequently arranged in four major categories. For manufacturing cases it is Manpower, Methods, Materials and Machinery. For Administration and service sectors, it is Equipment, Policies, Procedures and People. Ishikawa advocated the CED as a tool for breaking down potential causes into more detailed categories so that they can be organized and related into factors which help in identifying the root cause. 5.2 Interrelationship diagram (ID) Mizuno supported the ID as a tool to quantify the relationships between factors and thereby classify potential causal issues or drivers. The interrelationships among the operations are shown as ‘in and out’ in each stages of operation. The weight factors, which may include causes, effects, or both, of in and out are determined on the basis on logical sequence. 5.3 Current reality tree (CRT) Current Reality Tree is a tool to find logical interdependent chains of relationships between undesirable effects leading to the identification of the core cause. It depicts the real status under prevailing current conditions with regard to causality, factor relationships, usability, and participation. doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 30 D. Mahto; A. Kumar  What would you have done differently to have prevented the occurrence, disregarding all economic considerations (as regards operation, maintenance, and design)?  What would you have done differently to have prevented the occurrence, considering all economic concerns (as regards operation, maintenance and design)? 6.4 Management oversight and risk tree (MORT) analysis It is the methodology adopted by the root cause analysis team with the active support of management. To perform the MORT analysis:  Identify the problem associated with the occurrence and list it as the top event.  Identify the elements on the "what" side of the tree that describe what happened in the occurrence.  For each barrier or control problem, identify the management elements on the "why" side of the tree that permitted the barrier control problem.  Describe each of the identified inadequate elements or problems and summarize your findings. A brief explanation of the "what" and "why" may assist in using mini-MORT for causal analyses.  Barriers that surround the hazard and/or the target and prevent contact or controls and procedures that ensure separation of the hazard from the target  Plans and procedures that avoid conflicting conditions and prevent programmatic impacts.  In a facility, what functions implement and maintain these barriers, controls, plans, and procedures? doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 31 D. Mahto; A. Kumar  Identifying the hazards, targets, and potential contacts or interactions and specifying the barriers/controls that minimize the likelihood and consequences of these contacts  Identifying potential conflicts/problems in areas such as operations, scheduling, or quality and specifying management policy, plans, and programs that minimize the likelihood and consequences of these adverse occurrences  Providing the physical barriers: designing, installation, signs/warnings, training or procedures  Providing planning/scheduling, administrative controls, resources, or constraints. Verifying that the barriers/controls have been implemented and are being maintained by operational readiness, inspections, audits, maintenance, and configuration/change control  Verifying that planning, scheduling, and administrative controls have been implemented and are adequate  Policy and policy implementation (identification of requirements, assignment of responsibility, allocation of responsibility, accountability, vigor and example in leadership and planning). 6.5 Human performance evaluation Human Performance Evaluation is used to identify factors that influence task performance. It is most frequently used for man-machine interface studies. Its focus is on operability and work environment, rather than training operators to compensate for bad conditions. Also, human performance evaluation may be used for most occurrences since many conditions and situations leading to an occurrence ultimately result from some task performance problem such as planning, scheduling, task assignment analysis, maintenance, and inspections. Training in ergonomics and human factors is needed to perform adequate human performance evaluations, especially in man-machine interface situations. doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 32 D. Mahto; A. Kumar 6.6 Kepner-Tregoe problem solving and decision making Kepner-Tregoe is used when a comprehensive analysis is needed for all phases of the occurrence investigation process. Its strength lies in providing an efficient, systematic framework for gathering, organizing and evaluating information and consists of four basic steps:  Situation appraisal to identify concerns, set priorities, and plan the next steps.  Problem analysis to precisely describe the problem, identify and evaluate the causes and confirm the true cause. (This step is similar to change analysis).  Decision analysis to clarify purpose, evaluate alternatives, and assess the risks of each option and to make a final decision.  Potential problem analysis to identify safety degradation that might be introduced by the corrective action, identify the likely causes of those problems, take preventive action and plan contingent action. This final step provides assurance that the safety of no other system is degraded by changes introduced by proposed corrective actions. These four steps cover all phases of the occurrence investigation process and thus, Kepner-Tregoe can be used for more than causal factor analysis. This systems approach prevents overlooking any aspect of the concern. 7. Difference between RCA tools and techniques and RCA methods To differentiate Root Cause Analysis Tools and Root Cause Analysis Methods, a standard is needed to which they could be compared. It is generally agreed that the purpose of root cause analysis is to find effective solutions to our problems such that they do not recur. Accordingly, an effective root cause analysis process should provide a clear understanding of exactly how the proposed solutions meet this goal. To provide this assurance an effective process should meet the following six criteria  Clearly defines the problem and its significance to the problem owners. doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 33 D. Mahto; A. Kumar  Clearly delineates the known casual relationships that combined to cause the problem.  Clearly establishes causal relationships between the root causes and the defined problem  Clearly presents the evidence used to support the existence of identified causes.  Clearly explains how the solutions will prevent recurrence of the defined problem.  Clearly documents criteria 1 through 5 in final RCA report so others can easily follow the logic of the analysis Therefore, there is a clear distinction between an RCA Tool and RCA Method. A tool is distinguished by its limited use pertaining to particular phenomena or situation, while a method may involve many steps and processes and has wide usage with the flexibility to modify to some extent pertaining to particular phenomena or situation. Comparative differences of selected RCA Tools and RCA Methods have been shown in the Table 1. Tool / Method Type Defines Problem Defines all causal relationships Provides a causal path to root causes Delineates evidence Explains how solutions prevent recurrence Easy to follow report Causes-and-Effect Diagram Tool Yes Limited No No No No Interrelationship Diagram Tool Yes No No No No No Current Reality Tree Tool Yes No Limited No Limited No Why Why Analysis Tool Yes No Yes No No No Multi Vari Analysis Tool Limited Limited Yes No No Yes Events and Causal Factor Analysis Method Yes Limited No No No No Change Analysis Method Yes No No No No No Barrier Analysis Method Yes No No No No No Management Oversight and Risk Tree Analysis Method Yes Yes Yes No Limited Yes Human Performance Evaluation Method Yes Yes Yes No Limited Yes Kepner – Tregoe Problem Solving and Decision Making Method Yes Yes Yes No Limited Yes Table 1. “Comparison of selected RCA tools and RCA methods”. doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 34 D. Mahto; A. Kumar 8. Root cause analysis (RCA) process The RCA method brings a team of, usually 3 to 6 or as demanded, knowledgeable people together to investigate the failure using evidence left behind from the fault. The team brainstorms to find as many causes of the fault as possible. By using what evidence remained after the fault and through discussions with people involved in the incident, all the non-contributing causes are removed and the contributing causes retained. A fault tree is constructed starting with the final failure and progressively tracing each cause that led to the previous cause. This continues till the trail can be traced back no further. Each result of a cause must clearly flow from its predecessor (the one before it). If it is clear that a step is missing between causes it is added in and evidence looked for to support its presence. Once the fault tree is completed and checked for logical flow, the team then determines what changes has to be made to prevent the sequence of causes and consequences from again occurring. Root cause analysis is defined in the Canadian Root Cause Analysis Framework1 as “an analytic tool that can be used to perform a comprehensive, system-based review of critical incidents. It includes the identification of the root and contributory factors, determination of risk reduction strategies, and development of action plans along with measurement strategies to evaluate the effectiveness of the plans.” Root cause analysis in industries is best conducted by a multidisciplinary team, involving individuals knowledgeable about Productivity, as well as knowledgeable in the Quality area of focus. Information is gathered through interviews with staff members who were directly and indirectly involved, as well as family members when possible. In addition, the team reviews the location where the incident occurred, examines the products, devices, environment and work processes involved, and reviews relevant documentation and literature. To improve product quality and productivity, the analysis team proceeds through a series of probing questions focused on answering “why” and “caused by” questions to delineate the various factors that contributed to the event and which, if left unmitigated, could contribute to another event. The focus is on systems and processes and their interaction with individuals, with the understanding that the individuals involved did not intentionally act to cause harm, and given the same set of circumstances, the outcome would be the same for any individuals involved. The doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 35 D. Mahto; A. Kumar root cause analysis process encourages high-leverage system changes that, if implemented, will have lasting effects on product quality and productivity with safety. Relevant literature and practice standards are considered in formulating recommendations and actions. To make it understand the significance of such a system enhancement, it has been provided an analogous example from the automotive industry considering the steps for successful root cause analysis as given below. 8.1 Steps for successful root cause analysis (RCA) Root Cause Analysis (RCA) is a useful tool for trouble shooting breakdowns and efficiently coming to a solution. For successful implementation of RCA following seven steps are necessary and once completed that will naturally result in elimination of root causes and will increase profits. Seven points of RCA are…  Describe the actual Cause.  Define the physical phenomena of the Cause  Organize the details of the Cause by using the '3W2H' (with what, when, where, how, how much) tool.  Work as a team, respecting each other’s expertise and knowledge, rather than individually.  Consider every possible cause of the Cause.  Verify all logical causes and eliminate all illogical causes.  If determined that the cause among the causes was human error, separate that cause from the physical causes. 9. Outline of the empirical case study The experimentation was carried out at one of the automotive component manufacturing plants situated in Jamshedpur, India. The plant has sophisticated doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 36 D. Mahto; A. Kumar modern machine tools. This plant is professionally managed and it has got ISO 9002 and ISO 14001-system certification. It has also implemented Total Quality Management. The departments have been computerized and linked through Local Area Networking (LAN) to enhance accessibility. Maintenance management systems are in practice. It is also backed up by an advanced computer aided condition monitoring system. The plant processes may be roughly categorized into the following blocks (as per the material flow):  Raw Material Processes  Intermediate Processes  Final Processes The products manufactured by the plant comprise of different components, used in construction equipments and in conveying systems. To improve the product quality and productivity Oxy Flame cutting machine was chosen. This machine is composed of electrical and mechanical systems. The machine moves along X axis through L.T. Mechanism (Long Travel Mechanism) and through cross travel i.e. along Y axis travel is done in cross travel Beam by the three cutting torches which are fitted with sensors for which command comes from the CPU fitted with the machine. This machine has three cutting torches by which the materials are being cut by oxy flame. It uses multi channel data format for storage of time data, spectra, etc., including: function identifier, sampling frequency, input/output point and direction, input/output units, free text lines, X-, Y-, Z-axis labels, auxiliary custom fields. Extensive commands to extract information from headers including a search function. It uses dissolved Acetylene and Oxygen to generate flame to cut the material in a straight line or in curves. When a single torch is used, it can cut straight beveling. The torches have proximity sensors so that there should be a accurate distance between the Raw material to be cut and Torch tip. The tool holder is that part of the system where the sensor of the cutting edge is placed. When the machine begins cutting, the cutting torches move according to DNC (Direct Numerical Control) programme. This DNC programme is converted from a CNC (Computerized Numerical Control) programme for cutting. A detector is also placed to capture the problems in cutting, which enables an operator to make adjustments of speed, cutting gap or Gas flow. A photograph of the machine is presented in Figure 1. doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 37 D. Mahto; A. Kumar Now the seven points as described above has been adopted to study the empirical case. 9.1 Finding and Defining the Actual Cause (Step 01) The problem was encountered in the initial processing of the material. The capacity utilization of the plant was around 55% to 65% due to problems in processing of materials itself and there was always fire-fighting for want of material. But, no root causes were identified as to why there was such a problem. This reason was one of the key contributory factors for the lower level of productivity Figure 1. “Sample machine taken for root cause analysis”. 9.2 Physical phenomena of the cause (Step 02) The cutting operation was to be performed in all the items but how far it is related to or it was influencing the production processes had never been studied earlier. Therefore, a value stream mapping was done first by selecting a job (Pivot Frame) to understand the percentile impact of gas cutting operation on the production process. The defect on cut material increases the cycle time of each activity and adds more non-value adding times. Hence, to pin point non-value adding activities contributed by gas cutting, data were captured activity wise. Now, the Value Stream Mapping was done and its detail is tabulated in Table 2. Different operations have been categorized and entered in the table in abbreviated doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 38 D. Mahto; A. Kumar terms. The details of the abbreviation used are given below. The same abbreviations shall be used here in after.  VA = Value Adding Activities  NVA = Non Value Adding Activities  EOT = Electric Operated Tower Crane  DNC = Direct Numerical Control system  CNC = Computerized Numerical Control system  DTD = Desk to Desk (Lean tool)  SPC = Statistical Process Control Value Adding Non Value Adding Material Incoming Store Handling / Setting Gas Cutting Cutting Pieces Storage Grinding / Cleaning Handling / Setting Bending Handling / Setting Assembly Handling / Setting Welding Cleaning Turning Handling / Setting Boring Handling / Setting Drilling Handling / Setting Cleaning / Oiling Dispatch 1.5 0.3 2 4 2 3 1.5 2 1 5 0.5 5 2 4 0.3 4 0.3 4 0.3 1 2 SUMMARY Total Time (Hr) 46 Value Adding (Hr) 30 Non Value Adding (Hr) 16 Note: In process Inspection is carried out at every stage Figure 2. “Value stream mapping before root cause analysis”. RAW MATERIAL FINISHED PRODUCT TIME Hr doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 39 D. Mahto; A. Kumar In the above sample study, it was found that the non-value adding activities were higher than the value adding activities. The operations entered in serial numbers 3 to 5, 7 to 12, 18 to 20, 25 and 26 (Table 2) are non-value adding activities associated to gas cutting operation. It comes out to be 07 hours 40 minutes of non-value added activity with total throughput time of 12 Hours 50 minutes. Sl No Operations in Sequence Machine wise Activity Description Resources involved Time taken (Min) Category 1 Raw Material handling Handling from stock yard to Cortina Machine EOT Crane, 1 Operator, 1 Helper 25 NVA 2 Data conversion DNC to CNC Cortina Machine 1 Engineer, 1 Computer 10 NVA 3 CNC Cutting At Cortina Machine 1 Operator 35 VA 4 Material Removal shifting and Inspection Cortina Machine 2 helper, 1 Crane, 1 Inspector 55 NVA 5 Material Preparation Manual grinding 2 Operator, 2 Grinding machine 25 NVA 6 Inspection Manual grinding 1 Inspector 20 NVA 7 Segregation & Shifting Material Preparation, Bending, Assembly or Machining 1 Operator, 1 Helper, fork lifts, trolleys, Crane 30 NVA 8 Assembly Collection of prepared material for assembly 1 Operator, 2 Helper, Fixtures, Gauges 40 VA 9 Inspection Assembly 1 Inspector 20 NVA 10 Loading & setting at Manipulator For welding EOT Crane, 1 Operator 15 NVA 11 Welding Mig welding 1 Operator, Co2 Gas, Welding M/c 60 VA 12 Inspection Manually, UT machine 1 Inspector 25 NVA 13 Unloading & Shifting to Machining centre Unloading by EOT Crane 1 Helper, Fork lift 20 NVA 14 Setting at VTL For machining 1 Helper, 1 EOT Crane, 1 Operator, 20 NVA 15 Base Machining Fixture and special tool 1 Operator 50 VA 16 Inspection Vernier, Jig 1 Inspector 20 NVA 17 Unloading & Shifting to Boring Fork lift, EOT Crane 1 Helper, Fork lift, EOT Crane 20 NVA 18 Setting at Horizontal Boring For Machining 1 Helper, EOT Crane, 1 Operator, 20 NVA 19 Boring Ø 90±1., Ø 80±1 1 Operator 50 VA 20 Inspection Vernier, Jig 1 Inspector 20 NVA 21 Unloading & Shifting to Drilling & Tapping Fork lift, EOT Crane 1 Helper, 20 NVA 22 Setting at Radial Drill Drilling Fixing of Jig 1 Helper, EOT Crane, 1 Operator 20 NVA 22 Drilling & Tapping Ø 15.5+0.2, Ø 20+0.3 1 Operator 75 VA 23 Inspection Gauge, Tap 1 Inspector 25 NVA 24 Unloading & Shifting to cleaning Fork lift, EOT Crane 1 Helper, Fork lift, EOT Crane 15 NVA 25 Cleaning & surface treatment Phosphating and Rustoil 1 Operator, 1 helper 25 NVA 26 Inspection Visually 1 Inspector 10 NVA Total ∑770 Table 2. “Value stream mapping before root cause analysis”. doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 46 D. Mahto; A. Kumar Sl No Operation in Sequence Machine wise Activity Description Resources involved Time taken (Min) Category (VA/ NVA) 1 Raw Material handling Handling from stock yard to Cortina M/c 1 Helper, EOT Crane, 1 Operator 15 NVA 2 Data conversion DNC to CNC Cortina Machine 1 Engineer, 1 Computer 10 NVA 3 CNC Cutting At Cortina Machine 1 Operator 30 VA 4 Material Removal & shifting From Cortina Machine 2 helper, 1 Crane 10 NVA 5 Inspection At cortina machine 1 Inspector 15 NVA 6 Assembly Material prepared at different location are gathered & assembled 1 Operator, 2 Helper, Fixtures, Gauges 40 VA 7 Loading & setting at Manipulator For welding EOT Crane, 1 Operator 7 NVA 8 Welding Mig welding 1 Operator, Co2 Gas, Welding Machine 50 VA 9 Inspection UT machine 1 Inspector 15 NVA 10 Unloading & Shifting to Machining Fork lift, EOT Crane 1 Helper, Fork lift 20 NVA 11 Setting at VTL For machining 1 Helper, EOT Crane, 1 Operator 20 NVA 12 Base Machining Fixture & special tool 1 Operator 45 VA 13 Inspection Vernier, Jig 1 Inspector 20 NVA 14 Unloading & Shifting to Boring Fork lift, EOT Crane 1 Helper, Fork lift, EOT Crane 20 NVA 15 Setting at Horizontal Boring For machining 1 Helper, EOT Crane, 1 Operator 20 NVA 16 Boring Ø 90±1., Ø 80±1 1 Operator 30 VA 17 Unloading & Shifting to Drilling & Tapping Fork lift, EOT Crane 1 Helper, Fork lift, EOT Crane 15 NVA 18 Setting at Radial Drill For Drilling 1 Helper, EOT Crane, 1 Operator 20 NVA 19 Drilling & Tapping Ø 15.5+0.2, Ø 20+0.3 1 Operator 60 VA 20 Inspection Gauge, Tap 1 Inspector 25 NVA 21 Unloading & Shifting to cleaning Fork lift, EOT Crane 1 Helper, Fork lift, EOT Crane 10 NVA 22 Cleaning & surface treatment Phosphating & Rustoil 1 Operator, 1 helper 25 NVA Total 507 Table 8. “Value stream mapping after root cause analysis” doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 47 D. Mahto; A. Kumar Figure 5. “Trend analysis of cutting products after root cause elimination”. The value stream mapping was done after root cause elimination, which has been shown in Table 8 and Figure 7 respectively. Figure 6. “Comparative trend analysis of cutting products before after Root Cause analysis”. 155 178 165 170 188 190 196 191 198 195 200 198 201 145 170 160 167 184 188 194 189 196 195 197 197 201 10 853422220310 0 50 100 150 200 250 12345678910111213 No of items Days :January 2008 Trend analysis of Product Quality after Root cause analysis Total Item Cut Accepted Rejected 6.90 4.71 3.13 1.80 2.17 1.06 1.03 1.06 1.02 0.00 1.52 0.51 0.00 8.28 6.47 11.25 9.58 11.96 12.23 9.79 12.70 12.24 15.90 16.24 13.71 13.93 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 12345678910111213 Percentage Sample data taken for days Comparative trend analysis of product quality After Study Before Study doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 48 D. Mahto; A. Kumar New State Precedence Diagram Value Adding Non Value Adding Incoming g Setting Gas Cutting g Cleaning g Setting Bending g Setting Assembly g Setting Welding g Grinding Turning g Setting Boring Setting Drilling g Oiling Dispatch 1.5 2 2.5 3 1.5 2 1 2.5 0.5 3.5 2 3 0.2 4 0.2 4 0.3 2 SUMMARY Total Time (Hr) 36 Value Adding (Hr) 24 Non Value Adding (Hr) 12 Note: In process Inspection is carried out at every stage Figure 7. “Value Stream Mapping after root cause analysis of dump lever”. Table 9 represents the comparison of results of pivot frame before and after the root cause analysis. Sl No Status Before root cause After root cause elimination % Change 1 Time in Minutes Total Time % Total Time % (+ Or -) 2 Value Adding 310 40.25 260 51.28 +16.13 3 Non Value Adding 460 59.75 247 48.72 -46.30 Table 9. “Comparison of results of pivot frame” 12. Comparison of torch qualities and productivity After implementation of root cause analysis and elimination of defects, it has been observed that the quality trend has improved a lot, but still some problems have been left, which needs to be addressed. TIME Hr RAW MATERIAL FINISHED PRODUCT doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 49 D. Mahto; A. Kumar In order to test the variation in quality and productivity of Oxy Flame Cutting Machine by three torches A, B and C, data has been gathered for all the shifts. Thereafter, Multi Vari Analysis approach was adopted. It is imperative here to mention that Multi Vari Analysis tries to find the relation among cyclic effects, temporal effects and positional effect. An ANOVA test has been carried out to test the productivity contribution of each torch statistically. However, this ANOVA test may also be carried out by MINITAB 15 software. The data has been collected for January 2008. The weekly averages of no of good pieces produced by all the cutting torches have been worked out, which has been tabulated in Table 10. Production averages Week 1 Week 2 Week 3 Week 4 Week 5 Torch A 220 251 226 246 260 Torch B 244 235 232 242 225 Torch C 252 272 250 238 256 Table 10. “Weekly averages of production by the torches”. We have m independent samples, whose size is n and where the members of the ith sample – Xi1, Xi2, …, Xin are normal random variables with unknown mean µi and unknown variance 2  , as the Equation 1 shows: Xij ~N(  i,  2), where, i=1…m and j=…n Equation 1. “Samples”. The hypothesis to be tested is the productivity contribution is the same in the three torches or not. Let,  H0: µ1 = µ2 = …………=µm  H1≠ equal /i.e. means are not equal.) The algebraic identity, called the sum of squares identity, is useful in doing these types of computations. The Equation 2 shows the statistic for computing sum of square identity: doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 50 D. Mahto; A. Kumar wb m i n jij SSSSnmXX    22 11 )( Equation 2. “Statistic for computing sum of square identity”. Where SSb and SSw are calculated as the Equation 3 and Equation 4 shows: SSw(Xij j1 n  i1 m Xi)2 Equation 3. “Within samples sum of squares”. SSbn(Xi i  1 m X2 ) Equation 4. “Between samples sum of squares”. The above table can be simplified by subtracting each value with 220 that subtracting a constant from each data; value will not affect the value of test statistic. Hence the new Table 10 can be formed as follows Production averages Week 1 Week 2 Week 3 Week 4 Week 5  jij X jij X2 Torch A 031 626 40 103 3273 Torch B 24 15 12 22 578 1454 Torch C 32 52 30 18 36 168 6248 Table 11. “Modified weekly averages of production by the torches”. From the Table 11 (where m=3 and n=5) and the Equations 1-4, we obtain a value of 2.60 in the test statistic, as Equation 5 shows: TS = 60.2 12 5785.19912 3335.863 1   mnm ss m ss w b Equation 4. “The value of test statistic (m=3 and n=5)”. 12.1 Inference from Statistical Calculation Now, by comparing the calculated value with tabulated value of F.05,n,m, we see that F2.12,.05 = 3.89. Hence, because the value of the test static does not exceed doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 51 D. Mahto; A. Kumar 3.89, it cannot be, at 5 percent level of significance, rejected the null hypothesis that the torches give equal production i.e. all the torches produce equal quality and productivity at 5 percent level of significance. 13. Conclusions The conventional Root Cause Analysis Tools and Methods provide some structure to the process of human event problem solving. This empirical study shows as to how they can be used and how it can be communicated to others with full appreciation. How the solutions will prevent the problem from recurring. Thus, it is the only process which allows all stakeholders to have a clear idea and the reality to promote its effective solution all the time. The Root Cause Tools and Methods could be utilized according to prevalent conditions and situations of Man, Material, Machines, Systems and Processes. In the framework of this study, the following conclusions can be drawn  It has been observed that the percentage increase in value adding is + 20.00 %, whereas, percentage reduction in non-value adding is –25.00 % after implementation of root cause analysis of dump lever.  It has been observed that the percentage increase in value adding is + 16.13 %, whereas, percentage reduction in non-value adding is –46.30 % after implementation of root cause analysis of pivot frame.  The rejection has reduced from 11.87% to 1.92% on an average due to attaining the skill within a very short time after implementation of proper maintenance schedule and giving training to the operators and maintenance persons.  It has been observed that after application of Root Cause Analysis, the product quality and productivity of the plant has improved. The plant has now fixed a very high target, from 250 T per month to 450 T per month, which may not be possible in the prevailing situation. The company will have to achieve the capacity utilization or run factor of 77% instead of earlier stated value of 55 to 65 % for it. doi:10.3926/jiem.2008.v1n2.p16-53 ©© JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953 Application of root cause analysis in improvement of product quality and productivity 52 D. Mahto; A. Kumar  All the torches A, B and C produce same level of quality and productivity at 5% level of significance. References Anderson, B., & Fagerhaug, T., (2000). Root Cause Analysis: Simplified Tools and Techniques. Milwaukee: ASQ Quality Press. Arcaro, J. S. (1997). TQM Facilitator’s Guide. Boca Raton,FL: St. Lucie Press. Brassard, M., & Ritter, D. (1994).The Memory Jogger II: A Pocket Guide of Tools for Continuous Improvement and Effective Planning. Salem, NH: GOAL/QPC. Brassard, M. (1996). The Memory Jogger Plus+: Featuring The Seven Management and Planning Tools. Salem, NH: Goal/QPC. Brown, J. I. (1994). Root-Cause Analysis in Commercial Aircraft Final Assembly. 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(2000). The Measurement Nightmare: How the Theory of Constraints Can Resolve Conflicting Strategies, Policies, and Measures. Boca Raton, FL: St. Lucie Press. Sproull, B. (2001). Process Problem Solving: A Guide for Maintenance and Operations Teams. Portland: Productivity Press. Wilson, P. F., Dell, L. D., & Anderson, G. F. (1993). Root Cause Analysis: A Tool for Total Quality Management. Milwaukee: ASQC Quality Press. ©© Journal of Industrial Engineering and Management, 2008 (www.jiem.org) Article's contents are provided on a Attribution-Non Commercial 3.0 Creative commons license. Readers are allowed to copy, distribute and communicate article's contents, provided the author's and Journal of Industrial Engineering and Management's names are included. It must not be used for commercial purposes. To see the complete license contents, please visit http://creativecommons.org/licenses/by-nc/3.0/.