Application of Statistical Quality Control in Monitoring the Production and Marketing Process of Sachet Water at Ajayi Crowther Water Factory, Oyo, Nigeria
Open accessAbstract
This study aims to analyse the application of Statistical Quality Control (SQC) in monitoring the production and marketing processes of sachet water at Ajayi Crowther Water Factory, with a focus on integrating educational technology to enhance these processes. Data were collected from the management and personnel, including the total production of bags of sachet water and the number of defective products each day for 29 days, as well as total sales each day for 40 days. A pie chart was used to determine the percentage of defective products, revealing that 1% were defective while 99% were non-defective. P and nP charts were utilized to check whether the defectives were within control limits. The control charts indicated that the process was in control; however, the process capability indices showed that the process is not capable of consistent production within the pre-set limits. An Individual X? chart was used to determine whether the sales of sachet water were in control, and the control chart confirmed that the sales process was stable. This study concluded that SQC, complemented by educational technology, has the potential to improve the quality of sachet water produced by Ajayi Crowther Water Factory. It is recommended that the factory should consistently use SQC, alongside educational technology, to monitor their production and marketing processes to enhance quality control measures. These findings could inform policymakers and stakeholders in the sachet water production industry and contribute to improving the quality of water produced and marketed in Nigeria.
AI snapshot
Written from the paper’s full text.
TL;DR
The study applies statistical quality control to sachet water production and sales at Ajayi Crowther Water Factory, using 29 days of production/defect data and 40 days of sales data. It finds production and sales charts within control limits, but process capability below acceptable levels, so long-term consistency is questionable.
Key findings
- Over 29 days, defective bags were 1.00% and non-defective 99.00%; roughly 10 defective packs per 1,000, mainly from sachet leakage.
- P and NP charts showed all points inside control limits; a run test gave p=0.527, so the production process was judged statistically controlled.
- Process capability indices (CP, CPL, CPU, CPK) were all below 1, indicating the process could not consistently meet preset specifications.
- Sales data over 40 days on one distribution route fell within I-MR control limits; run test p=0.026, but the paper still concluded randomness/control, which conflicts with α=0.05.
Methods
- Secondary data from Ajayi Crowther Water Factory: daily total bags produced and defective bags for 29 days; daily total sales for 40 days.
- Used pie chart for defect proportion; P and nP attribute charts; process capability analysis; individual/moving-range chart for sales; Minitab and run tests for randomness.
- Analysis focused on control limits, process capability indices, and whether plotted points were random/in control.
Limitations
- No dedicated limitations section. The paper states sales data came from a single distribution route and that capability indices suggest long-term control may not be sustainable.
- Evident: single-factory case study with short observation windows (29 and 40 days), limiting generalizability.
- Evident: the sales run-test p-value (0.026) is below α=0.05, yet the paper concludes randomness/control, indicating an internal inconsistency; also, despite framing around educational technology, no actual ET intervention or training evaluation is reported.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 6 cited sources checked · 0 supported · 0 flagged
- Findingown data
The process capability indices (CP, CPL, CPU, and CPK) for the proportion of defective products were all less than one.
“The Potential Capability (within) indicators, CP, CPL, CPU, and CPK, are all less than one.”
- Also stated in: § 5. Recommendation · hedged
“The normal distribution curve is not centered according to the preset specifications, as indicated by a capability index of less than 1.”
Quote in the paperSubstantial claim - Also stated in: § 5. Recommendation · hedged
- Findingown data
The normal distribution curve for the proportion of defective products is not centered according to the preset specifications.
“The normal distribution curve is not centered according to the preset specifications, as indicated by a capability index of less than 1.”
Quote in the paperSubstantial claim - Findingown data
The run test for the sales data yielded a P-value of 0.026.
“Given that the P-Value is 0.026, which is less than the significance level α = 0.05, we do not reject the null hypothesis.”
Quote in the paperSubstantial claim - Findingown data
The individual moving average (I-MR) control chart for sales of bags of sachet water showed all plotted points within the upper and lower control limits.
“The individual moving average chart for the sales of bags of sachet water shows that all plotted points fall within the upper and lower control limits.”
- Also stated in: § 5. Recommendation · plain
“The control chart shows that the process is in control since all the plotted points all fall within the upper and lower control limits.”
Quote in the paperSubstantial claim - Also stated in: § 5. Recommendation · plain
- Premisecitation
SQC tools such as control charts, process capability indices, and Pareto analysis help in identifying and rectifying variations in the production process, thereby ensuring that the final product is consistent with desired quality standards.
“SQC tools such as control charts, process capability indices, and Pareto analysis help in identifying and rectifying variations in the production process, thereby ensuring that the final product is consistent with the desired quality standards [2].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [2] 2. Blanchard, G. D., & Fabrycky, W. E. (2011). Systems engineering and analysis (5th ed.). Prentice Hall.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
SQC, complemented by educational technology, has the potential to improve the quality of sachet water produced by Ajayi Crowther Water Factory.
“This study concluded that SQC, complemented by educational technology, has the potential to improve the quality of sachet water produced by Ajayi Crowther Water Factory.”
Quote in the paperSubstantial claim- Rests on an assumption: There is a lack of integration of educational technology in training personnel on advanced quality control methods, which hinders the effective application of SQC tools.
- Interpretationown data
The production process at Ajayi Crowther Water Factory is not capable of consistently producing products within the specified limits.
“The control charts indicated that the process was in control; however, the process capability indices showed that the process is not capable of consistent production within the pre-set limits.”
- Also stated in: § 4. Conclusion · hedged
“This indicates that the process is not capable of consistently producing products within the specified limits.”
- Also stated in: § 5. Recommendation · plain
“Consequently, the process is not capable of consistently meeting the preset specification limits over the long term.”
Quote in the paperSubstantial claim - Also stated in: § 4. Conclusion · hedged
- Interpretationown data
The plotted points for the sales data were random.
“Consequently, we conclude that the plotted points are random.”
- Also stated in: § 4. Conclusion · hedged
“Tests of randomness indicate that the points are random, leading to the conclusion that the sales process for bags of sachet water is in statistical control.”
Quote in the paperSubstantial claim - Also stated in: § 4. Conclusion · hedged
- Interpretationown data
The sales process for bags of sachet water at Ajayi Crowther Water Factory is in statistical control.
“An Individual X̅ chart was used to determine whether the sales of sachet water were in control, and the control chart confirmed that the sales process was stable.”
- Also stated in: § 4. Conclusion · plain
“Tests of randomness indicate that the points are random, leading to the conclusion that the sales process for bags of sachet water is in statistical control.”
Quote in the paperSubstantial claim - Also stated in: § 4. Conclusion · plain
- May combine two claims. 5
- Classified as probably not a substantial claim. 7
May combine two claims.
Findingown dataThe P and NP control charts for the production data showed all plotted points within the upper and lower control limits, indicating the production process was in a state of statistical control.
“The control charts indicated that the process was in control; however, the process capability indices showed that the process is not capable of consistent production within the pre-set limits.”
- Also stated in: § Control Charts · plain
“The plotted points all fall within the upper and lower control limits, hence it is in state of statistical control.”
Quote in the paperSubstantial claim- Also stated in: § Control Charts · plain
May combine two claims.
Findingown dataDuring the 29-day investigation period at Ajayi Crowther Water Factory, 1.00% of sachet water bags produced were defective and 99.00% were non-defective.
“A pie chart was used to determine the percentage of defective products, revealing that 1% were defective while 99% were non-defective.”
- Also stated in: § Figure 1: Proportion of defective bags · plain
“During the 29-day investigation period, the defect rate was 1.00%, with non-defective products constituting 99.00% of the total production.”
- Also stated in: § 4. Conclusion · plain
“During the 29-day investigation period, 99% of the total production output was non-defective, with a defect rate of 1%.”
Quote in the paperSubstantial claim- Also stated in: § Figure 1: Proportion of defective bags · plain
Classified as probably not a substantial claim.
PremisecitationSQC tools, particularly control charts and Pareto analysis, contribute significantly to identifying production bottlenecks and minimizing waste in beverage manufacturing.
“For instance, [1] highlighted how SQC tools, particularly control charts and Pareto analysis, contribute significantly to identifying production bottlenecks and minimizing waste in beverage manufacturing.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1] 1. Al-Salim, M., Ahmed, K., & Farouk, T. (2021). Enhancing production efficiency through Statistical Quality Control tools in beverage manufacturing. International Journal of Quality & Reliability Management 38(5), 1023-1045.
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
PremisecitationEducational technology can facilitate the understanding and application of SQC principles among employees, leading to more effective quality control measures.
“Educational technology can facilitate the understanding and application of SQC principles among employees, leading to more effective quality control measures [9].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [9] 9. Whitman, M. E. and Woszczynski, A. M. (2004). The handbook of information systems research. Idea Group Publishing. source
May combine two claims.
Interpretationown dataAlthough the P and NP charts indicate that the production process is currently in a state of statistical control, the process capability indices suggest that long-term control may not be sustainable.
“Although the P and NP charts indicate that the process is currently in a state of statistical control, the process capability indices suggest that long-term control may not be sustainable.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationreasoningThe integration of SQC and educational technology can enhance the production and marketing processes of sachet water.
“Together, they create a comprehensive quality control system that can significantly enhance the production and marketing processes of sachet water.”
Quote in the paperSubstantial claim- Rests on an assumption: There is a lack of integration of educational technology in training personnel on advanced quality control methods, which hinders the effective application of SQC tools.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIntegrating real-time data analytics with SQC improves decision-making in production environments.
“Similarly, [10] underscored the importance of integrating real-time data analytics with SQC to improve decision-making in production environments.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [10] 10. Zhang, Y., Li, P. and Chen, Z. (2022). Real-time data analytics and Statistical Quality Control in production processes: A case study approach. Quality and Reliability Engineering International 39(4), 1150-1167.
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationCombining SQC with digital training tools reduces operational errors and enhances product traceability across the supply chain.
“These findings are further supported by [8], who demonstrated that combining SQC with digital training tools reduces operational errors and enhances product traceability across the supply chain”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [8] 8. Singh, R. and Kumar, S. (2023). The impact of digital training tools on operational efficiency and product traceability in manufacturing industries. Journal of Industrial Engineering and Management 16(2), 245-260.
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
Interpretationnone givenThe findings of this study can be used to inform policymakers and stakeholders in the sachet water production industry.
“These findings could inform policymakers and stakeholders in the sachet water production industry and contribute to improving the quality of water produced and marketed in Nigeria.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationSQC employs various statistical methods to monitor and control production processes, thereby minimizing defects and ensuring that products meet predefined quality standards.
“SQC employs various statistical methods to monitor and control production processes, thereby minimizing defects and ensuring that products meet predefined quality standards [6].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [6] 6. Montgomery, D. C. (2013). Introduction to statistical quality control (7th ed.). John Wiley & Sons.
Not checked in the free run.
May combine two claims.
Interpretationown dataThe observed 1% defect rate corresponds to approximately 10 defective packs per 1,000 produced, primarily due to sachet leakage.
“This defect rate implies that out of every 1,000 packs produced, approximately 10 packs were defective, primarily due to sachet leakage.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Ajayi Crowther Water Factory faces persistent issues with volume dispersion during the filling process.
“Despite the implementation of quality control measures, Ajayi Crowther Water Factory faces persistent issues with volume dispersion during the filling process.”
The paper relies on this without support.
The factory has limited capacity to continuously and effectively monitor and control the quality of sachet water during production.
“The primary problem is the factory's limited capacity to continuously and effectively monitor and control the quality of sachet water during production.”
The paper relies on this without support.
There is a lack of integration of educational technology in training personnel on advanced quality control methods, which hinders the effective application of SQC tools.
“Additionally, there is a lack of integration of educational technology in training personnel on advanced quality control methods, which hinders the effective application of Statistical Quality Control (SQC) tools.”
The paper relies on this without support.
Traditional final product inspection methods are labour-intensive and often inadequate for ensuring consistent quality.
“The traditional final product inspection methods are labour-intensive and often inadequate for ensuring consistent quality.”
The paper relies on this without support.
The current quality control practices do not fully leverage modern technological advancements that could enhance process monitoring and efficiency.
“Moreover, the current quality control practices do not fully leverage modern technological advancements that could enhance process monitoring and efficiency.”
The paper relies on this without support.
The factory's limited monitoring capacity results in an inability to consistently produce sachet water within the pre-set quality limits.
“This limitation results in an inability to consistently produce sachet water within the pre-set quality limits.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
The R-chart method labels both limits as upper limits
MinorThe method says it calculates upper and lower limits for the R chart, but both displayed limit formulas are labelled UCL. No lower control limit is identified.
“ii. UCL = D 4 R (11 ) iii. UCL = D 3 R (12”
“The center line of an R chart is the average range, and the upper and lower control limits are calculated accordingly.”
The first run-test decision reverses the stated rule
MinorThe stated rule calls for rejecting the null hypothesis when the P-value exceeds the significance level. The production run-test conclusion instead does not reject it when 0.527 exceeds 0.05.
“Decision Rule Reject H 0 if the P-Value is greater than significance level otherwise do not reject H 0”
“Given that the P-Value is 0.527, which is greater than the significance level α = 0.05, we do not reject the null hypothesis (H0).”
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 7
- [1]1. Al-Salim, M., Ahmed, K., & Farouk, T. (2021). Enhancing production efficiency through Statistical Quality Control tools in beverage manufacturing. International Journal of Quality & Reliability Management 38(5), 1023-1045.
- [2]2. Blanchard, G. D., & Fabrycky, W. E. (2011). Systems engineering and analysis (5th ed.). Prentice Hall.
- [3]3. Crosby, P. B. (1979). Quality is free: The art of making quality certain. McGraw-Hill.
- [5]5. Juran, J. M. and Gryna, F. M. (1999). Juran's quality control handbook (5th ed.). McGraw-Hill.
- [6]6. Montgomery, D. C. (2013). Introduction to statistical quality control (7th ed.). John Wiley & Sons.
- [8]8. Singh, R. and Kumar, S. (2023). The impact of digital training tools on operational efficiency and product traceability in manufacturing industries. Journal of Industrial Engineering and Management 16(2), 245-260.
- [10]10. Zhang, Y., Li, P. and Chen, Z. (2022). Real-time data analytics and Statistical Quality Control in production processes: A case study approach. Quality and Reliability Engineering International 39(4), 1150-1167.
Cited by
Sorted by citations
Ayenigba, A. A., Afariogun, D., & Aina, O. (2025) · International Journal of Education, Management, and Technology · doi:10.58578/ijemt.v3i3.7174 · Cited by 2
“In a related study, Ayenigba and Ajao (2024) demonstrated the adaptability of such methods in the water packaging industry, using them to monitor both production efficiency and post production marketing processes.”
Self-citationHotelling’s T² Control Chart for Monitoring Correlated Wine Quality in Small-Scale Production
Ayenigba, A. A., Adegboye, D. A., Folarin, D. O., et al. (2026) · Jurnal Matematika · doi:10.24843/jmat.2026.v16.i01.p198
“The production of sachet water [2], manufacture of soap [1], processing of soybean meal [5], and quality control of crude palm oil [6].”
Self-citation
- Authors
- Ayo Ayenigba, A., & Michael Ajao, O.
- Venue
- Ajayi Crowther Journal of Pure and Applied Sciences
- Publisher
- Ajayi Crowther University
- Published
- 2025-03-31
- Volume
- 4
- Issue
- 1
- ISSN
- 3092-8729 (ISSN-L)
- 3092-8737
- OpenAlex
- W4412993512
- Semantic Scholar
- ebb6e8d271af6b9d181c06d2adeddc78cecc98d2
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Ajayi Crowther Journal of Pure and Applied Sciences (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Advanced Statistical Process Monitoring
- Social Sciences › Business, Management and Accounting › Strategy and Management › Management and Optimization Techniques
- Social Sciences › Business, Management and Accounting › Strategy and Management › Management Systems and Quality Improvement
- Keywords
- statistical quality control, sachet water, bottled water production, water marketing, p-chart, np chart, process capability index, sales monitoring, educational technology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-11.
Statistics and arithmetic are recomputed, not judged.
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