KPI

Top Operational Key Performance Indicators for Truck

Handb Ha ndboo ookk – 1s 1stt editi edition on OMCD/E, January 2008

Executive summary 8 top operational Key Performance Indicators (KPIs) were selected for standardization within Daimler Truck. The purpose of standardizing these these selected KPIs in all operating units units is to generate a common platform platform for steering manufacturing operations operations and achieve company-wide transparency and a platform for good practice sharing in order to ensure sustained continuous improvements improvements in Daimler Truck manufacturin m anufacturingg operational excellence This report documents the KPI standard definitions which were derived with a project team comprising representatives representatives from all Truck regions. The definitions were approved by members of the Manufacturing Leaders Council (MLC) and Truck Executive Committee (TEC).

Top Operational KPIs •HPU (hours-per-unit) •Throughput time •Direct run •K-factor •Ratio •0-ppm supplier •On-time-delivery •APA* (delivery product audit) * Auslieferungsproduktaudit Figure 1: 8 top operational operational KPIs for standardization throughout throughout Daimler Truck Truck manufacturing facilities facilities

A proposal for integration integration of the the KPIs into the Truck Truck Scorecard system system was approved approved in January 2008. The following chapters describe the steering steering goal, the calculation method, method, the measuring points and real plant examples examples for each of the top operational KPIs. The approved proposal for integration into the the Truck scorcard system and reporting lines is also documented. In addition, a Truck wide IT platform for collection, collection, consolidation consolidation and reporting of the KPI data is presented. Author: OMCD/E January 2008

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Handbook list of contents 1

Top Operational KPI project team and region representatives

2

Top Operational Operational KPIs KPIs – steering steering goals, goals, calculation, calculation, measurem measurement ent and examples examples 2.1 Hours-per-unit

2.5 Ratio

2.2 Throughput time

2.6 0-ppm supplier

2.3 Direct run

2.7 On-time-delivery

2.4 K-factor (aggregates), OEE (trucks)

2.8 APA (trucks), 0-ppm Customer (aggregates)

3

KPI integration into Daimler Truck scorecards

4

Reporting and KPI IT platform

5

Performance dialogue and best practice exchange

6

Appendix: Important project decision milestones; contacts at OMCD

Author: OMCD/E January 2008

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1. Top Operational KPI project team and region representatives Author: OMCD/E January 2008

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1

Project team incorporated all Daimler Truck OUs and relevant CFUs to enable cross divisional standardization MLC:

Dr. M. Dostal, Martin Daum, Roger Nielsen, Yoshitaka Taniyama, Ronald Linsmayer, Hermann Doppler, Dr. Holger Steindorf, Werner Thurner, Dr. Christoph Siegel

Project Core Team Project Leader

Back-office

T. Jung

McKinsey

Truck ASIA M. Kogame Y. Tokuda

Truck NAFTA G. Wootton T. Pax-Slotto

Project support

Truck EU C. Hinsen

Truck LA G. Heinz

P. Hoffmann

Subunit Axles/ Trans/Engines M. Ried

Manufacturing Planning TG Dr. H. Cronjaeger

IT-System A. Weichert / W. Dischler

PARTICIPANTS AT KPI STANDARDIZATION CONFERENCE: From left to right: A.Corcoran (OMCD/E), H.Cronjäger (TGP/MMA), M.Ried (BCF/EA - Kassel), G.Heinz (TGE/BMQ – Brazil), M.Lenz (OMCD/E), G.Wootton (Freightliner), T.Jung (OMCD/E and Project Lead), Y.Tokuda (Mitsubishi-Fuso), K.Hasegawa (Mitsubishi-Fuso), N.Heide (ITC/TO – Wörth), P.Hoffmann (OMCD/E), R.Jung (TGP/TT – Rastatt), W.Dischler (OMCD/E), A.Knuettel (TGP/ENP – Mannheim), not in picture C.Hinsen (TGE/O – Wörth)

Figure 2: Top operational KPI standardization conference June 2007 Author: OMCD/E January 2008

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1

Agreement on steering goals and definitions was the starting point for the KPI standardization KPI

Steering goal of KPI

Definition

0-ppm Supplier

Supplier quality management

Number of defect parts out of 1 million for parts received in selected month

Direct Run (assy)

Stability of manufacturing process

Ratio of units passing straight through final assembly without remaining defect or being taken offline for rework

K-Factor/OEE

Line productivity based on bottleneck equipment

Overall equipment efficiency of a plant based on actual vs. planned output of units (i.e. bottleneck)

HPU

Track total labor flexibility and efficiency

Average total hours worked (incl. all direct, indirect, salary) per production unit completed

Ratio

Direct labor productivity improvement

Ratio of direct labor improvement (total actually improved hours to planned standard hours)

Throughput Time

Reduce capital cost and handling time in the production process

Measures the time from giving production number to completion of final product release

On Time Delivery

Planning and process stability

Percentage of orders which achieved on time delivery (product released from production with ready to ship status on delivery date)

APA*

Focus production on final customerrelated quality

Audit forecast of how many defects the customer would find on the new vehicle

* Aggregates use 0-ppm customer instead of APA to reflect customer satisfaction Author: OMCD/E January 2008

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2. Top Operational KPIs steering goals, calculation, measurement and examples Author: OMCD/E January 2008

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HPU FACTSHEET

DEFINITION CALCULATION

MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.1

HPU (hours-per-unit) Abbreviation: HPU

Unit: hrs/unit

Description: average hours-per-unit (engine, axle,

Applicability:

x

TM

x

TE

x

TN

x

TA

Steering goal: Labor efficiency, labor flexibility

transmission or truck) based on total labor hours including direct, indirect and salary functions Level 1 calculation model:

Implementation / Measurement points:

HPU = actual working hours actual units produced

• Actual worked hours based on time-stamping (badging at FLLC) data. Where time-stamp data not available (e.g. salary functions) assumptions can be made

Base data required for KPI aggregation:

Primary shopfloor levers:

• Actual worked hours for direct, indirect and salary functions • Actual number of units produced

HPU CI* activities

Flexibility

Production volume

CI* continuous improvement

* Implies possible applicability to scorecard

Hierarchy relevance*: E2

x

E3

E4

Shopfloor KPI:

x

yes

no

x

E1

x

E5

Tracking of KPI on shopfloor boards recommended (direct workers only) Author: OMCD/E January 2008

Additional note:

Hours-per-engine, hours-per-transmission and hours-peraxle will report according to heavy, medium and light duty categories. Truck is not required to report according to product or product category. Daimler Trucks

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HPU FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.1

HPU – calculation model for Truck operating units reached at MLC meeting in Tokyo on December 3rd 2007 Actual paid direct hours worked

Total paid worked hours per period

Actual paid indirect hours worked

Actual hours/month e.g.ZEM@WEB

-

Assembly (body, paint, cab, final assembly)

-

Machining (strategic content, e.g. 5c’s)

-

Controlling/IT Human Resources Logistics Maintenance Planning/Organization Purchasing indirect Quality Service Operations Apprentices

-

Actual paid salary hours worked

HPU* (HPV,HPE, HPT,HPA)

Actual units/month e.g.TMC

-

Actual number of units produced

For more detail on HPU definition, including details of what‘s considered and what‘s not considered, please refer to the OMCD Harbour Guideline Contact: Ralf Hieber, [email protected]

*Definition based on the reference model of Harbour Consulting Inc., Quarterly report frequency – YTD-values Remark: paid working hours = actual worked hours (overtime effect not included) Author: OMCD/E January 2008

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HPU FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.1

Agreement on common HPU definition (MLC, Dec. 3rd) HARBOUR DEFINITION

TRUCK TM

TRUCK TE

TRUCK TN

TRUCK TA

MLC Agreement

DIRECTS in series production

IN

IN

IN

IN

IN



DIRECTS for internal major component transfer (e.g. door subassembly)

IN

IN

IN

IN

IN



DIRECTS for component manufacturing in plant (part machining or fabrications)

OUT - VEHICLE

(IN – AGGR)

OUT

OUT

OUT



DIRECTS for manufacturing – body shop, paint, cab trim, final chassis/finish&test

IN

IN

IN

IN

IN



INDIRECTS directly supporting production – 11 functional areas, e.g. logistics, mainten.

IN

IN

IN

IN

IN



INDIRECTS outsourced core functions –1.inplant logistics, 2.maintenance, 3.production

IN

IN

IN

IN

IN



INDIRECTS outsourced (non-core functions) – e.g. canteen, janitorial, fire service

OUT

OUT

OUT

OUT

OUT



SALARIES for series production (e.g. for series planning & engineering)

IN

IN

IN

IN

IN



SALARIES for future product planning & engineering

OUT

OUT

OUT

OUT

OUT



Quarterly report frequency – YTD-values

YES

YES

YES

YES

YES



HPU by segment (HD, MD, LD) or product and category HPV, HPE, HPT, HPA

YES

YES

NO

NO

NO

 


concensus

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HPU FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.1

Strategic product content: Manufacturing areas measured VEHICLE ASSEMBLY

TRANSMISSION

•Body Shop

•Carriers & Cases

•Paint Shop

•Converters

•Cab Assembly

•Clutches & Gears

•Final Assembly & Test

•Shafts

& Stampings

•Valve Body •Assembly and Test

ENGINE

AXLES

•Cylinder Block

•Axle

•Cylinder Head

•Drive

•Camshaft

•Carrier

•Crankshaft

•Planetary Gear

•Connecting

Rods

•Assembly and Test Source: Harbour Consulting Author: OMCD/E January 2008

housing shaft

•Hub •Front Knuckles •Assembly and Test Daimler Trucks

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HPU FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.1

Actionable levers to reduce HPU

Supported by HPU simulation tool

1. Continuous Improvement (KVP)

• Intensive the CI portion of T(e)-workers und GMK-AK • Realization of annual CI by indirect and salary people...

2. Flexibility

• Improving the operating point (“Betriebspunktes”) by block breaks • Flexibilisation of salary and indirect by new working models • ...

3. Production volume

• Production volume increase • Development of productions system towards “runner plant” • ...

4. Product (EHPU)

• Reduction of variants • Production-oriented product design (serie and new type) • ...

HPU Reduction

5.

Outsourcing or Automatisation*

• Reduction of value adding (“Fertigungstiefe”) by outsourcing of production and service functions • Reduction of actual working hours (“Anwesenheitsstunden”) of workers by automatization...

Outsourcing/automation will affect HPU figure, but is not an improvement as targets will be readjusted accordingly Author: OMCD/E January 2008

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HPU FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.1

Plant example - HPU split by functions and areas allows detailed analysis of current performance Measure performance of labor productivity including all labor classifications (Direct, Indirect and Salary Manufacturing Area Body Paint Cab Trim Chassis Ass./ Final

Focus on all functions for series production

11 Functional Areas Assembly (A) Machining (M)- only Aggregates Controlling, IT (C) MaintHuman Resources (H) Total Assb. Logistic Quality enance Other Logsitics (L) 19.3 Body 15.1 2.1 1.4 0.6 0.2 Maintenance (MA) 25.8 Paint 20.1 2.8 1.8 0.8 0.3 Planning//Engineering (P) 48.3 Cab Trim 37.7 5.3 3.4 1.4 0.5 Purchasing indirect (PU) 67.3 Chassis/Final 52.8 7.4 4.8 2.0 0.7 Quality (Q) Central Site Service Total 125.7 17.7 11.3 4.8 1.6 161.1 Operations (S) Apprentices (AP) (In add., e.g. Mercedes-Benz Cars have 50 measuring points of HPU to track, report and optimize – mainly center level)


AND… Labor Classification Direct Hourly Indirect Hourly Salary

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Throughput time FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.2

Throughput time Abbreviation: TPT

Unit: hours

Applicability:

x

TM

x

TE

x

TN

x

TA

Description: Measures the manufacturing lead

Steering goal: Reduce capital cost and handling

time from giving production number to completion of final product release

time in the production processes

Level 1 calculation model:


TPT = final product release time – earliest time at which production number stamped to frame or cab

• Final product release stamp • Earliest time of production number stamping to vehicle frame / cab for Truck plants • Final assembly begin for powertrain



• Sum of throughput times • Number of assembled units

Direct Run

Throughput Time

K-Factor * Implies possible applicability to scorecard


x

E3

Shopfloor KPI:

x

yes

x

E1

x

x

E4

E5

Change over time Inventory level

Note:

Throughput time for multiple lines to be based on weighted average.

no

Tracking of KPI on shopfloor boards recommended Author: OMCD/E January 2008

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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.2

Throughput time – calculation model agreed at KPI project standardization conference (June 2007) Key points Throughput time (hrs)

Final product release



Date of giving production number*

Report only for assembly lines at this time (Truck and Powertrain plants)



Measure throughput time from giving production number (assembly begin) to final product release



Measuring unit is working time in hours (without planned downtimes)

*Assignment of frame or cab number in Truck plants, assembly start for aggregate plants Source: Standardization Conference Author: OMCD/E January 2008

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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.2

TPT for vehicle assembly begins at the earliest assembly start point and ends with final release Measurement start point in this instance at cab-inwhite first fixturing as this begins earlier than frame assembly

CAB-IN-WHITE

Measurement end point directly after final inspection process (i.e. vehicle released)

CAB PAINT

FRAME/CHASSIS ASSEMBLY

CAB TRIMLINE

FRAME PAINT

FINISH/OFFLINE

FINAL ASSEMBLY

VEHICLE TESTING

FINAL INSPECTION

Timeline

Example of possible buffer points


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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.2

TPT for powertrain assembly begins at the earliest assembly start point and ends with final release Measurement start point after loading of first primary part onto final assembly line – „assembly begin“

Assembly stage 1

Measurement end point directly after final inspection process (i.e. aggregate release)

Assembly stage 2

Assembly stage n

Testing

Timeline

Example of possible buffer points Example of possible exit points for rework


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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.2

Throughput time actionable levers Throughput time

KPI tree as seen on shop floor

Actionable levers to improve KPI


Direct Run

OEE

See Direct Run..

See OEE..

Change over time

Inventory level

Problem follow-up

Problem follow-up

Dedicate machines

Create escalation levels

Separate manual/auto work content

Increase logistics frequency

Remove over-processing

Build to order

Multi-barrel

Change to flow layout

Fix change system

Strategic inventory layout

…

…

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Throughput Time FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.2

Throughput time example – São Bernardo do Campo Details Focus:

Improvement of the product delivery process

Process goal:

Reduction of the manufacturing time

Representation:

Bar chart

Calculation method:

Throughput Time (h) = Final product release - Date of giving production number*

Data source:

IT-Systems CGEM (MS-application) and Mag-Agera (Mainframe application)

Legend: * Assignment of frame number in Truck plants, assembly start for aggregate plants


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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.2

Implementation of throughput time in São Bernardo do Campo The throughput time is the total manufacturing time of an aggregate, measured between the beginning of the product assembly and the final release of the product, including the steps: • Product Assembly in main lines • Process Test • Assembly Process in the lines after test • Final Release If failures occur (product rework or fill up of parts) between the processes steps above, the respective overtime will be included in the calculation of the indicator.

Assembly Main Lines

Normal Flow

Failures

Rework


Test Process

Normal Flow

Failures

Rework

Assembly After Test Lines

Normal Flow

Final Release

Failures

Rework

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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.2

Throughput time measured from beginning engine assembly to final aggregate release Example São Bernardo do Campo: calculation model for throughput time Test Process

Assembly Main Lines 1 cycle time period

1 cycle time period

Unit

Unit

Start of Engine Assembly Beginning

Assembly After Test Lines 1 cycle time period

1 cycle time period

Unit

Unit

Unit Beginning

Final

B - A = Engine Assembly Lead Time

Final

Work Station

Work Station

Point A

Final Release of the Aggregate

Point B

Point D

Engine Test Lead Time

Point E

Point F

F - E = Assembly Powerpack Lead Time

F – A = Product THROUGHPUT TIME


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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.2

São Bernardo do Campo – throughput time in the powerpack assembly (engine and gearbox) Engine assembly start = Engine input data in the IT-systems

CGEM Mag-Agera

Powerpack final release = powerpack data input in the IT-systems

Point A Assembly Line beginning

Point B Assembly Line Final

Engine CarrinhoCarrinhoCarrinhoCarrinhoCarrinhoCarrinhoCarrinhoCarrinhoCarrinho

Assembly line Point E Paint Shop

Point D Engine Test

Point F Assembly Powerpack

Points D, E & F: Intermediary measurement points for traceability additional purposes

Engine Test Bench Author: OMCD/E January 2008

Powerpack Carrinho Carrinho Carrinho Carrinho Carrinho Carrinho Carrinho

Assembly line Daimler Trucks

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DIRECT RUN FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.2

Throughput time example for Kawasaki Plant Details Scope:

Cab welding ON to vehicle assembly OFF.

Focus:

Monitoring production lead time

Process goal:

Reduction of manufacturing time

Representation:

Bar chart

Calculation method:

Throughput time (Vehicle)= Cab welding lead time + Painting lead time + Trimming lead time + Assembly lead time (Refer to the structure)

Data source:

Calculate from units per hour every month


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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.2

Througput time for the Kawasaki plant

Cab welding

Painting

Painted Cab Storage

Trim

Final Assembly

Cab welding

Painting

Trim

Assembly

THROUGHPUT TIME OF VEHICLE


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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.3

Direct run Abbreviation: DIR

Unit: %

Applicability:

x

TM

x

TE

x

TN

x

TA

Description: DIR is the percentage of units passing

Steering goal: Stability and robustness of

straight through final assembly without remaining defects or being taken offline for rework

manufacturing processes to avoid quality errors



DIR = number of units without offline defects* Total number of produced units

• Measured at discharge points in final assembly lines. For powertrain multiple discharge points, for Trucks single discharge point • No multiple counts

* Offline defect is a defect which cannot be repaired in the line and is discharged to rework area in order to carry out repair / rewo rk



•Number of produced units • Number of direct run violations

Direct run Employee training



x

E3

Shopfloor KPI:

x

yes

x

E1

x

x

E4

x

no

E5

Defect reduction

Exceptions:

• Not measured for machining lines or subassembly lines at this time. To be installed on these lines later.


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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.3

Direct run – calculation model agreed at KPI project standardization conference (June 2007) Units with …

Number of units without offline defects

Direct run

Total number of units

Missing parts

Units with offline defects

Offline rejects

Key points •

For vehicles, only measure defects remaining after finishing, since finishing should be considered normal process

•

A Direct Run defect is a defect that can not be repaired in the line in cycle time and is thus discharged.

•

Measure start point is start of final assembly

•

Measure end point is after final assembly

Offline reworks (excl. finishing) Total number of units

Source: Standardization Conference Author: OMCD/E January 2008

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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.3

Measurement at every point at which the unit can be diverted from the main production line Assembly line 1 cycle time period

1 cycle time period

Unit

Rework area (z.B. Ausschleusepunkt)

Measure here Rework area (z.B. Ausschleusepunkt)

 

DR ok

Problem solved in line cycle time, direct run OK

× ×

DR not OK

Problem NOT solved in line cycle time ⇒ violation of direct run

End of line finish area

 

DR ok

End of line rework area

× ×

DR not OK

Any rework content at end of line is violation of direct run

Direct run ensures process stability in whole line. Only one violation count per unit. Q-Gate measurement points to be identified during KPI implementation phase Author: OMCD/E January 2008

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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.3

Direct run actionable levers on shop floor Direct Run Indicates recommendation to track values at line/station level



Employee training

Defects*

Trained members on cell

Std work sheet/audit

Supplier

Press

Paint

Assembly

Problem follow-up

Problem follow-up

Problem follow-up

Problem follow-up

Problem follow-up

Problem follow-up

Std work audit

Stop at detection

Training school

Solve quality problems

Increase quality standards visualization on shop floor

Quality task force

Manpower planning Sneaky checks …

Problem solving training Quality alerts Effective quality loops …

* All rejects and reworks not repaired in line in takt time Author: OMCD/E January 2008

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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.3

Direct run example Wörth plant Details Focus:

Improvement of process and product quality

Process goal:

Reduce rework levels

Representation:

Bar chart

Calculation method:

Numer of vehicles without rework per period* Direct run % = -------------------------------------------------------------------------Number of vehicles leaving assembly line in period*

Data source:

ZWA system

Target value 07/08:

Monitoring

Target responsibility:

TE/OP, TE/OS, TM/ME Legend: * Period = day, month or year


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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.3

Wörth system adapted to distinguish between planned and unplanned finishing content New definition of the system status signals „BA“ and „NA“. „BA“ represents the vehicles which go through finish area with no quality issues outstanding (i.e. good direct run). NA represents the vehicles which go into finish area and require rework as well as other planned work (i.e. violation of direct run). r e d r o r e m o t s u C

AZ, EP, BP, AW

Time line

RO

LA

Cab-inwhite

Paint

SE

Trim

Status NA is set in the FINISH system with an estimated final inspection target date and is passed to the next system.


SE

Assembly

f u a l b a d n a B

SA SL

BA

Finish

ZV

Vehicle Final Inspection delivery

NA

Yard Yard Nacharbeit Repair Repairshop shop Body Bodyrework rework etc. etc. Daimler Trucks

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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.3

Direct run plant example – São Bernardo do Campo plant Details Focus:


Process goal:

Reduction of rework and offline complementation

Representation:

Bar chart

Calculation method:

Number of products without rework per period* Direct run % = -------------------------------------------------------------------------Number of products leaving assembly line in period*

Data source:

CGEM (intern system) and Simsam

Legend: * Period = day, month or year


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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.3

São Bernardo do Campo implementation of direct run Measurement point is at the last Quality Gate at the end of the assembly line or engine test

CGEM Simsam

Point of Measurement Assembly Line Release

At the last Quality Gate a check list is fulfilled and the quality data is recorded in the IT-Systems * Point of Measurement Engine Test

Carrinho Carrinho Carrinho Carrinho Carrinho Carrinho Carrinho Carrinho Carrinho

Example of a Check-List


* reference for further investigation of root causes and performance statistics Daimler Trucks

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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.3

Direct run – São Bernardo do Campo IT-system CGEM

k i c C l

–

t a r S t

Definition of the problem type (assembly, missing part, etc.) Indication of the parts affected Registration of problem solving


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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.3

Quality problem follow-up, IT-system CGEM enables quality problem tracking Quality performance • per product • per cost center • per month / period Top failures Traceability Missing parts pending Others

Root Cause anaysis


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K-FACTOR FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.4

K-Factor Abbreviation: KFC

Unit: none

Applicability:

x

TM

TE

TN

TA

Description: KFC is a metric for monitoring and

Steering goal: Improve machining line / plant

improving the efficiency machining line bottlenecks

productivity by identifying and addressing bottleneck equipment



KFC = good parts × planned cycle time planned production time

• Measured for machining lines only • Line K-Factor is based on bottleneck machine • Plant K-Factor calculated by average K-Factor of bottleneck machines

* Planned production time based on planned shift hours including breaks, TPM and group meeting times



• Number of bottleneck machines • Sum of K-Factor values for bottleneck machines

K-Factor Equipment uptime



x

E3

Shopfloor KPI:

x

yes

x

E1

x

x

E4

x

E5

Workrate

Quality

Additional notes:

• Not applicable in truck/vehicle plants

no


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K-FACTOR FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.4

K-Factor – calculation model agreed at KPI project standardization conference (June 2007) Key points

Produced parts

•

Use for all machining shops

Reject parts

•

Planned production time includes the time for team meetings, lunch breaks and planned TPM (i.e. total scheduled time)

•

K-factor is measured only on bottleneck

•

If many product lines, report the average Kfactor for bottlenecks

Actual output good parts

Load/unload time Machine cycle time (TNG)

Machine auto cycle

K-factor

Planned Production Time**

*Unscheduled time is non-utilized shifts Source: Standardization Conference Author: OMCD/E January 2008

Total available time (24 hours/day) Unscheduled time

** Gross running time incl. all breaks

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K-FACTOR FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.4

K-Factor measurement based on bottleneck machines, aggregation to plant value by average

K-Factor per line is based on the respective bottleneck machine

Machining

(5 C‘s) K = 0,8

K = 0,7

K = 0,6

K = 0,5

K = 0,4

⇒

K-Faktor plant machining area = average of bottlenecks, e.g. 0,6

K-Faktor reporting value is 0,6


Daimler Trucks

36

OEE FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.4

K-Factor actionable levers K-Factor Indicates recommendation to track values at line/station level

TPM time

Equipment availability Breakdown

Member work rate Changeover

Absenteeism


Rejects

Reworks

Problem follow-up

Problem follow-up

STD work audit Problem follow-up


Trained members

Quality

Problem follow-up

Problem follow-up

TPM scheduling Solve breakdowns Simplify machine design SMED workshop SMED (Single minute exchange of dies) Dedicate machines New machinery 5s improvement …


Problem follow-up

Problem follow-up

Manpower planning Flexible manpower system Clean sheet bonus Std work audit Training / qualification Re-balance work content Accident alert … KAPITEL:

Stop at defect Problem solving training Containment Std. work improvement Quality alerts Random checks Design for quality … Daimler Trucks

37

K-FACTOR FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.4

Mannheim example shows impact of optimization measures on K-Factor 1. Before optimization

Stückzahlvorgabe: 35 FTE => 376 units (3 shifts) K-Faktor: 0.52

376 units in 1440min

K-Faktor

?

KFC = 376 units x 2.0min = 0,52 1440 min

0,75 0,52

2. Optimization: AuF Mannheim K-Faktor: 0,75 35 AK => 540 Stk (3 Schichten)

KFC = 540 Stk. x 2,0 min = 0,75 1440 min

540 Stück in 1440min


Before

After Optimization

+ 164 units in 3 shifts, e.g. through utilization of the total shifttime

Daimler Trucks

38

OEE FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.4

OEE – overall equipment effectivness Abbreviation: OEE

Unit: %

Applicability:

TM

x

TE

x

TN

x

TA

Description: OEE is a metric for monitoring and

Steering goal: Improve assembly line / plant

improving the efficiency of manufacturing processes

productivity by identifying and addressing bottleneck processes



OEE = good parts × planned cycle time planned production time*

• Measured for assembly lines only • Measure point is at end-of-assembly • If multiple lines, aggregate by weighted average

* Planned production time based on planned shift hours excluding breaks, TPM and group meeting times



• Line / plant OEE values • Line / plant production volumes

OEE Equipment uptime



x

E3

Shopfloor KPI:

x

yes

x

E1

x

x

E4

x

no

E5

Workrate

Quality

Exceptions:

• Powertrain plants will report K-Factor • Vehicle plants – for final assembly, trucks leaving line are considered good parts


Daimler Trucks

39

OEE FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.4

OEE – calculation model agreed at KPI project standardization conference (June 2007) Key points

Available time

Actual output good parts

Planned cycle time

Takt time

Demand forecast (based on 1-yr Prod Plan)

1

Breakdown percent Reject percent

OEE

Total available time (24 hours/day)

Team meetings Lunch, breaks

Planned downtime Unscheduled time* Planned TPM*

*Unscheduled time is non-utilized shifts and non-utilized shift time, TPM = Total Productive Maintenance. Source: Standardization Conference, Top Operational KPI project, June 2007






Planned loss

Planned Production Time



KAPITEL:

Use OEE for all assembly lines If many lines use weighted average Planned loss is planning function to derive planned cycle time, where breakdown and reject percentage is based on historical data.

Daimler Trucks

40

OEE FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.4

OEE consists of three factors 1. AVAILABILITY Availability takes into account down-time loss. That is, all events that stop planned production

2. PERFORMANCE Performance takes into account speed loss, which includes all factors that cause the process to operate at less than the maximum speed, e.g. equipment wear or operator inefficiency

3. QUALITY Quality takes into account quality loss, which factors out produced pieces that do not meet quality standards

OEE = AVAILABILITY × PERFORMANCE × QUALITY


KAPITEL:

Daimler Trucks

41

OEE FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.4

The KPI calculation model for OEE is derived by: 1. AVAILABILITY = operating time / planned production time

Captures any stillstands / downtimes

2. PERFORMANCE = (planned cycle time × total pieces produced) / operating time Performance factor will capture any deviation in line cycle time from intended cycle time

3. QUALITY = good pieces / total pieces produced For vehicle plants – all vehicles from end of line are considered good as quality aspect is captured using direct run. Thus, quality factor = 1.

OEE = planned cycle time × output / planned production time


KAPITEL:

Daimler Trucks

42

OEE FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.4

OEE measurement at end of assembly line, aggregation to plant value by weighted average Line 1

Line 2

Line 3

OEE = 0.9

OEE = 0.8

OEE = 0.7

Measurement points: OEE per line measures the parts which leave the line (measurement point at the end of the assembly line)

t n a l P

⇒

OEE plant value = weighted average e.g. 0.8 if production volumes of all three lines equal

Plant OEE calculated based on weighted average according to production volumes


KAPITEL:

Daimler Trucks

43

OEE FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.4

OEE actionable levers on shop floor OEE Indicates recommendation to track values at line/station level

TPM time

Equipment availability Breakdown

Member work rate Changeover

Absenteeism


Rejects

Reworks

Problem follow-up

Problem follow-up



Trained members

Quality

Problem follow-up

Problem follow-up

TPM scheduling Solve breakdowns Simplify machine design SMED workshop SMED (Single minute exchange of dies) Dedicate machines New machinery 5s improvement …


Problem follow-up

Problem follow-up

Manpower planning Flexible manpower system Clean sheet bonus Std work audit Training / qualification Re-balance work content Accident alert … KAPITEL:

Stop at defect Problem solving training Containment Std. work improvement Quality alerts Random checks Design for quality … Daimler Trucks

44

RATIO FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.5

Ratio Abbreviation: RAT

Unit: % (year-to-date)

Applicability:

x

TM

x

TE

x

TN

x

TA

Description: Ratio of total actual standard hours

Steering goal: Direct labor productivity

saved to planned standard hours based on actual production mix and volumes)

improvement



RAT = sum of standard hours saved to date time allocation based on reference standard hours

• Standard hours documented in production plans • Improvements approved by industrial engineering

for actual production program to date Base data required for KPI aggregation:

• Confirmed standard hours saved to date • Time allocation for actual production program based on reference standard hours from 31st of December of previous year * Implies possible applicability to scorecard


E2

x

E3

Shopfloor KPI:

x

yes

x

x

E4

E5

Primary shopfloor levers: Ratio CI* activities

Design changes

Equipment upgrades

CI* continuous improvement

Additional note:

-

no


Daimler Trucks

45

RATIO FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.5

Ratio – calculation model for monthly values agreed at KPI project standardization conference (June 2007) Note: Ratio evaluation in scorecards on the basis of year-to-date performance.

Key points

Actual standard hour (TE) improvement per unit

•

Set reference standard hours yearly, once, at the beginning of the year (31.12 previous year)

•

Calculate actual improved standard hours against the reference standard hours based on the actual production volumes

•

Definition considers only changes to standard hours (TE)

•

A set of reference products, representative for the full range, is ok to use if it >90% coverage

Total actual standard hours saved Actual produced units (by product or representative) Ratio

Standard hours (TE) at start of year Sum of planned standard hours for production mix Actual produced units (by product or representative) * Standard hour = standard planned time = Einheitenzeit TE Author: OMCD/E January 2008

Daimler Trucks

46

RATIO FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.5

Ratio measurement assesses the impact of improvement activities on defined work processes In plants where defined standard times per process / parts regulate the amount of direct labour required to manufacture / assemble a component, ratio is quantified based on approved and documented improvements in the work process. Approval is usually done by idustrial engineering.

Production plan with defined TE

Process optimisation, CI, TE improvement

Documentation and approval of improved process

Production plan with updated TE

Negative ratio: Part design changes or substitution of ”newer” parts can lead to negative changes in ratio – that means more standard time is required to fabricate / assemble the new part. Negative ratio effects due to design changes are not counted if the design change will be compensated by the customer paying a higher price for the product. For new parts / outsourced parts, reference time adjustment from month of introduction of new part or outsourcing


Daimler Trucks

47

RATIO FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.5

Ratio calculation based on TE changes of actual produced parts monthly Ratio calculation – at the end of month X

 (TE plan × n ) − (TE actual × n )  100 Ratioactual (%) = ∑  × TE plan × n 1   1  i

what: i = all parts based on parts numbers or individual representatives which were produced in month X n = actual produced number of specific part number or representative in month X *Premise: Representatives have to cover more than 90% of the actual produced parts spectrum

Source: TM Ratio Workshop – 2007-11-08 Author: OMCD/E January 2008

Daimler Trucks

48

RATIO FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.5

Ratio calculation based on month-by-month calculation with evaluation based on year-to-date performance Example from TM scorecard monthly values

yearly values

actual target

%

%

8

Color shows that although target reached in that month (3.0%), the year-to-date performance is not on track to reach the cumulative target of 3,4%.

8

7

7

6

6 5

5 4 3 2 1 0 2006

2007

2008

Compares sum of ratio hours until November with sum of standard hours on the basis of reference standard hours from 31st December of previous year

4 3 2 1 0 Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec





















Jan 0,5

Feb 1,1

Mar 1,6

Apr 2,1

May 2,7

Jun 3,2

Jul 3,7

Aug 4,2

Sep 4,8

Oct 5,3

Nov 5,8

Dec 6,4

223 42000

445 42000

668 42000

891 42000

1113 42000

1336 42000

1559 42000

1781 42000

2004 42000

2227 42000

2449 42000

2672 42000

0,6

1,1

1,4

1,4

3,0

3,4

3,9

3,9

4,9

5,6

5,6

460 41000

600 43500

600 41500

1300 43000

1400 41000

1700 43500

1700 43500

2100 43000

2300 41000

2300 41000

Production Plant 1 cumulative (%) target

2006 2007 ##### #####

Ratio improvement hours (tsd) Standard hours (tsd)

actual

##### #####

Ratio improvement hours Standard hours (Basis 12/2007)

2008 3,4

monthly ratio (%) target

17368 504000

Ratio improvement hours (tsd) Standard hours (tsd)

3,2 14700 465000

actual

Ratio improvement hours 240 Standard hours (Basis 12/2007) 43000

• 14700 = sum of saved standard hours = (240+460+…+2300) • 465,000 = allocated hours based on standard hours from December of previous Year = (43000 + 41000 + …. + 41000 ) Author: OMCD/E January 2008

#####

• 1700 = (TE ACTUALAug) – (TEPLANDec) for all parts produced in August • 43,500 = ( ΣTEPLANDecember) for all parts produced in August Daimler Trucks

49



MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.5

Freightliner example for Ratio calculation Details Focus:

Direct labor productivity improvements

Process goal:

To show the labor hour effect that CI events have in an area.

Representation:

Bar chart

Calculation method:

Benchmark improvement hours* Ratio % = ---------------------------------------------------------Current standard hours* + benchmark hours*

Benchmark pool improvement hours = ratio hours Standard hours + benchmark hours = Reference standard hours from 31st December previous year for actual produced units

Data source:

VPS system within IMS

Legend: * Period = day, month or year CI = continuous improvement


Daimler Trucks

50



MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.5

Freightliner example for Ratio calculation

= CI event at the plant Reports generated from VPS Plant performance with improvements from Web Focus reports

At the end of the year the benchmark hours are purged from the standard which sets a lower standard labor hour for the upcoming year. Author: OMCD/E January 2008

Daimler Trucks

51

0-ppm supplier FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.6

0-PPM supplier Abbreviation: 0SU

Unit: ppm

Description: Number of defect parts out of 1

Applicability:

x

TM

x

TE

x

TN

x

TA

Steering goal: Supplier quality management

million for parts received from suppliers (Daimler internal and external) in selected month Level 1 calculation model:


0SU = # defect parts from supplier × 1,000,000 Total number of parts received

• All supplied units which are to be part of our products are regarded for calculation of 0-ppm • PPM counting and rejecting policy to be conform with CVD Quality Guideline 21

Calculation method conform with CVD Quality Guideline 21



• Number of non-conforming supplier parts • Total number of supplier parts received

0-ppm Supplier Employee training



x

E3

Shopfloor KPI:

x

yes

x

E1

x

x

E4 no

E5

Supplier management

Additional notes:

0-ppm supplier should report only delivered quality defects (i.e. the Q-part of the 0-ppm CVD Quality Guideline 21).


Daimler Trucks

53



MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.6

0-PPM supplier – calculation model agreed at KPI project standardization conference (June 2007) Key points ppm counting as per CVD quality guideline 21*

Found at gate Rejected supplier parts Found in plant



O-PPM is already measured in plants



The reported figure should be the total PPM for the supply base (total defects/ total parts)

•

The 0-ppm figure reported reflects only the quality issues with the delivered parts.

Found at gate Defect parts × 1,000,000

Reworked supplier parts Found in plant

0-PPM

Found at gate Mislabeled supplier parts Total received parts

Found in plant

* The CVD quality guideline 21 is currently being redrafted by TE/QM. Expected sign-off date for new version is Feb. 2008 Source: Standardization Conference June 2007 Author: OMCD/E January 2008

Daimler Trucks

54



MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.6

Common PPM Concurrence letter forms basis for CVD quality guideline 21 “A common measure of quality is necessary in order to support the Board and EAC.“

PPM =

Nonconforming quantity Received quantity

x 1,000,000

M T E/ Q y b d d r a f t e e r g n e i n t ly b e r r u c

8 . 2 0 0 b e F i s r s i o n e v w n e e f o r t a d f o f s i g n d e t c e . E x p

i s e 21 n i l e u i d l it y g a u q V D T h e C “…reflects the common understanding in the

definition of the 0-km/0-miles PPM counting.“ The letter of agreement stipulates CVD guideline 21 for standarization of 0ppm counting


Daimler Trucks

55



MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.6

CVD guideline 21 outlines clear purpose and responsibilities for 0-ppm supplier counting •

CVD Guideline 21 outlines clear purpose and responsibilities

•

The guideline also details the scope for 0-ppm counting

•

The guideline clarifies the rules when parts are non-conforming / complaints

•

CVD guideline 21 sets clear rules when units are to be counted in the ppm counting

Source: CVD Guideline 21 – method of counting ppm Author: OMCD/E January 2008

Daimler Trucks

56



MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.6

Mercedes Benz special terms outlines rejecting policy with supplier Delivery

Line

Preliminary 0ppm Report 100 NC

Supplier inspection period

100 units

0ppm Report First 30 parts defect – assembler rejects whole box

Supplier has 20 working days to prove that not all 100 units are defect

Inspection and Determination of the acceptance rate in the case of a lot return: •

In the event of inspection by the supplier, DC and the supplier agree to status feedback with initial test results to DC within 10 working days of the supplier‘s receiving the goods

•

If, after a maximum of 20 working days as of receipt of the parts by the supplier, no concluding inspection result is available, the parts pertaining to this test report are regarded as accepted (periods may be extended by mutual agreement).

Source: Mercedes-Benz Special Terms 18/02 – excerpt from Section 2.3 Author: OMCD/E January 2008

Daimler Trucks

57



MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.6

The counting for PPM starts when the part contract specifies Daimler ownership Two delivery schemes are possible: • Supply ex-factory – ownership transfers to Daimler when parts leave supplier premises • Frei Haus (free shipping) – ownership transfers to Daimler upon delivery

PPM counting includes non-conformancies found at the gate (i.e. upon delivery) and found at the production lines


Daimler Trucks

58



MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.6

0-PPM supplier – actionable levers PPM*

Indicates recommendation to track values at line/station level

Member training

Trained members on cell

Std work audit


Problem follow-up


Problem follow-up

Manpower planning Flexible manpower system Clean sheet bonus Std work audit Training school Re-balance work content Accident alert …

Defects

Supplier

Press

Paint

Assembly

Reject

Rework

Reject

Rework

Rework

Rework

Reject

Rework

Problem follow-up

Problem follow-up

Problem follow-up

Problem follow-up

Problem follow-up

Problem follow-up

Problem follow-up

Problem follow-up

Stop at detect Solve quality problems Quality task force Problem solving training Design for quality Supplier development Change supplier …

* PPM as a general, both supplier and customer view Author: OMCD/E January 2008

Daimler Trucks

59

PPM Supplier FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.6

0-ppm supplier plant example – São Bernardo do Campo Definition Focus:


Process goal:

Reduction of defect parts received from suppliers

Representation:

Bar chart

Calculation method:

Defect parts x 1,000,000 PPM Supplier = -----------------------------------------------Total received parts

Data source:

SIGEQUALI System (IT system developed by MBBras)


Daimler Trucks

60



MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.6

São Bernardo do Campo – measurement for 0-ppm supplier through receiving inspection and ongoing analysis INPUT National Parts

Evaluation & Measurement by

- Sample quality analysis of supplied parts

TE/BTM (TCL - Supplier Management– Trucks MBBras)

Quality feedback about defect parts by TE/BT

- On going quality analysis of the supplied parts used in the vehicles’ assembly - Defects informed to TE/BTM to be considered in the ppm-Supplier

(TC - Production Trucks MBBras)


Daimler Trucks

61



MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.6

São Bernardo do Campo – 0-ppm supplier gate inspection Incoming goods area BGE/BTM check

Receipt bill

Data collection into the IT corporate logistic systems

IT-System SIGEQUALI Quality database for the registration of defect parts received * * reference for further investigation of root causes and rejection statistics


Daimler Trucks

62



MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.6

São Bernardo do Campo – 0-ppm supplier IT-system Data Source: SIGEQUALI - PPM Daily Situation National suppliers ppm (cumulative)

- On line update - PPM National

PPM Monthly – national suppliers

Details about the rejection: - # Item - Supplier - Reason for reclaimation - Quantity - Others


Daimler Trucks

63

OTD FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.7

OTD (on-time-delivery to the customer) Abbreviation: OTD

Unit: %

Applicability:

x

TM

x

TE

x

TN

x

TA

Description: OTD is the percentage of orders

Steering goal: Planning and process stability,

which achieved on-time-delivery from the customer persepective

customer satisfaction



OTD = number of units delivered on-time* Total number of units delivered

• For trucks, measured after completion of final inspection, i.e. ready-to-ship status approved • For aggregates, measured against on-timedelivery at truck plants

* on-time-delivery window is defined as -4 / +0 days for Truck, window for aggregates agreed between Truck plant and aggregates supplier



•Number of late deliveries •Total number of deliveries

OTD OEE



x

E3

E4

Shopfloor KPI:

x

yes

no

x

E1

x


E5

Direct run

Throughput time

Additional notes:

• For trucks, the tolerance for reaching OTD status is that the truck have ready-to-ship tolerance of -4/+0 days • For aggregates, the tolerance is agreed with the customer truck plant

Daimler Trucks

64

OTD FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.7

OTD (to customer) Key points

Calculation model for truck plants

On-time Delivery (percent)



Use freeze of production plan as start point



Use finish product release as end point



Common tolerance for Truck plants -4 / +0 days (approved in PEC 15.01.08)



Granularity: calendar day

Number of orders delivered on committed delivery time (based On call off)



Only measure for "not in plant" customer



Use "call-off" as start measure point

Total number of delivered orders



Delivering time as end measure point

Number of orders finished on Committed delivery date (finished product release) Total number of finished orders (ready to ship)

for aggregate and part plants

On-time Delivery (percent)

Source: Standardization Conference June 2007 Author: OMCD/E January 2008

Daimler Trucks

65

OTD FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.7

Measurement points: variances between on-time and missed days planning of finish date

planning fixed

planned finish date

start chassis

Vehicle released by production for shipment production distribution planning period on time

missed

examples

Variance examples

examples

OTD Reporting e.g. TMC

Set ready-to-ship status:

Variances in 2007:

Current OTD plant values:

EULA = 20 working days before

EULA = - 0/+3 working days

Werk Wörth = 79,2% (Nov. 2007)

FLLC = 26 days before

FLLC = <5 working days in offline

Mount Holly = 79.3% (Dec. 2007)

source COGNOS

FUSO = 7 working days before

FUSO = - 0/+1 working days

Kawasaki

source production office FUSO

= 75.6% (Oct. 2007)

source TMC

Variances for 2008:

ALL = - 4/+0 working days Author: OMCD/E January 2008

Daimler Trucks

66

OTD FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.7

OTD actionable levers on shop floor OTD

Ratio

OEE

Direct Run

Throughput time

See OEE..

See Direct Run..

See throughput time..




Daimler Trucks

67

APA FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.8

APA – Au Ausli sliefe eferu rung ngsp spro roduk duktt Aud Audit it–c –cus ustom tomer er Audit Audit Abbreviation: APA

Unit: faults/vehicle

Applicability:

TM

x

TE

x

TN

x

TA

Description: Audit forecast of how many defects

Steering goal: Focus production on final customer-

the customer would find on the new vehicle

related quality



APA = Σ (1s×0.01)+(3s×0.1)+(5s×0.4)+(9s×0.8) Total number of vehicles audited

• Vehicles subjected subjected to APA audit just before final final inspection. • Content of audit documented documented in APA handbook

Calculation method conform with CVD Quality Guideline 23


Primary Primary shopfloor shopfloor levers: levers:

• Sum of of all APA APA scores scores • Number Number of vehicles vehicles audited audited

APA Employee training



x

E3

E4

Shopfl Shopfloor oor KPI: KPI:

x

yes

no

x

E1

x

E5

Absenteeism

Quality control

Additional notes:

• Powertra Powertrain in plants plants will will report report 0-ppm customer customer • Categori Categorizat zation ion of 1s, 3,s etc. region region specific specific • Freightliner currently will not use the “0” score score


Daimler Trucks

68

APA FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.8

APA – cal calcul culati ation on model model agreed agreed at KPI KPI project project standardization conference (June 2007) Key points

Compliant with CVD Quality Guideline 23

Level 9

Level 5 ∑

APA (0+1+3+5+9)

Level 3

APA*

Level 1 Number of audits Level 0

Index 0.8

•

0's measured

Number of 9s

•

0, 1, 3, 5, 9 measured with indices

•

APA substitutes current 5's and 9's reporting

•

Market defines what is 0, 1, 3, 5, 9

•

Freightliner currently will not use the “0” score and will maintain their current process as they do not use a separate Product Audit.

Index 0.4 Number of 5s Index 0.1 Number of 3s Index 0.01 Number of 1s Index 0.00 Number of 0s

*Currently IPQ is reported in TG scorecard Source: Source: Standardiza Standardization tion Conference Conference Author: OMCD/E January 2008

Daimler Trucks

69

APA FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.8

APA me measurement point is is when the vehicle is ready for delivery Vehicle ready for delivery 7 Quality Gate Check

Fuso

Final Inspection

Assembly process

Rework

APA Finish

FTL

Quality Inspectors

Assembly process

End of Line Audit

Off line

Rework

Final Inspection

APA

MB Trucks

Section Inspection

Assembly process

BPA

Rework if necessary

Finish

Final Inspection 70% APA Dealer 30 % APA Plant

Source: Source: Dr. Dr. J. Hoffmann Hoffmann – „Quality „Quality Reporting Reporting TG TG KPIs – Status Status Report“ Report“ – Feb. 9th 9th 2007 2007


Daimler Trucks

70

APA FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.8

Scope and testing measures are fixed in the APA manual Customer feedback affects the contents and measures of the APA-Manual over the APA Coreteam. Thus it is guaranteed that from current customer view is examined.

Contents:

Clutch

Inspect: visual inspection of the tank from the outside: • fluid level

hydropneumatic gear change (HPS)

Inspect: visual inspection of the tank from the outside: • fluid level Cab must be tilted!

Scale:

Fluid level of the hydraulic clutch mechanism Minimum fluid level: The fluid level must lie at the upper m ark (1) (max.). Tolerance: ± 3.0 mm

APA Manual CV APA Manual CV Delivery – Product – Audit C ommercial Vehicles

Product – Audit Commercial Vehicles Edition:Delivery January–2006 Edition: January 2006

g e s a p 0 . 9 0 x o r p A p

Source: APA Presentation / Dr.2008 A. Fritz QCV / OF / 06-05-18 Author: OMCD/E January

Brake fluid, hydropneumatic gear change Minimum fluid level The fluid level must lie at the upper mark (max.). Tolerance: ± 3.0 mm

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APA FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.8

APA actionable levers APA Indicates recommendation to track values at line/station level

Employee participation

Absenteeism KPI tree as seen on shop floor

No 0’s

No 1’s

No 3’s

No 5’s

No 9’s

Problem follow-up

Problem follow-up

Problem follow-up

Problem follow-up

Problem follow-up



Trained members

Quality

Problem follow-up

Manpower planning Flexible manpower system Clean sheet bonus Std work audit Training school Re-balance work content Accident alert …


Stop at defect Problem solving training Containment Std. work improvement Quality alerts Ensure quality loops Design for quality … Daimler Trucks

72

APA FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.8

APA sc score MBTruck calculated based on nu number of faul fa ults ts* * mul multi tipl plie ied d by AP APA A in inde dexx FEF = 15 faults*/veh., of which: FEF sub-divided sub-divided in NQ- Groups Groups

GNQ NQ faults/veh. x 0.00 = 0.00 faults/veh. faults/veh. 0 0 % 2 faults/veh.

faults/veh. x 0.01 = 0.04 faults/veh. 1 1 % 4 faults/veh. 3 10 % 5 faults/veh. x 0.1 = 0.50 faults/veh. *Faults relates to a condition that will discover and complain (if asked) at a new vehicle up to 6 weeks after delivery.

5 40 % 3 faults/veh. x 0.4 = 1.20 faults/veh. 9 80 % 1 faults/veh. x 0.8 = 0.80 faults/veh.

2.54 faults/veh. APA = 2.54 faults/veh.

FEF = 15 faults/veh.

Dealer Plant Source: Pres 06-05-18 AuthoAPA r: OPresentation MCDentation /E Janu/arDr. y 2A. 00Fritz 8 QCV / OF / 06-05-18

Forecast Customers Daimler Trucks

73

0-ppm customer FACTSHEET


MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.8

0-PP 0PPM M cu cust stom omer er Abbreviation: 0CU

Unit: ppm

Applicability:

x

TM

TE

TN

TA

Description: Number of defect parts out of 1

Steering goal: Focus production on final customer-

million for parts delivered in selected month to customers

related quality



0CU = shipped defect parts × 1,000,000 Total shipped parts to all customers

• Defect Defect measurement measurement based based on direct feedback feedback from customer plants • Measured Measured for for plant plant intern internal al and plant plant exter external nal final final powertra powertrain in product product customers customers


Primary Primary shopfloor shopfloor levers: levers:

• Number of complaints complaints from from customer customer • Number Number of delivere delivered d units units

0-ppm Customer Employee training



x

E3

Shopfl Shopfloor oor KPI: KPI:

x

yes

x

E1

x

x

E4 no


E5

Quality

Additional notes:

• Vehicle Vehicle plants plants report APA to reflect reflect custome customerr satisfaction • QZA audit audit will be maintain maintained ed as internal internal product product audit for powertrain Daimler Trucks

74



MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.8

0-PPM customer – calculation model agreed at KPI project standardization conference (June 2007) Customer satisfaction index for aggregate and parts plants – PPM

Key points 0-PPM

Source: Standardization conference Author: OMCD/E January 2008

Shipped defect parts × 1,000,000

•

Keep QZA, but report OPPM from customer

Total shipped parts to all customers

defect parts = Nonconforming quantity Daimler Trucks

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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.8

0-PPM Customer is measured by customer and reported back to aggregate plant •

0-ppm customer complaints are based on reclamations from vehicle plants regarding aggregate units supplied to them

•

0-ppm customer should include feedback from aggregates supplied to all customers, Daimler internal and external.

•

0-ppm customer gives a direct and real indication of customer satisfaction levels based on aggregate quality 0-ppm reclamations 0-ppm reclamations

Vehicle plant

Vehicle plant Engine plant

Vehicle plant 0-ppm reclamations Author: OMCD/E January 2008

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MEASUREMENT POINTS

SHOP FLOOR LEVERS

PLANT EXAMPLE

2.8

0-PPM customer – actionable levers 0-ppm Customer

Member availability

Indicates recommendation to track values at line level

Absenteeism KPI tree as seen on shop floor

Trained members

Defect a

Defect b

Defect c

Defect …

Defect n

Problem follow-up

Problem follow-up

Problem follow-up

Problem follow-up

Problem follow-up



Quality

Problem follow-up

Manpower planning

Problem solving training

Flexible manpower system

Containment

Clean sheet bonus

Std. work improvement

Std work audit

Quality alerts

Training school

Re-align quality standards

Re-balance work content

Design for quality

Accident alert

…

… Author: OMCD/E January 2008

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3. KPI integration into Daimler Truck scorecards Author: OMCD/E January 2008

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3

On-Time-Delivery, Direct Run and Throughput Time KPIs shall be integrated in T-Scorecard in 2008, HPU and APA already in T-SC Standard definition: APA Additional KPI:

On-time delivery

r e m o t s u C & e c s n t e c i u r d e p o r x P E r o i r e p u S

Standard definition: HPV Additional KPI:

• Direct run • Throughputtime

e c n e l l e c x E l a n o i t a r e p O

1

Quality (internal)

APA (IPQ)

defects/ vehicle

2

Quality (external)

Fixed First Visit

%

3

Customer satisfaction, Consumer

Limes Brand Quality

4

Cost of ownership

Total cost of ownership

5

Warranty performance

9

Material cost

Net Production Material Cost Savings

%

10

Program spending **

Annual funding **

€ mill.

HPV

h/veh.

Pts. (11000) Score

Warranty expense at €/vehicle 12 MIS

Productivity

12

14

r o i r e p u S

Reporting begin Feb 2008

1

Quality (internal)

APA (IPQ)

defects/ vehicle

2

Quality (external)

Fixed First Visit

%

3

Limes Brand Quality

Pts. (11000)

4

Customer satisfaction, Consumer perception

On-time delivery

%

5

Cost of ownership

Total cost of ownership

Score

6

Warranty performance

10

Material cost

Net Production Material Cost Savings

%

11

Program spending **

Annual funding **

€ mill.

HPV

h/veh.

Direct run

%

new

14

Throughput-ti me

h/veh .

new

15

Manufacturing cost

€ mill.

Aftersales RoS

%

12

11

13

e c n e i r e p x E r e m o t s u C & s t c u d o r P

Aftersales performance (internal view) Aftersales performance (external view) Inverntory turnrate (new vehicles) Inventory turnrate (used vehicles)

Manufacturing cost

€ mill.

Aftersales RoS

%

CSI

pts

e c n e l l e c x E l a n o i t a r e p O

13

Warranty expense at €/vehicle 12 MIS

Productivity

16

Aftersales performance (internal view) Aftersales performance (external view) Inverntory turnrate (new vehicles) Inventory

new

Inverntory turnrate CSI pts 17 factor 15 (new vehicles) turnrate New KPIs to be integrated inInventory Focus Pillars according to Scorecard logic: only vehicle OUsfactor -TE, 18 figures for Inverntory turnrate (new vehicles) factor 16 (used vehicles) TN, TA- and Daimler Trucks. Total number of KPIs in T-SC is 28. Inventory turnrate


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3

Goal of policy deployment of the TOP operational KPI is to cascade objectives and achieve continuous improvement Scorecard

1. HPV 2. APA 3. Direct run 4. Throughput time 5. OTD 6. Ratio 8 7. OEE/K-Factor 8. 0-ppm Supplier

BENEFITS Policy Deployment

s w e i v C o R e DAIMLER t o r e d i r g a n a T t t i o n DT MBC DFS MBV e t n s i 5 & s n C a o C d e & MB Trucks FLC FUSO TM … r n c a l e u g e R To Reach the Targets

2

W60

W154

W152 W164 W575

W20 W34

…

Transparent status of corporate business objectives, understanding and accountability at all levels of the organization, identification of roadblocks inhibiting the attainment of objectives Indicates the number of Top Operational KPIs to be reported at each level


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3

Top operational KPIs for Truck Scorecard use existing reporting organization with clear responsibilities 2 operational KPIs in D-SC

DAIMLER Scorecard – H. Rudolph (S/P) TRUCK Scorecard – C. Bosmann / O. Haakshorst (S/T)

Truck Score5 operat. card TN KPIs in T-SC T. Pax-Slotto (CPMO)

Truck Scorecard TA

TM Scorecard

Truck Scorecard TE

A. Schmitz-Justen (S/T)

M. Ried (BCF/EPA)

S. Salzmann (S/TM)

Vehicles data

Trans I. Seitz

8 Operational KPIs reported to plant level

Vehicles data

TN FTL T. Marks (CPMO)


A. Knuettel

J. Hoffmann (TE/QM)

Axles M. Ried

Aggregates

Fuso Ulrich. Schmid (Controlling) Engines data

TA Fuso G.Noda (DCPS-Office)

All TN plants

Engines

Quality Cockpit

All TA plants

TM plants

Aggregates data

Vehicles data

Engines DDC

TM/TE MBBras

P. Gamache (DDC)

H. Araujo (TM/EBE) C. Dias (TE/BMQ)

Wörth

APA TE, TN, TA

C.Hinsen

Aksaray

PBS

Status: January 2008 Daimler Trucks

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4. Reporting and KPI IT platform


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4

Most practical IT solution for Truck-wide KPI platform incorporates hybrid solution with COGNOS frontend TEC Decision: COGNOS as mandatory system if any OU decides to implement new IT tool for KPI

TMC

Project team proposal: Top level reporting operational KPIs in FLC COGNOS system

Management system for top operational KPIs

E1 T o E3

E3

E4


•

KPI-data are collected separately using existing interfaces or file transfer for NAFTA (Cognos FLC) and EULA (TMC).

•

EULA-data are transferred via interface to Cognos FL

•

Top Management see all TOP Operational KPIs within Cognos FL using existing Cognos report templates

I E1 P - K E2 p T o

p E2 - K P I

E5 EULA

CONCEPT

Cognos FLC

E4 ASIA

NAFTA

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4

Data sources for TOP operational KPIs are linked back in manifold systems TMC E1 T o E2 E3 E4

Cognos FLC

Top Management

I E1 P - K E2 p T o

p - K P I

data exchange once per month

E5 file transfer of TOP KPI data to TMC for EULA and ASIA

E3 E4

file transfer of TOP KPI data to Cognos for NAFTA

files have to be generated manually in Step 1 using different data sources Origin data sources

Excel zem@web


Access

SAP Automotive

+ SISAM WEB, SAP Log, QSYS, MLS,…

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4

Proposal for Cycle and Rules of Monthly Reporting (Release 2) Timeline:

1.

up to 16th every month ?:

Delivery of data files to TMC (FileUpload, including data transfer Cognos -> TMC for level Trucks NAFTA!)

2.

17th every month ?:

Calculate roll up in TMC

3.

18th every month ?:

Transfer data file TMC -> Cognos

Definitions:

1.

19th to 21rd of every month:

Write comments or tasks in Cognos by managers or reporting persons

2.

22th every month:

Official reporting in Cognos for current month

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4

Proposal for Cycle and Rules of Monthly Reporting (Release 2) Rules:

1.

Every file includes all data from January to reporting month and overwrites the old data of files before (changes are only allowed inside of the current reporting year)

2.

All delivered data are visible in Cognos

3.

Reporting persons are responsible for data content and delivery in time

4.

Data files are delivered to Cognos without checkup of completeness

5.

The delivery of data files to TMC (Point 1 of timeline) is a fixed due date (hour and date: 16th of every month, MEZ 24.00). At this time all available data are processed as provided (a file which does not correspond to the defined rules will be completely rejected)

6.

It is necessary to deliver both targets and actuals for all KPI; otherwise the calculated targets would be misleading. If targets are not available to the reporting responsible, the targets have to be delivered corresponding to the actuals


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4

Draft: Reporting Structure TOP KPI 1/2 Levels of Reporting 1 2 3

TOP KPI 4

HPU

OEE / KFactor

Daimler Trucks Trucks Europe / Latin America (Mercedes-Be MB Trucks Brazil MB Trucks Wörth & Turkey MB Trucks Wörth MB Special Vehicles Wörth

1 1 1 1 1 1

MB Trucks Turkey Trucks Asia (Fuso) Fuso Trucks & Buses FUSO Trucks & Buses Domestic Trucks Kawasaki Trucks Nagoya Buses Toyama Light Trucks Tramagal (Portugal) Trucks Phantumthani (Thailand) Trucks NAFTA (Freightliner, Sterling, Wester Production Trucks NAFTA

1 1 1 1 ? ? ? ? ? 1 1

1 ? 1 1 1 1 1 1 1 ? 1

Production Trucks NAFTA & Portland

1

Trucks Cleveland Trucks Gaffney Parts Gastonia

? ? ?

APA RAT OTD TPT DIR 0SU / 0CU

n/a n/a ? ? 1 1 1 1 1 1 1 1

1 1 1 1 1 1

1 1 1 1 1 1

1 1 1 1 1 1

n/a 1 (APA ? 1 1 4 1 1 1 3 1 3

1 ? 1 1 1 1 1 1 1 ? 1

1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1

1 ? 1 1 1 1 1 1 1 ? 1

3 1 1 1 3? ? 3? ? ? 1 1

1

1

1

1

1

1

8

1 1 1

1 1 1

1 1 1

1 1 1

1 1 1

1 1 1

Truck BSC

Node

23

K0001 K0002 K0003 K0004 K0005 K0006

23

n/a n/a n/a n/a

n/a K0007 23 K0008 n/a K0009 n/a K0010 n/a K0011 n/a K0012 n/a K0015 n/a K0016 n/a K0017 23 K0018 n/a K0019

ass emb Data l/ Level TMC pow ertr.

Responsible Person Top Management (B -> E2)

EOD

A BS Andreas Renschler A OU Hubertus Troska A y PL Dr. Gero Herrmann A PL Martin Daum A y PL Ernst Wünstel A y PL Walter Eisele

T TE TE/B TE/O TE/OP TE/OV

A A A A A A A A A A A

y PL OU PL PL y PL y PL y PL y PL y PL OU SU

Hans-Ulrich Maik TE/OA Harald Boelstler TA Yoshitaka Taniyama TA/O Masashi Kogame TA/OA

Chris Patterson Roger Nielsen

n/a K0020 A n PL Alan Mayne

Responsible Person for Reporting

Christian Hinsen Charles Dias Christian Hinsen Christian Hinsen Christian Hinsen

TN TN/O

Christian Hinsen Genta Noda Genta Noda Genta Noda Genta Noda Genta Noda Genta Noda Genta Noda Genta Noda Tom Marks Tom Marks

TN/O

Rob Hopf

3 ? n/a K0021 A n PL John Pacillas 1 n/a K0022 A n PL Robert Harbin n/a n/a K0023 A n PL Erik Johnson (E3)

TN/OC Mike Puncochar TN/OF Bernie McNamee TN/OUG David Buswell

Trucks Mt Holly

?

1

1

1

1

1

1

2 ? n/a K0024 A n PL Mike McCurry

TN/OHA Ted Ingold

Trucks Portland

?

1

1

1

1

1

1

2 ? n/a K0025 A n PL Paul Erdy (E3)

TN/O

Tom Gertz

Trucks Santiago (Mexico)

?

1

1

1

1

1

1

5 ? n/a K0026 A n PL Knut Anderson

TN/OM

Lisy Rubio

Trucks St Thomas Thomas Built Buses (TBB)

? ?

1 1

1 1

1 1

1 1

1 1

1 1

2 ? n/a K0027 A n PL Robert Correll Jr. 1 n/a K0028 A n PL John O`Leary

TN/OT TN/OB

David Kuchma


Jenny Curry

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87

4 Levels of Reporting 1 2 3

TOP KPI 4

HPU

OEE / KFactor

RAT OTD TPT DIR 0SU

APA / 0CU

Truck BSC

Node

K0029 K0030 K0031 K0032 K0033 K0034 K0035 K0036 K0037 K0038 K0039 K0040 K0041 K0042 K0043 K0044 K0045 K0046 K0047 K0048 K0049 K0050

ass emb Data l/ Level TMC pow ertr.

n/a Truck Powertrain Production & Manufacturing n/a n/a Engines Trucks 1+3 n/a Engines Mannheim & FUSO 1+3 n/a Engines Mannheim 1+3 n/a Engines FUSO 1+3 n/a Engines MBBras 1+3 n/a Engines DDC 1+3 n/a Foundries Mannheim and South Africa 1 n/a Foundry Mannheim 1 n/a Atlantis Foundries (South Africa) n/a n/a Axles / Transmissions Trucks & Vans 2+7 n/a Transmissions worldwide 1+3 n/a Transmissions Gaggenau 1+3 n/a Transmissions MBBras 1 n/a Transmissions FUSO 3? n/a Axles FUSO 3? n/a Product Units Gaggenau n/a n/a Axles Trucks / Vans 1+3 n/a Axles Kassel 1+4 n/a Axles Gaggenau 1 n/a Axles MBBras 1+3 n/a Axles AAC n/a

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

n/a 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a

n/a

1

1

1

1

1

1

1

n/a K0051 P y PL Norbert Rehbein (E3)

Trailer Axle Systems

1

P P P P P P P P P P P P P P P P P P P P P P

y y y y y y

y y y y y y y y y

OU SU PL PL PL PL PL PL PL PL SU PL PL PL PL PL PL PL PL PL PL PL

Responsible Person Top Management (B -> E2)

Dr. Michael Dostal Hermann Doppler

EOD

Dr. Peter Vaughan Schmidt

TM TM/E TM/EM

Bart Laton

TM/EB

Dr. Henning Oeltjenbruns TM/ER

Ralph Wegener

TM/EF

Dr. Holger Steindorf TM/T Hans-Joachim Vogt TM/TT

Andreas Moch Ludwig Pauss

Dr. Holger Steindorf /

TM/TP TM/TA

Responsible Person for Reporting

Matthias Ried Angelika Knüttel Thorsten Speelmann Thorsten Speelman Genta Noda Jessica Passos Patrick A. Gamache Thorsten Speelmann Thorsten Speelman Thorsten Speelman Matthias Ried Reinhard Jung Reinhard Jung Jessica Passos Genta Noda Genta Noda Reinhard Jung ? Matthias Ried Matthias Ried Daniel Thiess Jessica Passos Soenke Scheffer (E

TM/TAS Matthias Ried

Legend:

BS: OU: SU: PL: HPU: Cluster:

Data input (Arbitrary node types, f.e. Cost centers, EOD Nodes...) Data export Cognos (NAFTA) -> TMC Reporting node (always EOD) Reporting node not consolidated Business Segment Operating Unit Sub Unit Plant number of partitions As: Assembly; Ax: Axles; E: Engines; T: Transmissions; O: Others


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Security Structure Release 3 (1) Renschler (1)

TECMembers

Troska (2)

Linsmeier

Moreira

1

2

Boelstler (8)

Dr. Herrmann (3)

Daum (4)

Wünstel (5)

Eisele (6)

Maik (7)

Taniyama (9)

Burkart

Bastian

6 Rules 1:

Mr. Renschler see all nodes

2, 3, 4, 5, 11: 6: 7:

TEC- members see nodes 1, 2, 8, 18, 29 and detail- nodes of his own Operating Unit All Top- Managers of TE see nodes 2 – 7 All Top- Managers of TA see nodes 8 –17

8: 9: 10:

All Top- Managers of TN see nodes 18–28 All Top- Managers of TM/E see nodes 29–38 All Top- Managers of TM/T see nodes 29 and 39-51


4

3

Johnson

Patterson (18)

Dr. Dostal (29)

4

next page

Nielsen (19)

Kogame (10)

Mayne (20)

Kawasaki (11)

Cleveland (21)

Nagoya (12)

Gaffney (22)

Toyama (15)

7

5

Gastonia (23)

Tramagal Portugal (16)

Mt Holly (24)

Phantumthani – Thailand (17)

Portland (25)

8

Santiago (26) St Thomas (27) TBB (28) Daimler Trucks

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89

Security Structure Release 3 (2) Renschler (1)

TECMembers

Dr. Dostal (29)

5

11 Dr. Kirchmann

Dr. Schmidt (31)

Laton MBBras (34)

Buchner

Weiberg

Dr. Steindorf (39)

Doppler (30)

Lemmermeier

4

Oeltjenbruns DDC (35)

Wegener Foundries (36)

Thiel

Vogt (40)

Moch (45)

Pauss (46)

Engines MA (32)

Foundry MA (37)

Transmissions Gaggenau (41)

Axles Kassel (47)

Engines FUSO (33)

Atlantis Foundries (38)

Transmissions MBBras (42)

Axles Gaggenau

Transmissions FUSO (43)

Axles MBBras (49)

Axles FUSO (44)

Axles AAC (50)

9

Rehbein (51)

(48)

10


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90

4

Methods for KPI consolidation and aggregation levels TM (Powertrain) is not consolidated in Truck KPI !!! KPI

(Abbreviation)

Consolidation method (targets and actuals) Σ

1. APA (APA)

(APA per model × units produced per model) / Σ units produced

2. O-ppm Customer(0CU)

(Σ PPM complaints / Σ delivered units ) × 1million (0CU)

3. On Time Delivery (%)

[1 - ( Σ late deliveries /

(OTD) Σ

4. HPU (hours) (HPU)

8. OEE

TM (0CU) Daimler Trucks / TM

units produced all models

TM: Sub-unit**

Σ

Daimler Trucks / TM

(throughput time per plant x units produced by plant) /

Daimler Trucks /

units produced all plants

TM: Sub-unit**

(TPT)

Σ

7. 0-ppm Supplier (0SU)

Daimler Trucks (APA)

Daimler Trucks /

[1 - ( Σ offline defect units / Σ units assembled) ] × 100

6. Throughput Time (hours)

units delivered) ] × 100

aggregation level

( attendance time per model × units produced per model) / Σ

5. Direct Run (%)(DIR)

Σ

Highest sensible

( Σ PPM defects / Σ supplier units received ) × 1million

(OEE)

Σ

OU

OEE indices / Σ assembly lines (OEE) OU

9. K-Factor

(KFC)

10. Ratio (%)

(RAT)

Σ

Σ

K-Factor indices / Σ bottleneck machines (K-Factor)

Ratio improvement hours / Σ standard hours × based on actual

OU

*

Reporting by monthly values; calculation of KPI based on year date (YTD; sum of counter and denominator from January until current month) production program in to month concluded

**

Consolidation of these KPIs to TM level does not yield meaningful information or values that can be tracked objectively. Individual scoring for engines, axles and transmissions shall in the TGP scorecard.


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4

Data flow between TMC and Cognos Cognos-Visualisierung

BSC TGP (temporär) H. Ried

Cognos-Datenbank Standard-Datenfomat TMCCognos (MQ) (to be defined) Mittelfristig alternative Option für primäre Dateneigner: Manuelle Erfassung über TMC (nur bei kleinem Datenvolumen praktisch relevant, offen: Security)

Export aus TMC in csv-Datei für weitere Verwendung TMC-Datenbank Extract Powertrain

Standard-Datenfomat Datenlieferant->TMC (FileUpload) (to be defined - csv-Datei) Primäre Datenquellen (EULA und ASIA) Dateneigner (Truck EULA, TRUCK Asia, Powertrain)


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5. Performance dialogue and best-practice exchange Author: OMCD/E January 2008

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5

The Performance Dialogue generates value for line-, plant-, SU- and OU-managers through good practice transfer 

Through the Performance Dialogue process managers have the opportunity to find cross regional and BUindependent good practices to improve their performance on basis of the standardized KPIs



The Performance Dialogue depends on the lean principles 

Share openly and borrow proudly



Go and see 9 Lean Principles Take the long view – Invest in tomorrow’s profits today

1

Go and See

2

Imagine you were your customer

Only empowered people produce powerful performance

4

Share openly and borrow proudly

5

Choose the process focus

7

Respect, support and challenge your partners and suppliers

8

Keep it simple

Learn quickly from triumphs and from tragedies


3 6 9

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5

Standardized KPIs enables efficient peer to peer exchange and good practice sharing Peer to Peer Review

Target Agreement

Performance review

DT DT Operating unit

Operating unit

Plant

Plant

Base of standardized KPIs

Shop floor

Target agreement • Set

Base of standardized KPIs

individual targets and

challenges to each plant KPI

processes (e.g. financials, customer requirements etc.)

Plant Base of standardized KPIs

Steering • Top-down

Steering

Sharing •Discuss issues and root causes openly and

• Align with other

DT Operating unit

honestly with peers in other OUs •Learning •No

and exchange oriented

• Target • Clear

deviation discussion

definition of roles and

scorecard responsibilities

target setting, no league table

PERFORMANCE DIALOGUE Author: OMCD/E January 2008

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5

Structured process for identification facilitates matching and transfer of good practices 3-month cycle

Input

Process part Responsible for Output

Output

Analysis of KPIs on Level line, center, plant

Good Practices Line, Center, Plant

Analysis plant

Analysis SU/OU

KPI coordinator plant

KPI coordinator SU/ OU/region, OMCD

Good Practices Line, Center, Plant


Opportunity Fields

Transferable Good Practices

Opportunity Fields

Transferable Good Practices

Opportunity Fields

Matching

Matching list

Priorization

Decision



Matching list

Transfer of good practice and evaluation of transfer

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5

To facilitate the Peer to Peer review and the exchange of good practices a suitable platform is required Selection of possible good practice processes KPI-Coordinator in line/center/ plant

KPIresponsibles, OMCD

On-going KPI analysis and identification of above average KPI improvement and performance

Identification of good practices and selection


Analysis of good practice and selection of opportunity KPIresponsibles, OMCD

Decision/ Presentation e.g. MLC

Selection of a Presentation of appropriate good practices partner for a good proposals with practice transfer savings potential regarding KPI estimation for performance production leaders

Implementation and tracking of good practices at target plants Plant Manager, good practice Expert

SU scorecard manager

Good practice Follow-up experience tracking of KPI exchange and improvement due implementation to good practice in target plants share - ensure sustainability

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5

Good practice transfer creates a ‘win-win’ situation for all involved parties Opportunity Plant

Good Practice Owner

•

Benefits through improvement in processes

•

Confirmation of good practice process performance

•

Achievement of savings

•

•

Better performance situation with positive effects on KPIs

Possibility of further improvement of this good practice


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Operational Excellence

•

Standardized processes adjusted to local circumstances

•

Common understanding of processes

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6. Appendix


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6

Summary of important decision milestones for Top Operational KPI project •

March 2007

• June • July

2007

2007

Assignment from TEC to standardize top operational KPIs for all OUs KPI standardization conference with participants for all OUs# Approval of KPI definitions by MLC (meetings Brazil, Portland)

•

October 2007

Preliminary approval of KPI definitions by TEC(some details regarding HPU outstanding)

•

December 2007

MLC finalise HPU definition for all OUs

• January

2008

Approval by PEC to integrate 5 top operational KPIs into T-scorecard

Roadmap for Top Operational KPI project – please see next slide.


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TOP operational KPI Roadmap supports the Lean Transformation of DAIMLER Trucks

Performancedriven CI dialogue DTs

Provide IT-system by Cognos/TMC Alignment of KPIs within DT Scorecards (Portfolio-Analysis) Approval standard op. KPIs / Integration in SCs TEC 15.01.08

Continuous training of Top Op. KPIs from DTs to GEMBA

Final Alignment in global MLC, Tokyo 03.12.2007

Standardization TOP op. KPIs in indirect areas (e.g. develop. by eHPV)

Discussion TOP Operational KPIs in plants, lighthouses, OUs, exe. councils

Today

Global implementation of TOP op. KPIs

Start of Pilot Implementation in plants (TM 07/07) TOP Operational KPI Kick-off and project assignment (15.05.07)


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KPI

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