Pet Kingdom Demand Forecasting and Quality Control Analysis Case Study

Case study 1

Analysis of and discussion of demand data of Pet Kingdom

The data of Pet Kingdom have been analysed based on two different categories of products those are "Dog Ball Launcher" and "Lightweight Dog Lead". The quarterly data analysis has been performed concerning each year and the product separately. Therefore, a quarterly basis annual forecast of both the product demand has been generated using the "Time series" model of forecast analysis.

Table 1: "Dog Ball Launcher" Demand

Table number 1 has been developed using the "Time series" model in terms of the generation of demand forecast of "Dog Ball Launcher" with respect from 2018 to 2021. However, the average of quarters in context to yearly manners has been performed in terms of generation of overall forecast comparison (Lawrence, 2019). The projection of the demand forecast has reflected that demand for the concerned product was at pick during the period of 2019. However, a downfall in concerned product demand has been noticed during the period of 2020 and 2021.

Table 2: "Lightweight Dog Lead" Demand

Once again using the "Time series" model the demand forecast concerning the "Lightweight Dog Lead" product has been also generated. In terms of the generation of the concerned forecast, quarters have been considered yearly and the average of each year has been coined out. This type of approach has helped to develop a yearly basis forecast of concerned product demand concerning data for 2021 and 2020. Besides that, a comparative forecast of product demand of the Pet Kingdom also has been generated in terms of assessment of demand (Mills, 2019). The calculation concerning the "Lightweight Dog Lead" product has been accurately given in table number 2.

Appropriateness of used forecast model

It has been observed that multiple models of forecast have been available in the field of statistical analysis. However, the time series model of analysis has a major significant role in terms of gaining a concise and accurate overview of data trends. Therefore, the reorganization of differences among outliers or random fluctuations can be coined out easily. Thus, in terms of financial and business analytics, this type of forecasting model has a major significant role (Li and Li, 2020). Therefore, the "Time series" model of the forecast is most suits for the analysis of data concerning the generation of a protective forecast.

Forecasting model consideration

The "Time series" analysis has been considered to perform this study in the context of given data. Therefore, in terms of developing a demand forecast of the concerned organization time series analysis has been considered. This has brought a major advantage in terms of the projection of demand data of Pet Kingdom concerning quarter-based annual trend projection (Livieris et al. 2020). Hence, it can be stated that an explicit and rigid overview of the product demands of the concerned organization can be gained and need business intervention can be amended.

The forecast for 2020 and 2021

The outcome of the "Time series" analysis of the Pet Kingdom concerning the "Dog Ball Launcher" has been performed. It has been observed that the demand value of the concerned product was nearly 29 by the end of all 4 average quarters of 2020. On the other hand, by the end of all 4 average quarters of 2021, the demand value of the concerned product was nearly 24.5. Figure 1 has featured the demand graph based on 2020 and 2021 in contrast to the concerned product. In contrast to the projection of demand forecast concerning obtained graph, a situation of downfall has been noticed. Thus, it can be stated that product demand has been reducing every year.

Figure 1: "Dog Ball Launcher" Demand

Besides that, the demand forecast of the "Lightweight Dog Lead" product quarterly in contrast to 2020 has been coined through time series analysis of data. The forecast has projected that demand for the concerned product has robustly increased in every quarter of 2020 which can be observed in figure number 2.

Figure 2: "Lightweight Dog Lead" Demand 2020

The demand forecast concerning the quarters of 2021 is based on demand data for the "Lightweight Dog Lead" product. It can be observed that demand has fallen during quarter 2 of 2021 and a robust hike in quarter 3 has been noticed. Once again, concerned product demand has fallen in quarter 4 thus fluctuations of demand by the quarters of 2021 have been noticed.

Figure 3: "Lightweight Dog Lead" Demand 2021

Based on the quarterly demand of 2021 and 2020, a comparative forecast has been brought into practice in terms of the assessment of demand. In contrast to the forecast graph of figure number 4, it can be stated that the concerned product has made a stable demand forecast in 2020. Along with that, instability of demand has been noticed during the quarters of 2021.

Figure 4: Comparison of "Lightweight Dog Lead" Demand 2020 and 2021

Determination of forecast error concerning the current model

It can be stated that no such forecast errors have been noticed during the analysis of the product demand data of Pet Kingdom. However, in case the errors in the analysis of the forecast, a straightforward method supposes to use in terms of mitigation of errors. Therefore, the sum value of occurred errors in the demand forecast needed to be divided by the value of total demand (Cao and Li, 2019). Hence, errors in time series analysis have been resolved with the "Mean absolute percentage error" (MAPE) method.

Recommendation

Based on the forecast of demand, concerning both products, a few recommendations have been enlisted below.

• The "Dog Ball Launcher" products have failed in market place thus product needs to be reconsidered in terms of marketing and production approach.
• The "Lightweight Dog Lead" product has also reflected instability thus product needs serious attention.
• The company have to make attention towards brand visibility and awareness along with product quality.

Case study 2

Analyse and explain

The organisation is concerned about product and service quality and the line manager focuses on mitigating the issues as the organisation received several complaints. The complaints mainly belong to billing errors and variations in weight in branded products named dried dog food bags. The company made two different tables, one was for billing errors and another was for variations in product weight. Data shows that a total of 30 days of billing details were calculated and as per the details, several billing errors occurred in a range between 1 to 8 billing errors per day. The first 10 days of the month show that number of billing errors is 1 to 3 per day and the middle 10 days of the month capture 1 to 3 billing errors per day. Out of that maximum day, the organisation performs 3 billing errors per day. The last 10 days' observations include billing errors in the range of 3 to 8 per day. Out of that, the organisation concedes an average of 3 to 5 errors per day. Therefore, the number of billing errors increased gradually and organisations faced a rise in the number of complaints from customers. This impact on reputation and loyalty value of the business and can impact the long-term advantage of the organisation. In the last few days, the organisation considered a lot of errors and the frequency of the number of errors was 4 to 5 per day.

Another table shows the mean weight of the dog food samples and this table includes 27 samples. the first 14 sample values include a mean value of approx in between 44 to 45 kg and a range value between 0.37 to 0.42. This inconstant value of product weight includes a list of errors in product and service quality of the organisation and customers get unsatisfied in the business process. The last 13 samples show a mean value of approx in between 44.50 to 45.50 and a range value between 0.38 to 0.45. This observation also includes calibration errors in product weight and negative feedback from customer increase gradually.

3 sigma control

The two 3 sigma control charts include two different issues and billing errors considering a list of parameters such as day and the number of incorrect bills and as per that calculation of CL, UCL and LCL are completed. The control limit (CL) value for this issue is 2.933333 and upper control limit value (UCL) is 7.849177 and the lower control limit (LCL) is -1.98251.

This table considers variables of the sample mean and range value to calculate CL, UCL and LCL values to determine 3 sigma controls for this business. The CL value for this observation is 44.99096, the UCL value is 45.20051 and the LCL value is 44.78142.

The graph shows fluctuation in the control process as errors fluctuate towards UCL and LCL. Therefore, the attention of the organisation is not too much on product and service quality. 3 sigma indicates that the acceptability of this issue cannot be considered as errors cross the UCL limit and some errors also down largely towards LCL value. Another graph shows the sample almost reach the UCL value and crossed the LCL value. Therefore, crossing LCL values creates concern about business transparency and customer dissatisfaction occurs due to this.

Controllable measurements

The business process is not in control as 3 sigma shows a large fluctuation present in the graph. The crossing or almost crossing of UCL and LCL values indicates less attention of management to the products and services quality of the organisation. Transparency and ethics do not maintain properly and this impact reviews of the organisation.

Findings and Consequences

Billing errors can occur due to miscalculations, system issues, lack of employment numbers and software issues in the billing section of the organisations. Miscalculations can occur due to a lack of efficiency and lots of billing at a time, system issues can occur due to consideration of backdated billing systems (Afthanorhan et al. 2019). A lack of employees in the billing section can create this issue as the completion of several billings in a day requires available employees. In addition, software issues can occur due to not considering updated versions and modern financial tools in the organisation. Therefore, this issue impacts the business market, customer numbers, loyalty, brand value and competitiveness of the organisation.

This product weight issue can occur due to technical issues, calibration issues, efficiency issues and product quality issues. The technical issue can create this variety of weight ranges in the same product and calibration issues can occur due to not considering zero to zero measurement in the calibration process (Suhartanto et al. 2019). Lack of efficiency among employees can create a variety of product weights in packaging as consideration of decimal value needs to manage perfectly to get a constant product weight value. Product quality issues can create this issue as a single piece of product grain needs to maintain an average range of weight to maintain constant weight in packaging the same average number of contains in a package. Therefore, the customers get dissatisfied with business products and services and launch several complaints regarding this.

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Implementation of TQM

Total quality management (TQM) can deliver a positive impact on business performance as cultural values and long-term advantages can cover through this management. The productivity rate can get increased and the reduction of business wastes and issues can complete through this management. Adaptability and market image can develop through this and competitive advantage can develop through this management. Therefore, this organisation can get effective support in decreasing issues of billing errors and product weight variable issues to develop product and service quality of the organisation (Asq.org, 2023). This can deliver effective support in decreasing customer complaints and developing loyalty and market competitiveness of the organisation.

Recommendations

This can be recommended that the DMAIC model of six sigma can consider developing business productivity and quality (Asq.org, 2023). The six-factor analysis can deliver a positive impact on business performance. This organisation can get effective support in maintaining the six sigma model by mitigating billing errors and packaging quality issues and this mitigation can consider the verification process after the initial process to continue with the effective business process in the organisation.

References

Journals

Afthanorhan, A., Awang, Z., Rashid, N., Foziah, H. and Ghazali, P., 2019. Assessing the effects of service quality on customer satisfaction. Management Science Letters, 9(1), pp.13-24. http://m.growingscience.com/msl/Vol9/msl_2019_16.pdf

Cao, J., Li, Z. and Li, J., 2019. Financial time series forecasting model based on CEEMDAN and LSTM. Physica A: Statistical mechanics and its applications, 519, pp.127-139. https://doi.org/10.1016/j.physa.2018.11.061

Lawrence, K.D., 2019. Robust regression: analysis and applications. Routledge. https://books.google.com/books?hl=en&lr=&id=8z33DwAAQBAJ&oi=fnd&pg=PP1&dq=+forecasting+model++analysis+books&ots=SHQhwF4Ycr&sig=CtE9RgpOSV8xKW9OmeKRTcJfeBU

Li, X., Kang, Y. and Li, F., 2020. Forecasting with time series imaging. Expert Systems with Applications, 160, p.113680. https://doi.org/10.1016/j.eswa.2020.113680

Livieris, I.E., Pintelas, E. and Pintelas, P., 2020. A CNN–LSTM model for gold price time-series forecasting. Neural computing and applications, 32(23), pp.17351-17360. https://doi.org/10.1007/s00521-020-04867-x

Mills, T.C., 2019. Applied time series analysis: A practical guide to modeling and forecasting. Academic press. http://repo.darmajaya.ac.id/4630/1/Applied%20Time%20Series%20Analysis_%20A%20Practical%20Guide%20to%20Modeling%20and%20Forecasting%20%28%20PDFDrive%20%29.pdf

Suhartanto, D., Helmi Ali, M., Tan, K.H., Sjahroeddin, F. and Kusdibyo, L., 2019. Loyalty toward online food delivery service: the role of e-service quality and food quality. Journal of foodservice business research, 22(1), pp.81-97. https://nottingham-repository.worktribe.com/index.php/preview/1424137/Loyalty%20OFD_22_JFBR_Main%20document_REVISED_Pak%20Hary.pdf