supply chain management on excel

Assignment Extra Mail Merge: combining Excel database and Word document Mail Merge is a function in Microsoft Office that combines two or more documents to create a customized output from a template. A common usage is for creating personalized letters or emails, where a template is created, with one or more “fields” for the recipients. The template, usually in MS Word, may say something like "Dear <Given Name>…", and when combined with another database (from Excel, for example), the mail merge creates a letter for each qualifying record in the database, so it appears that the letter is customized to each individual. Another common usage is for creating address labels from a customer database for mass mails. Here’s an example of a mail merge process, from a Word file and an Excel database to a completed, customized document:

Assignment: Create an email to people who have low absence rate (<1%) and who were born in December. You may need to think of a few intermediate steps to select the recipients. There is more than one way of doing it. Save the final document in Word and describe how you did it.

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A B C D E F G H I J K L EMPLOYEE DATABASE

LAST REVISION: 11/23/15

Basic Information Evaluations Employee ID First Name Last Name Birth Date Start Date MAT WA Absence Ability Accuracy Potential Loyalty

23855465 George Aguirre 03/16/54 08/21/81 88 Good 1.62% Poor Good Poor Good 10024136 Vanessa Anderson 05/05/59 09/14/84 91 Fair 1.24% Poor Good Poor Good 10017620 Darcie Biby 04/14/53 04/12/85 95 Good 0.64% Poor Good Poor Good 18002251 Robert Black 11/19/57 05/17/85 97 Fair 0.24% Poor Good Poor Good 16511510 Darren Brecht 05/12/53 07/15/88 87 Good 1.88% Good Fair Good Poor 18016263 Ricardo Bryan 07/30/57 04/21/89 72 Poor 1.90% Good Poor Good Fair 17991045 Daniel Bybee 03/17/53 09/30/83 83 Good 1.82% Good Good Good Good 23845991 Mark Carstensen 07/31/56 12/24/82 86 Good 1.70% Good Good Good Good 10002811 Sanjay Shiroma 07/01/58 06/17/83 82 Good 0.88% Good Good Good Good 10003198 Heath Jordan 08/03/54 12/18/87 96 Good 1.26% Good Good Good Good 21498602 Michelle Chiou 12/18/56 05/13/83 91 Good 1.16% Good Fair Good Poor 10005216 Vineeta Chisman 06/02/59 07/22/83 76 Good 1.30% Good Poor Good Fair 17960690 Michael Chung 09/25/56 11/17/89 99 Poor 1.12% Good Good Good Good 10003198 Starchild Scott 10/21/58 08/04/89 97 Good 1.22% Good Good Good Good 17961264 Rafael Das 05/07/57 10/15/82 85 Fair 0.78% Good Good Good Good 10012598 Kimberly Tsai 12/20/55 07/11/86 92 Good 0.50% Good Good Good Good 22456870 Robert duLac 10/22/57 11/13/87 95 Poor 0.44% Good Fair Good Poor 10013138 Deana Lee 08/04/53 07/02/82 100 Good 0.14% Good Good Good Good 10013235 Miguel Chavez 01/15/57 06/26/87 92 Good 1.44% Good Good Good Good 21698523 Patricia Embrey Jr. 02/12/57 10/28/88 97 Fair 0.26% Good Good Good Good 18009503 Hale Fisher 06/08/54 11/08/85 70 Good 0.12% Good Fair Fair Poor 10023244 Trevor Gaare 04/07/59 08/01/80 71 Poor 0.04% Good Good Good Good 10019985 Erika Ekins 10/27/53 02/10/89 94 Good 0.40% Good Good Good Good 10022654 Kale Pingel 05/10/55 10/30/81 99 Good 0.16% Good Good Good Good 17990872 Leticia Gomez 04/10/56 05/28/82 74 Good 1.72% Fair Good Fair Good 18013379 David Gossman 06/09/53 04/03/81 76 Good 1.20% Fair Good Fair Good 17992341 Kelly Green 07/05/55 11/23/84 78 Fair 0.74% Fair Good Fair Good 25136485 Evan Gustafson 11/24/53 08/30/85 79 Good 0.52% Fair Good Fair Good 17960289 RC Gustafsson 06/04/57 06/21/85 100 Good 0.80% Fair Fair Fair Poor 10024048 Caitlin Haag 09/02/52 09/25/81 85 Fair 0.58% Fair Poor Fair Fair 17955859 Kiprono Hadibrata 02/14/56 08/06/82 82 Good 0.06% Fair Good Fair Good 23945471 Ayumi Hamilton 02/19/52 09/04/87 87 Good 1.98% Poor Good Poor Good 17978635 Byron Harper 08/05/52 07/31/87 90 Poor 1.38% Poor Good Poor Good 17959430 Russell Hartman 03/11/58 06/30/89 94 Good 0.90% Poor Good Poor Good 22235481 Paul Haselwood 03/12/57 01/04/80 86 Good 2.00% Good Fair Poor Poor 20153246 Brenda Hess 05/13/52 02/01/85 71 Good 0.10% Poor Poor Good Fair 17990162 Samuel Holtzclaw 04/08/58 12/13/85 80 Good 1.94% Good Good Good Good 23511289 Brendan Hutchinson 06/10/52 12/04/81 82 Fair 1.84% Good Good Good Good 17982462 Lawrence Ianiri 03/13/56 06/01/84 85 Good 1.74% Good Good Good Good 23645448 Anthony Imparato 01/22/52 01/13/84 89 Good 1.50% Good Good Good Good 21465381 Sandi Johnson 05/06/58 12/09/83 90 Fair 1.28% Good Fair Good Poor 18007961 Ian Johnstone 08/31/54 12/02/88 75 Good 1.42% Good Poor Good Fair 10024167 Jernique Moore 02/15/55 09/05/80 92 Good 0.92% Good Good Good Good 10024286 Jorge Pineda 03/15/55 11/07/86 81 Good 0.34% Good Good Good Good 10047506 Dana Minier 02/17/53 04/23/82 91 Good 1.46% Good Good Good Good 26015482 Thomas King 01/13/59 06/06/86 84 Poor 0.84% Good Good Good Good 10483506 Hans Krohn 07/06/54 02/21/86 94 Good 0.56% Good Fair Good Poor 18015507 Willie LaBore 07/28/59 10/24/86 79 Fair 0.70% Good Poor Good Fair 23598410 Lon Langstaff 06/05/56 02/06/87 91 Fair 0.54% Good Good Good Good 10540506 Kevin Garcia 08/30/55 03/14/80 98 Good 1.40% Fair Good Good Good 10018608 William Lauprecht 06/30/59 03/13/87 96 Poor 0.30% Good Good Good Good 16750256 Ricardo Kilgras 08/27/57 12/28/84 80 Good 1.02% Good Good Good Good 23105843 Jeffrey Lopez 12/21/54 08/15/86 84 Fair 1.64% Poor Fair Poor Poor 20013456 Glenn Lua 04/13/54 02/12/82 69 Good 2.02% Poor Poor Poor Fair 17959146 Robert Martin 09/24/57 03/19/82 93 Good 1.00% Poor Good Poor Good 17990636 Ana Matura 10/30/51 10/09/87 98 Good 2.06% Good Good Good Good 23856550 Amanda Matura 10/02/51 04/17/87 96 Poor 0.42% Poor Good Poor Good 23788951 Douglas McDaniel 09/29/53 09/23/88 100 Good 1.96% Good Good Good Good 18017454 Travis McGlynn 03/10/59 05/26/89 88 Good 1.76% Good Fair Good Poor 23459121 Denise Meneely 09/01/53 11/19/82 73 Fair 1.66% Good Poor Good Fair 17986832 Bassam Meyers 03/18/52 08/26/83 88 Fair 1.56% Good Good Good Good 17954928 Sandra Mocan 06/03/58 08/10/84 95 Good 1.32% Good Good Good Good 17055240 Gustavo Mitchell 05/11/54 01/22/88 93 Poor 1.36% Good Good Good Good 17961060 Chris Castillo 11/25/52 10/04/85 90 Good 1.48% Good Good Good Good 17962213 Jason Ting 11/23/54 05/06/88 89 Good 0.62% Good Good Good Good 23445521 Tanya Moran 12/16/58 01/06/89 77 Good 1.06% Good Poor Good Fair 17974568 Adrian Edmiston 08/07/51 10/10/80 80 Good 0.46% Good Good Good Good 17980826 Kevin Goller 09/27/55 05/23/80 83 Good 1.18% Fair Good Good Good 18010739 Alex Lau 09/04/51 11/28/86 93 Good 0.48% Good Good Good Good 17963361 Kenneth Peltier 08/02/55 11/14/80 90 Poor 0.60% Good Good Good Good 12345987 Robert Perez 02/11/58 12/19/80 96 Fair 0.20% Good Fair Good Poor 18014820 Sonny Nam 09/23/58 09/08/89 83 Good 0.86% Good Good Good Good 22238911 Robert Walter 12/17/57 03/28/86 85 Good 0.00% Good Good Good Good 23744786 Sheldon Poole 08/26/58 04/08/83 86 Good 2.08% Poor Good Poor Good 18014800 Joshua Price 04/12/55 06/12/81 89 Good 1.58% Poor Good Poor Good 18002344 Karen Renoult 06/07/55 05/08/81 92 Good 1.10% Poor Good Poor Good 21900251 Kevin Robertson 10/25/55 09/10/82 85 Good 0.32% Poor Fair Poor Poor 10020744 Tamara Rodriguez 11/18/58 02/17/84 70 Good 1.78% Poor Poor Poor Fair 17987942 Bradley Roesler 04/15/52 06/27/80 99 Good 2.04% Good Good Good Good 17986983 Geoffrey Rohlfing 01/19/54 03/23/84 81 Poor 1.86% Good Good Good Good 23744875 Francis Rojano II 12/22/53 04/18/80 84 Good 1.80% Good Good Good Good 23891320 Kileen Rokitta 11/22/55 07/17/81 87 Poor 1.60% Good Good Good Good 25415110 Chong Ruiz 10/28/52 03/08/85 89 Poor 1.52% Good Fair Good Poor 17959961 James Saleh 10/26/54 09/19/86 74 Good 1.54% Good Poor Good Fair 24655410 Linda Schnagel 05/08/56 03/17/89 94 Fair 1.34% Good Good Good Good 22354810 Shanta Oza 07/29/58 12/22/89 88 Good 0.66% Good Good Good Good 17981023 Timothy Segal 02/10/59 03/04/83 100 Fair 1.08% Good Good Good Good 22589843 Peter Diaz 04/09/57 06/10/88 87 Good 0.72% Good Good Good Good 18015899 Carrie Shively 09/30/52 11/04/83 93 Good 0.68% Good Fair Good Poor 10024224 Andrew Stumpf 11/27/51 01/17/86 78 Poor 0.82% Good Poor Good Fair 23103654 Reid Karchesy 07/02/57 08/19/88 98 Good 1.14% Good Good Good Good 23514110 Jacqueline Van Horn 09/28/54 05/02/86 98 Good 0.18% Good Good Good Good 23541680 Connie Valevo 01/20/53 01/02/87 95 Good 0.36% Good Good Good Good 23910548 Jennifer Otero 01/18/55 02/27/81 86 Good 0.76% Good Good Good Good 23148973 Mark Vega 07/03/56 02/26/88 97 Good 0.08% Good Fair Good Poor 18006148 Christine Walsh 12/23/52 05/22/87 82 Good 0.22% Good Poor Good Fair 25146583 Robert Williams 01/14/58 01/08/82 99 Good 1.04% Good Good Good Good 17957408 Andrew Warmuth 12/25/51 04/27/84 73 Good 1.92% Fair Good Fair Good 12549506 Kimberly Westphal 01/17/56 04/01/88 75 Good 1.68% Fair Good Fair Good 17997171 Geoffrey Wilborn 02/16/54 07/26/85 77 Poor 0.96% Fair Good Fair Good 25800215 David Collins 07/07/53 01/28/83 81 Good 0.98% Good Good Good Good 10004808 Mary Woods 08/28/56 02/08/80 84 Poor 0.94% Fair Poor Fair Fair 17961141 Michele Yocum 11/20/56 01/23/81 80 Good 0.38% Fair Good Fair Good 21548788 Brian Young 07/08/52 10/19/84 81 Good 0.28% Fair Good Fair Good 10001305 Michael Zavala 10/23/56 07/06/84 83 Poor 0.02% Fair Good Fair Good