You work for a small manufacturing company called Textiles Group that produces batting (cotton filling material) for products such as mattresses, blankets, quilts, etc.The company h
Scenario:
You work for a small manufacturing company called Textiles Group that produces batting (cotton filling material) for products such as mattresses, blankets, quilts, etc.The company has six different production lines and has three different work shifts. They run the factory 24 hours per day, 7 days a week.Due to some production problems, you are asked to provide different analyses of the weekly data downloaded from the company’s custom ERP (Enterprise Resource Planning) software. You download last week’s production data that provides the production details from Sunday to Saturday.
B. ???? Memorandum
When you have finished your data analysis, compose a memorandum to all Plant Managers that summarizes the details of your different findings/analyses. Include the PivotTables as embedded objects in your memo to provide visuals of each of your analyses.Note: If memo goes onto two pages or more, please include an appropriate continuation page header.Be sure to include all the criteria provided in the marking sheet/checklist below and upload your files where indicated in the project submission area.
NOTE: please look at the microsoft word document titled project for the complete assignment guide. the assignment requires two to be submitted, one for excel attached below and create a new Microsoft word for the memorandum.
SWI511 – Integrated Projects
Project 1: Integrating Word and Excel – 20% /40 Marks
Scenario:
You work for a small manufacturing company called Textiles Group that produces batting (cotton filling material) for products such as mattresses, blankets, quilts, etc.
The company has six different production lines and has three different work shifts. They run the factory 24 hours per day, 7 days a week.
Due to some production problems, you are asked to provide different analyses of the weekly data downloaded from the company’s custom ERP (Enterprise Resource Planning) software. You download last week’s production data that provides the production details from Sunday to Saturday.
( How to Begin: )
Download, review, and save the Production Excel file (production.xlsx) to begin . You will use this file for your project.
( Next Steps: )
A. Pivot Tables and Pivot Charts
Summarize/analyze the data in two different ways using PivotTables and Pivot Charts.
· Be sure your PivotTable numbers are formatted properly (eg. Currency style, etc.)
· Be sure your charts are formatted attractively and include key elements.
· Your analyses should be located in separate sheets within your Excel workbook.
( 1 | P a g e )
B. Memorandum
When you have finished your data analysis, compose a memorandum to all Plant Managers that summarizes the details of your different findings/analyses. Include the PivotTables as embedded objects in your memo to provide visuals of each of your analyses.
Note: If memo goes onto two pages or more, please include an appropriate continuation page header.
Be sure to include all the criteria provided in the marking sheet/checklist below and upload your files where indicated in the project submission area.
Criteria to be Included:
|
Item |
Marks |
|
|
☐ |
Composed a detailed memo that outlines the purpose of the Excel analyses: · professional composition/writing skills applied · appropriate subject line · relevant summary of each Excel analysis |
/10 |
|
☐ |
Formatted a memo using proper formatting rules. · proper spacing · proper guide words · continuation page header included if required |
/5 |
|
☐ |
Analysis 1: PivotTable used and PivotChart created that appropriately analyzes the data |
/5 |
|
☐ |
Analysis 2: PivotTable used and PivotChart created that appropriately analyzes the data |
/5 |
|
☐ |
All PivotTables and Pivot Charts are formatted properly to include key elements such as correct number formatting, axis labels, legends, title, data labels, etc. |
/5 |
|
☐ |
Insert each PivotTable as an embedded object into the appropriate sections in your memo. · inserted as embedded objects · sized consistently in memo |
/10 |
|
☐ |
TOTAL |
/40 |
|
☐ |
-2 additional marks for spelling/grammar errors up to a maximum of -10 marks |
,
Sheet1
| Production Line | Shift | Date | Lbs (pounds) Produced | Row Labels | Sum of Lbs (pounds) Produced | |
| Line 1 | Day | 22-May-22 | 2400 | Line 1 | 4959 | |
| Line 1 | Day | 24-May-22 | 1500 | Line 2 | 6414 | |
| Line 1 | Day | 25-May-22 | 300 | Line 3 | 33173 | |
| Line 1 | Day | 26-May-22 | 360 | Line 4 | 40812 | |
| Line 1 | Day | 27-May-22 | 199 | Line 5 | 49927 | |
| Line 1 | Day | 28-May-22 | 200 | Line 6 | 1547 | |
| Line 2 | Afternoon | 22-May-22 | 1100 | Grand Total | 136832 | |
| Line 2 | Afternoon | 24-May-22 | 700 | |||
| Line 2 | Afternoon | 25-May-22 | 320 | |||
| Line 2 | Afternoon | 26-May-22 | 136 | |||
| Line 2 | Afternoon | 27-May-22 | 263 | |||
| Line 2 | Afternoon | 28-May-22 | 214 | |||
| Line 2 | Day | 22-May-22 | 600 | |||
| Line 2 | Day | 23-May-22 | 400 | |||
| Line 2 | Day | 24-May-22 | 650 | |||
| Line 2 | Day | 25-May-22 | 195 | |||
| Line 2 | Day | 26-May-22 | 300 | |||
| Line 2 | Day | 27-May-22 | 205 | |||
| Line 2 | Day | 28-May-22 | 21 | |||
| Line 2 | Midnight | 24-May-22 | 350 | |||
| Line 2 | Midnight | 25-May-22 | 230 | |||
| Line 2 | Midnight | 26-May-22 | 205 | |||
| Line 2 | Midnight | 27-May-22 | 235 | |||
| Line 2 | Midnight | 28-May-22 | 290 | |||
| Line 3 | Afternoon | 22-May-22 | 500 | |||
| Line 3 | Afternoon | 24-May-22 | 2500 | |||
| Line 3 | Afternoon | 25-May-22 | 1900 | |||
| Line 3 | Afternoon | 26-May-22 | 1900 | |||
| Line 3 | Afternoon | 27-May-22 | 1905 | |||
| Line 3 | Afternoon | 28-May-22 | 2563 | |||
| Line 3 | Day | 22-May-22 | 200 | |||
| Line 3 | Day | 24-May-22 | 2100 | |||
| Line 3 | Day | 25-May-22 | 2310 | |||
| Line 3 | Day | 26-May-22 | 1300 | |||
| Line 3 | Day | 27-May-22 | 3000 | |||
| Line 3 | Day | 28-May-22 | 0 | |||
| Line 3 | Midnight | 24-May-22 | 2100 | |||
| Line 3 | Midnight | 25-May-22 | 2602 | |||
| Line 3 | Midnight | 26-May-22 | 3606 | |||
| Line 3 | Midnight | 27-May-22 | 2000 | |||
| Line 3 | Midnight | 28-May-22 | 2687 | |||
| Line 4 | Afternoon | 22-May-22 | 2001 | |||
| Line 4 | Afternoon | 24-May-22 | 2500 | |||
| Line 4 | Afternoon | 25-May-22 | 3000 | |||
| Line 4 | Afternoon | 26-May-22 | 2560 | |||
| Line 4 | Afternoon | 27-May-22 | 2501 | |||
| Line 4 | Afternoon | 28-May-22 | 3000 | |||
| Line 4 | Day | 22-May-22 | 2000 | |||
| Line 4 | Day | 24-May-22 | 2200 | |||
| Line 4 | Day | 25-May-22 | 1000 | |||
| Line 4 | Day | 26-May-22 | 2400 | |||
| Line 4 | Day | 27-May-22 | 3000 | |||
| Line 4 | Day | 28-May-22 | 0 | |||
| Line 4 | Midnight | 24-May-22 | 3000 | |||
| Line 4 | Midnight | 25-May-22 | 2650 | |||
| Line 4 | Midnight | 26-May-22 | 3500 | |||
| Line 4 | Midnight | 27-May-22 | 2500 | |||
| Line 4 | Midnight | 28-May-22 | 3000 | |||
| Line 5 | Afternoon | 22-May-22 | 2600 | |||
| Line 5 | Afternoon | 24-May-22 | 3000 | |||
| Line 5 | Afternoon | 25-May-22 | 3020 | |||
| Line 5 | Afternoon | 26-May-22 | 2960 | |||
| Line 5 | Afternoon | 27-May-22 | 2980 | |||
| Line 5 | Afternoon | 28-May-22 | 2656 | |||
| Line 5 | Day | 22-May-22 | 2500 | |||
| Line 5 | Day | 24-May-22 | 2300 | |||
| Line 5 | Day | 25-May-22 | 3002 | |||
| Line 5 | Day | 26-May-22 | 2659 | |||
| Line 5 | Day | 27-May-22 | 3500 | |||
| Line 5 | Day | 28-May-22 | 3050 | |||
| Line 5 | Midnight | 24-May-22 | 3100 | |||
| Line 5 | Midnight | 25-May-22 | 2900 | |||
| Line 5 | Midnight | 26-May-22 | 3650 | |||
| Line 5 | Midnight | 27-May-22 | 3000 | |||
| Line 5 | Midnight | 28-May-22 | 3050 | |||
| Line 6 | Day | 22-May-22 | 100 | |||
| Line 6 | Day | 24-May-22 | 130 | |||
| Line 6 | Day | 25-May-22 | 130 | |||
| Line 6 | Day | 26-May-22 | 135 | |||
| Line 6 | Day | 27-May-22 | 196 | |||
| Line 6 | Day | 28-May-22 | 206 | |||
| Line 6 | Midnight | 24-May-22 | 120 | |||
| Line 6 | Midnight | 25-May-22 | 123 | |||
| Line 6 | Midnight | 26-May-22 | 126 | |||
| Line 6 | Midnight | 27-May-22 | 125 | |||
| Line 6 | Midnight | 28-May-22 | 156 |
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