Why Logistics Players Should Focus on Territory Optimization Over Route Optimization

First, let me quickly spell out the key takeaways from this post. It's how an advanced territory optimization strategy can help you ensure:

• A 10% to 28% potential cost savings by estimating the exact combination of fixed and freelance rider usage
• Roster for fixed delivery fleet and utilization of freelance riders are completely data-driven
• Cost-efficient management of expensive outliers

10% to 28% potential cost savings can be achieved using territory optimization

Now, let's get back to the subject. The way logistics businesses have been planning static routes to execute express deliveries might soon become obsolete, and we have good reasons to believe so. As order density grows, it will become increasingly challenging, if not impossible, to control logistics costs and scale deliveries per rider using such practices. Courier, express and parcel businesses must embrace a new way of thinking. Now, coming to the good part. There's a solution to this problem, and it's called 'territory optimization,' and one of the largest multinational express logistics companies in the world is leveraging it and reaping-never before-seen cost benefits. We will talk about these in a bit.

Limitations of Static Routes

Let's first establish why static route planning is a bigger problem than businesses might think it is. In the express logistics world, physical route codes printed on the labels act as an identifier when parcels get physically sorted. This means the labels cannot be changed. There are no dynamic routes, and riders need to follow the same static routes that are printed at the origin of the parcel's journey. This results in:

• The need to demarcate large cities into zones or clusters, thereby defeating the purpose of efficient route planning
• No room for optimizing routes based on real-time factors such as traffic, weather, road closures etc
• Difficulty in obtaining real-time updates on riders; by extension, difficulty in estimating ETAs
• Inability to frequently change the number of freelance riders due to the fundamental nature of static route planning - i.e., fixed routes for every driver along with no room for optimization
• Dissatisfaction amongst company's riders because of inequitable distribution of delivery tasks, which is directly proportional to their salaries

So, what is territory optimization?

Let's quickly define what exactly we mean by territory optimization. Territory optimization refers to taking a pre-defined geographical area, breaking that up into zones and subzones, and then assigning riders to these zones to make their respective parcel deliveries.?

Zones can be defined by: (i) pin codes, (ii) area names, or (iii) a set of polygons (and mini-polygons) on a map. This "partitioning" of the geographical areas is what results in optimizing the territory.

Further, riders are assigned to these zones and/or subzones to execute all the deliveries and are encouraged to service only their assigned zones for their tenure with the business. It helps solve multiple problems, such as increasing the riders' familiarity with their respective zones, creating a relationship with the end customer, and gaining deep knowledge to solve such physical-world problems that technology cannot.?

Identifying The Best Static Routes Using Territory Optimization?

Identifying the best static routes is the most critical problem the territory optimization technique can efficiently address. Therefore, to ensure we can clearly demonstrate its value, let's cite a real-life simulation we executed for our customer I mentioned initially.?

Territory optimization refers to taking a pre-defined geographical area, breaking that up into zones and subzones, and then assigning riders to these zones.

While running the simulation, we considered multiple constraints based on the customer's needs. Some of those are working hours for riders are 9 with a 1-hour break, zones shall not cross highways and primary streets, delivery times being 8 am to 6 pm, a 110% rider utilization on his zone and more.?

Results Delivered By The Simulation Were Outstanding

The results of the simulation were outstanding. It displayed a significant improvement in the efficiency of delivery tasks allocated to drivers post-running the simulation. The customer can now achieve 10% to 28% potential cost savings by quickly estimating the exact combination of fixed and freelance rider usage. It further helped the customer optimize costs by reducing expensive outliers from a particular zone and putting those in a zone that can better serve those locations.

This is a prelude to a much bigger story. To enhance your domain authority about the subject and get in-depth knowledge about the simulation approach, constraints and results, do get in touch with us. We will be happy to share the detailed white paper with you. Simply respond in the comments section or write to us at [email protected].