RESTAURANT SCHEDULING PROCESSES AND SYSTEMS

Methods and systems are provided to generate predictive food preparation schedules for restaurants or other stores or establishments. The processes include obtaining, at a control unit, data from one or more sources associated with a restaurant and generating a predictive food preparation schedule based on the obtained data, the predictive food preparation schedule indicating a number of items to be prepared in advance of at least one of a given time and event.

BACKGROUND

The subject matter disclosed herein generally relates to restaurants and food preparation establishments and, more particularly, to scheduling for food preparation at restaurants and food preparation establishments.

Often the demand for food products to be prepared in a restaurant is variable and sporadic, particularly at fast-food or other eat-in or take-out dining establishments. For example, preparation of grilled foods in particular may be of such variability that waste and/or time delays may result due to variable states of demand and preparation. Such variability can lead to personnel of a restaurant cooking and/or preparing more food product than is required, which can result in waste, or not cooking enough product resulting in long customer wait times. The waste issue may arise due to mandatory limits or restrictions on how long a food product can be left waiting for a customer (e.g., time from being cooked to time of serving). Such issues are further complicated by having customers arrive in large groups, on busses or queuing up at a drive-through, which can cause visibility issues related to incoming demand for the personnel inside the restaurant.

SUMMARY

According to some embodiments, methods to generate predictive food preparation schedules are provided. The methods include obtaining, at a control unit, data from one or more sources associated with a restaurant and generating a predictive food preparation schedule based on the obtained data, the predictive food preparation schedule indicating a number of items to be prepared in advance of at least one of a given time and event.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the methods may include that the one or more sources comprise at least one of: a first point-of-sale system associated with a counter within the restaurant; a second point-of-sale system associated with a drive-through of the restaurant; occupancy sensors associated with one or more areas within the restaurant; monitoring sensors associated with a drive-through line or area; parking lot sensors associated with a parking lot of the restaurant; historical data related to orders made at the restaurant; weather data associated with a location of the restaurant; social media data associated with events in a locale of the restaurant; local news data associated with the locale of the restaurant; event data associated with the locale of the restaurant; day of week information; time of day information; proximity information related to nearby businesses, parks, stadiums, highway exits; mobile and online order information made from remote customers for food from the restaurant; and kiosk ordering within the restaurant.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the methods may include displaying the generated predictive food preparation schedule on a display within a kitchen of the restaurant.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the methods may include controlling an automated food preparation system based on the predictive food preparation schedule to prepare food in accordance with the predictive food preparation schedule.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the methods may include that the automated food preparation system is an automated grill system.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the methods may include that the control unit is located at least one of within the restaurant, located at another restaurant, and located on one or more remote servers.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the methods may include receiving a manual input that overrides the predictive food preparation schedule.

According to some embodiments, methods to control automated food preparation systems are provided. The methods include obtaining, at a control unit, data from one or more sources associated with a restaurant, generating a predictive food preparation schedule based on the obtained data, the predictive food preparation schedule indicating a number of items to be prepared in advance of at least one of a given time and event, and controlling an automated food preparation system based on the predictive food preparation schedule to prepare food in accordance with the predictive food preparation schedule.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the methods may include that the one or more sources comprise at least one of: a first point-of-sale system associated with a counter within the restaurant; a second point-of-sale system associated with a drive-through of the restaurant; occupancy sensors associated with one or more areas within the restaurant; monitoring sensors associated with a drive-through line or area; parking lot sensors associated with a parking lot of the restaurant; historical data related to orders made at the restaurant; weather data associated with a location of the restaurant; social media data associated with events in a locale of the restaurant; local news data associated with the locale of the restaurant; event data associated with the locale of the restaurant; day of week information; time of day information; proximity information related to nearby businesses, parks, stadiums, highway exits; mobile and online order information made from remote customers for food from the restaurant; and kiosk ordering within the restaurant.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the methods may include displaying the generated predictive food preparation schedule on a display within a kitchen of the restaurant.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the methods may include that the automated food preparation system is an automated grill system.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the methods may include that the control unit is located at least one of within the restaurant, located at another restaurant, and located on one or more remote servers.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the methods may include receiving a manual input that overrides the predictive food preparation schedule.

According to some embodiments, automated food preparation systems are provided. The automated food preparation systems include a control unit, a food storage station for holding unprepared food items, a cooking station for receiving one or more items from the food storage station and cooking the one or more unprepared food items, and a staging station for preparing and assembling ordered items form the unprepared food items and the cooked food items, wherein the control unit controls each of the food storage station, the cooking station, and the staging station, and wherein the control unit obtains data from one or more sources associated with a restaurant and generates a predictive food preparation schedule based on the obtained data, the predictive food preparation schedule indicating a number of items to be prepared in advance of at least one of a given time and event, the control unit controlling the food storage station, the cooking station, and the staging station based on the predictive food preparation schedule.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the automated food preparation systems may include that the one or more sources comprise at least one of: a first point-of-sale system associated with a counter within the restaurant; a second point-of-sale system associated with a drive-through of the restaurant; occupancy sensors associated with one or more areas within the restaurant; monitoring sensors associated with a drive-through line or area; parking lot sensors associated with a parking lot of the restaurant; historical data related to orders made at the restaurant; weather data associated with a location of the restaurant; social media data associated with events in a locale of the restaurant; local news data associated with the locale of the restaurant; event data associated with the locale of the restaurant; day of week information; time of day information; proximity information related to nearby businesses, parks, stadiums, highway exits; mobile and online order information made from remote customers for food from the restaurant; and kiosk ordering within the restaurant.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the automated food preparation systems may include a display for displaying the generated predictive food preparation schedule on a display within a kitchen of the restaurant.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the automated food preparation systems may include that the cooking station is an automated grill station.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the automated food preparation systems may include that the control unit is located at least one of within the restaurant, located at another restaurant, and located on one or more remote servers.

DETAILED DESCRIPTION

As shown and described herein, various features of the disclosure will be presented. Various embodiments may have the same or similar features and thus the same or similar features may be labeled with the same reference numeral, but preceded by a different first number indicating the figure to which the feature is shown. Although similar reference numbers may be used in a generic sense, various embodiments will be described and various features may include changes, alterations, modifications, etc. as will be appreciated by those of skill in the art, whether explicitly described or otherwise would be appreciated by those of skill in the art.

Embodiments described here are directed to food preparation-management systems and processes that utilize multiple data sources to predictively determine and output a predictive food preparation schedule (e.g., predictive schedule for food to be prepared). Embodiments of the present disclosure can include collected data from multiple sources, including, but not limited to, average hourly demand for the past three days at the location, hourly demand for the same hour on the same day for the past three years, sensors or occupancy information for a lobby/dining area, and sensors or occupancy information for drive-through lanes. The collected information can then be aggregated at a processing unit to generate a schedule for food preparation. For example, taking all the aggregated data a prediction of demand needed for food preparation can be generated. In such an example, with respect to grilling, suggested products to be cooked/prepared prior to an actual demand from a register or other point-of-sale (POS) system can be provided. The predictive load can be overridden in a positive or negative count manually at the location (e.g., using a computer or the processing unit) based on actual demands needs.

Turning toFIG. 1, a schematic illustration of a restaurant100that can incorporate embodiments of the present disclosure is shown. The restaurant100may be a location serving fast-food, and thus include a first service counter102(e.g., in-store service) and a second service counter104(e.g., drive-through service). The first and second service counters102,104include respective point-of-sale systems106,108that are configured to receive inputs regarding service orders, such as customer food orders. The point-of-sale systems106,108are in communication with a control unit110that receives the inputs at the point-of-sale systems106,108and generates an order list for prompting preparation of food to fulfill the orders.

The food is prepared within a kitchen112of the restaurant100. The kitchen includes, for example, a cooking station114, a staging station116, and a food storage station118. The cooking station114can include a grill, fryers, stove tops, cooking surfaces, ovens, microwaves, heaters, etc., as will be appreciated by those of skill in the art. The staging station116is used for assembly of orders. For example, the staging station116may receive cooked foods from the cooking station114and then combine the cooked food with appropriate additional items. In one non-limiting example, a hamburger patty may be cooked at the cooking station114and then the cooked patty is transferred to the staging station116to assemble a hamburger assembled to a customer's order request (e.g., additional of condiments, vegetables, and assembly on a bun). Similarly staging may be applied to salads, fries, or any other food order that the restaurant offers. The food storage station118can be a freezer, fridge, and/or dry storage area where raw and/or unassembled food items are stored.

When an order is made at one of the service counters102,104and such order is entered into a respective point-of-sale system106,108, the food orders are input into the control unit110. Simultaneously, typically, personnel in the kitchen112will hear the orders as they are made, and thus may begin preparation of the orders in substantially real-time.

Issues may arise related to food waste and/or customer experience due to load variability. That is, at times, too many orders may be entered into the control unit110such that long wait times may arise. During these periods, the personnel may attempt to compensate for this by making large quantities of food in anticipation of continuing orders at a high volume. However, once a rush dies down, any excess, unserved food may sit for too long, and thus must be discarded. Accordingly, it may be advantageous to have a system or process for anticipating load demands at the restaurant100such that waste is minimized while also providing an efficient delivery and/or completion of food orders made by customers within the restaurant.

To enable a predictive food preparation-management system, in accordance with the present disclosure, the restaurant100may include one or more sensors for obtaining real-time information or data related to future order demand by customers of the restaurant100. For example, a first sensor120may be arranged to monitor an ordering area122to determine the presence of lines of people waiting to make orders. The ordering area122is proximate the first service counter102within the restaurant100. The first sensor120may be an optical sensor, proximity sensor, people-counting sensor, or other type of sensor as will be appreciated by those of skill in the art. The first sensor120is arranged to count the number of customers within the ordering area122to determine an anticipated ordering volume. Similarly, a second sensor124is arranged to monitor a lobby area126in a similar fashion. The information associated with the lobby area126can be used to determine if the ordering area122may be impacted by additional new customers not yet located within the ordering area122. However, in such arrangements, the second sensor124may be arranged to discount customers that are leaving the restaurant or merely waiting for others in a party to order.

Further, the restaurant100can include a third sensor128that is arranged to monitor a parking lot130of the restaurant100. The data collected by the third sensor128can be used to determine the number of vehicles in the parking lot, detect arriving vehicles, customers walking toward the restaurant, etc. Additionally, a fourth sensor132can be arranged to monitor a drive-through area134to detect the presence of additional customers located behind a current customer in the drive-through area134that may be currently ordering.

The sensors120,124,128,132are in communication with the control unit110. The control unit110can then aggregate the collected data from the sensors120,124,128,132to determine an anticipated, predictive ordering load that may occur at the restaurant100. The control unit110can then output an anticipated food order schedule to be displayed to personnel within the kitchen112and/or may be used for other methods of food preparation. Accordingly, the control unit110can make predictive, anticipatory schedules for obtaining food from the food storage area118, cooking food at the cooking station114, and assembling food at the staging station116. In this way, the restaurant100can provide an efficient delivery of prepared food to customers while minimizing waste.

The control unit110is configured with a program, algorithm, or other programming to enable predictive scheduling in accordance with embodiments of the present disclosure. The control unit110includes processes that utilize multiple data sources to predictively determine and output a predictive food preparation schedule (e.g., predictive schedule for food to be prepared). In addition to receiving real-time sensor data from the sensors120,124,128,132, the control unit110can further include analysis of one or more historical data sources, internet or external sources, etc. Historical data can include, but is not limited to, average hourly demand for the past three days at the restaurant100, hourly demand for the same hour on the same day for the past three years, and/or other historical data associated with food preparation demand and/or scheduling. External sources of data can be associated with physical location of the restaurant100, proximity to other locations (e.g., mall, retails outlets, hotels, highway exits, schools, etc.), social media sources (e.g., information related to local events, including but not limited to sports events, concerts, parades, etc.), etc. The collected information can then be aggregated at within the control unit110to generate a schedule for food preparation. For example, taking all the aggregated data, a prediction of demand needed for food preparation can be generated for a given period of time or event.

Although shown with the control unit110located within the restaurant100, such configuration is not to be limiting. For example, the control unit110can be located remote from the restaurant100, and the predictive food preparation schedule can be transmitted to the restaurant100, e.g., to a display system within the restaurant100to display the predictive food preparation schedule. In some embodiments, the predictive food preparation schedule can be sent to an automated food preparation system (e.g., an automated grill, fryer, salad assembly station, etc.). A remote controller or a remote portion of a controller can be located on one or more remote servers (e.g., “in the cloud”), located at a centralized store, restaurant, headquarters, etc., located as an aggregate of different computing systems located in a collection or group of stores/restaurants (e.g., local or regionally connected locations).

Turning now toFIG. 2, a schematic illustration of a restaurant demand-management system236in accordance with an embodiment of the present disclosure is shown. The restaurant demand-management system236can include various components, including, but not limited to, one or more sensors238, one or more point-of-sale systems240, and a control unit242(collectively “electronic devices”). The sensors238may be similar to that described with respect toFIG. 1and may be positioned at various locations associated with a restaurant (e.g., monitoring interior and/or exterior areas or spaces of the restaurant). The control unit242can be located within the restaurant and/or located remote therefrom. In some embodiments, the control unit242comprises multiple different electronic components, with some components located on-site and other components located off-site. As shown, the various sensors238, point-of-sale systems240, and the control unit242are operably connected and/or in communication with each other through a network244, as described herein.

One or more of the electronic devices may include processor(s), memory, communication module(s), etc. as shown and described herein. Communication can be established between the various electronic device can be by wired or wireless communication, through the internet, through a direct connection, etc. as will be appreciated by those of skill in the art.

The sensors238and the point-of-sale systems240are in communication with the control unit242. For example, the sensors238and the point-of-sale systems240and the control unit242may communicate with one another when the restaurant is open and orders by customers may be received and/or during preparation time prior to the restaurant being opened. As noted, wired or wires communication may be employed. Wireless communication networks can include, but are not limited to, Wi-Fi, short-range radio (e.g., Bluetooth®), near-field infrared, cellular network, etc. In some embodiments, the control unit242may include, or be associated with (e.g., communicatively coupled to), one or more networked system elements, such as computers, routers, network nodes, etc. The networked system element(s) may also communicate directly or indirectly with the sensors238and the point-of-sale systems240using one or more communication protocols or standards (e.g., through the network246).

In some embodiments, the control unit242(or functionality thereof) can be integrated into the point-of-sale system240. In such embodiments, the point-of-sale system240can communicate with the sensors238using near-field communications (NFC) (e.g., network246) and thus enable communication therebetween. In some embodiments, the control unit246and/or the point-of-sale system240may establish communication with one or more sensors238that are outside of the structure or building of the restaurant. Such connection can be established with various technologies including GPS, triangulation, or signal strength detection, by way of non-limiting example. In example embodiments, the control unit242can communicate with the sensors238and the point-of-sale systems240over multiple independent wired and/or wireless networks. Embodiments are intended to cover a wide variety of types of communication between the control unit242and the sensors238and the point-of-sale systems240, and embodiments are not limited to the examples provided in this disclosure.

The network246may be any type of known communication network including, but not limited to, wide area networks (WAN), local area networks (LAN), global networks (e.g. Internet), virtual private networks (VPN), cloud networks, intranet, etc. The network246may be implemented using a wireless network or any kind of physical network implementation known in the art. The sensors238and/or the point-of-sale systems240may be coupled to the control unit242through one or more networks246(e.g., a combination of cellular and Internet connections) so that not all sensors238and/or the point-of-sale systems240may be coupled to the control unit242through the same network246at the same time. One or more of the sensors238and the control unit242may be connected to the network246in a wireless fashion. In one non-limiting embodiment, the network246is the Internet.

Embodiments provided herein are directed to apparatuses, systems, and methods for collecting data, aggregating said data, and generating a predictive food preparation schedule. In some embodiments, the collected data may be communicated over one or more lines, connections, or networks, such as network246, e.g., data collected by a sensor238and transmitted through the network246to the control unit242. The transmission from the sensor238may be transmitted in real-time to the control unit242.

As provided herein, the control unit242can be associated with an automated cooking station and/or a display system for displaying information to personnel of the restaurant. The control unit242can be used to aggregate collected data and stored data to generate a predictive food preparation schedule that is predictive and designed to minimize waste while increasing efficiency of delivery of orders from customers. The data can be received through the network246from the one or more sensors238and/or the point-of-sale systems240, from local or remote memory, from the internet, etc. One or more of the sensors238may be associated with a particular area of observation, which can be weighted differently than other areas of observation (e.g., different weighting of information from different sensors).

As noted above, the control unit of the present disclosure can receive different types of information in generating a predictive food preparation schedule. Data that may be employed by control units of the present disclosure in generating predictive food preparation schedules can include, but is not limited to: real-time orders made at first point-of-sale systems (e.g., counter); orders made at second point-of-sale systems (e.g., drive-through); occupancy sensors (lobby, ordering area, dining area, etc.); sensors monitoring a drive-through line or area; parking lot sensors; historical data (e.g., recent days, year-to-date days, etc.); weather data; social media data; local news data; local event data; day of week information; time of day information; proximity information related to nearby businesses, parks, stadiums, highway exits, etc.; mobile and/or online order information made from remote customers for food from the restaurant; and kiosk ordering within the restaurant.

In some embodiments, the control units can generate predictive food preparation schedules that are applied to automated food preparation systems, e.g., automated grills, etc. Such automated grilling systems can be arranged as continuous-cook-conveyor systems, pick-and-place systems, and/or carousel-to-conveyor systems, for example. Such automated food preparation systems may integrate the separate, personnel-manned stations into a single unit. For example, automated food preparation system can incorporate a food storage station, a cooking station, and a staging station. In such system, raw/uncooked food can be obtained from the food storage station, moved to the cooking station for cooking, and then moved to the staging station for assembly in accordance with an order.

The predictive food preparation schedules generated are a number of items to be prepared at a given time. For example, based on a number of preceding days, the predictive food preparation schedule may indicate that a specific number of a specific food item should be prepared in advance of a specific time or event. In one non-limiting example, at lunch for three days one hundred hamburgers have been ordered within a fifteen minute window, starting at 12:00. On the following day, the predictive food preparation schedule may indicate that at least sixty hamburgers (or more) should be prepared such that they are ready to be served at 12:00.

In some embodiments, the predictive food preparation schedule may be manually overridden. For example, on the fourth day of three sequential days, the predictive food preparation schedule may indicate that a large number of food items should be prepared. However, due to a holiday, business may be slower than anticipated, and thus a user can override the predictive food preparation schedule to prepare fewer than originally predicted numbers of items. Alternatively, a manual override can instruct more than predicted, as needed.

Advantageously, embodiments described herein provide predictive food preparation schedules that enable preparation of food in advance of when the food will be ordered by customers (e.g., an estimated time or event). As such, a reduction of food waste may be achieved by preventing preparing too many items products which may result from human error attempting to manually compensate for a large volume of customers, followed by a large reduction in the number of customers. Further, advantageously, embodiments provided here can improve personnel efficiency because the food preparation will be tied to a predictive schedule based on various input information instead of instantaneous load demands. Moreover, embodiments provided herein may provide an improvement in finished product quality because the cooking stations (e.g., grill) will produce products that are not held for extended periods of time prior to serving.