What is Business Intelligence?

What is Business Intelligence?

Discover the tools for data-driven decision-making applied to supply chain optimization.

Business Intelligence is a process that leverages software and services to transform data into actionable intelligence supporting decision-making.

This intelligence helps businesses to gain insights into their operations, improve their process efficiency and create a competitive market advantage.

Business Intelligence process in five steps — (Image by Author)

Business Intelligence tools combine a wide range of applications including data warehousing, data discovery, and visualization.

The goal is to simplify the procedure of analyzing raw data by transforming it into summarized insights used for strategic decision-making.

In this article, we will explore the concept of Business Intelligence to understand how it can support operational continuous improvement initiatives for a more efficient and sustainable supply chain.

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SummaryI. What is Business intelligence?1. Distribution Operations for Fashion Retail2. Business Intelligence for Operational Management3. Business Intelligence is not Advanced Analytics4. Supporting Operational Performance ImprovementII. The Mechanics of Business Intelligence1. What is a Data Warehouse?2. A central source of harmonized data for reportingIII. Why is Business Intelligence Significant?1. Answering a simple business question2. Become a data-driven green organizationIV. Conclusion

I. What is Business intelligence?

This can be defined as a range of software applications used to analyze an organization’s raw data.

1. Distribution Operations for Fashion Retail

We can use the example of an international clothing group that has stores all around the world.

Supply Chain Network — (Image by Author)

The stores are delivered from local warehouses that are 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).

Systems Architecture — (Image by Author)

Multiple IT systems orchestrate the complete distribution process

Transactional data with timestamps are created and stored in the databases of these systems.

Distribution Process Timestamps — (Image by Author)
  • After the order creation in the ERP, a second timestamp is recording the time when the order is transmitted to the WMS
  • The order is then tracked by the WMS from preparation to loading
    [From Start Preparation To Truck Leaving]
  • 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?

2. 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.

Examples of two shipments (On-time: top, Late: bottom) — (Image by Author)

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
  • Order Reception Expected Time: 21–04–2020 12:30
    Order Reception Actual Timestamp: 21–04–2020 12:04 [On Time]
  • Order Picking Expected Time: 21–04–2020 14:30
    Order Picking Expected Time: 21–04–2020 15:12 [Delayed]

And you can continue this until the store delivery.

Based on these timestamps you can create automated rules:

  • If the actual delivery date is after the expected date: Late Delivery
  • 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]
Delayed Delivery Root Cause Analysis — (Image by Author)

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

3. 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.

BI vs. Advanced Analytics — (Image by Author)

Business Intelligence provides Descriptive and Diagnostic Analytics solutions focusing on “understanding past events”.

  • What happened?: how many orders have been delivered with delay?
  • When?: when the order 1878497 has been loaded at the warehouse?
  • Who?: which carrier delivered store 12 last week?
  • Why?: why the order 1878497 arrived at the airport 1 hour late?
Business Intelligence vs. Advanced Analytics — (Image by Author)

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.

4. Supporting Operational Performance Improvement

However, this kind of visuals helps the planning managers to comprehend their data better, understand patterns and extract insights.

  • 37.8% of shipments are delayed because of transmission
    Action: “I should contact IT teams to solve these issues.”
  • 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 what exactly business intelligence is and how to implement it in your company.

II. The Mechanics of Business Intelligence

Business Intelligence platforms conventionally base their information on a data warehouse that aggregates data from several sources into a centralized source.

1. What is a Data Warehouse?

This consolidation simplifies business analytics and reporting.

Data Warehouse combining data from multiple sources — (Image by Author)

In our example, we are trying to track delivery orders:

  • Created in the ERP with creation date, expected delivery date, destination, SKU, quantity, […]
  • Transmitted to the Warehouse Management System (WMS) that tracks order receiving time, order picking time, order shipping time
  • Transmitted to the Transportation Management System (TMS) to follow the shipment from the loading at the warehouse to the store delivery.

BI solutions interact with these systems to:

  • Process and translate the acquired data into a single harmonized source
  • Build user-friendly reports, charts and maps
Simple Example of Data Processing for Harmonization — (Image by Author)

In this simple example,

  • Data is stored in three different tables (from WMS, ERP and TMS) that have a common key: ORDER NUMBER
  • The harmonized dataset is created by merging these three tables to get the complete flow from order creation to store delivery.

2. A central source of truth for reporting

Using this harmonized table, we can categorize orders by status looking at the last time stamp that is non-null.

Automated rules can be implemented to flag the orders

  • If the delivery time is null we have
    Order status = In transit
  • The last non-null timestamp is used to flag the status
    For instance, if the last non-null time stamp is Order Picking Time: Order status = PickPack
Transit Orders Chart — (Image by Author)

This source of harmonized data is the founding pillar of your data analytics journey as it can also feed

Four types of analytics — (Image by Author)

These solutions, which are outside the scope of Business Intelligence, still rely on the harmonized cleaned dataset built by Business Intelligence solutions.

In the next section, we will understand how Business Intelligence is so important for the digital transformation of a company.

III. Why is Business Intelligence Significant?

Business Intelligence empowers organizations to transform data into actionable insights.

A company can also come up with an actual operational problem and seek the help of business intelligence teams.

Let us illustrate this with the example of the green transformation of our fashion retail company.

1. Answering a simple business question

The main indicator of the environmental footprint of your company is the amount of CO2 emissions to produce, deliver and sell products.

In order to measure it, BI can be used to create a single source of truth across the supply chain to track emissions across different business units and time periods.

What is Life Cycle Assessment?

Life cycle assessment (LCA) is a methodology used to evaluate the environmental impacts of a product.

Life Cycle Assessment —( Image by Author)

It is covering the entire life cycle from raw material extraction to disposal (end of life).

As you can imagine, this methodology mainly relies on aggregating and processing data from multiple sources.

2. Become a data-driven green organization

Our fashion retailer can rely on BI to become a green data-driven company by implementing sustainability indicators across the supply chain.

A data warehouse can centralize data from multiple sources to track your environmental impact at each step of the life cycle.

Data Architecture to track environmental impact — (Image by Author)
  • Measure the production output by connecting to factories systems
  • Collect utilities and raw material data usage of your factories, warehouses and suppliers from Excel files
  • Receive CO2 emissions, fuel consumption and routing data from logistics companies via APIs
You can’t manage what you can’t measure.

The results of data processing will provide a granular vision of the environmental footprint from the product point of view.

For instance,

  • Water consumption per T-shirt produced is 3,500 L/Unit
  • 25% of the CO2 emissions are due to sea freight
  • 0.8 g of SOx and 0.5 g of NOx are emitted per T-shirt produced
Example of LCA-related KPIs along the supply chain — (Image by Author)

These indicators can be used to build your sustainability roadmap:

  • Improve the efficiency of your manufacturing facility considering the water and energy used per unit produced
  • Challenge your freight forwarders and track their emissions to reduce the footprint of your transportation
  • Improve the raw materials sourcing to reduce the amount of water used

💡 For more details about solutions for environmental footprint visibility,

VI. Conclusion

The most effective BI tools facilitate operational management, detect issues and support your business transformation journey.

By connecting to a variety of data systems, they can provide valuable insights and uncover hidden patterns.

Business Intelligence tools do not only provide solutions for descriptive analytics (dashboards, reports), they create a central source of trusted data that can be used by more advanced predictive or prescriptive algorithms.

In order to meet environmental targets, organizations must embrace these tools to extract operational insight from the data they generate.

About Me

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