When we turn to a search engine with an urgent request, we expect targeted results -because who has the time to filter through dozens of marginally relevant entries? But when you run a search in a web search engine, you can't always count on receiving the output you were hoping for. To improve the relevance of search results, search engine developers advise users to take advantage of advanced search options and special operators.
Of course, the volume of information stored in a restaurant chain database cannot be compared to the amount indexed by a web search engine; nonetheless, restaurant databases are far from small. In restaurant chains with extensive menus, it is often difficult to find a specific item. Keeping in mind common issues faced by users of Tillypad XL, the software's developers have improved the search and filter mechanisms in its tree-structured directories.
Ordinarily, when a search is made by stock item name, the search results will include both purchased stock items and the restaurant's offerings. For example, suppose you need to register a delivery of potatoes. Before the delivery can be added to the stock-in record, you need to find the stock item in question in the directory. If the group filter is not used, the search results will contain both the purchased stock item 'Potatoes' and prepared items, such as fried potatoes, baked potatoes, and mashed potatoes.
In order to instantaneously find an item in the directory, you can customise the search to be conducted only within certain groups, subgroups and items – depending on the requirements of your task and how specific you want to be. For example, you can set up a search in the kitchen or bar or in both groups simultaneously. After selecting the necessary items, subgroups and groups and choosing the Filter by group command in the context menu, the Tillypad XL user will see a modified results set: only items of the desired specification level from the selected venues, divisions and menu groups will appear on-screen.
When the system displays the smallest possible data set, you avoid the distraction of unnecessary information and can focus on what's important. Save time by viewing, collecting and analysing only the information you need.