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vorhandene Tickets bezüglich der Zeitschriftenverwaltung bewertet. |
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Proposal UB Leipzig and SLUB Dresden
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Proposals on periodicals and subscriptions - extended version (Google-Präsentation) |
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03-07 |
Introduction
- In German libraries, specific demands on the management of periodicals exist, which are mostly not available in Folio. Especially in big libraries the ordering, receiving, claiming, … of several subscriptions of one periodical title are necessary and should be assisted by automatisms.
- Example: SLUB Dresden holds 7.432 active print subscriptions and in 2021 (January until September) around 30.200 issues were checked in.
- The aim of the presentation is to explain the necessary adjustments. To reach the aim, in the following sections planned developments and missing elements and functions are described.
- Jira-tickets concerning the claiming process in Folio
- Functions, which should be implemented or improved in Folio
Child pages (Children Display) page Zeitschriftenverwaltung - Funktionsanalyse
Description Jira-tickets
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- Description:
- If check-in pattern and frequency can be discerned, create pending check-in pieces automatically, e.g. if monthly, with annual renewal, create 12 pending check-in pieces
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- Description:
- If a user checks in a piece on an ongoing order, and there's no pending piece, create the next pending piece automatically. Maybe use the most recent existing item record as the default/template for the next one, so the user only has to change the enumeration/chronology and barcode
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- Description:
- Problem: User has to add each expected piece individually. Pieces are added almost daily while ordering is active and this can consume a significant amount of time. Patterns might also trigger automated claiming when an issue is overdue
- Workflow mockups: https://drive.google.com/drive/folders/1EDEZAWpteiV-NZc_W9sjfTWlr4bpmd7N?usp=sharing
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- Description:
- Claims are filed via multiple communication methods (Eg. EDIFACT, e-mail or printed). This is different depending on if this is an ongoing order (maybe based on prediction or patterns) or one-time order (based on order date and expected delivery period from vendor). Consider splitting in order to prioritize feature.