Demand Driven MRP Case Studies Erik Bush CEO, Demand Driven Technologies
Transcript
1. Demand Driven MRP Case Studies Erik Bush CEO, Demand Driven
Technologies
2. Objective of this presentation Provide an inside look at 3
successful Demand Driven MRP implementations Highlight how specific
attributes of DDMRP are used to address a wide range of user
environments
3. Agenda Amore Pacific Satuerca StemCell
4. Amore Pacific
5. Together We Can AMORE PACIFIC
6. 6 Index Part 1 : About AMORE PACIFIC Part 2 : SCM History of
AMORE PACIFIC Part 3 : Lunching DDMRP Part 4 : Results &
Lessons Learned
7. Our Brand : Beauty & Health History of Endless pursuit
for beauty 1930193019301930---- Mother, the root ofMother, the root
ofMother, the root ofMother, the root of
AMOREPACIFICAMOREPACIFICAMOREPACIFICAMOREPACIFIC
1960196019601960---- Start of doorStart of doorStart of doorStart
of door----totototo----doordoordoordoor sales systemsales
systemsales systemsales system Category diversificationCategory
diversificationCategory diversificationCategory diversification
(make(make(make(make----up, personal care)up, personal care)up,
personal care)up, personal care) IPOIPOIPOIPO 1980198019801980----
Reshaped labor relationsReshaped labor relationsReshaped labor
relationsReshaped labor relations Corporate restructuringCorporate
restructuringCorporate restructuringCorporate restructuring
Intensified competition andIntensified competition andIntensified
competition andIntensified competition and sales channelsales
channelsales channelsales channel
diversificationdiversificationdiversificationdiversification (Dep.
store, Specialty store)(Dep. store, Specialty store)(Dep. store,
Specialty store)(Dep. store, Specialty store) 2000200020002000----
Global expansionGlobal expansionGlobal expansionGlobal
expansion
8. 8 29 Brands : across all categories in Beauty & Health
Skin Care, Make-up, Fragrance, Hair Care, Body Care, Oral Care,
Green Tea, Beauty Food
9. 9 Part 2. SCM History of AMORE PACIFIC SCM Strategy & IT
System History 1996 2002 2007 2014 Excel Base MRP System MRP System
developed by COBOL Lunch SAP R3 ERP Add on SAP APO Module Implement
DDMRP R+ System Weekly Cycle Product planning Reduce lead time,
Setup time, Lotsize Increasing frequency of order flexibility
Synchronizing Demand planning Strong network with Supplies Share
all of Information Operation Excellence Decouping Demand planning
Independent of purchasing Available of stock
10. Good forecasting? Sales Forecast : The accuracy of M+3
forecast is less than 10%. Purchasing team could not depend on the
sales forecast, used only as a reference. Best policy of purchasing
department : Try to reduce the lead time because the accuracy of
short term forecast is high. [ Accuracy of Sales Forecast]
11. 11 Part 3. Lunching DDMRP [ 30 ] The Five Components of
DDMRP Demand Driven Material Requirements Planning Strategic
Inventory Positioning 1 Buffer Profiles and Levels 2 Dynamic
Adjustments 3 Demand Driven Planning 4 Visible and Collaborative
Execution 5 Modeling/Re-modeling the Environment Plan Execute
12. 12 Product BOM Raw material, component package material,
bulk
13. HubPack BulkWeig htingRaw C.P Supplier's Amore pacific
Injecti on W/H Buffering in front of the suppliers drum work center
andAPs warehouse Reduced the finished goods L/T Strategic Inventory
Positioning W/H Decor ation 3days3days 14days 7days
14. All parts Stocked () Non-Stocked () Replenished Replenished
Over-ride Min-max Non-buffered Lead Time Managed DDMRP Part type
Raw materials Packing materials Buying goods Non Stocked
Non-Buffered N/A Short L/T, 80% items(cheap, easy) All Lead Time
Managed N/A N/A N/A Stocked Min-Max Outside stored Bulk raw
material N/A N/A Replenished All items except Min-Max parts Long
L/T, top 20% items All imported tube All items of glass Other
Strong items - Injection, Pumps, tubes, single box, tray, etc Some
items of large supply variability (suppliers capacity limit, etc)
Replenished _override New items expected to be used the Replenished
method
15. Red Zone Base Red Zone Safety Yellow Green Step 2: Buffer
profile Implementation division : cosmetic plant all of raw
material 100%(about 1500 items) some of long LT packing materials
(about 300 items) Lead Time Lead Time Variability Long/High 30% 80%
Medium 50% 50% Short/Low 80% 30% ADU : Past 90 days Spike Order :
Demand plan of 2~5 times Lead time MOQ : Packing unit as a
multiple
16. Step 3 : Dynamic Buffer Management Decided to utilize
adjustments to Lead Time to flex buffer instead of using Planned
Adjustment Factor Seasonality :Addressing Chinese New Year Adjusted
lead time from 90 days to 120 days to increase yellow zone size
Items supplied from China Worked well only 3 days of shortage
17. 17 Utilization of lead time adjustments to address Chinese
New Year Delivery has delayed : 60K Standard Lead time increased
from 90days to 120days One supply order wasnt delivered on time But
its ok buffer was able to absorb the impact
18. ResultsResultsResultsResults 1. Raw materials Reduce the
Inventory level 20 % Reduce the load of order management 30% 2.
Packing materials Service level (purchasing response rate 60% ->
90%) Imported packing materials : Service level 99% Possible to
share the long term purchasing plan with supplier
19. Result Reduce Raw material inventory & work load
20. Result Component packaging material On time delivery has
improved Major improvement in only 2 months stabilized
21. 21 Closing The Meaning of Implement the DDMRP with TOC
concept? => Change the planning paradigm change from point to
range estimation at the same confidence level => Change the
planning paradigm from forecast and inventory precision to
establishing appropriate buffer ranges trust the system Sales
forecast, S&OP : mixed use with a valid technique Started small
and built confidence as results were achieved
22. Satuerca
23. March 27th, 2015 Presented at the Demand Driven World
Conference Houston, TX
24. SATUERCA ACTIRO ESTAMCAL MECANIFRAN 24 INTRODUCTION MARKET
PRODUCTS PRODUCTION PROCESS DDMRP RESULTS Founded in 1967
Horizontal forging (parts between 50g and 2kg) Machining Focused on
the automotive supply sector 2 factories located in Spain and 1 in
Romania 135 workers 15,000 m2 between the 3 factories Founded in
1967 Horizontal forging (parts between 50g and 2kg) Machining
Focused on the automotive supply sector 2 factories located in
Spain and 1 in Romania 135 workers 15,000 m2 between the 3
factories
25. Our productsOur products 25 Gears Clutch Bodies Cams
Bearings Nuts INTRODUCTION MARKET PRODUCTS PRODUCTION PROCESS DDMRP
RESULTS
26. Special Nuts Cams 26 Our productsOur products Gears
Bearings Clutch bodies INTRODUCTION MARKET PRODUCTS PRODUCTION
PROCESS DDMRP RESULTS
27. 27 Why DDMRP?Why DDMRP? PLANNING Critical process Situation
description Customer forecast/orders varying even on due date
Delays of raw material deliveries Daily consumption from
consignment stocks Planning history Until 2012, planning was
dependant on a single persons analysis From 2012 to 2013 use of
Excel September 2013 until present day: DDMRP Methodology applied
Necessities Methodology to manage this information on a daily
basis: Stock levels Detection of new Work Orders (WO) requirements
New raw material purchase requirements Which raw material Open
Supply orders need to be controlled In Process WO that need to be
expedited INTRODUCTION MARKET PRODUCTS PRODUCTION PROCESS DDMRP
RESULTS
28. 28 Changing to DDMRPChanging to DDMRP 1st- Strategic
Inventory Positioning 2nd- Buffer profiles and Level Determination
3rd- Dynamic Buffers 4th- Demand-Driven Planning 5th- Execution
DEMAND ANALYSIS Change in the way DEMAND is understood
Forecast/Orders beyond a reliable horizon are no longer taken into
account Only committed orders (date and quantity) are considered
DEMAND INTRODUCTION MARKET PRODUCTS PRODUCTION PROCESS DDMRP
RESULTS
29. 29 CUSTOMER B RESULTS: Bad forecasts meant that stock was
out of control, what was needed wasnt available and the forged
parts were no longer required Urgencies in both factories The
forecast for the next three weeks was the input to start the
forging process 2 weeks later, when the forged parts were delivered
to Mecanifran, a new forecast would tell us whether they were then
required for the next process. INTRODUCTION MARKET PRODUCTS
PRODUCTION PROCESS DDMRP RESULTS BEFORE
30. NOW 30 RESULTS: Buffers controlled in every strategic
position LT reduction of 50% (from 20 to 10 days) Stock reduction (
3,700,000 parts to 2,700,000 parts 35% in three months) No more
urgencies, the distortion that bad forecasts produced has been
eliminated Machine Capacity has increased by using them for what is
really needed INTRODUCTION MARKET PRODUCTS PRODUCTION PROCESS DDMRP
RESULTS CUSTOMER B
31. 31 3th- Dynamic Buffer Adjustments The Planning Manager
should know of any extraordinary changes to products so as to be
able to indicate such changes in the buffer parameters. In addition
to this, they must review the behaviour of the buffers periodically
to be aware of whether readjustments are necessaryINTRODUCTION
MARKET PRODUCTS PRODUCTION PROCESS DDMRP RESULTS
32. 32 RESULTS Reduction in production planning changes Machine
capacity improved Production for one week is planned Daily
information updated vs Weekly information updated Improved reaction
to changes Facilities are used more efficiently maintaining same
service levels (98+%) Regulation of the stock levels - stock is
available for all production requirements Customer B is the best
example INITIAL REQUIREMENTS Part and Inventory data must be
correct and updated DEMAND has to be filtered INTRODUCTION MARKET
PRODUCTS PRODUCTION PROCESS DDMRP RESULTS
33. 33
34. 34 CUSTOMER B 445945 PART
35. 35
36. 36 AVERAGE STOCK 175 TN DDMRP TARGET: 184 TN ADU 9,228
(3/16/2015) RAW MATERIAL STOCK (2 WEEKS) = 92TN MATERIAL IN PROCESS
(2 WEEKS) = 92TN TOTAL = 184 TN CAMS 13% OF TOTAL PRODUCTION
37. 37
38. BECOMING DDMRP 38 INTRODUCTION MARKET PRODUCTS PRODUCTION
PROCESS DDMRP RESULTS
39. StemCell
40. STEMCELL Technologies Experiences from our Demand Driven
journey March 2015 - Juan Abbud, Aida Mujkanovic CONFIDENTIAL AND
PROPRIETARY Any use of this material without specific permission of
StemCell Technologies is strictly prohibited
41. Dr. Allen Eaves Founder, President & CEO A Family Owned
Company We can move quickly in a fast moving market We listen to
our employees & customers Growth over 20 years dependent on
sales revenue a great discipline! Sales are largely dependent on
the new products that we develop New products are developed by our
outstanding R&D scientists - who are essential to STEMCELLs
success! Planning and prioritization of new product development is
done carefully and confidentially Leadership & decisions are
focused on long-term growth and stability
42. The promise of stem cell research Cells for transplantation
Adult Tissue Specific Stem Cells Bone marrow for leukemia Nerve
cells for Parkinson's & Alzheimer's disease Heart muscle cells
for heart disease Pancreatic islet cells for diabetes Cultured
Cells Drug screening and potential therapeutics Toxicity testing
Study cell differentiation Understand prevention & treatment of
cancer/disease Pluripotent Stem Cells
43. STEMCELLs Planning Considerations
44. STEMCELLs Planning Puzzle Global, High Mix, Low Volume
Sales by productInventory by product Planned Product Introductions
5 years Seasonality and growthOff the shelf sales
45. Deep Bills, Long lead times & growth Cell Separation
Cocktail Bill of Material, LT & CLT Changing Demand - 517 day
cumulative leadtime, 300 days in prequalified base raw material,
difficult to obtain, requires QC qualification - strategic part -
Supports multiple products within a family 239 finished goods -
Growing sales and product lines require buying for growth - Lot
Consistency and Shelf life constraints intermediates and their
effect on FG
46. Manufacturing constraints - MOQ - Sample based QC testing
significant LT, capacity constraint (i.e. 55 of 56 days) -
Dedicated equipment for some product lines long setup vs run time
strategy is to size batches of low volume product to cover long
periods and maintain equipment availability minimum equipment batch
size - Inventory footprint is low - Manufacturing variability
multiple possible uses for certain parts use is test result based -
Shelf life synchronization considerations, lot mixing
constraints
47. Keeping up with growth
48. Problem Statement Limited ability to identify and exploit
opportunities for improvement in the Supply Chain during day to day
operations Labour intensive processes consume planning and
procurement resources on tactical activities and limit operational
agility Lack of data consistency limits metric development on the
performance of the supply chain Difficult to maintain inventory at
optimum level, opportunities for reduction exist according to
analysis to date exceptions driving special cases are not always
captured in the system and are difficult to analyze
49. Operational objective Improve Plan to Produce Improve
service levels while maintaining the optimum inventory level
establish and maintain optimum inventory, control oscillation
Establish process efficiencies in the Plan to Produce process by
optimizing the use of the ERP and supporting systems Develop and
establish the required datasets to support the Plan to Produce
process and its continuous improvement
50. Adopting DDMRP
51. DDMRP a better fit for our planning goals System Comparison
DDMRP is directly aligned with the objectives of our current
planning system This eliminates manual work to achieve our desired
output from the planning process The DDMRP model provides simpler
and more effective metrics System design creates opportunities for
process automation Simulation based on historical data validated
buffer performance and service levels
52. Simulation sample - ADU vs. variability PART - A
53. Solution Proposal Adopt DDMRP as planning methodology
Update BOM configurations to support strategic inventory
positioning Capture complete demand and supply pipeline on ERP
system (some information for special cases on other systems)
Certify Planning and Procurement teams - CDDP
54. Implementation process
55. System Design BOM Structure
56. Steady usage constant availability Metrics and Analytics
System inputs, setting part types 20 % 80 %
57. Spotty usage constant availability, strategic parts Steady
usage constant availability Metrics and Analytics System inputs,
setting part types 20 % 80 %
58. Metrics and Analytics System inputs, setting part types
Demand Driven Planning 20 % 80 %
59. Measuring System Inputs and Performance
60. Metrics and Analytics Buffer Performance Details
61. Identify parts with risks in Supply and higher variability
Assign to correct buffer, address cause when possible Complements
LT variability metrics Identifying opportunities for improvement
Leadtime Variability Purchased Parts
62. Future Considerations what is next for us? Deploy metrics
and link them to execution, periodical metric reviews buffer status
meetings Dynamic adjustment model for applicable RO and MM parts
Continue monitoring and adjusting parts attributes Rollout DDRMP
for pilot product end to end (Raw FG) Build BOM dataset strategic
buffer analysis Explore opportunities for improvement MOQ, Leadtime
and Variability invest to redefine constraints as appropriate
63. Key implementation factors at STEMCELL Secure executive
sponsorship by demonstrating value Simulation with your data helps
Understand the scope of applicability and expected benefits fit to
business An implementation partner that knows your system is highly
valuable Train everyone! Make sure the BOM definition in your
system is correct Strong data team ability to run trending
calculations and validate settings is invaluable focus on data
quality Plan for the transition support users by providing them
context to understand the new ordering patterns Plan the
integration of the planning system into all related processes