The rapid evolution of the Indian eCommerce industry over the last decade has spurred the need for more reliable last-mile services. The last mile is the last leg in the journey of a shipment from the serving distribution center or hub to its final destination at the customer's doorstep. For logistics providers, the last mile poses a significant challenge as it contributes approximately 40-55% of the total delivery cost. In cost-sensitive markets like India, where free delivery often influences buying decisions, these costs are rarely passed on to the customer. What's more, final mile logistics is probably the most important step in the delivery process for customer experience and satisfaction. Consumers are more likely to make purchases from retailers that offer fast, easy, and transparent delivery and express their dissatisfaction over a bad delivery experience on social media. Hence last mile delivery experience is a very important factor in maintaining the reputation of a retailer. This, and other factors, make the industry hyper-competitive on cost and overall customer experience, both for the service providers and eCommerce players.
Encouraged by evolving delivery standards, logistics and eCommerce companies are now experimenting with various methods and models to make last-mile deliveries more efficient and cost-effective. The task, however, is less simple than it seems, since demand is often sporadic and unpredictable. Let’s consider the food delivery segment, here, demand rises mid-day and again in the evening as customers order meals from various restaurants and cloud kitchens around town. Milk and grocery deliveries on the other hand, usually occur in the early morning. For eCommerce shipments, however, the activity and volume continue throughout the day, peaking before noon.
The daily task for a delivery planner is hence, to take into consideration the dynamic nature of demand and estimate how much capacity is to be built in order to ensure efficient and transparent last-mile services. In addition to the factor of highly sporadic demand, the planner also has several other factors to consider. For instance, the distance between the fulfillment center and the customer’s location, the route and traffic density, the availability of the customer, etc. These challenges and others like inventory shortages, and errors in order tracking can cause process inefficiencies in last-mile logistics.
Making the correct estimates and provisions to meet dynamic demand is a difficult job and one that cannot be done with traditional hiring practices.
If a logistics player were to employ a model with fixed contract or salaried staff, they may end up with excess capacity, resulting in a sub-optimum cost per shipment. Meanwhile, underestimating demand and capacity requirements will surely result in service failure and poor customer experience. The traditional hiring model of fixed working hours and salaries does not suit the last-mile requirement of fluctuating volume, varying time shifts, and cost sensitivity.
Hence, companies are rethinking their hiring practices and building new models for capacity planning and provisioning. These models should enable flexible and cost-effective last-mile services and help manage surges and lean periods better. They require planning that considers the location, the city’s population and demography, geopolitical conditions, and overall market demand and supply factors, to determine the percentage of staff required at a variable instead of fixed cost.
Below are the key enablers for improving last-mile capabilities
Mobilization of a disparate workforce: The gig economy has already taken over several other industries like hospitality and cab services. The logistics and eCommerce industry too are embracing this new crowd-sourcing dynamic within the labor market. Logistics and eCommerce companies must work towards identifying and sourcing individual delivery staff, part-timers delivery 'runners', delivery agents, Kirana stores, and individuals with space and capacity to offer delivery services.
Employing more variable pay structures: Instead of hiring a fleet for delivery and paying executives fixed compensation, companies can develop novel data-driven compensation schemes. Crowd-sourced delivery executives can be compensated and rewarded through remuneration models that consider diverse factors like the probability of acceptance of the delivery task, better customer satisfaction ratings, and an overall track record of the number of shipments accepted. Companies can even opt for simpler time-based models.
Transparency in payments and remuneration: By digitizing their payment methods, companies can offer their staff more transparency and understanding of their earnings, reducing the room for error and conflict. Building the technology capability to manage and execute these complex transactions and calculations can help manage crowd-sourced staff and their payments more efficiently. For example, a mid-sized logistics company might need to make a pay-out of anywhere between Rs. 20- 45 thousand per day for staffing. Making accurate and transparent calculations for individuals based on the shipments delivered, bonuses, applicable incentives, fuel payments, etc. can be quite complex and difficult to manage manually.
New and dynamic training methods: Through training and development programs, companies who hire their own delivery fleets may have the benefit of greater control and alignment with company culture, standards, safety, and product knowledge. New technologies and digital platforms can offer micro-learning, gamification, and reinforced learning modules that help level the playing field, and allow companies to identify, train, retain and conduct refresher programs for their crowd-sourced delivery executives.
The Variable pay-out scheme benefits both staff and eCommerce players. It helps last-mile delivery executives earn better compensation while exerting more control over their time and income. It offers opportunities for higher rewards and incentives for executives delivering during peak hours and for those who are able to manage more shipments. There is more flexibility as lean periods allow executives to engage in other tasks and employment, ensuring that they can further boost their earning capacity. Service providers meanwhile, benefit from the increased flexibility in their capacity and the overall cost-effectiveness offered by these variable models.
