PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to Indian Application No. 201821039466, filed on Oct. 17, 2018. The entire contents of the aforementioned application are incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates generally to optimization process, and, more particularly, to a system and method for concurrent dynamic optimization of replenishment decision in a networked node environment.

BACKGROUND

Replenishment planning (e.g., in retail) is triggered based on a demand forecast, store inventory levels, sales, replenishment schedules and various replenishment parameters (shelf capacity, safety stock, lead times, shelf life etc.). These inputs are used to compute quantity of each product that must be supplied to each store, in order to maintain sufficient stock to meet customer demand. Currently, entire replenishment process starts with creation of suggested store order quantity looking at demand forecast, item level attributes (shelf life, pack quantity, etc.), store attributes (shelf capacity, safety stock, min pres., etc.), inventory levels and various replenishment parameters (schedules, lead times, etc.) which are pre-dominantly static and based on pre-set rules. The suggested orders are taken as fixed requirements for a warehouse and a transportation network, and are first executed in the warehouse in best possible way that suits warehouse operations and subsequently in transport operations. Thus, while the order quantities are optimized from point of view of stores, the efficiency of rest of the supply chain is hampered by the one-way propagation of requirements. The current system also doesn't consider future delivery quantities and adjust current order appropriately (stock build-up or ramp-down).

In this sense, the current replenishment system is a pure pull-based system, where the replenishment quantities are demanded by stores and have to be met by the rest of the supply chain. Local optimization of replenishment quantities without cognizance of operational capacity throughout the supply chain has huge implications on product availability and wastage. Typically, these localized optimizations are done through disparate systems using classical optimization approaches which have certain inherent pitfalls: a. Localized sequential optimization by independent modules without any joint information sharing resulting in overall suboptimal operations, b. Hand off between modules lead to noise and difficulties in course correction, resulting in inability to adapt to business dynamics, c. Time consuming execution and computationally infeasible to re-run frequently in-turn affecting the delivery times to stores and customers, d. No learnings from historical data and minimal simulation of operational environment leveraged in optimization decision, e. Static rules, large periodicity (high latency) and incapable of dynamic changes in short time.

Traditional replenishment planning involves dealing with a moving target, since each component's target or objective is only relevant to the localized optimization step for process/function under consideration. In initial step (store replenishment), availability of products in store is the objective and when the plan goes through subsequent processes, which may undergo optimization to leverage volume benefits from supplier, or at operational level it may undergo warehouse pick optimization and transport load optimization.

In this scenario of sequential optimization following problems, as representative examples, occur: (i) On shelf availability may get impacted if the transport or warehouse capacity is constrained and if the system decides to forego some of the suggested replenishment quantity; (ii) If more quantity is picked and delivered than the original suggestion to fill the trailer capacity, this may potentially result in storage issues at the stores and product obsoleteness or wastage; (iii) The impact of changes made to quantities, by downstream systems, across stores is not considered, hence increasing or reducing the quantity at a later stage is not typically decided based on the potential to sell at every store; (iv) The impact of changes made to quantities, by downstream systems, across current and future delivery schedules is also not considered.

SUMMARY

Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one aspect, a processor implemented method of concurrent dynamic optimization of replenishment suggestive actionable element in a networked node environment is provided. The processor implemented method includes at least one of: receiving, via one or more hardware processors, at least one current state attribute associated with the networked node environment at a predetermined time interval; obtaining, via the one or more hardware processors, an optimized entity actionable matrix by mapping the at least one of current state attribute and an associated actionable entity; computing, via the one or more hardware processors, a predictive replenishment matrix for an impending cycle of the networked node environment based on the optimized entity actionable matrix; identifying, via the one or more hardware processors, a reward function for the predictive replenishment matrix; and determining, via the one or more hardware processors, the replenishment suggestive actionable element based on the reward function. In an embodiment, the reward function includes a computation of overall cost with regard to a realized revenue associated with aggregation of the plurality of nodes.

In an embodiment, the at least one current state attribute may include at least one of (i) product levels across a plurality of stores in the networked node environment, (ii) real-time tracking information of plurality of delivery vehicles, (iii) availability of labor in the predetermined time interval, and (iv) inventory levels of the plurality of products in the networked node environment. In an embodiment, the optimized entity actionable matrix may include a plurality of parameters associated with corresponding the at least one of current state attribute. In an embodiment, the predictive replenishment matrix may include a plurality of nodes and a plurality of pre-trained historical parameters. In an embodiment, the at least one suggestive actionable element may correspond to at least one decision.

In another aspect, there is provided a processor implemented system for concurrent dynamic optimization of replenishment suggestive actionable element in a networked node environment is provided. The system comprises a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: receive, at least one current state attribute associated with the networked node environment at a predetermined time interval; obtain, an optimized entity actionable matrix by mapping the at least one of current state attribute and an associated actionable entity; compute, a predictive replenishment matrix for an impending cycle of the networked node environment based on the optimized entity actionable matrix; identify, a reward function for the predictive replenishment matrix; and determine, at least one suggestive actionable element based on the reward function. In an embodiment, the reward function includes a computation of overall cost with regard to a realized revenue associated with aggregation of the plurality of nodes.

In an embodiment, the at least one current state attribute may include at least one of (i) product levels across a plurality of stores in the networked node environment, (ii) real-time tracking information of plurality of delivery vehicles, (iii) availability of labor in the predetermined time interval, and (iv) inventory levels of the plurality of products in the networked node environment. In an embodiment, the optimized entity actionable matrix may include a plurality of parameters associated with corresponding the at least one of current state attribute. In an embodiment, the predictive replenishment matrix may include a plurality of nodes and a plurality of pre-trained historical parameters. In an embodiment, the at least one suggestive actionable element may correspond to at least one decision.

In yet another aspect, there are provided one or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes at least one of: receiving, via one or more hardware processors, at least one current state attribute associated with the networked node environment at a predetermined time interval; obtaining, via the one or more hardware processors, an optimized entity actionable matrix by mapping the at least one of current state attribute and an associated actionable entity; computing, via the one or more hardware processors, a predictive replenishment matrix for an impending cycle of the networked node environment based on the optimized entity actionable matrix; identifying, via the one or more hardware processors, a reward function for the predictive replenishment matrix; and determining, via the one or more hardware processors, the replenishment suggestive actionable element based on the reward function. In an embodiment, the reward function includes a computation of overall cost with regard to a realized revenue associated with aggregation of the plurality of nodes.

In an embodiment, the at least one current state attribute may include at least one of (i) product levels across a plurality of stores in the networked node environment, (ii) real-time tracking information of plurality of delivery vehicles, (iii) availability of labor in the predetermined time interval, and (iv) inventory levels of the plurality of products in the networked node environment. In an embodiment, the optimized entity actionable matrix may include a plurality of parameters associated with corresponding the at least one of current state attribute. In an embodiment, the predictive replenishment matrix may include a plurality of nodes and a plurality of pre-trained historical parameters. In an embodiment, the at least one suggestive actionable element may correspond to at least one decision.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

