Plan better with V-eyeQ

One of the striking V-eyeQ features is the ability to create and manage collection plans. This feature is sometimes called "rational collection management". The "rational" part refers to the ability to create and modify collection plans based on "objective" data instead of "subjective" opinions and knowledge. Obviously, data that is not stored is not available for planning purposes, which tends to lay a relatively big focus on circulation performance.

In these plans the (typically: yearly) goals for selections (acquisitions) and weeding are defined based on library goals and "objective" data. So they define, based on the library's goals: given the available budget, how much to purchase & weed, - and for which branches. As such, V-eyeQ supports a centralized selection process.

There are many reasons to create a collection plan. In such plans targets can be defined to optimize collection usage. Typical examples are: weed (old) items to make collections look more attractive; decrease the desired loan frequency, so that more items on the shelf, thus making more items available for customers; purchase more popular titles to implement a more demand-driven policy. The library determines what the answers to these questions are and puts them into the plan. In the next step, the plan will reveal the differences between available and necessary budget. Based on this, the library can decide how to act. The plan is the translation of the library's policy / strategy. It is the library that decides, not the application.

There is a direct link between the collection plans and the selection process. As such the plans are "directive": based on yearly goals, they suggest weekly targets. How many works (books) should be bought? The weekly selectins have a direct effect on the yearly goals, which are thus dynamically changed by the actual actions in the real world.

Collection rules

Collection plans are based on (structured per) collection rules. These are elements of the collection that are relevant for collection management. These rules "map" these elements to definitions that the application can understand and interpret, based on literally any available metadata element such as material type, genre, language, level or subject category. Examples of rules are crime & adventure, fantasy & science-fiction, Spanish language novels, philosophy, economy, cookbooks, travel guides, DVD movies, X-box games, pre-school book, comics for children, daisy-ROMs for adults, music CD's, sheet music, etc. The application supports collection clusters, a mechanism to "group" the rules. In practice this means: the smaller a branch, the more rules will be clustered. A typical example of such a cluster could be: while a bigger branch has separate rules (and separate goals) for crime & adventure, fantasy & science-fiction, detectives & thrillers, for a small branch these are clustered into a single group (e.g. adventure).

Though "technically" this is not limited, a library typically maintains between 100 and 200 rules. For each rule (collection part) the application calculates how many items to purchase, how many items to weed and how popular the collection part is. It suggests which items to weed, which items to replace, for which titles to purchase additional copies, which titles to transfer or promote and which new titles to purchase (if "potentials" are used in acquisitions).

Plan elements

The library defines which elements are included in a collection plan. One can decide, per analysis, which data elements are included (and in which order). One can choose from:

• locations

• period

• "scope": display per location, per location cluster (a grouping of branches), per collection cluster, total

• collection rule

• collection rule wording

• collection cluster (small, medium, large, …)

• percentage of loans

• percentage of collection

• balance (ratio between percentage of loans and percentage of collection)

• loan frequency

• desired on the shelf (KPI)

• desired number of items in collection (KPI)

• desired purchase (KPI)

• desired weeding (KPI)

• desired loan frequency (KPI)

• desired minimal collection size (KPI)

• number of items

• number of items more than n times on loan

• number of items less than n days old

• number of items older than n days

• number of items older than n days and never on loan

• number of loans

• number of loans now

• number on the shelf

• number of items on loan in last n years

• average (book) price.

Workflow

The collection plan creation workflow is a multi-step process:

1. The plan is defined by the library, based on the aforementioned rules
2. The application collects the data. This data harvesting collects or calculates all sorts of potentially relevant performance indicators, such as percentage of loans, percentage of collection, checked out more than n times, number older than n days, number older than n days and not checked out since, number currently on loan, number checked out in last n years, number of items, number of loans and loan frequency.
3. The library defines the desired KPI's (with regard to loan frequency and weeding percentage). These are basically: the library goals.
4. Based on available budget, defined KPI's and average (book) price, the application calculates the goals for weeding and purchase.
5. Finally, the library defines the actual goals. If desired, the targets that are calculated by the application (number of purchases, number to weed) can be manually changed.

Steps (2-5) can be repeated as many times as wanted.

(Please note that the above workflow is more sophisticated than described here, as there are multiple parameters that influence the outcome.)

Link with acquisitions

Once the plan is created, it can (should?) become the basis of the plan of real-life actions such as purchasing, weeding and transferring. V-eyeQ is integrated in acquisitions and the actual progress can be compared with the goals that are defined in the collection plan. If needed, the plan can be modified.

The plan data and the performance data are accessible / visible from (integrated with) selections.

Purchase quota are created per collection rule: the (yearly) goals in the collection plan are "translated" to weekly targets: the yearly targets, the number of copies that were already ordered this year and how many items are on order currently determine the "weekly" targets. The expected performance value (both the average and the lowest/highest value per branch) are visible and usable as a sorting criterion.

On top of that, (detailed) performance data is accessible not only from within selection management (acquisitions), but also from within metadata management (cataloguing). The next blog post will describe the performance data capabilities in more detail.

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