Driving Business Success Through Data, Analytics and ... · 2. Simple Definition applied to business: • “A really important plan…to achieve a designated objective” 3. Therefore…A

Driving Business Success

Through Data, Analytics and

Business Intelligence

Dr. Raj VeeramaniUNIVERSITY OF WISCONSIN–[email protected]

Shawn HelwigTOTAL VIEW [email protected]

Presentation Outline

Driving Business Success Through Data, Analytics

and Business Intelligence

• The strategic opportunity for manufacturers

• Case-studies

• A structured approach for doing it right

2

Impact of irreversible, converging technology trends

Digital Business Transformation

Embedded sensing and

smart devices

Real-time analytics and

Contextual intelligence

Pervasive connectivity and Cloud computing

5

Manufacturing Systems

Smart & Connected Systems

6

Your company’s data: A strategic asset

Data from every facet of your business,

not just the shop floor!

Analytics and Data-driven Decision-making

ActionData

Descriptive analyticsWhat happened?

Diagnostic analyticsWhy did it happen?

Predictive analyticsWhat will happen?

Prescriptive analyticsWhat should happen?

in context

Examples of Analytics Use-Cases for Manufacturers

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Front Office

• Demand Forecasting

• S&OP

• Product Design

• Customer Satisfaction

• Supply/Demand

Balancing

Production

• Enhancing OEE (Overall

Equipment Effectiveness)

• Predictive Maintenance

• Machine Downtime

Analysis

• Quality Analysis

• Defect Tracking

• Production Scheduling

• Capacity Planning

• Real-Time Parts Flow

Monitoring

• Inventory Optimization

Back Office

• Spend Management

• Supplier Performance Tracking

• Energy Efficiency

Improvements

• Margin Contribution Analysis

by Customer / Product

• Overhead Tracking

• Labor Cost Optimization

Copyright © 2020 by Total View Analytics

Case Study #1 – Part One

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1. Demand Forecasting

• Challenge: Forecast accurate demand for chicken when the supply is extremely limited

• Approach: Leverage technology to “crunch the numbers” to identify a more accurate forecast

• Method: Gather customer data → Cluster & Segment similar customers based on volume & ordering behavior → Auto-generate a unique forecasting model for each customer segment


Case Study #1 – Part Two

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2. Predictive Maintenance

• Challenge: Avoid “air-chill” system downtime

• Approach: Establish a model to predict failures

• Method:

1. Gather data leading up to previous failures 2. Use IoT sensors to measure conveyor belt motor amperage,

temperature & tension 3. Monitor data through a predictive model for signals


Case Study #2 WI-Based Mfg

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1.OEE Success

• Challenge: Reduce cost to suppliers or lose contract

• Approach: Implement OEE to identify potential efficiencies

• Method: 1. Collect data from stamping operations2. Combine data with ERP data to calculate OEE3. Display OEE performance on Flat Panels screens

• Result: Eliminated ENTIRE 3rd shift – saved contract


OEE = Availability x Performance x Quality

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• Connect OEE data to Operational Data

o Identify Root Cause: Part design? Customer? Operator?


Vendor Soup – Who to Choose?

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Question: Where to Begin?

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This is the common challenge facing most manufacturers today

Why?

• So many use cases to tackle

• So much technology

• Too many vendors – stop e-mailing us!

• Data is everywhere

• The complexity! Who can I trust?

• What should I do first?


1. Meaning often comes from the origin…

• Greek word = Strategia…meaning “art of the general”

2. Simple Definition applied to business:

• “A really important plan…to achieve a designated objective”

3. Therefore…A Good Strategy has Two Parts -

• Covey: “start with the end in mind”

• Sun Tzu: “…he who is destined to defeat first fights and afterwards looks for victory.”

Start with a Strategy

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1 -Objective(s)2 –

Plan(s)

Strategy


1. Use Structure – 4-Step Method with Tools & Templates

2. Be Practical – Avoid getting into “the weeds” – stay at 14,000 ft

3. Focus – Complete the method quickly – in days, not weeks

GOAL: A Concise Road Map that Defines & Prioritizes your Data-Related Efforts (a Strategy)

How to Craft a Data Management & Analytics Strategy…

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Introducing: Analytics to Win® - for Wisconsin ManufacturersA Practical & Simple Method for Crafting a Data Management & Analytics Strategy


1. Define the Corporate Objectives

2. Define the Strategic Data Management & Analytics Objectives

3. ALIGN THEM !

• Which ones support the others?

Analytics to Win®

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Objective:

To DEFINE your organization’s strategic data management & analytics objectives

Audience:

Executive Leadership


Analytics to Win®

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Sample Tool: One way to help Identify Strategic Data Management & Analytics Objectives

The Data Supply Chain

©2019 Total View Analytics Last Updated: 9/2/2019

DATA

MOVE STORE ORGANIZE ANALYZE DISTRIBUTE

Cloud

Devices

ETL

Data Enrichment

Data Warehouse

Data Lake

Data Governance

Master Data Mgt

Data Base

Data Catalog

Dashboards

Excel / Spreadsheets

Reports

via Websites

Embedded in Applications

Data Science

Virtual D/W

Data Profiling/Mapping

Data Quality

Data Wrangling / Data Prep

Data Streaming

A overview of how data moves and common functions performed or utilized along the supply chain

Data Mart(s)

Data Lineage

Where are the weaknesses in your organization’s Data Supply Chain?


Assess…

• Data Management Practices

• Data Project Results

• Data Governance

Analytics to Win®

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Objective:

To ASSESS your organization-wide data management and analytics environment and related competencies to determine areas of improvement

Audience:

IT Leadership

Analytics to Win®

Data Governance Assessment Step 2 - ASSESS

1 - Untrue to

5 - True

SCORE (1 to 5)

1 The organization uses a dedicated data governance council or committee to oversea data-related matters 1

2The data governance council utilizes sub-committees to oversea domain-specific glossaries or data dictionaries (customer vs supplier vs

product, etc.)1

3There are one or more technology solutions in place to enable how data governance policies, standards and rules are utilized with the

data.2

4 There are specifications defining which data elements are considered important , confidential or sensitive . 3

5 Data Stewards have been identified for each primary source system and/or data sets. 1

6 It is easy to generate a list of users who have access to reports/dashboards that contain confidential or sensitive information. 2

7 The source systems that contain important and/or sensitive information are documented and easily available. 3

8 Integrations that move important and/or sensitive source data from one system to another are documented. 1

9There are standard policies and procedures that define and document all aspects of data governance, including who has access to what

data and how the data is updated.2

10 The organization conducts regular data security audits. 2

11 Policies are in place to govern or regulate decisions about sharing or exchanging data with other business entities, vendors, etc. 2

12 The organization uses Active Directory or other LDAP-like groups and/or policies as part of the data governance approach. 5

13There is a master schedule of when key data assets are refreshed from key data sources. Ex. Online customer transactions are updated

in the analytics repository every 60 minutes.2

14There is a list of all users who have security access that enables them to change the security access for other users so they could access

sensitive data.1

15 The organization has well-defined methods or procedures in place to operationalize the data governance policies, standards & rules. 2

16 There are standards or specifications in place used to validate the quality and integrity of production and analytical data. 2

17 It is easy to generate a list of active standard reports that are on a schedule to be generated and distributed. 3

18 Data governance is perceived as a barrier to projects by the organization, not an enabler. 5

19There is a list of data assets that includes a basic overview of the system/service containing data, the types of data contained,

integrations with other systems, and an overview of the user base.4

20 The executive leadership team is aware of the general state of data governance competencies of the organization. 1

Click Here

For Results

\

Instructions: On a scale of 1 to 5, with 1 being completely UNTRUE and 5 being completely TRUE, enter a score that applies to your

organization.

Analytics to Win®

Data Governance Assessment Step 2 - ASSESS

Overall Data Governance Assessment Results

The OVERALL Data Governance competency score is shown below. A HIGHER score indicates more mature data governance competencies.

TOTAL RESULT ==> 41

0 - 25 WEAK

26 - 50 MARGINAL <== Your Organization

51 - 70 OK

71 - 90 STRONG

91-100 WORLD-CLASS

Data Governance Competency - Areas for Improvement

Data Governance is comprised of FOUR competency areas. Areas that may need improvement are shown below:

If Improvements are needed, add potential improvement projects to the Project Portfolio Sizing Summary

1) Organization TOTAL RESULT ==> 5

0 - 5 WEAK <== IMPROVEMENT NEEDED

6 - 12 MARGINAL

13 - 20 GOOD

21 - 25 STRONG

2) Policy TOTAL RESULT ==> 11

0 - 5 WEAK

6 - 12 MARGINAL <== CONSIDER IMPROVEMENTS

13 - 20 GOOD

21 - 25 STRONG

3) Security TOTAL RESULT ==> 9

0 - 5 WEAK

6 - 12 MARGINAL <== CONSIDER IMPROVEMENTS

13 - 20 GOOD

21 - 25 STRONG

4) Operations TOTAL RESULT ==> 16

0 - 5 WEAK

6 - 12 MARGINAL

13 - 20 GOOD <== Your Organization

21 - 25 STRONG

<BACK

Data policies govern aspects of all phases of the data lifecycle, from

requirements assessment through modeling, acquisition, storage and

management, integration, protection, security, quality, and disposition.

Data policies are often organized around operational functions.1

Data Governance requires structure and commitment from the

organization. The practices associated with this competency revolve

around setting the organizational constructs in place to execute Data

Governance, as well as working with the other areas of the business to

adopt Data Governance practices.

Data Governance competencies around Security are necessary to meet the

growing demands of customers, employees and regulatory bodies. Security

breaches and the misuse of data have created heightened awareness of

data security. Understanding which data to secure and then setting the

standards and procedures to execute proper security are vital.

Operations competencies are oriented around practices and procedures to

execute data governance policies. These are the competencies that

question whether or not the data operations team(s) have adequate

capabilities in place, and question the ease by which they can execute

those capabilities.

Sample Assessment

Sample Results


Analyze…

• Function-Level Data Practices

• Key Performance Indicators

• User Adoption

Analytics to Win®

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Objective:

To IDENTIFY specific projects and/or initiatives that will address department-specific data and analytics-related challenges and gaps

Audience:

Department/Function Management

KPI Questionnaire

SIFROC™ Template

KPI Breakdown Template


Assemble…

• Improvement Opportunities

• Architecture

• Strategy Matrix

• Project Plans

Analytics to Win®

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Objective:

To ASSEMBLE the final Analytics to Win® deliverables, including the Strategy Matrix - to gain leadership approval

Audience:

IT Leadership - then Executive Leadership

Project Portfolio Summary

Architecture Diagram

Analytics to Win®

Strategy Matrix


The Key Deliverable = The Analytics to Win® Strategy Matrix

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BENEFITS:

• One Pager

• Easy to Communicate

• Higher Approval Rate

• Links Plans to Objectives

• Subjectively Empirical

• Easier to Prioritize

• Easy to Adapt/Vary

Analytics to Win®

Strategy Matrix

SAMPLE SHOWN DURING PRESENTATION


SUMMARY: How to Craft a Data Management & Analytics Strategy

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The Analytics to Win® Method

1. Use Structure – 4-Step Method with Tools & Templates

2. Be Practical – Avoid getting into “the weeds” – stay at 14,000 ft

3. Focus – Complete the method quickly – in days, not weeks

OUTCOMES:

A Concise Road Map that Defines & Prioritizes your Data-Related Efforts (a Strategy)

QUESTIONS?

Shawn [email protected]

(608) 514-1801

Creator of: Analytics to Win®

www.totalviewanalytics.com

Dr. Raj [email protected]

Documents

Driving Business Success Through Data, Analytics and ... · 2. Simple Definition applied to business: • “A really important plan…to achieve a designated objective” 3. Therefore…A