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Top1. Introduction
Recent surge towards online retailing (ecommerce) across both developed and large developing nations presents significant challenges for warehouse management for both indigenous and multi-national ecommerce players (Yu et al., 2017). In particular for a retailer’s profitability, its warehouse operations must be efficient and responsive in that the associated warehouses must be successful in their role of receiving, storing, and shipping goods to the right customer at the right time and in the right quantities. Efficient and responsive warehouse operations hold even more primacy due to the fierce competition amongst e-commerce players resulting in constant cost pressures and need to ensure efficient operations on a sustainable basis (Cui, et al., 2015). In view of the challenges enumerated above, it is quite prudent to infer that resource management in context of warehouse operations assumes criticality for retailers managing their warehouse operations.
In context of resource management within warehouse operations, management of manpower, equipment and processes are relevant in that these impact the overall accuracy of delivery, determine fixed and variable costs, and influence level of flexibility in context of dynamic demand. Warehouse operations have typically four stages of dealing with goods viz. receiving, storing, order picking and shipping. Receiving and storing relates to inbound logistics, while order picking and shipping pertain to outbound logistics (Krishna, 2016). Resources such as space, manpower (skilled and semi-skilled) and equipments are allocated to different warehouse locations owned by the retailer following organizational policies to achieve desired operational performance in terms of capacity, service level, and throughout at minimum cost (Krishna, 2016). Essentially, the intent is to ensure maximum profitability by optimizing the concerned resources.
Within the resource planning for warehouses, manpower allocation is imperative in that this directly influences costs and thereby profitability of warehouse operations. Extant research literature has categorized manpower planning in three broad categories: a) predicting current demand for manpower; b) predicting the future supply of manpower; and c) reconciling the difference between demand and supply (Leeuw, et al., 2015). Predicting current demand for manpower is relatively easier in context of relatively stable demand of goods. However, retailers’ approach needs to be much more nuanced in case their goal is to predict future supply of manpower.
Consider a case of a retailer wishing to predict the future manpower requirements in terms of number of teams (consisting of manpower adept at handling warehouse operations) that need to be allocated to its warehouses located at multiple locations. From an existing baseline, the retailer needs to determine the number of teams that need to be deployed to respective warehouses such that additional profit can be maximized while optimizing the allocation of teams. In this context, it would also be important to note that owing to the manpower constraints, the retail cannot violate allocation constraints in that if N is the maximum number of teams available for allocation to M number of warehouses, then N and M would be associated in terms of some kind of requirement constraint. Further, the allocation would also have to keep in mind of contributions of allocation of each team to respective warehouses such that total additional profit can be maximized. To cater to the above mentioned research nuances, in this research a dynamic programming based manpower allocation for warehouses is conceptualized. To this end, the number of teams available for allocation is modeled as different exclusive states, while the warehouses are modeled as exclusive stages. The measure of performance is additional profit that the warehouse can make given certain number of teams allocations such that higher number of teams allocated to a warehouse, higher the associated additional profitability. In context of the warehouse-team allocation problem and in the parlance of dynamic programming, state (number of teams) at the next stage is completely determined by the state and policy decision at the current stage. Therefore, we model the problem using deterministic dynamic programming approach.
Rest of the article is arranged as follows. Section 2 presents a review of extant research literature followed by enumeration of the evolved model in Section 3. Section 4 presents the illustration of our devised model followed by concluding remarks in section 5.