Exporting AusPaper’s paper products to more than 75 countries
“Pinnon Paper Industries” has a subsidiary named “AusPaper”. The Australian company, “Pinnon Paper Industries” is has an extended history in local production of products of paper. “AusPaper” trades products of papers to the two market segments like “the newspaper industry” and “magazine industry”. The newspaper industries that receive paper products are Herald Sun and Australian Financial Review. The magazine industries that obtain paper products are Homes & Gardens and Men’s Style magazine. The products are indirectly retailed to the customer and through a broker also.
The “AusPaper” company exports the paper-products to more than 75 countries of Indian subcontinent, Europe, Asia, Middle East, Latin America, and Africa. In last few years, “AusPaper” purchased more than 690000 tonnes of products as well as produced 619000 tonnes of paper products to the markets locally and overseas.
It causes a change in choices of end-consumers alike the preferences of online magazines, preferences of readers across newspapers and preference of social media. More of it, the consumers are developing to undertake a tactical move to have a strong strategic partnership with their customers. These days, “AusPaper” management textures the requirement for ensuring a stable customer base and supremely a solid strategic alliance with their consumers of magazine and newspaper industry. Not only is that “AusPaper” setting up to insert a formal process to be capable of prospecting future financial turnovers with the support of historical data, but also develop their business with proper process. In spite of successful operations and firm financial turn-overs over the last two decades, “AusPaper” company is forecasting a vivid shift within the forthcoming seven years in the business environment.
The concerns of “AusPaper” highlights the contracted managers of firms purchasing from “AusPaper”. It invigorated them to contribute in an online survey. The gathered data is accompanied by other assembled information. It tabulated the data regarding sales of “AusPaper” warehouse, its sale and manageable through the decision support system.
For the analysis in MS Excel, the researcher used the add-ins “RealStats”. The logistics regression and stepwise multiple regression are executed with the help of this add-ins. The researcher clarified his objectives from the analysis to summarise the major research questions. The analysis with the assistance of considered factors clarify consumer loyalty with operations of a firm. The information intends to discover crucial factors that estimate satisfaction of consumers perceived from previous purchases from “AusPaper”. The analysis aims to increase minute insights into variables that estimate the “likelihood of “AusPaper” customers building strategic alliance” with the firm. The report analytically focuses to develop a predictive model to anticipate the turnover of AusPaper in the 2nd, 3rd and 4th quarters of 2017. For the forthcoming financial year 2017, the analysis would demonstrate whether “AusPaper” would be in a decent position or not.
Establishing solid strategic alliances with newspaper and magazine industry customers
200 perceptions along wide 18 factors are included in the data file. Mostly two kinds of data are resent in this database. First one is the perception of performance of “AusPaper” on 13 characteristics that are measured utilizing 0-10 scale where “0” denotes “Poor” and “10” is “Excellent”. The other information links to include outcomes and business connections, for example, amount of consumers and length of purchase association and in addition quarterly turnover operations of “AusPaper”.
Task 1
Summary of frequencies of Strategic alliance:
- The frequency distribution and frequency table of the variable “Extent to which the customer/respondent perceives his or her firm would engage in strategic alliance/partnership with “AusPaper”” determines that out of 200 samples, 114 samples incurred the strategic alliance or strategic partnership.
- On the other hand, 86 people conveyed that they are engaged in strategic alliance or relationship. The percentage share of these two cases are 57% and 43% respectively
Summary of Customer satisfaction:
- The average customer satisfaction with past purchases from “AusPaper “is found to be 6.95.
- The standard deviation indicates the spread of the distribution of customer satisfaction with purchases from “AusPaper” that provides the value 1.24.
- The middle most value in terms of median of the satisfaction data set is 7.05.
- The location measures refer that 25% of the bottom values are less than 6 and 25% of the top values are more than 7.9.
- The mode is 5.4 that indicates that the frequency is found highest when customer satisfaction rate 5.4.
- The minimum customer satisfaction of previous purchases from “AusPaper” is 4.7 and maximum customer satisfaction of previous purchases from “AusPaper” is 9.9.
- Hence, the customer satisfaction with previous purchases from “AusPaper” ranges between 5.2.
- The customer satisfaction level of slightly rightly and positively skewed. The graphical visualization indicates that its left tail is longer than its right tail.
Task 2.1
The 15 samples are detected for analysis and tabulated samples are attached in the appendix section of the assessment file. These chosen variables are – Cstmr_Type, Indst_Type_Dummy, Size_Dummy, Region_Dummy, Distn_Sys_Dummy, Prdct_Qual, E_Comm, Cmplnt_Supp, Advert, Prdct_Line, Image, Pricing, Warranty, New_Prdct, Billing. “Sats” is assumed to be a dependent variable. All the variables except “Sats” are assumed to be independent variables. The analyst is interested to build a predictive model with the single dependent and multiple (fourteen) independent variables.
Task 2.2.
The value of R2 is not authenticate in this analysis as it might contain multi-collinearity. Therefore, adjusted R2 is the suitable measure of finding association among variables and goodness of fit. The predictive multiple regression model refers that altogether the 14 predictors explain 83.3% variability of the response variable. Stepwise regression ultimately selects 8 significant factors that are customer type, dummy of size, dummy of distribution system, product quality, complaint residuals, product line, image and new product.
From ANOVA table of the multiple regression model, the analyst observes the p-value of the whole model (0.0). Hence, the model is fitted well and the predictors all together predict the model well with 95% probability. The significant p-values of the independent factors refer that four factors have p-values less than 0.05. These variables are- Cstmr_Type (p-value = 0.0000), Size_Dummy (p-value = 0.0000), Distn_Sys_Dummy (p-value = 0.0000), Prdct_Qual (p-value = 0.0000), Cmplnt_Res (p-value = 0.0000), Prdct_Line (p-value = 0.009), Image (p-value = 0.0000) and New_Prdct (p-value = 0.016). Hence, these four variables have linear and statistical significant association with dependent variable “Sats” at 5% level of significance. Rest of all the variables have insignificant effect on the response variable as the calculated p-values for those variables are less than 5%. The negative and positive slopes or beta values refers the negative or positive association between that the variables. All the factors have positive association with dependent variable.
Surveying contracted managers of firms purchasing from AusPaper
Task 2.3.
The analyst in the next part of the analysis caused that the depth and breadth of “Product line” of “AusPaper” is a significant estimator of the variable “Customer Satisfaction”. The previous analysis referred that the strength of this association may vary according to the location of customers.
Among 200 consumers, 81 customers are from in Australia and New Zealand. 119 consumers are from outside Australia and New Zealand. Three multiple regression models are executed with the help of four variables that are product line, region, customer satisfaction and interaction effect of region and product line.
Here, 0 = Outside ANZ and 1 = ANZ region. The multiple regression model has considered the interaction variable of “Region” and “Product line” as predictor variables. The interaction variable is calculated multiplying two predictor variables. Hence, the predictor variables are “Region”, “Product line” and “Interaction effect”. The response variable is as usual as “Customer satisfaction”.
In the multiple regression model, all the three variables Product line, Region and Interaction are linearly, positively and significantly associated with “Customer satisfaction” where product line has co-efficient is 0.865218 and p-value is 0.0. Here, “Region” has co-efficient 2.927282 and significant p-value is 0.0003 and “Interaction” has co-efficient has (-0.55504) and significant p-value is 0.0. Therefore, among the three variables “Product line”, “Region” and the “Interaction effect”, “Product line” and “Region” have linear significant influence on the dependent variable “Customer satisfaction”. The regression model has p-value less than 0.05. Hence, according to the most suitable predictive model, all the three predictors most suitably predicts the dependent variable – Customer satisfaction.
Task 3.1.
The main target of the data analysis is to finalise an advanced predictive model utilising key variables that impact the “likelihood of building a strategic alliance of partnership” with “AusPaper”. The advanced model building analysis takes into account five factors that are “Product Quality”, “Product Line”, “Personnel Image”, “Flexibility” and “Competitive Pricing”.
The response of logistic regression model is the dichotomous binary variable used as “Strategic Alliance”. In the first model, the predictor variables are “Personnel Image” and “Product Line”. In the second model, the predictor variables are “Product Quality” and “Price Flexibility”. The probabilities of strategic alliance are found with the two logistic models.
Task 3.1.a.
“Personnel Image” and “Product Line” are the predictor variable and “Strategic Alliance” is dependent variable in the predicted logistic regression model. The predictive regression model could be stated as-
………(1)
Analyzing data using RealStats add-ins and multiple regression models
The p-values of two predictor variables, “Product Line” and “Personnel Image” in this logistic regression model are both 0.0. As, p-value is less than level of significance, both the dependent variables, “Personnel Image” and “Product Line” are significant explanatory variables at 5% level of significance.
Task 3.1.b.
“Product Quality” and “Price Flexibility” are predictor variables and “Strategic Alliance” is dependent variable in the predictive regression model. It is given as-
…………. (2)
The calculated p-values of two predictor variables “Product Quality” and “Price Flexibility” in the logistic regression model are 0.0 in both cases. As, calculated p-values are less than level of significance, both the explanatory variables are significant at 5% level of significance.
The futures prediction of “AusPaper” is necessary for the growth of business and sales. To predict the future turnover amount, the analyst considered “Time” as explanatory variable and “Turnover ($’000)” as response variable. The variable “Turnover ($’000)” indicates for total turnover amount within the time span. The variable “Time” is actually the chronological frequency of quarters starting from 1st quarter of 2008 to 1st quarter of 2017. The estimated turnover amounts of the 2nd, 3rd and 4th quarters of 2017 are found to be $4531.955, $4991.969 and $5043.73 respectively
As an outcome, the quarterly turnover amount of “AusPaper” also has enlarged significantly. The quarterly turnover amounts of 2nd, 3rd and 4th quarter are more in 2017 than any other quartiles of previous year. Same as first quarter, the turnover amounts of other quarters also have grown effectively in 2017. The future of “AusPaper” seems to be promising. The research analysis concludes that the consumers of “AusPaper” has a high satisfaction level. Most of the consumers are allied in strategically. Strategic alliance, product quality, E-communication and Image has significant influence on the Customer satisfaction. Not only that, “Product line”, “Region” and their “Interaction” effect has significant relevance to the satisfaction of customers. “Image” and “Product line” significantly predicts the types of Strategic alliance of “AusPaper”. “Product quality” and “Price flexibility” of “AusPaper” also significantly influence the dependent variable “Strategic alliance”.
As per discussion, the analysis determines the predicted possibility of building strategic alliance with varying levels of “Product Quality” and “Product Flexibility” in several perceptions. Conversely, the constant proportion to the “Product Line” and “Personnel Image” for effecting “Customer Satisfaction” is also asserted. The superior values of independent variables are growing “Customer Satisfaction” and types of “Strategic alliance” with “AusPaper”. The organization should focus on the significant issues that strongly impacts the strategic alliance and customer satisfaction.
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