Root Cause Analysis Of Quality Control Failure In Independent Testing Laboratory

Calculating Mean and Standard Deviation

Calculating the mean and the standard deviation of the 20 values of control data.

510	475	485	460 Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper	490	505	500	505	515	510
480	500	525	485	520	440	495	510	510	495

Table 1: Control data

From MS Excel, 

1. Using the obtained mean and the standard deviation to construct a control chart.

Below is the data obtained for the first, second, and third standard deviations from the mean.

	Mean	1st Deviation	2nd Deviation	3rd Deviation
Sample value	Control line	Up control line	Lower control line	Up control line	Lower control line	Up control line	Lower control line
510	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
475	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
485	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
460	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
490	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
505	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
500	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
505	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
515	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
510	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
480	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
500	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
525	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
485	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
520	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
440	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
495	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
510	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
510	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447
495	495.75	516.479	475.0215	537.20702	454.29298	557.93553	433.56447

Table 2: Data obtained for construction of a control chart

Application of the control chart to the test results using the Westgard multi-rules to assess the implication of the results

For convenience purposes, the shorthand notations are deployed to abbreviate different controls such as 1_2S for indicating a control measure exceeding 2s limits of control. From the obtained control graph above, the following was observed on application of the Westgard multirules to the sixty data samples used in this assignment. It is noted that the 1_3S control rule, which is used with the Levey-Jennings chart when the limits are set as the control line plus 3s and minus 3s are violated according to (Iqbal and Mustansar, 2018, pp.106-109). The run is therefore rejected when the samples exceed the third deviation upper or lower limits.  

Moreover, the 1_2S used when the control limits are set as the control mean plus/minus 2S or the boundaries of the second deviation from the mean is violated. Furthermore, the rule is used as a trigger to scrutinize the control data carefully when following the rejection rules.

(Park et al., 2017, pp.362-364) attributes that the 1_2s is usually used by the analysis’s to determine if any sample falls out of the plus or minus two value to the average value. The authors further regarded it as a warning rule that calls for attention in examining the existence of the violation of other regulations. Moreover, when the samples are found to conform to this rule, the control chart is accepted immediately. However, if some data scores are found to violate of this rule, the analyst is set to examine the other states of the samples. However, the 1_3S rule is used as a rejection rule. When a single sample of data is found in violation of the rule, it leads to rejection of the control chart obtained. Additionally, violation of this rule implies that some samples are beyond the third standard deviation limits thus would lead to inaccurate results should the control chart be used in the analysis of the data samples since a random error(s) has occurred (Westgard and Westgard, 2016, pp.32-50). 

Every success and failures in any organization are centred on leadership and management. (Doppelt, 2017) elaborates that aspects such as customer satisfaction that define a successful operation and management are attributed to attributed to effective leadership whereas factors such as regular customer complaints and work logs are pieces of evidence of low quality of service delivery associated with poor management. Organizations such laboratory are very critical areas when it comes to quality as they determine the lives of human beings who heavily rely on the lab results to define their food substances and drinks such as water among

The aspects identified in the case scenario take root in the defects in the management that contributed to a sudden and ambiguous change in the customer’s laboratory results. The newly appointed manager, Fred Mark, took the roles without conducting management review (clause 4.15) on the contracts signed by the former manager before he took office. Without having the knowledge of the terms and policies of the previous policies, Fred has no clue of the legal status of the contracts he is running putting him in position to violet the requirements of ISO 17025 on management organization clause 4.1 that demands the consent of legal knowledge on contracts (Lebrun and Taylor, 2017, pp.393-422). Furthermore, the provisions of clause 4.4 that illustrates the procedures of reviewing the tenders and contracts are violated when the manager takes office without undertaking these practices.  

Application of Control Chart

Additionally, there is a poor control on documentation and records that gives access to different files as evidenced in the scenario where the manager experienced difficulties in retrieving the supplementary procedure 306 (Total Plate Count) and 602 (Nutrients) files that defined the procedures for the water tests for the specified client. These aspects are highlighted in the certification of ISO 17025 clauses 4.3 and 4.12 (Elhuni, 2016, pp.964-976). This incidence illustrates that the manager and his team do not make any follow up on test results given to the clients after experiments have been done in the laboratory. Moreover, it also depicts bad leadership skills having not conducted any internal audits (clause 4.14-procedures of internal auditing) to review the roles and duties of every employee at the firm. How can one lead without being aware of the responsibilities of their team? This result in the poor quality of services rendered to the clients according to (Purbaningtias and Huda, 2017, pp.1-8).

Concisely, another aspect that could have contributed to the complaint by Larry is the regular defaults of the laboratory equipment reported by the technician. Furthermore, there exists a gap in the workforce due to the training on handling and maintenance of the equipment used at the lab, for instance, the technician could not replace the injection needle on the instrument as simple as it sounds (Hill and Finster, 2016). These factors put the accuracy and reliability of the results being obtained at the laboratory that could be the potential reasons for customer dissatisfaction. The provisions of ISO 17025 on personnel (clause 5.2) that ensures the laboratory staff members are skilled and well qualified and the policies governing equipment, testing and calibration (clauses 5.5 and 5.4) are violated by the firm. However, there is poor communication observed at the firm, which dates back to the previous management where instructions are delivered verbally without clear consents to the recipients. (Linton, Boersma, Traczyk, Shaw, and Nicholas, 2016, pp.150-159) discourages poor communication in the organization as it could lead to massive mistakes and financial losses while explaining the importance of an effective flow of information in operations and management of laboratories.

However, the customer complaints in the scenario can be addressed using the root cause analysis that ensures such challenges do not occur in the future (Sciacovelli et al., 2017, pp.348-357). This involves a situational analysis to determine the possible causal factors to the identified problem in the first step. From the scenario, the root cause to the customer complains is the dominance of ignorance and poor record keeping observed at the firm. As much as a laboratory environment has many pieces data that should be monitored, the persons in charge should have a clue on the status of contracts and tenders being done at the firm. Additionally, another cause is poor organizational and management practices at the company. It is reported that there are procedures and policies of conducting activities, which are barely followed through by the team. However, these are facilitated by the poor flow of information and communication at the firm where memos are verbally delivered to the junior staff by their bosses. Another finding from the analysis is the poor training offered to the new recruit making them incompetent (Yusof and Arifin, 2016, pp.766-773).

Westgard Multirules

It is recommended that for the manager to ensure quality services at the lab, he should conduct a review of all the contracts and tenders to familiarize with the terms and conditions agreed upon by the previous managers through internal audits as a requirement of the ISO 17025: 2005 (AI-Ghanimi, 2016, pp.154-169). The management should further outline, define the roles of every employee, and establish standard procedures for performing the tasks and responsibilities that include effective communication channels that would help in timely reporting of problems encountered by the team. It is also important that the management to organize training seminars to brainwash the skills among the employees to bring them up to the speed of competence. Moreover, a data management software could be implemented to replace the tedious paperwork at the laboratory to enhance efficiency and faster retrieval of data and information (Tauxe et al., 2016, pp.2450-2463). 

References

AI–Ghanimi, M. N. A. (2016). Compatibility Assessment of the Quality Management System in the College of Engineering Laboratories of the University of Kerbala with ISO 17025: 2005 requirements. journal of kerbala university, 14(3), 154-169.

Doppelt, B. (2017). Leading change toward sustainability: A change-management guide for business, government and civil society. London. Routledge.

Hill, R. H., & Finster, D. C. (2016). Laboratory safety for chemistry students. New Jersey. John Wiley & Sons.

Iqbal, S., & Mustansar, T. (2017). Application of sigma metrics analysis for the assessment and modification of quality control program in the clinical chemistry laboratory of a tertiary care hospital. Indian Journal of Clinical Biochemistry, 32(1), 106-109.

Lebrun, P., & Taylor, T. (2017). Managing the Laboratory and Large Projects. In Technology Meets Research: 60 Years of CERN Technology: Selected Highlights (pp. 393-422).

Linton, S. J., Boersma, K., Traczyk, M., Shaw, W., & Nicholas, M. (2016). Early workplace communication and problem solving to prevent back disability: results of a randomized controlled trial among high-risk workers and their supervisors. Journal of occupational rehabilitation, 26(2), 150-159.

Park, H. I., Cho, J., Lee, S. M., Son, J. W., Kim, S. R., Yoo, S., & Lee, S. S. (2017). Development and Application of a Laboratory-Developed Quality Control Program for Blood Glucose Monitoring Systems: A Single Institute Experience. Annals of laboratory medicine, 37(4), 362-364.

Purbaningtias, T. E., & Huda, T. (2017). Improving Understanding of Application of ISO/IEC 17025 with the Role-Playing and Simulation Methods in Laboratory Management. International Journal of Chemistry Education Research, 1(1), 1-8.

Sciacovelli, L., Lippi, G., Sumarac, Z., West, J., del Pino Castro, I. G., Vieira, K. F., … & Plebani, M. (2017). Quality indicators in laboratory medicine: the status of the progress of IFCC working group “laboratory errors and patient safety” project. Clinical Chemistry and Laboratory Medicine (CCLM), 55(3), 348-357.

Tauxe, L., Shaar, R., Jonestrask, L., Swanson?Hysell, N. L., Minnett, R., Koppers, A. A. P., … & Fairchild, L. (2016). PmagPy: Software package for paleomagnetic data analysis and a bridge to the Magnetics Information Consortium (MagIC) Database. Geochemistry, Geophysics, Geosystems, 17(6), 2450-2463.

Westgard, J. O., & Westgard, S. A. (2016). Quality control review: implementing a scientifically based quality control system. Annals of clinical biochemistry, 53(1), 32-50.

Yusof, M. M., & Arifin, A. (2016). Towards an evaluation framework for Laboratory Information Systems. Journal of infection and public health, 9(6), 766-773.