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Record W2152400726 · doi:10.18438/b8x32x

Labour Costs for Inventory Control Less Expensive than Repurchasing

2010· article· en· W2152400726 on OpenAlexaffvenue
Laura Newton Miller

Bibliographic record

VenueEvidence Based Library and Information Practice · 2010
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsCarleton University
Fundersnot available
KeywordsLaptopComputer scienceEconomic order quantityOrder (exchange)Inventory controlBarcodeLibrary scienceOperations researchInterlibrary loanCollection developmentControl (management)World Wide WebDatabaseBusinessMathematicsMarketingOperating systemFinanceArtificial intelligence

Abstract

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A Review of:
 Sung, J. S., Whisler, J. A., & Sung, N. (2009). A cost-benefit analysis of a collections inventory project: A statistical analysis of inventory data from a medium-sized academic library. Journal of Academic Librarianship, 35(4), 314-323.
 
 Objective – To describe an inventory system that was created within the library and to show the cost-effectiveness of using the inventory system compared to the price of reacquiring mis-shelved books. 
 
 Design – Bibliometric study and cost-benefit analysis.
 
 Setting – Medium-sized academic library in a rural community of the United States.
 
 Subjects – Approximately 300,000 books from LC classifications D through H, N, P and Q, representing two thirds of the library’s entire monograph collection.
 
 Methods – The library created its own electronic inventory and shelf-reading program, using a laptop computer equipped with a hand-held scanner, to scan barcodes in the stacks. Library staff used the Microsoft Access database to update two files containing a shelf-list and an active-status list while the books were scanned. The program alerted the worker if books found had an active status (i.e., Missing, Renewed, Overdue, Charged), were not in the correct order, or were not in the system. Each transaction created a log which contained a time stamp (to the second), the call number and the barcode number. It also took note of scanning errors, books that were out of order, and books that were not on a shelf-list. After a complete section was examined, a list was produced to reveal the distance of mis-shelved books from their correct location and the amount of time between each scan. The researchers used statistical analysis (using SPSS 15.0) to measure scan speed for each scan, mis-shelving rate and error distance of each mis-shelved book. In order to analyze the cost of labour to replace a book versus the cost of inventorying, the researchers estimated the salary costs of staff members involved in selection, acquisition and cataloguing. The library spent $440,000 USD in labour costs to purchase 15,000 monographs in one fiscal year (approximately $30.00 USD in labour costs per book). They multiplied this by 5300 books that were found to be “badly” mis-shelved (found beyond 25 books away from the proper position). Labour fees were used to determine costs of inventorying by calculating average scanning speed and cost per hour to pay someone to scan the entire half-million monograph collection.
 
 Main Results – It took approximately 707 hours to scan 305,000 monographs. The average (mode) calculation for scans was 5 seconds for 80% of the barcodes, with an average (mean) of 8.35 seconds between scans. The longest average (mean) time for scanning barcodes was in the N section, followed by G, H, P, Q, D, E and F. A total of 291 books were found on the shelves with an “active” status (i.e., Charged (4), Overdue (7), Renewed (4), In Transit (24), and Missing (228)). Twenty-four books with the status “Miscellaneous” (i.e., At Bindery, Call Slip, Cataloguing Review, Damaged, and Mending) were also found on the shelves. Of the 15 active books in the categories “Charged”, “Overdue” and “Renewed”, ten were found in the proper position on the shelf. Of the 228 “Missing” books, 30% were scanned in the correct location, 10% were found 26 to 100 books away, and half were located over 100 books from their proper location. In addition to the books already marked as “Missing” in the catalogue, there were 516 books (.17% of the entire scanned section) still not found on the shelf after three searches over a period of 6 months. Of the 291 active status books found on the shelves, 52% were reused as of July 2008. (The inventory was completed at the end of 2006). Over 36% of books mis-shelved further than 25 books from their correct location were reused. However, among all books scanned, only 17% were reused during the same time period. The researchers noted that inconsistencies between the call number as shown on the book label and how it appeared in the catalogue occurred 565 times. Of these discrepancies, 40% of the labels resulted in books being misplaced ten or fewer books away, 10% misplaced between 10 and 100 books away, and 35% misplaced more than 100 books away from the correct position. In general, 82% of mis-shelved books were found within 1 to 25 books away from their correct location. By calculating that 5300 books were mis-shelved beyond 25 books away from their proper position, labour costs were estimated to be at least $159,000 USD (5300 x $30.00 USD per book in labour costs). Costs for interlibrary loan were calculated at approximately $30.00 USD per transaction, and patron’s time wasted trying to locate misplaced books was estimated at 30 minutes per book. This was much more than the labour costs associated with scanning books, which at an average speed of 8 seconds per book and $10.00 US per hour for scanning worked out to be 2.2 cents per book, or $11,000 USD to scan the entire half-million monograph collection.
 
 Conclusion – The results appear to reveal that the labour costs for inventory control are less expensive than repurchasing or borrowing the same number of books.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.368
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.239
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2010
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