An automated shopping order entry system has particularly advantageous features for use in a supermarket having a delicatessen station. A processor subsystem, a customer subsystem, and an employee subsystem are provided. The customer subsystem can be located in the view of customers entering at a customer entrance that is typically remote from the delicatessen station. The customer can select delicatessen items and gets a printout of item selection and of estimated waiting time based on an accumulated variable workload that depends on items ordered by customers. Shopping efficiency is increased by making automated shopping available to the customer at an advantageous place and time in the shopping pattern, and by handling waiting time in an advantageous manner.
A portable electronic shopping list device with the familiarity of a pocket calculator comprises a display, keyboard with control keys, microprocessor and memory for storage of a library of standard lists and custom lists, input and output port for communication with external devices, and support software. A product is added to a user list by searching and selecting within a standard list or adding a new item not on the list. In a search, entry of each alphanumeric character causes a pointer to advance in the list to the first list item having that character in the same character position in the item name. Alternatively, a user can scroll through a list to a desired item. Item quantity may be entered with the item on the user list as well as item price. Total price sums through the list for reference at any time. Price comparison is also enabled by entering a product price per unit and a second price per different unit with the device determining the lowest price of the two. Required entries are prompted on the display.
A queued task/queued resource state forecaster employs a binomial distribution fitter to fit a composite binomial distribution to operational data of a work management system, such as a call center. The fitter obtains arrival statistics for calls and agents in the call center, determines moments for the net calls in-queue from the obtained arrival statistics, determines parameters of binomial distributions that corresponds to the moments, and fits the determined moments to a linear combination of offset binomial distributions to obtain a composite binomial distribution. The composite binomial distribution is then evaluated by a scheduler which adjusts the operation of the call center accordingly. For example, a task scheduler evaluates the distribution to obtain a probability of an additional call being enqueued by the future point in time in order to determine whether an outbound call should be launched. Or, a resource scheduler evaluates the distribution to obtain an expected number of enqueued calls at some future point in time in order to determine whether another agent should be dispatched.
To improve the prediction of wait times of calls (23) waiting to be processed, or to improve the prediction of completion times of calls that are presently being processed, in low-volume call centers (10) or splits/skills (21), additional data points are created--either manually or automatically--during the processing of each call that is presently being processed, for use in effecting wait-time estimates of the waiting calls. At each of the plurality of points in the processing of each of the plurality of calls, information about the call that is relevant to how long completion of the processing of the call is likely to take is obtained (200). This information is applied to historical information about previously-processed calls to estimate (202) how long completion of the processing of each of those calls is likely to take, i.e., how long it will be before the agents (25) who are processing those calls become available to handle the waiting calls. The estimated times are then used (210) as the predicted wait times of that plurality of the waiting calls. The technique is applicable to workflow management in general.
Order entry apparatus for automatic estimation a transformation model comprising a pattern composed of a plurality of parameters representing custom product specifications, production line conditions and factors for composing the estimates for production cost and completion date. The parameters of this transformation model are specified by learning, leading to estimation for the requested product specifications, in broad consideration of conditions such as those of the production line. The use of a neural network model as this pattern transformation model makes pattern transformation more flexible and pattern learning more efficient. Estimation accuracy is also increased by entering values of predicted charges in production line conditions such as loads or stock obtained from resource requirements planning, process design, capacity requirements planning, etc.
An interactive communications system for facilitating the management of purchases performed by individuals or certain businesses in repetitive manner, characterized in that the apparatuses making up the system comprise input means (10, 16), confirmation means (17), suggestion/selection means (18), memory means (13), and display means (11) for displaying the articles to be purchased, which articles may be selected from preceding lists and/or files without omission or repetition. The list of articles to be purchased is then printed on paper by means of a printer (14) or is transferred to other apparatuses of the system by interface means (15). By networking resident and portable apparatuses, freestanding terminals for giving assistance in decision-making and situated at the point of sale, and optionally information or transaction terminals in a temporary or permanent network, it is possible for the purchaser to obtain in real time all of the information and means required for managing the purchase function, and for the vendor to obtain better knowledge of customers and to satisfy customer requirements better.
