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By Elian Zimmermann
12 October 2020
CUSTOMER SUCCESS

Streamlining Vertical Data Integration for AB InBev

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PRODUCTS: FLOW

INDUSTRY: FOOD & BEVERAGE

INTEGRATOR: ADVANSYS, NEXT INTEGRATION

END USER: ANHEUSER BUSCH INBEV

1. Introduction

AB InBev world’s largest brewer. AB InBev is a multinational drink and brewing company based in Leuven, Belgium. AB InBev has a global functional management office in New York City, and regional headquarters in São Paulo, London, St. Louis, Mexico City, Bremen, Johannesburg and others.

2. Problem

AB InBev needed to migrate from a disjointed collection of breweries throughout Africa to a unified Zone where comparisons are made like-for-like.

2.1 Key Drivers

  • Standardisation in regions’ routines and reporting.
  • Improving efficiency at plant level.
  • Getting the data to a central location in a unified format for rapid SIC.
  • It is so important to know where you have been, what worked, and what didn’t, to correctly plot your course into the future.

2.2 Paint Points AB InBev Faced

  • Inefficiencies due to employees using different/disparate systems for reporting.
  • Data can be lost/corrupted easily.
  • Single point of truth tends to be difficult to implement.
  • Poor standardisation of KPIs across breweries. Some reporting on 10 KPIs, others on 40 KPIs. A definite need to streamline KPIs that are deemed critical to optimal plant operation.
  • Poor standardisation within KPI definitions. Although in name the same…differing calculations.
  • Very difficult to get correct data up to Zone HQ for analysis:
    – Very slow transfer of data, only at a Monthly interval.
    – Time (and resources) spent putting data together in single format.

3. Solution & Results

The Flow Information Platform was selected as the solution to solve AB InBev’s problem.

3.1 Phase 1: Flow to Regions (Tier 1)

  • A Flow instance was created at each plant.
  • Training is given to each plant from a UI point of view.
  • The use of Excel for reporting immediately decreased.

3.2 Phase 2: Flow as a Template

  • Template Flow instance created at Zone HQ – Template Server (TS).
  • Standardised Metrics and Measures/KPIs created on the template server.
  • Department-specific Reports and Dashboards, as well as data entry Forms created on the template server.
  • All brewery instances have a template server configured and pull the templates down.
 
Problems Resolved:
  • Standardisation of KPIs across Africa. What was being reported and when.
  • KPI definitions are standardised. Comparing like-for-like focuses attention where it should be.
 

3.3 Phase 3: Regional to Zone Replication (Tier 2)

  • A Flow instance was created at the zone for data replication and reporting, the Africa Report Server (RS).
  • Bulk Replication Configuration was done at each site (100 000 measures).
  • Each site posts data up to a corresponding measure within the RS.
    – The data posting is triggered when the source data changes.
    – The Tier 1 server is responsible for replication to the Tier 2 server.

Problems Resolved:

  • Data is replicated to the zone within minutes of changing at any brewery.
    – Analysis is rapid and upper-level management can make the best decisions quicker.
    – Data is also always the latest and most recent…if it changes at the plant, it will re-post to the Tier 2 server.
  • Data comes through in the same format onto already configured Dashboards.
    – Middle management no longer needs to spend hours collating data.

4. The Future - BiG Data

4.1 Data Value Through Visualisation

  • AB InBev has a central Flow server that has plenty of data they can utilise.
  • AB InBev is looking at using graphical tools within Flow much more.
  • Flow admittedly does not seek to be a Power BI or QlikView – Flow provides the mechanism to provide its data via Data Consumers to other visualisation systems, so one can use those tools’ visualisation capabilities while leveraging Flow’s data acquisition and transformation strengths.

4.2 Data to Cloud

  • Standardised way of posting data to the cloud via MQTT consumers.
    – No more Excel dumps, SQL DB replication, etc.
  • The data can be utilised by machine learning solutions that are specifically focused on how to use data effectively.
    – “We are busy interfacing with our global structures about getting data globally into a Sustainable Development algorithm.”
    – Reverse Osmosis/Water Treatment companies that are seeking to use the data to decrease brewery maintenance costs.
  • 29 sites
  • 4150 events (total)
  • 542 500 measures (total)
  • Connected to different data sources:
    – Microsoft SQL Database
    – AVEVA Historian
    – Metering Online
    – OPC UA Historian
    – Web Service
  • Reports that have been generated so far:
    – 1680 charts (template chart instances across all sites)
    – 721 charts (Zone/HQ-specific)
    – 285 dashboards
  • Flow is used to integrate with third-party applications including Flow Consumer, Microsoft SQL Database and PostgreSQL Database.
  • Flow tiering, an Enterprise decision-making solution is used extensively at AB InBev. Approximately 5500 measures were replicated from each of the 29 sites.
  • We have a template server which all sites pull their templates from. Flow templates are used to ensure standardisation and governance.
  • More than 500 people across all 29 sites are using Flow reports and dashboards daily.