IL293899B2 - Task manager system and a method thereof - Google Patents

Description

TASK MANAGER SYSTEM AND A METHOD THEREOF TECHNOLOGICAL FIELD Embodiments of the presently disclosed relate generally to systems and methods for task managing.

BACKGROUND Warehouses and storage centers, for example ones that facilitate ecommerce orders, commonly use manual or semi manual processes to perform order fulfillment processes which are the activities preformed once an order is received. Some systems are based on operators standing in the picking area while boxes are moving and others create complex rack structures for space utilization. Some solutions utilize mobile robots to fetch cases and bins to a picking area. Moreover, order fulfilment of orders placed must take place within a relatively short period of time in order to be commercially competitive. Such order fulfilment is known as E-commerce and places demands on an order fulfilment system to meet such obligations. Each unique item has a specific inventory identification, known in the industry as a stock-keeping unit (SKU). Each item usually bears an optical code, such as a barcode or radio frequency identification (RFID) tag that identifies the SKU of the item. Picking stations in automated warehouses work in a way that the box arrives (by a robot) from storage to a particular station, the picker (human or robotic), picks from the specific box, one or several items, and places the items in a put wall so that packers can packages the one or more items to provide one or more packages that are outputted from the automated warehouse. The box awaits till the picker picks the one or several items and then immediately return the box to the storage.

GENERAL DESCRIPTION As described above, order fulfilment of orders must take place within a relatively short period of time in order to be commercially competitive. The warehouse usually has a number of predefined resources including a predefined number of picking stations being associated with a certain number of picker persons working usually a predefined number of shift working hours and a predefined number of robots. Generally, the predefined resources are attributed arbitrarily according to the averaged orders received during a certain period or according to the size of the warehouse affecting the number of picking stations. However, it should be noted that if the orders are not fulfilled on time, the picker persons may work overtime, maintenance tasks may be postponed, or even worse, the delivery time may be delayed. On the contrary, if the number of orders does not correspond to the predefined resources, some picking stations may be closed, and the time of the picker persons may be wasted. Moreover, it should be noted that when the operator of the automated warehouse attributes the number of resources arbitrary, the operator is not able to evaluate the consequences of the different attributions of resources and/or tasks on the system throughput. Moreover, the operator is not capable to calculate the time at which the tasks would be completed. Additionally, the operator is also not capable to decide when maintenance tasks should be applied, if any. Moreover, in the order fulfillment processes, special events (order triggering events) can accelerate the sales of specific items. For example, certain seasons may accelerate the sales of certain types of food and/or items for cooking such foods (turkeys before thanksgiving, grills and/or barbeque equipment at the beginning of spring), rain and/or snow can accelerate the sales of umbrellas and/or raincoats and/or boots, the beginning of summer may accelerate the sales of bathing suits and/or sunscreens and/or sun glasses, holidays may accelerate the sales of certain items such as Christmas ornament every Decembers, flowers on Valentine’s day or costumes before Halloween. Therefore, the need for resources (e.g. number of picker persons, allocation of tasks to picker persons when not be dedicated at the picking station, number of "open" picking stations and number of robots ...) is not uniform all over the year. More specifically, some periods of the year may require a certain number of picker persons working every day in a full-time position and some periods of the year (that might be very short) may require to double the number of picker persons and to extend the picking hours to be able to match the delivery time. Therefore, there is a need to forecast an optimal execution of the operational tasks of an automated warehouse, to optimize the resources of a warehouse management system to be able to manage or predict the trends in demand in general, and on specific items in particular, for example based on the past, e.g. an item is demanded every Friday, every 1st of the month, every Christmas, especially at peak times - such as black Friday, cyber-Monday or Christmas, i.e. the months of November and December to be able to meet the customer's needs.

The presently disclosed subject matter relates to a task manager system being capable of performing algorithms providing to an operator, in real-time or in a predicted manner, a recommendation regarding an optimization of a warehouse with respect to optimal resources (e.g. number of picker persons, allocation of tasks to picker persons when not be dedicated at the station, number of "open" picking stations and number of robots ..) to be deployed at each period of time in the actual day/week/month or as anticipation for future days/weeks/months as well . The optimization data may include at least one of the followings: number of picking stations that should be actuated, number of actuated robots of each type, optimal time of operation of the picking stations, managing time of the picker persons, and also optimization data regarding maintenance tasks such as managing the inventory, filing the stock, optimal location of selected items before or after the pick-up … Therefore, according to one broad aspect of the present disclosure, there is provided a task manager system for a warehouse comprising a processor being configured and operable for receiving a time data being indicative of a predetermined period of time at which the preparation of orders to be prepared using items from the warehouse should be completed, processing the time data, and generating a recommendation including an optimization data for the predetermined period of time comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one maintenance task. In this connection, it should be noted that the predetermined period of time may define any desirable period of time including but not limited to any one of: part of a day, a single day, a plurality of days, an entire week or even an entire month. The optimization data provides the optimization parameters for the predetermined period of time. During this period of time, the optimization data can provide sub-period of times during which the task to be performed relates only to order preparation and sub-period of times for maintenance tasks. Therefore, in some sub-period of times, only orders can be prepared without processing with a maintenance task. For example, replenishment or consolidation or cycle count or replacing batteries should not be performed every day. The recommendation may provide to the operator the optimized time to perform maintenance tasks, so the throughput required of the order preparation is not impacted. As will be described further below, for example, the optimization data may recommend performing replenishment and/or consolidation tasks the week before black Friday because the load on picking is low and the inventory to fulfill the picking is needed during this peak. In some embodiments, the processor is configured and operable for receiving order data being indicative of the orders to be prepared using items from the warehouse, wherein generating a recommendation including an optimization data comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one of completion of the preparation of the orders and at least one maintenance task. The order data may comprise at least one order line or recall data. Each order line may comprise at least one order related parameter including an item identifier, a due date data for supplying the item and a quantity. The due date data may comprise an expected delivery due date and/or an indication for a prioritized handling. In this connection, it should be noted that, typically, the purpose of an automatic warehouse is to fulfill orders. However, the ordering can be suspended at certain period of time (e.g. at the company yearly report time when cycle count should be performed on every item). The robots should then bring every box to the picking station(s) to perform the cycle count. The task manager of the present disclosure can then provide a forecast of time/picking stations/robots needed for the operation. Also, days before a special event, the number of orders can drop to zero, since customers wait for these events to order items. Thus, at this time, the warehouse workers can prepare the special events operation by performing at least one maintenance tasks such as consolidating, recalling and replenishing inventory as will be detailed further below. In some embodiments, the optimization of a warehouse may include a special planning in real-time of the warehouse (e.g. location of the items) according to the orders (amount and/or type of items) or to the predicted orders being related to special events. The optimization of a warehouse may also include planning an optimized organization of the warehouse before the special events. The optimization data may take into consideration the orders or the predicted orders. In some embodiments, the optimization of a warehouse may include optimizing the number of actuated robots according to the maintenance data of the robots and/or of the movement data of the robots in the warehouse and/or data relating to the size of the warehouse and the distance/time of the round trip.

In some embodiments, the processor is configured and operable for receiving historical data being indicative picking parameters including averaged picked-up time. the optimization of a warehouse may thus include providing recommendations based on historical data including averaged picked-up time. In some embodiments, the plurality of optimization parameters comprises at least one of predicted optimized number of robots of each type that should be actuated, predicted optimized number of picker persons, predicted optimized number of picking stations and allocation of at least one maintenance task to at least one picker person when not be dedicated at the station. The maintenance task may comprise replenishment and/or consolidation and/or cycle count and/or space reduction and/or warehouse maintenance and/or recall and/or replacement of batteries. The optimization data may comprise a plurality of options, each option being indicative of a different optimization of the plurality of the optimization parameters. In some embodiments, processing the time data (and optionally order data) comprises performing a plurality of simulations on the plurality of the optimization parameters. The order data may be received from a customer management system and the time data may be received from an operator, the customer management system and the operator being in data communication of the processor. In some embodiments, generating the optimization data comprises generating the optimization data before the preparation of the orders including the day at which the orders are prepared or at least one day before the preparation of the orders or in real-time during the preparation of the orders. In some embodiments, when the optimization data is generated before the preparation of the orders, the optimization data comprises data being indicative of at least one of the followings: optimal location of selected items before or after pick-up, optimal time of operation of picking stations before and/or during the preparation of the orders, managing time of picker persons. In some embodiments, the order data comprises predicting data being indicative of at least one of predicted orders enabling to provide an optimization data being related to special events. The predicting data may be based on historical data predicting the special events. According to one broad aspect of the present disclosure, there is provided a method for optimizing a warehouse management. The method comprising obtaining, by at least one computerized system, a time data being indicative of a predetermined period of time at which the preparation of the orders should be completed, processing the time data, and generating a recommendation including an optimization data for the predetermined period of time comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one of completion of the preparation of the orders and at least one maintenance task. In some embodiments, the method further comprises receiving together with the time data, an order data being indicative of orders to be prepared using items from the warehouse and wherein generating a recommendation including an optimization data comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one of completion of the preparation of the orders and at least one maintenance task. In some embodiments, the method further comprises receiving historical data being indicative picking parameters including averaged picked-up time. In some embodiments, the method further comprises receiving a predicting data being indicative of at least one of predicted orders enabling to provide an optimization data being related to special events. According to one broad aspect of the present disclosure, there is provided at least one non-transitory computer readable medium that stores instructions that once executed by a computerized system causes the computerized system to execute a process for optimizing a warehouse management, the non-transitory computer readable medium stores instructions for: obtaining, by at least one computerized system, a time data being indicative of a predetermined period of time at which the preparation of the orders should be completed, processing the time data, and generating a recommendation including an optimization data for the predetermined period of time comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one maintenance task. According to one broad aspect of the present disclosure, there is provided an automated warehouse that comprises: a storage configured to store multiple items, wherein the multiple items are stored in item containers; a plurality of picking stations that comprise at least one picking station; one or more robots that are configured to convey item containers to the plurality of picking stations; and a task manager system as defined above.

BRIEF DESCRIPTION OF THE DRAWINGS In order to better understand the subject matter that is disclosed herein and to exemplify how it may be carried out in practice, embodiments will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which: Fig. 1 is a functional block diagram showing the task manager system according to some teachings of the presently disclosed subject matter; Fig. 2 is a functional flow chart showing the optimization method according to some teachings of the presently disclosed subject matter; and Fig. 3 is a schematical illustration of an automated warehouse according to some teachings of the presently disclosed subject matter.

DETAILED DESCRIPTION OF EMBODIMENTS Any reference in the specification to a method should be applied mutatis mutandis to a device or system capable of executing the method and/or to a non-transitory computer readable medium that stores instructions for executing the method. Any reference in the specification to a system or device should be applied mutatis mutandis to a method that may be executed by the system, and/or may be applied mutatis mutandis to non-transitory computer readable medium that stores instructions executable by the system. Any reference in the specification to a non-transitory computer readable medium should be applied mutatis mutandis to a device or system capable of executing instructions stored in the non-transitory computer readable medium and/or may be applied mutatis mutandis to a method for executing the instructions. Any combination of any module or unit listed in any of the figures, any part of the specification and/or any claims may be provided. The specification and/or drawings may refer to a processor. The processor may be a processing circuitry. The processing circuitry may be implemented as a central processing unit (CPU), and/or one or more other integrated circuits such as application- specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), full- custom integrated circuits, or a combination of such integrated circuits. A computerized system may include one or more processors and may also include additional units or components such as memory units, communication units and the like. An automated warehouse may be managed by an automated warehouse control system (WCS) being configured and operable to perform automated warehouse control and management operations. The automated warehouse includes a storage for storing multiple item containers (e.g. boxes). The WCS may be executed by any type of computers - one or more servers, one or more computers, may be operated in a centralized or distributed manner. The WCS may include WCS parts that may manage different parts of the automated storage. The WCS may obtain (receive and/or generate) information relevant to the management of the automated warehouse. Thus may include at least one out of orders, received items, availability of trucks or any other output entities to output items from the automated warehouse, content of item containers (items stored per box and/or quantity of items per box), a mapping between item identifiers (SKU, barcodes and the like) and items, locations of items (storage, picking stations), any information regarding an item (including item type, expiration period, storage parameter such as storage temperature, conveying parameter, fragility, position of conveying, and the like), packaged boxes, content of picking stations, historical data (including history of orders), popularity information, environmental information, and the like. The WCS may be fed from sensors and/or any tracking systems and/or robots and/or picker persons about the locations of the item containers and the content of the item containers (including for example the amount of one or more items per box). The term 'robot' refers hereinafter to any mechanical or electro-mechanical agent that is guided by a computer program, electronic circuitry or remote controlled. Sensors may be of any type - including visual sensors, cameras, RFID readers, NFC readers, and the like. The WCS is configured to manage the storage and/or provision process of the items. A provision process may include at least one out of picking an item container (including the item), providing the item container to a picking station, returning the item containers to the storage, managing the storage, performing the picking and the like. When the picking is managed by a human then the WCS may provide suggestions regarding the picking. For example - the WCS may add received items to an overall inventory, allocated boxes for items, may fill or partially fill boxes by items, may add boxes to a box inventory, and the like. The WCS may be configured to determine locations of boxes within the storage - for example by taking into account the distance to one or more picking stations and/or by taking into account the popularity of the items. Reference is made to Fig. 1 , showing a functional block diagram of a task manager system 100 of the presently disclosed subject matter which may be a part of the WCS. Task manager system 100 comprises a computer system comprising a processing utility 100B and being a part of and connected to a computer network. Task manager system 100 may comprise a general-purpose computer processor, which is programmed in software to carry out the functions described herein below. Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as "determining", "processing" or the like, refer to the action and/or processes of a computer that manipulate and/or transform data into other data. Also, operations in accordance with the teachings herein may be performed by a computer specially constructed for the desired purposes, or by a general-purpose computer specially configured for the desired purpose by a computer program stored in a computer readable storage medium. Task manager system 100 includes a data input utility 100A including a communication module for receiving order data being indicative of orders to be prepared using items from the warehouse and a time data being indicative of a predetermined period of time at which the preparation of the orders should be completed, an optional memory (i.e. non-volatile computer readable medium) 100C for storing the input/output data, a database or the computer program as will be detailed below, and a processing utility 100B adapted to processing the order and time data, and generating a recommendation including an optimization data comprising a plurality of optimization parameters being indicative of optimal (e.g. minimum) resources that should be optimally attributed to complete at least one task including at least one of completion of the preparation of the orders and at least one maintenance task and an optional data output utility 100D being configured and operable to provide the optimization data. The time data is generally directly defined by the operator via an interface being in communication with data input utility 100A , representing the optimal time at which all the orders should be fulfilled or the time at which the operator needs to close the working day. The time data may include a predetermined period of time which may be for example from few hours to a week, and/or a specific time at which all the tasks should be completed (for example at 4:00 PM). As illustrated in the figure, the order data may be received, for example, from a customer management system being in data communication of the processor/ data input utility 100A . The order data comprises at least one order line or recall data. Each order line typically includes an item identifier (e.g. SKU), a due date data for supplying the item and a quantity. The order data may also comprise an approximate distribution of orders along a certain period, defining "regular" and "peak" days. Usually, the operator is already aware of the number of orders (at least approximately) that should be fulfilled one day before. An indication for a prioritized handling may be also added to the order line. The recall data may comprise an item identifier such as SKU and/or specific box and/or specific batch, and a quantity . The batch refers to a production number enabling the manufacturer to identify the batch of production of items. The production number enables to recall, for example, for any reasons (e.g. detection of bacteria), some items of certain production batches. The plurality of optimization parameters may comprise predicted optimized number of robots of each type that should be actuated and/or predicted optimized number of picker persons and/or predicted optimized number of picking stations and/or allocation of at least one maintenance task to at least one picker person when not be dedicated at the station. Therefore, the optimization data may comprise optimization data generated in real-time (i.e. during the preparation of the orders), if it seems that the different tasks would not be completed on time or if the time of some pickers seems to be wasted. Alternatively, the optimization data may be generated before the preparation of the orders including the day at which the orders are prepared or at least one day before the preparation of the orders, to anticipate and save the number resources (and optionally their availability) required for accomplishing the different tasks. When the optimization data is generated before the preparation of the orders, the optimization data comprises data being indicative of at least one of the followings: optimal location of selected items before or after pick-up to, optimal time of operation of picking stations before and/or during the preparation of the orders, managing time of picker persons. For example, if it is expected that some specific items would be popular in the next orders, the item containers holding these specific items may be displaced within the warehouse to be placed on storage casing(s) being closer to the picking stations to enable a shorted path to be run through for the robots. Moreover, the task system manager is also capable of readjusting the optimization data in real-time when the optimization data is generated before the preparation of the orders. The optimization data may provide one or more optimization parameters according to the customer's needs. More specifically, the customer may decide which optimization parameter(s) he is interested in, and a plurality of options may be provided to him, each option being indicative of a different set of optimization parameters being indicative of different optimization of the plurality of the optimization parameters. This may be implemented by performing a plurality of simulations (e.g. on-demand) on the plurality of the optimization parameters, to determine the optimal set of optimization parameters. For example, the optimization data may be provided for any predetermined period of time defined by the operator: daily, weekly or monthly. The optimization data may provide the number of picker persons required for completing the orders only, for alternating between completion of orders and maintenance tasks. More specifically, the operator may decide whether he desires to include maintenance task(s) in the optimization parameters or not. The maintenance task(s) may comprise replenishment and/or consolidation and/or cycle count and/or space reduction and/or warehouse maintenance and/or recall and/or replacement of batteries. A certain prioritization may be established between the different maintenance tasks and the pick-up task to be able to fulfill the operator's requirements. The replenishment task refers to a task during which a predefined number of new item containers are introduced into the WCS. The replenishment task requires picker person(s) and robot(s) resources. The new item containers are scanned at their specific location (i.e. specific storage casing in the warehouse and specific storage shelf on the storage casing). The replenishment data is entered in the WCS. The consolidation task refers to a task during which the item containers being partially filled are identified, and some items are displaced from one item container to another to completely fill the item containers and to thereby minimize the number of item containers in the system. The consolidation task requires picker person(s) to move the items from and to boxes and robot(s) resources to displace the boxes. The cycle count refers to a task during which the number and optionally the type (being defined by the item identifier) of items in each item container is/are identified and correlated with the item data in the WCS, to verify that there are no discrepancies in the WCS. The cycle count requires picker person(s) and robot(s) resources. For example, if a box has been brought to the picking station for picking purposes, and after the picking, the number of items is under a certain threshold, the system can recommend to the picker person to perform a cycle count. Alternatively, the cycle count can also be decorrelated from picking when the operator is required to perform a cycle count on a particular box or SKU not needed for picking and/or if there are no picking tasks that should be performed for a certain period of time, then the robots can bring the box/SKU to the picking station for cycle count. The warehouse maintenance refers to at least one of cleaning the warehouse, inspecting the condition of the warehouse's equipment, verifying the operation of the robots, unloading item containers from a truck etc. For example, the maintenance time of the robots, the time of swapping between the batteries, the charging time of their batteries if any, the waiting time on each robot path, as well as the time of a round trip for each robot according to the warehouse size may be considered. The space reduction task refers to a task during which the pick-up is proceeded from multiple item containers for the same order line in order to empty the warehouse. The space reduction task is time consuming and may be generally implemented when no time constraint exists. The space reduction task requires picker person(s) and robot(s) resources. For example, the data input utility 100A may receive that tomorrow, 1000 order lines should be treated on the same day before a cut-off time of e.g. 15:00. The data recommendation may advise that it is preferable to achieve replenishment today. Additionally or alternatively, the data recommendation may include a daily planning with hourly operation. For example, the data recommendation may propose to start with replenishment from 8:00 AM to 9:00 AM, to continue with picking from 9:00 AM to 11:00 AM, to perform consolidation from 11:00 AM to 12:00 AM and then to perform another session of picking with space reduction feature from 13:00 to 16:00. For example, the data input utility 100A may receive that a certain number of item containers should be entered into the WCS. The data recommendation may advise on the optimal resources (e.g. number of picking stations that should be opened and time to fulfill the replenishment together with the pick-up). The simulations enable the operator to understand the effect of the attribution of the different number of resources. Moreover, the operator may also be able to control and change in real-time or by anticipation the different attributions of the resources of the system. For example, more or less picking stations may be opened or closed, more or less robots of the same or different types may be used… For example, new picking stations may be opened, if the number of orders that has been received exceeds the forecast. If the operator decides to perform maintenance tasks together with the picking up of the orders, he may define several maintenance tasks parameters such as a certain number of boxes/items to replenish, a number of cycle count that should be proceeded with, a number of consolidation and/or recall tasks, to obtain the number of picker persons required to accomplish these tasks. This enables to calibrate the operation of the warehouse to the operator's needs. Memory 100C may be integrated within task manager system 100 or may be an external storage device accessible by task manager system 100 . The software may be downloaded to task manager system 100 in electronic form, over a network, for example, or it may alternatively be provided on tangible media, such as optical, magnetic, or electronic memory media. Task manager system 100 comprises at least one computer entity linked to a server via a network, wherein the network is configured to receive and respond to requests sent across the network, and also transmits one or more modules of computer executable program instructions and displayable data to the network connected user computer platform in response to a request, wherein the modules include modules configured to: receive and transmit order and time data, transmitting a recommendation based on the optimization, for display by the network connected user computer platform. The presently disclosed subject matter may include computer program instructions stored in the local storage that, when executed by task manager system 100 , cause task manager system 100 to receive order and time data and determine the optimization data. The computer program product may be stored on a tangible computer readable medium, comprising: a library of software modules which cause a computer executing them to prompt for information pertinent to an optimization data recommendation, and to store the information or to display optimization data recommendations. The computer program may be intended to be stored in memory 100C of task manager system 100 , or in a removable memory medium adapted to cooperate with a reader of the task manager system 100 , comprising instructions for implementing the method as will be described below. More specifically, the computer program may be in communication with an interface to receive order and time data. In some embodiments, data input utility 100A may receive historical data being indicative of picking parameters such as averaged picked-up time per picker persons or per number of orders. In particular, different picker persons may have different pick-up time, and the different pick-up time of the different picker persons working at a specific time shift may be taken into consideration in real-time. For example, to be able to determine how many picker persons are required to fulfill a certain number of orders and if the working hours of a regular working day would be enough, the WCS may generate historical data, such as the averaged picked-up time per picker persons (or for a predetermined number of picker persons) for a predetermined number of orders, and calculate the optimized number of picker persons and/or picking time according to the number of received orders or predicted orders. Additionally or alternatively, in some embodiments, order data may include predicting data being indicative predicted orders enabling to provide an optimization data being related to the special events. As defined below, special events are related to a significant increase of ordering of specific items in a specific period of time. In this connection, it should be understood that the special events may increase by a large factor (e.g. five) the number of orders treated during time periods outside these special events. For example, events such as black Friday typically multiplies by 4 to 5 the numbers of items to be shipped compared to a normal routine day. Normal routine days are typically defined as 85% of days of the year. The precise optimization of the different resources may be critical to appropriately handle the orders during these special events periods. The operator has several degrees of freedom (different tasks, different number of resources), however, without the recommendation of the task manager of the presently disclosed subject matter, he is not capable to appreciate whether he would be able to fulfill all the orders at the end of the requested time, when the pick-up time would finish and how the different resources should be distributed. The optimization of a warehouse may include a special planning in real-time of the warehouse (e.g. location of the items) according to the orders (amount and/or type of items) or to the predicted orders being related to special events. The optimization of a warehouse may also include planning an optimized organization of the warehouse before the special events. For example, consolidation and/or replenishment maintenance tasks may be programmed before Black Friday, or season changing (recall of the spring/summer items at the end of summer or recall of fall/winter items after the winter and restock with the new season). The predicting data may be based on historical data predicting the special events or not. The prediction data includes at least one of the timing of the special event(s), their expected duration, the item(s) related to these special events or their expected quantities. As mentioned above, the item containers holding specific items being related to the special events, may be displaced within the warehouse to be placed on storage casing(s) being closer to the picking stations to enable a shorter path to be run through for the robots.

In some embodiments, the predicting data may be calculated by the task manager of the present disclosure. The processing utility 100B may receive special events data being indicative of prediction of special events and/or historical data being indicative of the history of the orders to generate an optimization data being indicative of at least one predicted order being related to the special events. The optimization recommendations relating to the treatment of the special events would probably increase the numbers of pickers, the time of picking and would probably anticipate the replenishment and would delay other maintenance tasks such as cycle count or consolidation. In a specific and non-limiting example, the operator is aware that he received all the orders that should be fulfilled for tomorrow and that he would like to limit the shift working hours to eight hours. The task manager would advise that to fulfill all the orders, he should activate twenty robots on the thirty available robots and request for the presence of only five picker persons for pick-up on the seven picker persons available. Since ten robots and two picker persons are free, they can be attributed to perform maintenance tasks. Reference is made to Fig. 2 , showing a functional flow chart of a method for optimizing a warehouse management 200 of the presently disclosed subject matter. Method 200 comprises obtaining in 202 an order data being indicative of orders to be prepared using items from the warehouse and a time data being indicative of a predetermined period of time at which the preparation of the orders should be completed, processing in 204 the order and time data, and generating a recommendation in 206 including an optimization data comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one of completion of the preparation of the orders and at least one maintenance task. Generating the optimization data may comprise generating the optimization data in real-time during the preparation of the orders in 206A or before the preparation of the orders in 206B including the day at which the orders are prepared or at least one day before the preparation of the orders. When the optimization data is generated before the preparation of the orders, the optimization data may comprise data being indicative of at least one of the followings: optimal location of selected items before or after pick-up, optimal time of operation of picking stations before and/or during the preparation of the orders, managing time of picker persons. In some embodiments, when the optimization data is generated before the preparation of the orders in 206B , the optimization data may be readjusted in real-time in function of the real accomplishment of the different tasks to fulfill all the orders. The readjustment may be due to different unexpected factors in real-time such as orders requiring immediate attention, delay in the picking up due to the picker persons, to a malfunction of a robot or unexpected peak of orders or picker persons having slower pick-up time… In some embodiments, the processing of the order and time data comprises performing in 208 , a plurality of simulations on the plurality of the optimization parameters, to determine the optimal set of optimization parameters. The different simulations enable to the customer to select the optimal set of the optimization parameters according to some other parameters not defined in the system. Additionally to the order data and time data, historical data being indicative picking parameters of previous orders such as averaged picked-up time may also be taken into account in 210 to adjust the optimization parameters. Additionally or alternatively to the historical data, predicting data being indicative predicted orders may also be taken into account in 212enabling to provide an optimization data being related to the special events. As mentioned above, predicting data may be based on historical data predicting the special events. In some embodiments, prior to the processing of the order and time data in 204 , the method may comprise in 214 receiving special events data being indicative of prediction of special events and/or historical data being indicative of the history of the orders to generate the predicting data in 216 being indicative of at least one of predicted orders being related to the special events. For example, the customer can inform the WCS on clearance sales on specific items, or seasonal sales. Reference is made to Fig. 3 , showing a schematic diagram of an automated warehouse 300 of the presently disclosed subject matter. Automated warehouse 300 comprises: a storage 1 and 2 configured to store multiple items, wherein the multiple items are stored in item containers; a plurality of picking stations 7,8,9 that comprise at least one picking station; replenishment/recall station 10 , one or more robots 3 and 4 that are configured to convey item containers to the plurality of picking stations; and a task manager system 310 as defined above. As defined above, task manager system 310 may be a part of a Warehouse Control System (WCS) being configured to perform automated warehouse control and management operations. Although storage 1 and 2 represent storages under different conditions, the presently disclosed subject matter is not limited to any type of storage, which may be of the same or different type. Similarly, although different picking stations 7-9 are shown in the figure, the presently disclosed subject matter is not limited to any type and any number of picking station, which may be of the same or different type. Also in this case, although robots 3 and 4 are shown in the figure, the presently disclosed subject matter is not limited to any type and any number of robots, which may be of the same or different type. For example, two type of robots may be provided. One type of robots (e.g. robotic carts) may be configured for accessing the boxes of one or more lower shelves and providing the boxes the picking stations and another type of robots (e.g. robotic lift units) may be configured for accessing the boxes of the higher shelves and providing the boxes to the picking stations. The robots may differ by size, complexity, cost, height, span of movement, and the like. The WCS is in data communication which each robot to determine and control the parameters of its displacement (path and speed). The WCS may update in real-time or near real-time the position of each robot in the warehouse and provides the robot with routes. The WCS may request a robot to retrieve a box from its current location and deliver it to a certain picking station. The WCS may instruct a robot to move a box from one picking station to another. Table 1 below shows a specific and non-limiting example of some possible data recommendations being generated by the task manager system of the presently disclosed subject matter. As shown in the table, a set of different optimization parameters is proposed to the operator. These recommendations may be based on a specific number of orders already entered in the system, on an averaged number of orders, or on an expected number of orders (calculated by the system or not). In the first set, for one open picking station, it would take 16 hours to complete the orders, the time of the picker would be occupied at 95% and the robot would be occupied at picking up at 30%. Since the robot is not occupied enough, the task manager system would recommend performing replenishment task to optimize the utilization of the robots. The number of boxes for which the replenishment task should be accomplished would be 157. In the second set, for two open picking stations, it would take 9 hours to complete the orders, the time of the picker would be occupied at 80% and the robot would be occupied at picking up at 60%. The task manager system would advise that there is no time for performing replenishment task if new orders are entered in the system. In the third set, for three open picking stations, it would take 7 hours to complete the orders, the time of the picker would be occupied at 60% and the robot would be occupied at picking up at 90%. The task manager system would advise that there is no time for performing replenishment task. The task manager system would advise, for the second and third set of optimizations that, the operator should renounce proceeding with a replenishment task or deciding to close a picking station and work two additional hours after picking to proceed with the replenishment.

Number of Picking Stations 1 2 Hours to finish 16 9 % occupancy of picker 95 80 % occupancy of robot fleet for picking 30 60 Recommendation Do replenishment No time if new orders enter the system No time for replenishment How many boxes do you need to replenish 1You need to close a Picking Station to replenish and work 2h more after picking Table 1The task manager system may also advise that, if a forecast of 1000 orders is suddenly expected or received, two more picking stations should be opened to complete the orders on time. In some embodiments, as mentioned above, the task manager of the presently disclosed subject matter is capable distributing the different resources between the different tasks optimally. Therefore, the set of the optimization parameters may be controlled by the operator. For example, the operator may decide that he is ready to reduce the picking efficiency by a certain percentage to increase the replenishment. Additionally or alternatively, the operator may also decide that he needs to reduce the picker throughput to perform maintenance tasks such as cycle count or consolidation. Additionally or alternatively, the operator may also decide that he prefers to bring a certain quantity of multiple boxes for one order line to empty boxes. Additionally or alternatively, he may also simulate how many orders can be completed if a replenishment is completed for 1000, for a predetermined period of time. In each case described above, the task manager system is configured and operable to simulate operator's demands and to provide to the operator optimization parameters based on these specific demands/constraints and to distribute the tasks and/or the resources optimally. As described above, these simulations may be proceeded in real-time or by anticipation on forecast or real data. The simulations enable to the operator to understand the impact of the attribution of the different number of resources. Moreover, the operator may also be able to control and change in real-time or by anticipation the different attributions of the resources of the system, to understand the impact of the attribution of the different number of resources.

Claims (26)

293899/- 20 - CLAIMS:

1. A task manager system for an automated warehouse, said task manager system comprising: an input data utility for receiving: (i) at least one maintenance task to be performed in the warehouse, wherein the at least one maintenance task comprises at least one of replenishment, consolidation, cycle count, space reduction, warehouse maintenance, recall or replacement of batteries; (ii) time data indicative of a determined period of time at which preparation of orders using items from the warehouse should be completed; and (iii) warehouse resources to be attributed for the preparation of said orders in the determined period of time including at least one of a predefined number of picking stations being associated with a certain number of picker persons and a defined number of robots; a processor configured to: perform a plurality of simulations on a plurality of optimization parameters for the given time data and generate simulation data indicative thereof, wherein the optimization parameters include resources of the warehouse and allocation of the at least one maintenance task; determine a set of optimization parameters selected from said optimization parameters based on the generated simulation data including the allocation of the at least one maintenance task to be completed within the determined period of time, to thereby generate recommendation optimization data regarding an optimization of a warehouse, wherein the recommendation optimization data configured to control and adapt in real-time, or by anticipation, attributions of the resources of the warehouse; and an output data utility being configured and operable to provide the recommendation optimization data.

2. The task manager system of claim 1, wherein said selected set of optimization parameters comprises at least one maintenance task parameter, wherein said at least one 293899/- 21 - maintenance task parameter comprises at least one of a certain number of at least one of box and item to replenish, a number of cycle count, a number of consolidation and/or recall task.

3. The task manager system of claim 1 or claim 2, wherein said processor is configured and operable for receiving order data being indicative of the orders to be prepared using items from the warehouse.

4. The task manager system of claim 3, wherein the order data comprises at least one order line or recall data.

5. The task manager system of claim 4, wherein each order line comprises at least one order related parameter including an item identifier, and/or a due date data for supplying the item, and/or a quantity thereof.

6. The task manager system of claim 5, wherein the due date data comprises an expected delivery due date and/or an indication for a prioritized handling.

7. The task manager system of any one of the preceding claims, wherein said processor is configured to receive historical data being indicative of picking parameters including averaged picked-up time.

8. The task manager system of any one of the preceding claims, wherein the recommendation optimization data comprises a plurality of options, each option being indicative of a different optimization of the plurality of the optimization parameters.

9. The task manager system of any one of claims 2 to 8, wherein said order data is received from a customer management system and said time data is received from an operator, the customer management system and the operator being in data communication of the processor.

10. The task manager system of any one of the preceding claims, wherein generating the recommendation optimization data comprises generating the recommendation optimization data before the preparation of the orders including the day at which the orders are prepared or at least one day before the preparation of the orders or in real-time during the preparation of the orders.

11. The task manager system of any one of claims 2 to 10, wherein the order data comprises predicting data being indicative of at least one predicted order related to special events. 293899/- 22 -

12. The task manager system of claim 11, wherein the predicting data is based on historical data predicting the special events.

13. A method for optimizing warehouse management, the method comprising obtaining, by at least one computerized system, (i) at least one maintenance task to be performed in the warehouse, wherein the at least one maintenance task comprises at least one of replenishment, consolidation, cycle count, space reduction, warehouse maintenance, recall or replacement of batteries, (ii) a time data being indicative of a determined period of time at which preparation of commanded orders should be completed, and (iii) warehouse resources to be attributed in the determined period of time including at least one of: (i) a predefined number of picking stations; (ii) a number of human pickers associated with each of the predefined stations and (iii) a predefined number of robots; performing a plurality of simulations using different resource attribution parameters and generating simulation data indicative thereof; generating a plurality of optimization parameters comprising the warehouse resources and allocation of at least one maintenance task; determining a selected set of optimization parameters based on the simulation data, the selected set of optimization parameters including: (i) the allocation of at least one maintenance task to be completed within the determined of of time; and (ii) an optimized attribution of the resources of the warehouse, to thereby generate a recommendation optimization data.

14. The method of claim 13, further comprising receiving together with the time data an order data being indicative of orders to be prepared using items from the warehouse.

15. The method of claim 13 or 14, wherein the order data comprises at least one order line or recall data.

16. The method of any one of claims 13 to 15, wherein each order line comprises at least one order related parameter including an item identifier, and/or a due date data for supplying the item, and/or a quantity thereof.

17. The method of claim 16, wherein the due date data comprises an expected delivery due date and/or an indication for a prioritized handling.

18. The method of any one of claims 13 to 17, further comprising receiving historical data being indicative of picking parameters including averaged picked-up time. 30 293899/- 23 -

19. The method of any one of claims 13 to 18, wherein the recommendation optimization data comprises a plurality of options, each option being indicative of a different optimization of the plurality of the optimization parameters.

20. The method of any one of claims 13 to 19, wherein the order data is received from a customer management system and said time data is received from an operator, or from the customer management system.

21. The method of any one of claims 13 to 20, wherein generating the recommendation optimization data comprises generating the recommendation optimization data before: the preparation of the orders, including the day at which the orders are prepared or at least one day before the preparation of the orders; or in real-time during the preparation of the orders.

22. The method of any one of claims 13 to 21, further comprising receiving a predicting data being indicative of at least one predicted order related to special events.

23. The method of claim 22, wherein said predicting data is based on historical data predicting the special events.

24. The method of any one of claims 13 to 23, wherein said selected set of optimization parameters further comprises at least one maintenance task parameter, wherein said at least one maintenance task parameter comprises at least one of a certain number of at least one of box and item to replenish, a number of cycle count, a number of consolidation and/or recall task.

25. At least one non-transitory computer readable medium that stores instructions that once executed by a computerized system causes the computerized system to execute a process for optimizing a warehouse management, the non-transitory computer readable medium stores instructions for: obtaining, by at least one computerized system, (i) at least one maintenance task to be performed in the warehouse, wherein the at least one maintenance task comprises at least one of replenishment, consolidation, cycle count, space reduction, warehouse maintenance, recall or replacement of batteries; (ii) a time data indicative of a determined period of time at which preparation of the orders should be completed, and (iii) warehouse resources to be attributed in the determined period of time including at least one of a predefined number of picking stations being associated with a certain number of picker persons and a defined number of robots; processing the time data by performing a plurality of simulations on a plurality of optimization 293899/- 24 - parameters, for the given time data, and generating a simulation data including a plurality of sets of optimization parameters, wherein the sets of optimization parameters includes the warehouse resources and allocation of at least one maintenance task, determining a selected set of optimization parameters based on the simulation data including the allocation of at least one maintenance task to be completed together with the preparation of the orders and an optimized attribution of the resources of the warehouse, to thereby generate a recommendation optimization data regarding the selected set of optimization parameters.

26. An automated warehouse that comprises: a storage configured to store multiple items, wherein the multiple items are stored in item containers; a plurality of picking stations; one or more robots configured to convey item containers to the plurality of picking stations; and a task manager system of any one of claims 1 to 12. 15