improving operational efficiencies.ppt

8/10/2019 Improving Operational Efficiencies.ppt

http://slidepdf.com/reader/full/improving-operational-efficienciesppt 1/56

Improving Operational Efficiencies Through“Lean Manufacturing”

May 2010



n

2

ADVICE IMPLEMENTATION

OperationalAssessment

OptimizedFacility

Design

ImprovementImplementatio

DetailedDesign

Support

Manufacturing Advisory Services



•

•

•

– •

•

•

•

•

•

•

Definition of “Lean”

A management philosophy/ strategy that focuses on eliminatingwastes in manufacturing. Waste inflate costs, lead times andinventory requirements. By eliminating waste, quality is improved,and production time and cost are reduced

Lean principles come from the Japanese manufacturing industry.

For many, Lean is the set of "tools" that assist in the identification andsteady elimination of waste.

The original seven wastes are:Transportation

Inventory (raw material ,WIP, and finished product)

Motion (people or equipment moving or walking more than is required)

Waiting (Down time)

Overproduction (production ahead of demand)

Over Processing (resulting from poor tool or product design creating activity)

Defects (the effort involved in inspecting for and fixing defects)

3



•

•

•

–

–

• •

“Wastes” in Injection Molding

Waste in:1. Raw material

2. Overall Equipment/ Plant EfficiencyMachine “Availability” (Scheduled/ Unscheduled down time)

Machine “Performance” (Cycle and cavitation)

Rejects

3. Labor

4. Over productionConformance ratio

5. InventoryWIP, FG, and spare parts

6. Product flow/ Transportation

7. Production lead timeValue stream mapping“Push” vs. “Pull” system

8. Energy

9. House keeping

4



•

•

•

–

–

• •





Rejects

3. Labor




7. Production lead time

Value stream mapping“Push” vs. “Pull” system

8. Energy

9. House keeping

5



•

•

6

Material is the largest contributor to operational cost regardless of

market type

Any reduction/ elimination of wastage in material directly impacts

bottom line

9%2%

3%7%

3% 2% 2%1%

2%

24%

Automotive

Large Tonnage

60%

PET

Preform

85%

Material

Primary Equipment

Energy

Labor

16%

6%7%

1%1%

Closure

molders

18%

5%2%

Custommolders

Building & Infrastructure

Maitenance 5%

69%

Waste In “Raw Material”

6% 64%



•

7

Material waste typically happens in the following areas:

– Part weight

2004 2007 2009

Lightweighing Weight Annual Resin

Opportunity

Sub-total Savings

Assumptions

Savings (g)

2.8

Savings

$3,148,235

Preforms (3 machine plant)

PET Resin Cost

HDPE Resin Cost

850 Million/yr

$1.40 $/kg

$1.10 $/kg

14.5g 12.3g 11.7g




•

–

8


– Part weight

Unrecoverable scrap




%variationfromnominal

•

–

–

•

•

9


– Part weight

Unrecoverable scrap

Over packing

0.90%

0.70%

0.50%

0.30%

Weight variation due to process control

0.10%current range

0 10 20 30 40 50 60 70

-0.10%

-0.30%

-0.50%

-0.70%

-0.90%

-1.10%

-1.30%

-1.50%

Resin consumption reduction: 312,000 kg/yr ($450,000/yr)

Based on plant with 69 SKU (preforms) and 13 IMM


improved range

quality spec



•

–

–

–

10


– Part weight

Unrecoverable scrap

Over packing

Spillage, Purge, Start up




•

–

–

–

–

11


– Part weight

Unrecoverable scrap

Over packing

Spillage, Purge, Start up

Excessive use of colorant




•

•

•

–

–

• •





Rejects

3. Labor






8. Energy

9. House keeping

12



•

•

•

Common Goal Between Departments

Best-in-class molders enable collaboration amongmaintenance, production, and financial groups

Maintenance, operation, and engineering have traditionally

been compensated on different goals

Best-in-Class molders address this challenge by establishinggoals that are aligned (i.e. OEE)

OEE = Availability x Performance x Quality

Connecting these metrics will ensure that functional groups

work collaboratively to achieve common goal

13



Overall Equipment/ Factory Efficiency

OEE/ OFE = Availability x Performance x Quality

OFE

OEE

=

=

Scheduled time = (Total available hours - Hours not scheduled due to lack of sales)

14



15

Asset Intensive Companies: Source: Aberdeen Group, Nov. 2009

Best-in-Class

(Top 20%)

Industry Average

(Middle 50%)

Laggards

(Bottom 30%)

OEEUnscheduled Down Time

88%2%

81%11%

75%18%

Injection Molding Companies:

PET (1) Closure (2)

Thinwall /

Medical (2)Custom Automotive

OEE

Unscheduled downtime

>96%

< 3%

>90%

< 5%

>90%

< 5%

> 80%

< 8%

> 85%

< 8%

(1) Preforms only

Best-in-Class Metrics



•

16

Increasing “Availability” through predictive maintenance


Unscheduled and Scheduled Down time

Shift from Reactive to Predictive Maintenance



•

17







MachineAvailability

•

18




Predictive

100,0%

98,0%

96,0%

94,0%

Machine Availability

Preventive

Program

Program

92,0%

90,0%

88,0%

86,0%

84,0%

82,0%

80,0%

0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5 4,0 4,5 5,0 5,5 6,0 6,5 7,0 7,5 8,0 8,5 9,0 9,5 10,0

Years of Production

Data curve generated from more than 200 Husky machine audits




20.00

-

19

300,000.00

250,000.00

200,000.00

Spending 150,000.00

100,000.00

50,000.00

-

Breakdown of Maintenance Spend

35.00

30.00

25.00

20.00

15.00

10.00

5.00

-

Lost Production caps in Milions per year

2005 2006 2007 2008 2009 2005 2006 2007 2008 2009

Lost Prod'n/Year

Emergency Maintenance Parts

Revenues lost K$

•

•

•

New policy started 2007 to minimize

maintenance spend

Postponed PM visits

Affect on reducing maintenance resulted inincreased total spend and production loss

120.00

100.00

80.00

60.00

40.00

• By Q3 of 2008 PM practices were renewed 2005 2006 2007 2008 2009

Revenue Loss

Predictive Maintenance - Case Study



20

• Increasing “Availability” through Quick Mold Change (QMC)



Reduce Scheduled Down Time by QMC



2.

4.•

•

•

21




Steps to reduce mold change time:

1. Separation of internal from external

times

Conversion of internal to external

3. Parallel operations

Automation (hardware)Quick connects and disconnects

Hydraulic, Pneumatic, Magnetic clamping

Ejector Couplers

5. Elimination of adjustments




2.

3.

•

•

•

Other

22






times


Parallel operations

Organization Up to 60% immediatereduction through

the first three steps

4. Automation (hardware)Quick connects and disconnects


Ejector Couplers

Tool positioning

10%

Press settings

6%

Water line

7%

Clamping

9%

Ejector

6%

5. Elimination of adjustments4%

Organization

58%




2.

4.•

•

•

23






times


3. Parallel operations

Automation (hardware)Quick connects and disconnects


Ejector Couplers

5. Elimination of adjustments




•

24

Increasing “Performance and Quality” through “Asset Performance Optimization Program”


Improving Equipment Life Cycle Costs



•

25






CostperPart

•

Life

26



Existing System with Technology

Upgrades /Encore

New System

Today

Economical End of

LifeNew End of Life

Upgraded system




•

•

•

•

•

Key Strategies to Increase Return On

Assets (RoA) in Molding

1. Adopt a “Risk based strategy” to enable efficient decision making

Plant and operational assessment to “Identify, Quantify, and Prioritize” risks that are inherent to the organization (i.e. unscheduled down time)

2. The key strategy that differentiates Best-in-Class is to implement APM

(Asset Performance Management) as the company moves fromreactive to predictive asset management

One of the main goals of the best-in-class molders is to maximizeOEE and minimize down time.

The only way to achieve this goal is by being predictive in managingassets and proactive in controlling the process

This key strategy enables Best-in-Class molders to perform at amaximum efficiency and minimum unscheduled down time.

Best-in-Class molders avoid asset failure before they actually happenresulting in lower unscheduled down time

27



PercentageofParticipants

•

•

Key Strategies to Increase RoA in Molding

3. The successful execution of previous strategy requires molders to havereal time visibility into asset performance (also a differentiating factor)

Making effective asset decisions requires molders to equip their employees

with the right data at the right time in the right form

Best-in-Class differentiate themselves from industry average and laggards

by more effectively leveraging data and turning data into action70% Best-in-Class

60%59%

50%

59%

52%

Industry average

Laggards

50% 45%

40%

30%

21%

20%

10%

0%

Failure data is used to perform root

cause analysis

Historical as well as real time data is

used as actionable intelligence

Source: Aberdeen Group, Nov. 2009

28



•

•

•

–

–

• •





Rejects

3. Labor






8. Energy

9. House keeping

29



•

Labor Reduction without Automation

Case study: Workcells pared for labor sharing

Warehouse

3 1 1 1

1 1 1 1 1

30



• •

•

Labor Reduction with Automation

Utilize the existing automation (i.e. robots)Consider other financial metrics in addition to simple payback when evaluating automation

Life cycle costing

31



•

•

•

–

–

• •





Rejects

3. Labor






8. Energy

9. House keeping

32



%ofscheduledproduction

• • •

4 6 1

Over Production

Is “Conformance Ratio” one of your main KPIs? Over production leads to excess inventory

High unscheduled down time leads to under production

which in turn leads to low service level

180%

160%

140%

120%

100%

80%

60%

40%

20%

0%

Over production due toHigh scheduled down time

Under production due to highUnscheduled down time

12 37 99 41 17 36 97 13 40 24 14 11 42 25 22 22 48 11 98 18 91

Machine number

33



•

•

•

–

–

• •





Rejects

3. Labor






8. Energy

9. House keeping

34



Cost($)

35

Combined cost

Set Up Costs

Carrying Costs

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Annual Inventory Turns

Optimum Inventory turns



Cost($)

36

Combined cost

Set Up Costs

Carrying Costs

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25





Cost($)

37

Combined cost

Savings

1 2 3 4

Combined cost

Set Up Costs

Carrying Costs

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25





A

B

C

Mold Change Time and Inventory

UTILIZATION

Total production: 24

2 changes, I hour each


Lot size reduced in half.

Lot size reduced in half.

5 Changes, I hour each

5 Changes, 24 min. each


Production

Mold change

38



ServicelevelInventorylevel(x1000)

0

39

Category A

Category BService level and inventory @ 85% OEE

Category C

Top 10

Inventory Top 10

102.0%

100.0%

98.0%

96.0%

94.0%

92.0%

90.0%

88.0%

86.0%

700

600

500

400

300

200

100

84.0%

Category A

Category B

Category C

Top 10

Inventory Top 10

Base

93.5%

95.1%

96.0%

92.4%

512

50% lot size

97.0%

95.0%

95.9%

99.4%

466

25% lot size

97.5%

95.2%

96.1%

99.6%

352

200% lot size

92.0%

92.3%

91.8%

89.6%

591

• Simulated the effects of different lot sizing policies on inventory and service level

• Lot size reduction improved service level and reduced inventories

Case study- lot size reduction simulation



Valueofinventory

40

Sensitivity to spare parts inventory

$2,100,000

$2,000,000

$1,900,000

$1,800,000

$1,700,000

$1,600,000

$1,500,000

$1,400,000

$1,300,000

$1,200,000

$1,100,000

• Preventive Maintenance

• Mean Time Between Failures (MTBF) 10% - reduction of parts not used

25% - reduction of parts not used





$1,000,000

$900,000

$800,000

$700,00050% reduction in not used parts

Today

Reduction to 3 months for parts used in 2009

$600,000$500,756

$500,000

$400,000

$300,000

$200,000

$100,000

$0

Goal

2 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Years of inventory (parts used in 2009)

Case Study – Spare Parts



•

•

•

–

–

• •





Rejects

3. Labor






8. Energy

9. House keeping

41



42

ASSEMBLY, PACKAGING, MACHNE HALLand PALLETIZING

Assembly

Assembly/

Packaging/ ConveyorPalletizing

WAREHOUSE

Product Flow – “Before”



43

ASSEMBLY, PACKAGING,PALLETIZING

Assembly/WIP

MACHNE HALL

Packaging

Palletizing

STAGING

Product Flow – “After”



44

Suppliers

Forecast PRODUCTION CONTROLSYACC

Forecast

CustomerDemand: 127K

Daily/ Weekly orders

Injection:

part #: 8720, 90ml vialCavitation: 24 cavityCycle: 9.6 sec.Parts/ hr.: 9,000Daily demand: 127,000/ day

12 sec. 7 days

Scheduling

Daily/ Weekly ordersUnits/day

Injection:

part #: 9962, 48 mm capCavitation: 16 cavityCycle: 10 sec.Parts/hr.: 6,000Daily demand: 127,000/ day

10 sec. 7 days

14 days

Assembly:

Part #: B 90210Cycle: 0.58 sec. / unitParts/hr.: 6,200Daily demand: 127,000/ day

Sterilizing:

Outside supplierDaily demand: 127,000/ day

8 days

Receiving/ Shipping:

Daily demand: 127,000/ day

Production Lead Time:

22 days

Processing Time:22 sec. 0.58 sec. 22.6 sec.

“Value Stream Map” - Existing System



Lleno

Vacío

7 days 15 days

45

Forecast PRODUCTION CONTROL Forecast

Suppliers

Daily/ Weekly orders

SYACC

FullDaily/ Weekly orders

CustomerDemand: 127KUnits/day

Scheduling

Empty

Injection:

part #: 8720, 90ml vial

Cavitation: 24 cavityCycle: 9.6 sec.Parts/ hr.: 9,000Daily demand: 127,000/ day

12 sec.

ProductionKan Ban

WithdrawalKan Ban

Injection:

part #: 9962, 48 mm capCavitation: 16 cavityCycle: 10 sec.Parts/hr.: 6,000Daily demand: 127,000/ day

10 sec.

3 days

Assembly:

Part #: B 90210Cycle: 0.58 sec. / unitParts/hr.: 6,200Daily demand: 127,000/ day

Sterilizing:

Outside supplierDaily demand: 127,000/ day

8 days

Receiving/ shipping:

Daily demand: 127,000/ day

Production Lead Time:

11 days

Processing Time:22 sec. 0.58 sec. 22.6 sec.

“Value Stream Map” - Future System



CarryingCostAverageInventory

•

46

1,600,000

Push$2,000,000

$1,800,0001,400,000

$1,600,000

1,200,000

$1,400,000

1,000,000$1,200,000

800,000

600,000

Pull

$1,000,000

$800,000

$600,000

400,000

$400,000

200,000$200,000

0

Push: 46 machines Pull: Summer + reduced mold change Pull: Level summer + reduced mold

$0

time (26 machines)

Average Inventory

change time (21 machines)

Inventory Carry Cost

Pull System reduced :

– Batch size by an average of 56% on high volume products and 74% on lowvolume products

– Safety Stock from an average of 15 days to 3 days

– Inventory by 68%

Push Vs. Pull – Simulation Analysis



47

• Shift from “Push” to “Pull” system requires standardization, process

stability and repeatability

• Stabilize operation prior to lean conversion

Material Control

Methods LevelProduction

• Reduce response times or changes in demand

• Reduce upstream schedule variability

Pull

System

• Material replenishment linked to customer withdrawals

• Constraint management applied to

manage bottlenecks

SynchronizedProduction

• Synchronize operations with customer

requirements

• Create disciplined process repeatability

ContinuousFlow

• Standardize work

• Increase process flexibility

• Reduce WIP inventory, time loss and defects

• Create repeatability between workstations

Stability• Bring processes under control

• Provide an environment to eliminate waste

• Show a quick business impact Start here

Time

Shift from Push to Pull



•

•

•

–

–

•

•





Rejects

3. Labor




7. Production lead timeValue stream mapping

“Push” vs. “Pull” system

8. Energy

9. House keeping

48



Social Responsibilities with Profits

• Reducing energy consumption of assets is an untapped resource in both the quest for profits and social responsibilities

ENERGY

Lighting

HVAC

8%

Air compressors

6%Others

1%

3% Labour Infrastructure

d cooling

12%

Machines

Energy

3%

2% 2%Maintenance

2%50%

Dryers

20%

Equipment5%

Resin

86%

49



•

•

•

Two Approaches to Reduce Energy Cost

1. Reduce the cost per unit of energy ($/KWh) through

negotiation and risk mitigation

Numerous consulting firms provide “Negotiation and risk

mitigation” services

Alternative Energy generation

2. Reduce the amount of energy used (KW/lb):

Certain utility companies offer programs that provide molders

rebates towards the purchase and installation of qualified

equipment that improves their facility’s energy efficiency

These two approaches alone without an “Energy Management

Program” can not be sustainable

50



1-

5-

Total Energy Management Program

Estimate and verify site energy profile

2-

3-

4-

Understand your “Base” and “Process” loads

Understand when and how much energy is used

Identify, Quantify, and Prioritize opportunities

Phase 1:Energy audit

& reduction

strategy

Eliminate waste and reduce consumption through

- Implementation of selected energy reduction projects

6 - Monitoring and Targeting

- Understand Where energy is used

7 - Data analysis and reporting energy KPIs

- (Energy dashboard) by department

Phase 2:Sustainability

Through

M&T

8 - Conduct internal and external benchmarking

9 - Repeat the steps – Continuous improvement

51



0

0

52

Estimated

Capital Cost

Estimated

Annual Savings

Payback Electricity

Reduction

Carbon

Reduction

Identified Opportunities ($) ($) (year) (KW) (ton)

Process Water SystemFree cooling

Option 1Option 2 (4)

$ 60,000$ 128,000

$ 29,000$ 26,000

2.1 years4.9 years

322,222288,889

7264

Compressed Air

Air LeaksReduced PSI for IMMs

Install ES control solution

$ 2,500 (1)$0

$ 8,500

$ 37,500$ 8,376 (2)

$ 12,000

24 daysimmediate

0.7 years

416,667

150,000

93

33

Cost avoidance of new Air comp.$50,000

Lighting

Retrofit to T5s as per layout $ 98,560 $ 46,689 2.1 years 449,206 100

Power Conditioning

Power cure to condition power $ 55,300 $ 23,000 (3) 2.4 years 204,254 45

and improve power factor

Cycle times

Estimated 6% reduction plant wide

TOTAL ESTIMATED COSTS & SAVINGS

$ 24,000

$ 248,860

$ 25,000

$ 178,564

0.96 years

1.39 years

318,000

1,827,016 KW

71

407 ton

Percent reduction compared to current usage

Case Study

17.77%



•

•

•

–

–

•

•





Rejects

3. Labor




7. Production lead timeValue stream mapping

“Push” vs. “Pull” system

8. Energy

9. House keeping

53



54

Visual Kan Ban to eliminate excessive material handling

Implement “5S”



Action Plan

• Start with assessing your current operation• Plant and operational assessment to “Identify,

Quantify, and Prioritize” Waste reduction

opportunities that are inherent to the organization

• Husky’s Manufacturing Advisory Services team

can assist you in developing and implementing a“Lean Manufacturing” program tailored to your

operation

55



Contacts

• Santiago Archila, – [email protected]

– 905-951-5000, Ext. 3810

• Sean Golzarian, – [email protected]

– 905-951-5000, Ext. 3550

• Husky website: www.husky.ca

56

improving operational efficiencies.ppt

Documents