This study focusses on a decision-making tool to assist an organization in planning for capacity needed for the Last Mile Delivery (LMD) services which is the most expensive part of the entire supply chain. Considering the use of Crowdsourcing for Logistics (CSL), the decision-making tool’s objective is to provide an optimal combination of fulltime, seasonal and CSL resources that lead to minimum operational LMD costs and meet the variable demand. To achieve this, a three phased approach is used, where in the first analytical phase an expected cost model is numerically validated. In the second stochastic program phase, the capacity and cost of the CSL resources are varied. Finally, in the third simulation phase, the approach is further extended to consider the daily employee attrition rate and unsatisfied demand being carried over to the next day. Lastly, the use of automation or newer technologies, such as robots, for LMD services is introduced in this simulation phase to show the benefits in terms of the operations costs. The results from the analytical model described the optimal values of fulltime and seasonal considering the utilization of CSL and experienced some penalty costs. In this case, the parameters being fixed, does not capture the differences due to the variability of CSL availability or costs, which is addressed in the stochastic program phase. Though the output from the stochastic model is higher, it does consider the variability in the CSL capacity and cost, which is practically observed. The simulation section gives a further refined optimal combination of fulltime, seasonal and CSL that meets the demand considering the attrition rate of fulltime and seasonal, and rollover the units by one day. Within this simulation, the consideration of automated delivery systems like using a robot for LMD services leads to further cost savings opportunity. Here, the fulltime delivery cost is benefited, with low utilization of seasonal and CSL limited for optimizing delivery strategy. In conclusion a tool is provided for aggregate delivery capacity planning that would consider an optimal combination of fulltime, seasonal and CSL resources lowering the LMD costs and meeting the variable demand.