What is Business Intelligence?
Business Intelligence is a process that leverages software and services to transform data into actionable intelligence supporting decision-making.
This intelligence helps businesses gain insights into their operations, improve process efficiency, and create a competitive market advantage.
Business Intelligence tools combine various applications, including data warehousing, discovery, and visualization.
How can use analytics solutions to create a central source of truth with harmonized data?
The goal is to simplify analyzing raw data by transforming it into summarized insights for strategic decision-making.
In this article, we will explore the concept of business intelligence to understand how it can support continuous operational improvement initiatives for a more efficient and sustainable supply chain.
Summary
I. What is Business intelligence?
1. Distribution Operations for Fashion Retail
2. Business Intelligence for Operational Management
3. Business Intelligence is not Advanced Analytics
4. Supporting Operational Performance Improvement
II. The Mechanics of Business Intelligence
1. What is a Data Warehouse?
2. A central source of harmonized data for reporting
III. Why is Business Intelligence Significant?
1. Answering a simple business question
2. Become a data-driven green organization
3. Automate ESG Reporting
IV. Conclusion
1. Generative AI to boost Business Intelligence
2. Product Segmentation for RetailWhat is Business intelligence?
This can be defined as a range of software applications used to analyze an organization's raw data.
Distribution Operations for Fashion Retail
We can use the example of an international clothing group that has stores all around the world.
The stores are delivered from local warehouses and replenished by factories producing garments in Asia.
As a distribution planning manager, you would like to measure the lead time to deliver a store (time between order creation and store delivery).
Multiple IT systems orchestrate the complete distribution process
- A delivery order is created in the ERP by a distribution planner.
- Planners enter a list of items with quantities, store codes and requested delivery dates (c.f OTIF – On Time In Full performance indicator)
- This order is transmitted to the Warehouse Management System (WMS) for preparation, packing and loading.
- After loading, the order is tracked by the Transportation Management System (TMS) until store delivery.
Transactional data with timestamps are created and stored in these systems' databases.
- After the order creation in the ERP, a second timestamp records the time when the order is transmitted to the WMS
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The order is then tracked by the WMS from preparation to loading [From Start Preparation To Truck Leaving]
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And the transportation part is managed by the TMS with time stamps following the shipments until store delivery [From Arrival to Airport to Store Delivery]
How can we exploit this data to analyze past events?
Business Intelligence for Operational Management
BI can help convert this data into meaningful information to support descriptive and diagnostic analytics for operational or strategic decision-making.
For each timestamp, BI solutions can help to automatically compare the expected time with the actual one to detect where delays occur.
The objective is to provide reports, dashboards and data visualizations to automatically provide insights to the operational teams.
Let us take an example of the tracking of a delivery order
- Order Creation Time: 21–04–2020 11:00
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Order Reception Expected Time: 21–04–2020 12:30 Order Reception Actual Timestamp: 21–04–2020 12:04 [On Time]
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Order Picking Expected Time: 21–04–2020 14:30 Order Picking Expected Time: 21–04–2020 15:12 [Delayed]
You can continue this until the store delivers it.
Based on these timestamps, you can create automated rules:
- If the actual delivery date is after the expected date, Late Delivery
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For each process, if the actual date is after the expected, you can add them to the list of reason codes For instance: Late Delivery due to [Transmission, Loading, Customs Clearance]
This donut plot is an example of a visual showing the different root causes of late store deliveries
- 1,842 shipment orders have been delivered late
- 37% of the delays are due to order transmission issues only
For more details, you can have a look at these short explainer videos
Why do we need business intelligence?
Business Intelligence is not Advanced Analytics.
The purpose of Business Intelligence is not to advise the operations about the best mitigation plan or to predict future performance.
Business Intelligence provides Descriptive and Diagnostic Analytics solutions focusing on "understanding past events".
- What happened? How many orders have been delivered with delay?
- When? Has the order 1878497 been loaded at the warehouse?
- Who? Which carrier delivered store 12 last week?
- Why? Why did order 1878497 arrive at the airport 1 hour late?
The other types of analytics, using past data to provide predictions and prescriptions,are more advanced but still require the building foundations of business intelligence.
Supporting Operational Performance Improvement
However, this kind of visual aid helps the planning managers comprehend their data better, understand patterns, and extract insights.
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37.8% of shipments are delayed because of transmission Action: "I should contact IT teams to solve these issues."
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3.3% of shipments are delayed because of late loading Action: "Align with warehouse ops to increase loading capacity"
In general, BI solutions are included in a performance management process where data is used to
- Understand the Past: measure the performance, detect issues
- Implement Mitigation Plans: optimize a process, increase resources, solve IT issues
- Track the Operational Improvements: implement key performance indicators (KPIs), issues logging
In the following section, we will delve into the details of business intelligence and how to implement it in your company.
