Write a
tentative outline and introductionfor the report(in the attachment).
And reformat the report to the following:
Your formal report follow the guidelines addressed in class and must include:
1.
Letter (Memo) of Transmittal
2.
Title Page
3.
Executive Summary
4.
Table of Contents/List of Illustrations
5.
Introduction
6.
Discussion or “Body” (include visuals with written discussion)
a.
Establishment and Example of Problem (at least 2 visuals)
b.
Statement of Solution
c.
Example of Solution [include researched, real example(s)] (at least 2 visuals)
d.
Solution Implementation (include financial discussion) (at least 3 visuals)
e.
Potential Results (at least 1 visual)
7.
Conclusion and Recommendations
8.
Reference Page for Research Documentation
After you done, please submit 2 files. One is the entire work,another one is theatentative outline and introductionfor the report.
EGL310 - Technical Writing Incorporation of Visuals into Reports PURPOSE The objective to carry out this activity is to understand the business problem of an organization with the help of visuals & report. We have considered a sales transactional dataset of one of the chain of Superstore at US. The dataset consists of transactional id and the product corresponding to that particular ordered, mode of transport. Each product is categorised into particular categories and subcategories. Figure 1 – Sample snapshot of Dataset VISUALIZATION: 1) BAR GRAPH: Scope – We shall understand the pattern of sales in various subcategories across different categories. Figure 2 – Bar Graph Conclusion (Category wise) - 1) Furniture- Chairs has maximum share in the sales, but there is not much difference between the Chairs and the Book cases. 2) Office Supplies- Storage has maximum share in the sales. Additionally, around 50% of the share in Office supplies is accounted by Storage and Appliances. There is a huge gap in the contribution between the first Two and the others. 3) Technology- Phones has maximum share in the sales and followed by the Copiers- which together account for 50% of total Technology sales. Recommendation – It is required to focus on Office supply category where we have very small contribution within the sub-categories as compared to other 2) QUADRANT ANALYSIS: Scope - Here we will try to find out the discounting pattern on various products & whether the discounts given on various products is really a profitable deal or not. By doing the Quadrant analysis, the products are segregated into four quadrants, according to the Average Discount and Profit Ratio (Profit per unit of sale) Figure 3 – Quadrant Analysis Conclusion - The products falling into Upper Right Quadrant are- Higher Discount and High Profit Ratio The products falling into Upper Left Quadrant are- Lower Discount and High Profit Ratio The products falling into Lower Right Quadrant are- Higher Discount and Low Profit Ratio The products falling into Lower Left Quadrant are- Lower Discount and Low Profit Ratio Recommendation – Therefore, we need to concentrate on those particular products which are falling into lower right quadrant- where the discounts are high but the profits on those particular products are low. Considering the above facts, it is not a favourable condition to give discounts on these products. (Rita L. Sallam, 2011) 3) PARETO ANALYSIS: Scope - Here we will identify those products which are contributing the most to our total sales. Figure 4 – Pareto Analysis Conclusion - By drawing the Two -coloured Pareto chart we get to know how many products are contributing to the 75% of the total sales. These products can easily be identified and option to provide offers to these products can be explored. Besides this, it helps in identification of the products which are contributing very less share in the total sales. These products needs’ further concentration and identify whether it would be a feasible option to promote these products. Recommendation – There is a dire need to focus on the products which are contributing just 20% of the share in total sales (Baldwin, 2019.1) 4) STACKED CHART: Scope – We will try to analyse the delivery lead time in each subcategory. The Standard delivery time is 3 days from the date Order is placed Figure 5 – Stacked Chart Conclusion - The Orange portion in each graph shows the Percentage of orders which takes more than 3 days to be delivered within each Sub category. It can be seen from our analysis that within each subcategory the percentage of orders delivered beyond the 3 days is almost same that is around 30%. So, we can conclude that the products being delivered late is not due to any particular product, so there might be a bigger issue with overall supply chain that is being currently followed to Ship the Products. Recommendation - It is highly recommended to do the Root Cause Analysis & check store’s Supply Chain Operations & bridge the gap for smooth running of operations leading to overcome the delay in delivery lead time 5) BAR CHART: Scope - Any data can be analysed according to different granularity level. For e.g. this particular data set which are dealing with has Regions and their corresponding sales. In this particular section we want to analyse how the products preferences moves across the various regions for Top 10 products. We have drawn a Bar graph and added our Top 10 products Set on the filters shelf. Further to gain insights for Each region, we add Regions to filter shelf and convert the Region filter into the context filter- which means for each Region get me the Top 10 Products by Sales. Figure 6 – Bar Chart Conclusion - It can be seen that the products popularity is different across the various regions, but Phones in the Technology segment is at the Top in almost all the region under consideration. This Analysis helps us to gain insights about the Product popularity for each Region, which in turn helps in the specific strategy for each Region. Recommendation - As product popularity is different across various regions so there is a need for customized strategy for each region rather than following a standard SOP for all regions 6) LINE DIAGRAM: Scope - Data can be interpreted in several ways as per the specific requirement and can be used to gain further insights to get the real picture of the current situation. Here we will be going to get a picture of how good are bad the various Regions are performing on the Sales front. Figure 7 – Line Chart Conclusion - Here we can easily view that there is a huge gap Between the Central and the south region The Sales in the Central region is the highest in all the Years under consideration. Moreover, as the Slope of the central region is upward sloping, so we can conclude that sales in the Central region is increasing overtime. But as far as the other regions are concerned, the sales is either increasing at a very slow pace or either stagnated, particularly for the Caribbean and Canada Region. Recommendation - Caribbean and Canada Region are performing below expectations. Respective managers need consultation. 5W2H analysis (Pacaiova, June 2015) can done based upon external & internal parameters in the respective regions References Aldowah, H. (2019). Educational data mining and learning analytics for 21st century. Educational data mining and learning analytics for 21st century. Baldwin, D. (2019.1). Mastering Tableau . Packt Birmingham-Mumbai. Pacaiova, H. (June 2015). Analysis & Identification of Nonnonforming Products by 5W2H method. International Quality Conference, (pp. 33,34). Rita L. Sallam, J. R. (2011). Magic Quadrant for Business Intelligence Platforms. Gartner. Ritesh Chugh, S. G. (2013). Why Business Intelligence?: Significance of Business Intelligence Tools and Integrating BI Governance. Order IDOrder DateShip DateShip ModeCustomer NameSegmentStateCountryMarketRegionProduct ID CA-2012-12489131-07-201231-07-2012Same DayRick HansenConsumerNew YorkUnited StatesUSEastTEC-AC-10003033 IN-2013-7787805-02-201307-02-2013Second ClassJustin RitterCorporateNew South WalesAustraliaAPACOceaniaFUR-CH-10003950 IN-2013-7124917-10-201318-10-2013First ClassCraig ReiterConsumerQueenslandAustraliaAPACOceaniaTEC-PH-10004664 ES-2013-157934228-01-201330-01-2013First ClassKatherine MurrayHome OfficeBerlinGermanyEUCentralTEC-PH-10004583 SG-2013-432005-11-201306-11-2013Same DayRick HansenConsumerDakarSenegalAfricaAfricaTEC-SHA-10000501 IN-2013-4236028-06-201301-07-2013Second ClassJim MitchumCorporateNew South WalesAustraliaAPACOceaniaTEC-PH-10000030 IN-2011-8182607-11-201109-11-2011First ClassToby SwindellConsumerWellingtonNew ZealandAPACOceaniaFUR-CH-10004050 IN-2012-8636914-04-201218-04-2012Standard ClassMick BrownConsumerWaikatoNew ZealandAPACOceaniaFUR-TA-10002958 CA-2014-13590914-10-201421-10-2014Standard ClassJane WacoCorporateCaliforniaUnited StatesUSWestOFF-BI-10003527 CA-2012-11663828-01-201231-01-2012Second ClassJoseph HoltConsumerNorth CarolinaUnited StatesUSSouthFUR-TA-10000198 CA-2011-10298805-04-201109-04-2011Second ClassGreg MaxwellCorporateVirginiaUnited StatesUSSouthOFF-SU-10002881 ID-2012-2840219-04-201222-04-2012First ClassAnthony JacobsCorporateKabulAfghanistanAPACCentral AsiaFUR-TA-10001889 SA-2011-183027-12-201129-12-2011Second ClassMagdelene MorseConsumerJizanSaudi ArabiaEMEAEMEATEC-CIS-10001717 MX-2012-13001513-11-201213-11-2012Same DayVicky FreymannHome OfficeParanaBrazilLATAMSouthFUR-CH-10002033 IN-2013-7395106-06-201308-06-2013Second ClassPeter FullerConsumerHeilongjiangChinaAPACNorth AsiaOFF-AP-10003500 ES-2014-509995531-07-201403-08-2014Second ClassBen PetermanCorporateIle-de-FranceFranceEUCentralOFF-AP-10000423 CA-2014-14356703-11-201406-11-2014Second ClassThomas BolandCorporateKentuckyUnited StatesUSSouthTEC-AC-10004145 ES-2014-165177408-09-201414-09-2014Standard ClassPatrick JonesCorporateTuscanyItalyEUSouthOFF-AP-10004512 DATA SET OF SUPERSTORE SALES