Designing Data Collection for Consistency that Improves Process Management

©2011 21CMS All Rights Reserved

Factors for

Manufacturing Analytics Success - Part 2:

Designing Data Collection for Consistency

to Improve Process Management


Standards Liaison

for Manufacturing Operations Charlie Gifford

• Thomas Fisher Award for Best Standards Book of Year 2010

• MESA International Outstanding Contribution Award 2007

• Chairman, ISA-95 Best Practices Working Group

• Published over 45 papers and 4 books on Mfg Operations IT

• Director, MESA Global Education Program

• Certified TQM Facilitator / Process Action Team (PAT) Leader, 22 years

• Voting Member, ISA-88 & ISA-95 Committee

• ISA-95 Representative, ISA-95/SCOR Alignment Working Group

• Information Member: ISA-99 (Security), ISA-100 (Wireless)

• Director, ISA Computer Technology Division 96-99

• Coauthor, SCOR MAKE Section

• Chairman, Editorial Board, Industrial Computing Magazine 98-02

• Standards Work: ISA-84, 88, 95, MESA, SCOR, Many DOD Standards

2


Agenda

• The Data Collection Problem for Standards-based

Manufacturing Intelligence

• What Data to Collect

• How to Collect for Data Integrity

3


Mfg 2.0 Requirement: Design for

Global Manufacturing Environment

Mfg 2.0: Evolve Demand-Driven Manufacturing as

a Scalable Adaptable Business Model

• Synchronize manufacturing and supply chain work processes

• Dynamically reconfigurable global supply network

to a known profit per order fulfillment path

• Reuse of Model-based architecture provides scalable

continuous improvement capability

• Scalable Continuous Improvement “Network”

4


Mfg Operations Contribution Required

Supplier

Quality

Supplier

On-Time

Purch

Costs

Dir Mtl

Costs

RM

Inv

Cost

Detail

Production

Sched

Variance

Plant

Utilization

WIP + FG

Inventory

Order

Cycle

Time

Perfect

Order

Detail

AP AR Inventory

Total

Cash-to-Cash

Perfect

Order SCM

Cost

Demand

Forecast

• Right product

• Right Quality

• Right place

• Right time

• Right profit margin

Enterprise Manufacturing Intelligence Copyright © 2011 Gartner Group

5

©2011 21CMS All Rights Reserved 6

• Importance of Perfect Order Performance

• 1/10 of the stockouts of their peers

• 15% less inventory

• 17% stronger perfect

order fulfillment

• 35% shorter cash-to-cash

cycle times

Mfg Operations Contribution Required


Mfg 2.0 Innovates

Operations Process

Effectiveness


Top Line Opportunities are Compelling,

But More..

3 - 12 mos. 12 to 36 mos. 3 years +

1. Reduce

Operating

Costs

2. Increase Volume

and /or Margins At

Same Cost

3. Increase Market Share

And/Or Pursue New Markets

Faster NPI cycle – shorten TTM for innovation

Customer audit requirements: traceability and genealogy

MES marketed as competitive tool

Promotes flow manufacturing

Supports collaboration Supply chain visibility Platform for continuous improvement

Lower WIP and FGI Reduce indirect labor costs Reduce waste/scrap/materials Shorten cycle/flow time Reduce cost of regulatory compliance Improve quality/ reduce process & product Reduce rework variability Reduce maintenance costs

12 to 36 mos.

Faster NPI cycle: Shorten TTM for innovation

Customer audit requirements: traceability and genealogy

MOM marketed as competitive tool

Supports collaboration Supply chain visibility

Platform for Continuous

Improvement

Lower WIP and FGI Reduce indirect labor costs Reduce waste/scrap/materials Shorten cycle/flow time

Reduce cost of regulatory compliance

Improve quality/ reduce process & product Reduce rework variability

Reduce maintenance costs

$$

Va

lue

of

Be

ne

fits

Project payback ranges 6 to 24 months

Average

payback

12 Months on

1X Benefits

1X

10X

3X

Larger benefits from

continuous

improvement:

MOM is necessary to

achieve this level

MOM Systems justified

on cost reduction Copyright © 2011 Gartner Group Report: MES Provides Long-Term Revenue and Market Benefits

Beyond Easy-to-Quantify Operational Cost Savings

8


Mfg Data Sophistication Determined by

Mfg Work Processes

9


Broadcast

WIP

Track

Production

Monitoring

Scheduling

SCADA

ANDON

Data

Collection

Re

Sequence

eKanban

Logistics

Suppliers Corp

Systems

Order

Mgmt

Quality

Error

Proof

Asset

Mgmt.

Required Agility Forces Change

10


Plant Data Collection Issues • Primary Plant Data Collections:

• Process and Work Process Data

• Operations KPIs and Metrics

• Business Process Data

• Business Process Metrics

11


Plant Data Collection Issues • Too many Shop Floor GUIs and Paper Forms from Manual

Data collections for too many applications

• Too many paper forms are manually transcribed into

applications with point-to-point interfaces to other applications

Has Led To…..

• Non-value added activities

• Large data translations error propagates poor data Integrity

• No “same shift” feedback:

• Operators and supervisors MUST CARE about their manual

data collections

12


“Automate” Manual Data Collection

0%

5%

10%

15%

20%

25%

30%

35%

40%

Fully automated Partially automated Keyed intospreadsheets

Manual recording

Data Collection Mechanism for Metrics

Business movers Others

Source: Correlating Plant Performance to Business Performance, © 2010 MESA International & Cambashi Inc.


Focus on Value-Add Data Collections

• User-centric User Interfaces (UI) streamline activities by

contextualizing all applications to a single UI for each operation

• Orchestrated Manual Data Collection:

Minimize Typing or Writing

• Wireless UIs with Single action methods:

Bar Code Sheets, Menus, Value Inputs, Error Proof ranges

• Mobile Applications: MS OS, Apple IPAD, Android

• RFID Mesh Networks

• Contextualized Automated Equipment Data Collections

• Standard OEM equipment interfaces

• Rationalize equipment state models for OEE data integrity


Business Movers Show Improvement Requires Rapid Feedback

0%

20%

40%

60%

80%

100%

Within a shift Longer than a shift

How rapidly operational KPIs showed to those managing the operations measured

Business movers OthersSource: Correlating Plant Performance to Business Performance, © 2010 MESA International & Cambashi Inc.


High Value Realized by

Actionable Accurate Decisions

© All rights reserved. Industrial Management Enhancement, 2011


Competitive Framework

for Process Capabilities

Best-in-Class Average Laggards

Process

Standardize processes across the enterprise

for optimizing manufacturing operations

64% 37% 30%

Standardize measurements of KPIs across

enterprise

68% 58% 51%

Standardize processes for response to

adverse events

64% 51% 19% Copyright @2008 Aberdeen Group, All rights reserved.

17


Agenda





18


Best-in-Class Focus on Perfect Order and

NPI Supported by Actionable OEE

© 2011, Aberdeen Group. All Rights Reserved.

19


Manufacturing Intelligence Foundation…

VISUALIZE CONTEXTUALIZE

e.g. Capable/Profitable to Promise ANALYZE

DEVICE I/O

TAGS

EQUIPMENT

& ASSET

ORDERS

SPECIFICATIONS

INSTRUMENT

BUSINESS

RULES

MATERIAL

& PRODUCT

FLOWS

PRODUCTION MODELS,

RECIPES/ BOMS

& ROUTES

COST-BASED

MODELS

Large volumes of extremely detailed

production data from multiple back-end

data sources.

Operating data transformed into asset

performance KPIs

Correlate of work process data,

equipment data and product data

Overall process

performance metrics

Performance

to schedule

Perform

To Demand

Incre

asin

g S

trate

gic

Valu

e t

o t

he E

nte

rpri

se

Copyri

ght

© 2

011 G

art

ner

Gro

up

20


Business Scope

Production

RPO/

PSO

APC Logistics

BPM

Collaborative Infrastructure

Enterprise Domain Business

Customers Suppliers

Value Chain Domain

Lifecycle Domain

Automation

ERP

PLM/S

PLM/D

SRM CRM TMS

CPS

HR

EAM

FIN

GLS

…Enables Collaborative Manufacturing

Management

MES MOM

Source: ARC Advisory Group

APS/

FCS

21


MESA Metrics Conceptual Framework

• Focus on Actionable

metrics for improvement

• Link metrics from

operations to finance

• Logical links do exist

• Focus on Financial drill

downs to operations

improvement efforts

Inc

reas

ing

ag

gre

ga

tio

n

Audience:

CFO, CEO

Plant Accounting,

Finance

Plant Management,

Operations

Management

Operators, Supervisors,

Quality, Engineers,

Technicians

Profitability

Inc

reas

ing

ab

ility to

take

ac

tion

CorporateFinancials

Aggregated Financial& Operations Metrics

Operations-level KPIs &Dynamic Performance Metrics

External

Investors

& Creditors

Internal

Strategic

Business

Planning

Plant floor sensors, Operator, and

machine to machine interface

Machine to

Machine


Knowledge is a Key Enabler of the Knowledge Worker to

Support Problem Solving and Troubleshooting

Information

Understanding

Knowledge

Structure Data

Understanding Relationships

Understanding Patterns

Understanding Principles

UNDERSTANDING

CO

NT

EX

T

IND

EP

EN

DE

NC

E

Structured Data Provides

Greater Understanding

23


Mfg

Master

Data

Mgt.

24

A Recipe

Management

Example:

Master Data

and its mMDM

24


Production

Performance

Batch Production

Record

Work Production

Record

Master Recipe Master Work

Definition Product

Definition

Production

Schedule

Control Work

Definition Control Recipe

Work

Schedule Batch List

Product Related

Definitions

Output from

Scheduling

Executable

Elements

Execution

Results

Site Recipe

Align

Mfg Ops

Master

Data

25


Agenda





26


First, Top-down Orchestrated… Next, Bottom-up Optimized. Links Plant to Business Performance

0%

5%

10%

15%

20%

25%

Business Movers Others

Links between operations and business KPIs are very effective

Source: Correlating Plant Performance to Business Performance, © 2010 MESA International & Cambashi Inc.


Supplier

Plan

Customer Customer’s

Customer Suppliers’

Supplier

Make Deliver Source Make Deliver Make Source Deliver Source Deliver

Internal or External Internal or External

Your Company

Source

Understand Plant’s Role in Supply Chain:

SCOR’s 5 Management Processes

SCOR Model

Return Return Return Return Return Return Return Return

Building Block Approach

Processes Metrics

Best Practice Technology

Copyright © 2011 Supply-Chain Council All rights reserved.


Top-Up Metrics Based on Business

Process Metrics Decomposition



Bottom-Up KPI Hierarchy based on

VSM and URS Process Definitions



Lean Attacks Waste

Process

Non Value Add Time

Value Add Time

Lead Time/Cycle Time

Six Sigma Attacks Variation

FOCUS on continuous improvement data collections:

• Lean … Cycle time reduction and waste elimination

• Six Sigma … Defect reduction and variation control

Data Collections from

Value Stream Maps and 6 Sigma…

Enable Manufacturing Transformation

31


Business Logistics Management

LEVEL 2 Data Inputs and Outputs: Manual and Automated

URS Defines Operations, Information

Flows, Data Collections, and Timings

Common Material

Segment

Final Material

Segment

Final Product

Segment

Make Material Segment

Inven

tory

Inven

tory

Deliver

Batch Batch Batch

Test

Mix

Deliver

Fill Cap Label Package

Deliver

Test

Setup/

Maintain

Setup’

Maintain

Production Operations

Management

Quality Operations

Management

Maintenance Operations

Management

Inventory Operations

Management

Inve

nto

ry

Copyright © 2011 ISA

32


ISA-95 Object Models Define

Data Exchanges & Data Models

33

People Materials Equipment

Resources

Process & Operations

Segments

Structure / View

Production & Operations Schedule

Production & Operations Performance

Production

Product

Time

Production & Operations

Capability

Capability

Product & Operations

Definition

Product/Operations

4 Resource Categories 4 Information Categories


Production Resource Management

Production Capability

Product Analysis

Production Data Collection

Production Execution

Production Dispatching

Production Tracking

Production Performance

Detailed Production Scheduling

Production Schedule

Level 2 Process Control / Plant Work

Product Definition Management

Product Definition

Production Analysis

Process Analysis

Three Types of MOM Analytics for KPIs

Correlate Data to Construct Metrics

and Complete Production Genealogy

34

Metrics Categories

From ANSI/ISA-95.00.03-2007 Copyright © 2010 ISA. Used with permission. www.isa.org


Maintenance Production Quality Inventory

Production

data

collection

Production

execution

management

Production

resource

management

Production

dispatching

Production

tracking

Production

performance

Detailed

production

scheduling

Production

schedule

Product

definition

management

Production

performance

analysis

Production

capability

Product

definition

Maintenance

resource

management

Maintenance

response

Detailed

maintenance

scheduling

Maintenance

request

Maintenance

definition

management

Maintenance

capability

Maintenance

analysis

Maintenance

definitions

Maintenance

data

collection

Maintenance

execution

management

Maintenance

dispatching

Maintenance

tracking

Inventory

resource

management

Inventory

response

Detailed

inventory

scheduling

Inventory

request

Inventory

definition

management

Inventory

analysis

Inventory

capability

Inventory

definitions

Inventory

data

collection

Inventory

execution

management

Inventory

dispatching

Inventory

tracking

Quality

analysis

Quality

test resource

management

Quality test

response

Detailed

quality test

scheduling

Quality test

request

Quality

definition

management

Quality test

capability

Quality

definitions

Quality

test data

collection

Quality test

execution

management

Quality test

dispatching

Quality test

tracking

Level 2 Process Control: Inputs and Outputs are Bi-Directional Data Collections

Production Operations Depends

on Operations Data Response

• Shaded elements define information flows within Level 3

areas to support Production

• Some information may flow to other Level 4 systems

ANSI/ISA-95.00.03-2006

Copyright © ISA 2011.

35


Conclusion • Too much data…Poorly collected for today’s modern manufacturing

environment…with predictably disastrous results

1. Assess and Define processes for each operation to understand quality

impact of product and processes: Their information flows, data, & timings

2. Successful systems

1. Define and maintain consistent data formats

2. Design for compliance with industry standards for MDM governance

3. Design value-add data collections for Actionable control of process

4. Design data collection methods to deliver high integrity data

3. Provide actionable “same shift” feedback on processes to establish a solid

foundation for process management and continuous improvement

36


Question and Answer

Charlie Gifford

President

21st Century Manufacturing Solutions LLC

[email protected]

208-309-0990


Working Notes • Problem

• Too many GUIs, too many applications

• Non-value added activity

• Data translations, Data Integrity

• Same shift feedback: Metrics that matters

• What

• VSM required

• Metrics SCOR, MESA

• How and Governance? Master Data

• Mfg 2.0 User centric interfaces: Work and work cell specifics

• RFID, Mesh Networks, Wireless, Automated, Paper, spreadsheet, HMI, IPAD

• MS OS, Apple, Android

• Bar Codes, Menus, Value inputs, Error Proofing ranges

• Equipment interfaces

• State models

• Companies that fail to manage their data properly can’t remain competitive. Product data

management, though, is only as effective as the quality of data being managed. Poor data

quality can lead to endless headaches and poor decision making.

38


MOM User and Functional Requirements

Define Data Structure for Mfg Intelligence 1. MOM URS:

Open O&M

Process Model

2. MOM URS:

Open O&M

Information Flows

3. MOM FRS: Open

O&M Data Definition,

Structure, Transactions &

Rules

• Manufacturing Intelligence Requirements:

• URS define processes, resources, data, KPIs, and metrics

• Governance, Definitions, and Structure of Manufacturing Data

• Mfg Master Data Mgt: Mapping and Synchronization Processes

• Metrics: Operations and Financial

• KPIs: Quality and Work Processes

• Align Master and Meta data for each application

• Align Syntax data for each application

• Mfg Integration Semantic Models (Processes and resources)

• Systems of Record: Incidence and Historical Data

39

Designing Data Collection for Consistency that Improves Process Management

Business

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