Consumer habits have changed a lot in the last few years, especially after Covid. Consumers now are more enticed by comfortable options at their place to avoid any hassle in their routines.
Gen-Z has a high monthly income with a hectic lifestyle. Due to this, the task of regular buying becomes a problem. People demand a quick way to satisfy their needs.
The kind of facility users ask for is served only by a high-end app to smoothly manage everything. Hence, businesses are focused on fostering the demands and services of the customers. The quick operations of the delivery fleet, sync of staff picking and delivering to the last mile customers, etc. all are done by assigning different delivery executives to food/grocery delivery logistics zones.
Before we move forward with how these delivery clusters work, let’s understand what are Delivery Logistics Zones.
- The risk of spoilage in grocery delivery
- Dock Scheduling:
- Delay in deliveries due to no real-time visibility
- Limited control of last-time delivery
- Lack of integration between order fulfillment and errors
What Are Delivery Logistics Zones?
Delivery logistics zones are the geographical areas (point of origin) that are assigned in the heuristic assignment model, to reach the order to the place where the warehouse is situated. The delivery area further divides into delivery logistic zones to reduce the pressure felt by delivery businesses.
With the grocery/food delivery app development companies integrate feasible and advanced features with the technologies. The transformational elements can balance the performance of the delivery fleet system and warehouses.
More about the Grocery Market for Logistics and Supply Chain…
The grocery delivery market is one of the fastest-running economies in the marketplaces. The estimated $11.9M in the year 2021 is touched and has been growing continuously. It will touch the figure of $300M by 2027. There are many reasons that ensure that the grocery market is more impulsive than ever.
The customers have more eating habits without discomfort. This increases the rate of consumers buying their groceries online with satisfaction.
It does open new horizons to the grocery delivery app development business to render a one-stop solution to the market to accelerate the needs of the market. However, the solutions that will come in the future may or may not satisfy the customer’s experience but will surely give a drift to the rate of the market.
Today, in this blog, we will understand a quick way to solve grocery delivery problems for assignment clusters of restaurants. Assignment clusters are nothing but the delivery zones. But before continuing with this, let’s encounter some of the common challenges that exist in grocery delivery logistics.
So without any wait, let’s begin!
Challenges Faced by Grocery Delivery Logistics
Grocery logistics are always challenging and exhausting. The challenge occurs due to market competition, customer demand for fast and personalization, problems faced by the delivery executives, and proper security management. The dairy products like Yogurt and Milk have a short lifespan to be consumed in the estimated time, which causes a tedious task to deliver in time.
The risk of spoilage in grocery delivery
The grocery delivery system has a 24 hours life span in stock, which will reflect the risk of spoilage. This happens due to the improper run of the dock systems that cause dock rescheduling.
It is the warehouse calendar, which is used to manage the groceries deliveries under the time constraints for operating the process of transportation and assigning the labor for each.
Due to the dock’s improper handling, the entire system breaks its harmony. That will increase the resource demands and cost of the delivery.
The problem is solved using advanced technology that ensures quick delivery within the estimated time schedule prior to the deliveries. Especially for the items which have a high need for storage and have a risk of decay, like beverages or dairy products. With modern-age analytics like to predict earlier climate transportation, the deliveries can be run in a faster manner.
Delay in deliveries due to no real-time visibility
In the grocery delivery industry, handling out-of-stock products causes a major challenge.
When the item is in shortage in the warehouse and if it is predicted earlier, then it would be easy to fill it in advance to avoid any delay. The notification alerts can easily monitor the orders in inventory and inform the responsible person to refill them immediately. The real-time monitoring of things and processes is mandatory.
Limited control of last-time delivery
Most of the delivery partners are highly affected by last-mile delivery. With expert tracking options, the delivery partners can monitor and optimize the deliveries. The dedicated delivery partners with the shipping and courier fleets can hassle-free do the process. It is easier for them to look after real-time management and make the process of deliveries to the last mile flawless.
Lack of integration between order fulfillment and errors
It is the need of every system to work without any disruptions. But this is actually not possible in real-time. One of the reasons is the improper involvement of the middlemen. In the delivery, if for any reason the delivery fleet is stuck in the middle, that establishes an awkward situation, while the warehouse keeps on waiting for an estimated time.
This would cause a tough deal to manage for the delivery partners to reach the last mile in real-time. Whereas if every one of them is not able to communicate, that situation will cause an error.
We have talked a lot about the challenges, and now we want to resolve it using the machine learning heuristic approach.
How to solve the Grocery Delivery Logistics problem?
We have discussed the challenges faced by the grocery delivery industry. The factors on which we need to focus are: to improve fleet management, customize the order handling, schedule the flexible delivery system, and add on customer preferences, with real-time responsiveness.
To consider the above, the grocery delivery management system must adopt an automated system that can make the fleet or logistics management faster. Only that can improve the delivery management and optimize the time and cost parameters in reality. However, the market has witnessed big companies like BigBasket, Amazon Pantry, etc adopt agile technology to run their deliveries on time, reducing the risk of security.
They run smooth delivery management that is functional, controlled, and run. Let’s dive into the right solution using the assignment to develop the logistic zones.
The solution would enhance the customer experience and the delivery partner’s problems.
To harness the problem, then we can approach assignment heuristics. In short, create the delivery logistics zone for the delivery executives to assign orders to similar zones. The algorithm helps to cut down the effective cost of the deliveries and reduce the waiting time of the delivery executives.
Next, come to the algorithm part:
Input objective: take data sets from different trained Machine learning predictions that are potential variables.
Objective constraints = no. of orders
Target Variables: time traffic, customer expectation (CX), and Delivery Executive (DE) familiarity with the areas.
Associated Problem: the orders are kept in pilling the warehouses while the DE has to wait.
Using the algorithm, the restaurant ensures that 1 DE has been assigned with one batch. That means, the solution that is expected is to minimize the cost of delivery with a great customer experience.
The derivation of the delivery zones or cluster creation is for the number of batches with partners present in a particular city.
The delivery cost for the grocery can be calculated as = The total distance traveled by the DE + Time spent waiting at the restaurant
To comprehend the solution, we adopt the Machine Learning heuristic model and integrate it with the APIs.
We can break the solution into;
- Logistic zone or cluster level assignment: Here, the main focus is to reduce the number of orders overhead on the delivery executives. Further, we specify different zones in a city, choose the nearest delivery areas into a cluster, and assign it to a DE.
- Suboptimal in the cluster-level assignment: Here, the zones/clusters are optimized with auto-scaling. This method picks the right clusters depending on the expected runtime.
The next move is to create the logistics or delivery zones. There are particular steps that help to divide the clusters as per the DE availability.
Elementary steps to train Delivery Logistics Zone with the Word-Embedding using Geohashing
The main part to solve the grocery delivery problem with logistic zones for assignment is following the number of steps.
STEP1: training restaurant embeddings
Let the locations be divided into D1, D2, D3, D4, and D5.
Restaurants such as R1, R2, R3, and R4.
The restaurants that are situated near each other come under a single cluster. The location of delivery partners that are close to the delivery locations is packed in a single cluster.
While the restaurants that do not belong to the same cluster have less similarity and are not batched with grocery orders to deliver. But if that happens then, the waiting time at the customer end and delivery partner both increase.
STEP2: derive the clusters from embeddings
This is the crucial step that ensures delivery within the same cluster or zone, to reduce any cross-cluster assignment. That will impact the waiting time factor and cost parameter.
STEP3: dynamic nature of the clusters
The clusters are nothing but the partition of the city where delivery has been placed and assigned to DE. Here, our main motive is to reduce the latency or delay. In the hierarchy of the data sets, a tree is created where the top has no cluster, and the down we traverse the demand for more clusters rises. This will increase the latency so we have to make it under control, to optimize the waiting time and delivery time.
STEP4: restaurant embeddings
Our main objective is to train the embeddings with orders in close proximity from similar restaurants that are assigned the similar location DE. This is achieved by NLP (compared with the humongous data of restaurants ).
With the NLP intelligence, we conclude the following thing: Derive the word embeddings; word2vec, fastText, convert into the geohash, and n-gram.
The restaurant’s location is traced by the geohashes.
Let’s understand briefly.
What is Geohashing?
Geohashing: It is the hierarchy of precision points where each point is divided into small grids of latitude and longitude. It is used to determine the location of a specific identity to the most precise and accurate point.
The geohash contains a string format which is a combination of numbers and characters, i.e alphanumeric (base-32 alphabet encoding, every 5 bits is converted into one character).
For example, the location Taj Hotel, Mumbai, is found with the coordinate pair: 18.922028, 72.833358. It has the geohash code of te7g9kesu1xv, which is a string. With every additional bit, the precision and accuracy are increased. The geohash helps to find the nearest neighbor and is easily stored inside the database.
If you want to understand a little bit more about the geohashing then, let’s dig inside the training of the data. For every path (edge) we take the coordinates of the batch and DE. There are different geohash for both restaurants (g(r)) and DE (g(de)). The geohash is nothing but word embedding in the form of strings, which will train and apply in real-time to match with the input dataset.
The word embedding is extracted using the word2vec or FastText. We choose FastText here due to its short time complexity.
Word2vec: it works for 2 words with similar meaning or close to meaning that are paired as similar embeddings.
fastText: it is an extension to the word2vec. That main aim is to embed the new words, that’s why it is faster than word2vec.
N-gram: it extracts the geographical information of the geohashes.
The trained data is found with less risk in fastText as compared to the word2vec. There is a clear difference of 5%. With the embedding method, we can reduce the percentage risk in assigning more than 2 batches from the same cluster.
With the solution, the grocery delivery business can target location-based delivery zones and allocate the partners if the input matches with the geohashes.
Deliver a Fresh Grocery Logistic Experience with the Right Partner Business Solution
As the competition increases, the daily utilization of needs keeps on growing, which urges the saturated business solution. Using the logistic management of the grocery deliveries with the assignment and cluster formation, the delivery partner can serve quick solutions to both the customer and the delivery partner. The solution can become more feasible due to technology involvement and facilitate faster deliveries, and optimize the transportation of grocery stores so that the delivery network can easily be monitored and scheduled.
If you’re new in the market of grocery deliveries and need some advanced solutions, you can get connected with us. We have a dedicated team of experts who are sound in tech stack and functionalities that can scale your solution in the market.