In an age where every company is promising steep delivery times like “delivery within 24 hours”, express shipping, and so on, there is no place for delayed shipments. In order to keep up these tall claims, companies end up shouldering almost 25% of the delivery costs to provide shipping at almost no cost to their customers. The costs come up to almost $70 billion to the retailers.
Given this tight scenario, on-time delivery or OTIF becomes the cardinal metric to optimize, which means they neither can be early nor late! Premature deliveries may seem desirable, but they come with their own slew of problems. Every company has a different way of calculating this metric, but no matter how they calculate, they are looking for a high OTIF score and lesser missed deliveries.
Introduction to our Partner
The company we recently worked with is a household name in the logistics space in Mexico. They have a powerful fleet of over 2500 vehicles and over 5600 staff members. With their footprint in over 50+ cities, they also have a strong client base of 2000 regular customers. Let us, deep dive, into how Locale enabled them to improve the kingpin of their metrics - OTIF.
They noticed that their deliveries were constantly being delayed, and these late fulfilments led to expensive fines and customer dissatisfaction. This is because the retailers expect top-notch fulfilment and on-time deliveries. In case of early, late, or incomplete deliveries, they impose substantial fines which impact the revenue.
Despite having their final OTIF score recorded regularly, it did not provide clarity on each stage of the supply chain process and go granular into the problem. Their existing technology stack lacked the analytics framework that could handle a compound and challenging metric like OTIF. Moreover, the KPIs need to be constantly updated based on the focus of the company, and the process of creating new metrics every time they had a new requirement was a painstaking process.
Therefore, they needed an intelligence framework that would enable them to go deeper, to understand the extent that could help solve this problem. With customer satisfaction being on the line, this was a top priority for the company. Along with this, they wanted to be able to get real-time insights and collaborate with their team member seamlessly to improve their overall performance.
The How’s and Why’s of the Solution
An overview of the metrics is a good starting point, but it becomes very challenging to find patterns among these data points, pinpoint the reasons behind failed deliveries, and identifying bottlenecks.
Before we approached the solution, we worked towards asking the right questions to understand the most important insights required by the company. All these factors had to be accounted for at every leg of the journey from the first mile to the last mile.
1. Get visibility on real-time data on shipments that did not meet the on-time requirements.
- What external factors(weather, traffic, protests) played a role?
- Are the pickup and drop-offs on-time?
2. Determine reason and impact relationships to find the root cause of the failure of OTIF
- How does the metric change for one-time delivery?
- What is the performance for recurring deliveries?
- How many partial shipments or wrong items were sent?
- Can we pinpoint the facilities which are causing issues?
3. Understanding the correlation between time and areas
- What are the specific routes causing the problem?
- At what time of the day do we notice a high failure rate?
- Do weekdays show lesser fulfilment of OTIF deliveries?
- How does the performance change during different times of the day in different areas?
We have to pay special attention to the first mile as it is still lacking transparency and is inefficient. Many issues that arise during the last mile of the journey may have been a result of issues that arose way up during the initial stages. It is imperative that they had visibility on the first mile as well, depending on the model.
How did Locale.ai help them?
- Using Locale, they first analyzed the routes which caused the most delays and analyzed the type of shipments and the distance ranges: It led them to understand that the delays were caused due to 2 fulfilment centers which were becoming the bottlenecks with low throughput because of the low availability of delivery personnel in that area.
- After identifying the problem, they tried to approach it in two different ways: They assigned more number of delivery personnel to only one of the fulfilment centers because the cost of delivery vs revenue made sense. In the other center, they reduced the SLA as they planned to deliver from these areas only twice a week.
- Since they had tested out different methods, they wanted to measure the impact to know which one worked out. Using the impact analysis feature, they found out that their OTIF increased significantly as a result of their experimentation.
Apart from this with Locale, they were also able to elevate their workflow and make it more efficient.
- They got alerts when a metric showed abnormal behaviour. Open lines of communication help keep supply chains moving. Without the ability to quickly talk to a manager, team members, suppliers, manufacturers, and other important parties, everything would soon bog down.
- They could see where and how it’s going wrong in real-time. They were able to take immediate action to mitigate the situation - placing the workforce in the right place or sending back up fleet in case of issues with the vehicle.
The company was able to record an increase in their on-time deliveries and paid significantly less amount for penalties. Its OTIF metric improved from 72% to nearly 89% within a year.
With this improvement, they were also able to improve their other metrics like fleet utilisation and productivity, ability to react to problems real-time, have a control tower for the planning team to get a holistic view of their operational KPIs in one central place.
Therefore, the answer lies in a robust analysis of the data beyond statistics and charts. For a lot of logistics and courier companies, analytics comes as an afterthought. Without the right tools to analyze their complex data, the KPIs are limited to charts and bar graphs of the number of deliveries across different days and weeks and they are not able to get answers into their "where" and "why."