Performance Management

Logistic Performance Management Using Data Analytics

Implement operational indicators to monitor and improve the performance of international distribution networks

Article originally published on medium.

Objective
Understand how to use Data Analytics to measure and improve the end-to-end performance of your logistic network.

Introduction
A supply chain is a goal-oriented network of processes and stock points used to deliver goods and services to customers.

The performance of your network can be summarised in one sentence

Are you delivering your end customers on time in full?

Behind this simple question, there is a set of complex KPIs needed to monitor and understand your performance.

In this article, we will try to understand the minimum set of indicators and the methodology required to pilot complex logistic operations.

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SUMMARY
I. Example of a Simple Supply Chain Network
Production and delivery of garments for a fashion retailer
II. End-to-End Lead Times
Lead time between the order creation and the store delivery
1. Information flow
Each step of the process is tracked by different systems
2. End-to-End Visibility
Record time stamps from order creation to shipment delivery
3. KPIs and Lead times
Actual versus targets lead times
IV. Conclusion
1. Root Cause Analysis
What can go wrong?
2. Next steps
Root cause analysis and improve the performance

I. Example of a Simple Supply Chain Network

Scenario

You are a logistic performance manager in an international clothing group that has stores all around the world.

The company is producing garments, bags and accessories in factories located in Asia.

Stores are delivered from local warehouses that are directly replenished by factories.

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Our focus here will be on the distribution. The aim is to ensure on time delivery assuming that we have the stock on hand in the warehouses.

Stores Replenishment

Distribution planners are managing the inventory in the stores to meet the demand.

Supply Chain Network from Manufacturing to Retail — http://samirsaci.com — Supply Chain Network — (Image by Author)

When the inventory level, for a specific reference, reaches the minimum level

The distribution planner creates a store replenishment order in the ERP with the quantity for each item and a requested delivery date
The order is transmitted to the warehouse management system
Warehouse operational teams prepare the orders for shipment
Transportation teams organise the pick-up at the warehouse
Shipments are delivered and received at the stores

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In our example, store replenishment is managed by a team of distribution planners. But it can be also completely automated using ERP or inventory management systems.

If you want to learn more about inventory management policies, have a look at the article below

Warehouse Replenishments

The same process is done at the warehouse level by Supply Planning teams to ensure a minimum level of inventory for order preparation.

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This part of the process will be excluded because it involves complex processes linked with the upstream flows from factories. We will focus only on the distribution to stores.

Overall Performance: On Time In Full (OTIF)

The overall process performance can be measured by the capacity of the network to deliver the stores on time and with the right quantity of items (in full).

On Time in Full (OTIF) Examples — http://samirsaci.com — On Time in Full— (Image by Author)

As a logistic performance manager, your focus will be on improving this indicator that is driving the satisfaction of your internal customers (i.e. the stores).

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For more information about the KPI On Time In Full have a look at this short explainer video in my youtube channel: Link to Youtube

End-to-End Analysis

However, this indicator alone cannot give you full visibility of what is happening in the chain.

Therefore, we will need to break it down and understand what is impacting the overall performance using data analytics.

II. End-to-End Distribution Process

Let us break down the different steps between order creation and shipment delivery.

1. Information flow

In Supply Chain Analytics, everything starts by understanding the flow of information.

By connecting to the right systems, you’re going to extract and process the correct information.

Scenario: Store Replenishment
The inventory levels of some references are below the minimum safety level in several stores.

A distribution planner is creating a replenishment order in the ERP with quantities and requested delivery dates
The order is transmitted to the WMS before the expected picking date
Warehouse operational teams prepare and pack the orders
A truck is assigned for pick-up at the warehouse
Shipment information is transmitted to the TMS for tracking
The goods are delivered to the store
Items are received in the ERP by store teams

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In a perfect world, we will assume that these systems are perfectly connected using API/EDI to ensure a continuous traceability of the flow.

2. End-to-End Visibility

After connecting the different systems, you can get detailed visibility of each step between replenishment order creation and store delivery.

End-to-End Time Stamps — (Image by Author)

Order reception time: timestamp when the order is received in the WMS and ready to be prepared in the warehouse
Warehouse Operations: picking, packing and shipping are tracked by the WMS
Transportation: tracking of the orders from shipping to delivery
Store receiving: timestamp when store teams are receiving the shipments in the ERP

During the order creation, planners are adding a requested delivery date that can be used to calculate the targeted timing for each process.

End-to-End Time Stamps vs. Target — (Image by Author)

Thus, we can know at each step, if operations are behind the schedule and find potential bottlenecks.

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Time stamps are estimated considering the requested delivery date. We use the target lead times of each step from creation to delivery to estimate the time stamps.

3. Lead times

There is no point, from an operational point of view, to look only at the OTIF at delivery.

Lead time definition — (Image by Author)

The segmentation by sub-process is mandatory to monitor the performance of each leg of the logistic network:

Order transfer is impacted by infrastructure & software
Order preparation is linked with the capacity and productivity of warehouse operations
Pick-up scheduling lead time between the end of packing and shipping time
Transportation from the warehouse to the store

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The added value at this stage is to provide detailed visibility of the performance of the process. Your role is to support operational teams to improve their performance by implementing a continuous improvement culture backed by data.

IV. Conclusion

Now that we have visibility on each step of the process, what can we do to improve the overall performance?

1. Root Cause Analysis

To improve the overall performance, you need to spot the root cause(s) in case of late deliveries.

What can go wrong in the process?

IT Infrastructure & Softwares
It starts with the systems, you can face some delays due to capacity issues or system failures. If the order is not received by the WMS, your warehouse teams cannot proceed with preparation and shipment.

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The human factor can also impact the performance with data entry errors, delays on some key tasks or just misalignments between the teams.

Warehouse Operations
In the warehouse, the lead time can be impacted by

Stock-out: products missing causing back orders and cancellation
Capacity: resources shortage to absorb the workload (people, equipment)
Transportation sourcing: you cannot find trucks to pick up packed orders

Transportation
After the truck left the warehouse, the lead time can be impacted by

Road conditions or delays during transfers for multi-modal transportation
Postponement of delivery due to availability constraints: for instance shortage of store staff to receive the goods, store closure, …

Transportation Root Causes — (Image by Author)

These are general examples that must be adapted to your specific situation.

2. Next Steps

Reaching this step is already challenging. Indeed, getting all the root causes requires a tremendous effort of coordination, analysis and systems mapping.

The logistic teams need then to work on mitigation plans to ensure that these issues will not occur in the future.

Thus, you are able to build around this reporting tool a recurrent process for performance management with:

Weekly or monthly analysis of the data
Implementation and follow-up of continuous improvement plans

Data analytics tools will not provide the solutions but they can help the operational teams to find the bottlenecks and failures in the process.

About Me

Let’s connect on Linkedin and Twitter, I am a Supply Chain Engineer that is using data analytics to improve logistics operations and reduce costs.