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INVENTORY IN SMALL LIBRARIES ATLA 2014 Meagan Morash Booth University College.

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Presentation on theme: "INVENTORY IN SMALL LIBRARIES ATLA 2014 Meagan Morash Booth University College."— Presentation transcript:

1 INVENTORY IN SMALL LIBRARIES ATLA 2014 Meagan Morash Booth University College

2 SO WHY DO IT? Too few staff Software too expensive or too complicated Can’t shut library for the time it would take Not worth the time investment- ROI too low

3 Benefits  Greater % of items in their proper place – it is where we say it is  Greater patron satisfaction  Less staff time spent looking for items out of place  Accurate collection count  Costs less than replacing titles listed as missing  See opportunities for weeding and/or collection development Are these benefits actually realized?

4 WHAT THE RESEARCH SAYS Patron Satisfaction – 90% drop in patron missing item reports over 5 year period (Nixon, 2009), Ernick, 2005) Replacement costs – $11,000 vs $159,000 to replace (Sung, Whisler, Sung, 2009) Weeding & collection development/assessment – Multi- tasking & targeting scarce $ more wisely (Shouse & Teel, 2006; Teel, 2008)

5  Time is minimal, thanks to technology 2 ½ hours per day for 5000 items  Software needed can be what you use everyday in an Office Suite package  Any basic text program, Notepad, Word, etc.  Excel 2007  Hardware – scanner & basic device (laptop, tablet or even a smart phone)

6 FAIRBANK MEMORIAL LIBRARY  No systematic inventory ever done  Entire collection re-barcoded in 2003 as part of switch to mainstream consortial ILS  ILS inventory package too expensive  60,000 items | 3 staff | 1 barcode reader  MS Office suite of software

7 SET- UP  Divide collection into blocks of 10 bays ( items) & assign staff to specific blocks  Used scanning tracking document in Word – same document our assistants use for shelf-readingscanning tracking document  Schedule staff – calendar inside tracking duo-tang and calendar booking function on  Could also use Google docs when dealing with larger staff  Train one or more staff to manipulate data in Excel  Done during the early summer when circ is low but staff have not yet taken holidays

8 PROCEDURE  Used laptop & USB scanner (the one from the circ desk!) to collect barcodes  One staff member used a personal tablet  Recorded section #, time to scan shelf, staff name, date scanned  Scanned barcodes into a text file or Word as a list, each barcode on a separate line  Save after every 4 shelves.  Naming convention - Bay #-Bay #.txt

9  Barcodes are located in the upper right corner of the back of the book, so scan times were pretty minimal – 1 hour 10m on average, 1.6 seconds per title  Barcodes located inside books will take significantly longer  Laptop was connected to network by Wi-Fi, so the file didn’t need to be uploaded at all. Tablet file needed to be saved to USB or ed and then saved to a network drive

10  Staff member then created a review file (shelf-list of that block) in Millennium containing the following information  Call #  Author  Title  Copy #  Status  Date due (our system needed this & status as the date due is stored separately)  Barcode  Time to create file – 5 minutes. A saved query made this quick and made sure staff didn’t forget a field

11  The review file MUST be created as soon as possible after scanning the section in order to accurately capture the check-out status of items  Export review file with all listed fields as a delimited file or.csv file Export review file  Now you have the shelf-list and a list of barcodes, in two different files.  Here comes the fun part.

12  Open delimited file in Excel and save as.xls or.xlsx  We named each file for the block it contained – First call # - Last call # (bay #-Bay #)  Highlight any rows containing errors, such as missing barcodes  Open matching.txt file and copy all barcodes  Paste into new column in the Excel file, to the right of the shelf-list barcode column

13 You now have a file that looks like this: Stacks s11_s20 inventory - Demo.xlsx

14  Excel has a function called conditional formatting  Use it to remove duplicates from the inventory list  Then select the two barcode columns and apply Conditional Formatting to highlight duplicates  Every barcode that appears in both columns will change to the designated colour

15 Stacks s11_s20 inventory - Demo.xlsx

16 With this simple command we can now quickly find the following:  Items on the shelf but still signed out to a patron (anything with a due date, but coloured highlighting  Items that are in mending, display, etc. (anything with a status showing that it is temporarily somewhere else)  Items that are supposed to be on the shelf, but were not found (any barcodes in the first barcode column that are still black on white)  Items that were on the shelf, but weren’t supposed to be there (any barcodes in the second barcode column that are still black on white)

17  You can use the filter function in Excel to show only the non-coloured barcodes, items with different statuses etc. Stacks s11_s20 inventory - Demo.xlsx  The reasons for why an item isn’t there could be varied.  We created two lists to take to the shelves –  things that weren’t there (call #, barcode & copy #)  items that were there, but shouldn’t be (barcode & call # of the item scanned before and after it)

18  One trip to the shelves with these two lists found 70% of the items  Item might have been missed in scanning  Items that were mis-shelved, had labelling errors, or had location discrepancies in the record.  Labelling errors and location discrepancies were determined by comparing the physical item to the electronic record. Since our library is all on one floor, we brought them back to our desks to work on as time permitted during the rest of the day.

19  The entire data manipulation & printing process, all three to four comparisons and filterings, took 15 minutes, maximum.  Time to locate the items varied depending on the # of items on the two error lists and the nature of the errors

20 OUR DATA Based on 1/3 of collection  Mis-shelved  15 titles = 0.075% significantly mis-shelved  Call number errors on spine label or in record  120 = 0.6%  Incorrect location code  188 = 0.9%  No record or barcode in system  46 = 0.23%  Procedural errors  75 = 0.375%  Barcode scanning error  19 = 0.095%

21 THINGS WE LEARNED  Do working sub collections, like processing, display & mending first  Then you can confidently ignore these statuses when they show up in the records  Keep track of types of errors  We weren’t consistent first time around, so it’s more difficult to know how to adjust procedures, if needed  Not all ILSs allow for detailed call # range searching

22 MEASURES OF ASSESSMENT/OUTCOMES/BENEFITS Anticipated  Identified missing items  Found status exceptions  Located items missing barcodes

23 UNANTICIPATED  Systematic look at the condition of materials  Opportunity to weed damaged and multiple copies  Librarian did this as she scanned.  Technicians ID’d obvious problems & put on cart for librarian to decide  Discovered location inconsistencies  Worry over lack of mis-shelving within the section paled in comparison to types of errors we were finding

24 WHAT ABOUT MIS-SHELVED BOOKS WITHIN EACH SECTION  Drawback  Items mis-shelved within each block cannot be located using Excel  Many were located by staff as they were scanning.  Research suggests that ~80% of books are mis-shelved within 25 items of it’s proper location (Sung, Whisler, Sung, )  Average # of books on a Booth is 24  This will likely be found by staff searches or patron browsing

25 Questions?


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