Implementing APO Demand Planning at a small company

10,326 views

Published on

2 Comments
6 Likes
Statistics
Notes
No Downloads
Views
Total views
10,326
On SlideShare
0
From Embeds
0
Number of Embeds
6
Actions
Shares
0
Downloads
798
Comments
2
Likes
6
Embeds 0
No embeds

No notes for slide

Implementing APO Demand Planning at a small company

  1. 1. 2003 Supply Chain Management Forum Implementing APO Demand Planning at a small company Session Code: 710 Presenters: Mike Beller, Entegris Chandra Mohan, Bristlecone Joan Ellison, Entegris
  2. 2. Who is Entegris? • Core competency: Materials Integrity Management • Core markets: Semiconductor, Data Storage, Life Sciences, Fuel Cell • Core products: Silicon wafer carriers, wafer shippers, disk shippers, fluid handling • Fiscal year 2003 revenue approx. $245M
  3. 3. Geographical Supply Map: Indicative Manufacturing Locations DCs Customers
  4. 4. SAP landscape • Went live on SAP R/3 4.0B in August 1998 (MM,PP,FI,CO,SD,HR) • Upgraded to 4.6C in August 2001 • R/3 currently implemented at all major facilities outside of Japan • Licensed mySAP suite in 2002 • Outsourced hardware maintenance for SAP systems in spring 2003.
  5. 5. Importance of Planning • Semiconductor has traditionally been Entegris’ largest market. • Semiconductor market has historically been subject to rapid and significant changes in demand. • Failure to anticipate upturns leads to high level of expedites, long lead times, and loss of market share • Failure to anticipate downturns leads to excess inventory and capacity
  6. 6. Supply chain planning at Entegris: the old days • Planning/forecasting processes ranged from inconsistent to nonexistent • Planning responsibilities divided into four groups: – Sales: Generated a high-level dollar-based forecast on a quarterly basis – Product management: Refused to provide forecasts – “our products are too unique to forecast.” – Operations: No unit forecast available, planned at a high level based on the dollar forecast – Logistics: Received forecasts from certain key customers, data not visible to other groups
  7. 7. Early attempts to improve • In February 2002, implemented a custom forecasting system in R/3 for selected product lines – “Bare-bones” functionality – Low participation – Ongoing programming support • No experience with R/3 Sales & Operations Planning functionality or budget for investigating it • No business push for consistent practices and tools
  8. 8. Business emphasis on supply chain • In early 2002, internal reorganization placed greater emphasis on supply chain planning – Supply chain functions consolidated under one senior VP – Creation of a demand management group responsible for forecasting – Executive push for a project to address supply chain planning processes and tools
  9. 9. Project goals • Define one global forecasting/demand planning process based on industry best practices to apply for all Entegris facilities/product lines. • Implement tools within the SAP environment to enable the realization of the global demand planning process. • Match inventory to customer demand • Reduce lead times by reducing schedule disruption
  10. 10. Planning tool selection • For cost reasons, decided to make use of existing and available functionality within SAP for demand planning – did not consider third-party supply chain planning systems. • Considered R/3 Sales & Operations Planning • APO Demand Planning selected: – SAP’s long-term direction – Collaborative capabilities
  11. 11. Consulting partner evaluation • Selected three candidate consulting partners based on past experience/other contacts • Each candidate received a detailed requirements document, and conducted a series of fact-finding interviews to gather further data • Each candidate then presented: – Their proposed demand planning business process – A demo of how this process would be implemented within APO-Demand Planning
  12. 12. Why we selected Bristlecone • High level of knowledge and experience with demand planning business practices • Significant experience with APO-Demand Planning implementation • Close ties to SAP • Local resource
  13. 13. Business case prep • Project budget approved, but difficulty in quantifying projected benefits/ROI. • Before implementation, build a detailed business case to quantify benefits and justify the project
  14. 14. Chandra Mohan Bristlecone
  15. 15. Bristlecone’s Company Statistics Founded 1998 Ownership Private Number of 125 Employees Headqrtrs San Jose, CA Areas of Supply Chain Management, Product Lifecycle Expertise Management, Enterprise Resource Planning Key Process Manufacturing, Consumer Packaged Goods, Industries Apparel/Footwear, High Tech, Industrial, Oil & Gas Global United States, Germany, India Presence Business Santa Clara, California; Atlanta, Georgia; Cleveland, Offices Ohio; Houston, Texas; Richmond, Virginia.
  16. 16. Bristlecone Services • SC Opportunity Assessment Consulting • SC Network Strategy • SC Process benchmarking Services • SCOR framework • SC Business Process Improvement • SAP APO Implementation • “Meta-app” development • SCM-PLM • SCM-CRM SCM ERP SMB Packaged PLM Managed Solutions Services • plannerDATM • APO Help Desk • SAP APO Value Accelerators • “Planning Expert •Model Validator On Call” •Planning Results Analyzer
  17. 17. Bristlecone’s Supply Chain Experience SAP APO Implementation SC Strategy ITT Night Vision Nike GaAsTek Pennzoil • Highest number of Nestle USA Shell Millennium Chemicals successful APO Motorola implementations SC Process HP Dow Corning Nike Palm LSI Logic • Supply Chain domain Pennzoil Entegris expertise that spans Owens Corning VWR several industry Eastman Chemicals Entegris verticals Hasbro Baker Hughes SC system evaluation/selection Celestica Ferro Corporation
  18. 18. Objective, Scope and Deliverables • Identify business benefits: – Identifying Supply Chain Opportunities for Realization – Benchmark against other companies – Estimating the realization value • Scope for the assessment exercise – Demand Planning – Supply Planning
  19. 19. Objective, Scope and Deliverables • Benefit Scope – Inventory Reduction – “Order to Delivery” Cycle Time Reductions • Drivers: – Demand Plan Accuracy – Improved Supply Planning Capabilities – Where,When, How Much to produce • Deliverables: – Value matrix
  20. 20. Approach using, Supply Chain Analysis (SCAn) Secondary Data Gathering Primary Data Gathering Data Analysis Estimation of Realization Value Benchmarking & Gap Analysis (PMG)
  21. 21. Understanding of Entegris Business • Revenue forecast was the only prevalent ‘forecast’ • Unit level forecasting was introduced for a small subset of products recently – Lack of a formalized consensus demand plan building process (Product Management, Sales, Production and Procurement function in isolation) – Forecast accuracy was difficult to estimate as the interim forecasting system had been operational for a couple of months only.
  22. 22. Understanding of Entegris Business • No demand visibility upstream in manufacturing and procurement – ‘Expedites’ was all pervasive – Manual changes to the production schedules were common • Capacity planning was done once a year • Three manufacturing strategies were prevalent, though not formally used: – 50% Made-to-Stock – 35% Made-to-Order – 15% Assemble-to-Order
  23. 23. Understanding of Entegris Business Forecast Accuracy arrying Cost 90% 80% 70% 60% MAPE % 50% Estimated Entegris forecast 40% accuracy 30% 20% 10% 0% Best In Class Median Entegris 2002 2001 Inventory Total 2002 2001 2002 2001 nished Goods 19.17 24.3 2.13 2.7 21.3 27 P 1.17 11.34 0.13 1.26 1.3 12.6 100 M 3.35 3.8 3.35 3.8 6.7 7.6 80 Days of Supply 2002 2002 60 nished Goods 19.17 2.13 The benchmarks are based on Fiscal P 1.17 0.13 2001 Entegris data M 40 3.35 3.35 20 Inventory Carrying Cost - 7 Best In Class 5 Median Entegris
  24. 24. Finished Goods Inventory Reduction Opportunity Financial Value ($ mil) 1. One time Inventory Reduction 1. One time Inventory Reduction = $X Mil Current days of supply = A Savings in $ per year Target days of supply(#) = B X * Cost of Capital % = $Y Mil # Calculated based on assuming customer service requirements, an improvement in forecast accuracy and reduction in supply variability – all for made to stock products Root Cause APO Enabler 1. Inaccurate forecast Demand Planning 2. Lack of Formal Demand Supply Planning Planning & Supply Planning Processes 3. Lack of Planning System
  25. 25. Raw Materials Inventory Reduction Opportunity Financial Value ($ mil) 1. One time Inventory Reduction 1. One time Inventory Reduction = $W Mil Current days of supply = C Savings in $ per year Target days of supply = D W* Cost of Capital % = $Z Mil Root Cause APO Enabler 1. Inaccurate forecast Demand Planning 2. Lack of Formal Demand Supply Planning Planning & Supply Planning Processes 3. Lack of Planning System
  26. 26. Other Benefits (not quantified) • Reduction in obsolescence costs • Potential increase in revenue due to better customer service • Reduction in expediting costs (Transportation costs) • Better plant throughput • Reduction in warehousing costs • Visibility of RM demand to suppliers – Reduced RM delivery variability – Negotiate better terms with suppliers
  27. 27. Benefit: Total Quantified Value • Annual $ benefit per year, resulting from inventory reduction due to better Demand and Supply planning. This was based on the following assumptions: – Improvement of forecast accuracy – Support customer service requirements (Line Fill Rate) of up to 95% • Future Potential (based on further improvement in forecast accuracy, reduction in manufacturing batch sizes and reduction in planning cycle times)
  28. 28. Joan Ellison Project Leader and Sr. Business Analyst, IT Entegris, Inc
  29. 29. APO-DP Implementation details … Phase 1 Nov-02 till May-03 This is the development of the initial forecast process for products that are identified as High Revenue and make-to- stock strategy. “Go-Live” March 3rd 2003. This includes moving the system hardware & basis support to our “Hosting Vendor”. “Go-Live” mid May. Project Team = core team + extended team IT Project Lead/Business Analyst ( 1 ) Demand Management Business Project Lead ( 1 ) Bristlecone Consultants ( 2 ) IT Business Warehouse Analyst ( 2 ) Extended Team from the Business ( 6 )
  30. 30. APO-DP Implementation details … Initial fears and concerns … • Getting the business to own the process, not the IT department. • Resistance to forecasting from the business as a whole. “We can’t forecast our products, it’s not possible” • First use of BW in a productive environment – any prior BW efforts were only in a development project. • Entegris knew very little about APO & BW and their dependencies. We were relying heavily on our consulting partner, Bristlecone, to educate us. • Decisions and timing of the hosting move impacted hardware size, performance & client availability. Initial system was in- house and only a Dev to QA landscape. APO and BW shared a box.
  31. 31. Forecast Process … Blueprinting determined we needed to develop two forecasting processes in APO. Monthly Forecast - by Product Managers • System generated forecast that is adjusted by the Product Manager. The Adjusted PM Forecast is reviewed in a Monthly Consensus Meeting and finalized. The Consensus Forecast is then moved to R/3 as Independent Requirements.
  32. 32. Forecast Process … Blueprinting determined we needed to develop two forecasting processes in APO. Weekly Forecast – by Logistics Managers • Customer forecast is what drives this process. The Logistics Manager gets forecasts from a select group of customers who we have established contracts with. This Customer Forecast can be adjusted by the Logistics Manager. The Adjusted Logistics Forecast is moved to R/3.
  33. 33. Forecast Process … Blueprinting determined we needed a way to identify the products that were to be forecasted in APO Developed the Forecast Tier / Manufacturing Strategy relationship based on revenue. Tier 1 = 80% revenue + Tier 2 = 95% revenue + Tier 3 = 100% Make-to-stk Assemble-to-order Build-to-Ord Tier 1 350 products Tier 2 Tier 3
  34. 34. Monthly forecast process Monthly Product Manager Forecast Process User roles : End User – display only ( 4 users ) Supervisors & Managers End User – change capability ( 18 users ) Product Managers Location of users - US based in Minnesota and Colorado Forecast timing – first week of the fiscal month is the duration of this forecast cycle. Forecast horizon – 12 months in the future with current month as unchangeable. Forecast includes only Tier 1010 – Tier 1 / Make-to-stock approx 350 products. Forecast profile – Univariate profile – #14 Weighted Moving Average
  35. 35. Monthly forecast process Timeline for Monthly Forecast Process Day 0 – • Extract sales order history and master data elements from R/3 into BW. • Transfer data from BW to APO. • In APO, run clean up jobs to prepare for the forecast. • In APO, run the forecast jobs and alerts. Day 1 – Day 3 • Distribute the Forecast vs. Actual report from BW. • Product Managers and Logistics Managers make their adjustments in their planning books. Day 4 • Run jobs in APO and reports in BW for use in the Consensus meetings. Day 5 • Finalize the Consensus Demand Plan. • Transfer to R/3.
  36. 36. Weekly Logistics forecast process Weekly Logistics Forecast Process User roles : End User – display only ( 1 users ) Manager / Director End User – change capability ( 4 users ) Logistics Managers Location of users – Global - based in Germany, Singapore, Portland Or. Forecast timing – Monday & Tuesday of each week is the duration of this forecast cycle. The first week of every fiscal month the Logistics Weekly forecast cycle overlaps the Monthly Product Managers forecast cycle. Forecast horizon – 17 weeks in the future with current week as unchangeable. Forecast products – Tier 1010 in Logistics Distribution Plants. Forecast profile – Univariate profile - #56 Forecast with automatic model selection.
  37. 37. Weekly Logistics forecast process Timeline for Weekly Forecast Process Day 0 – • Extract sales order history and master data elements from R/3 into BW. • Transfer data from BW to APO. • In APO, run clean up jobs to prepare for the forecast. • In APO, run the forecast jobs and alerts. Day 1 – Day 2 • Distribute the Forecast vs. Actual report from BW. • Logistics Managers make their adjustments in their planning books. • Finalize the Adjusted Logistics Forecast. Day 2 • Transfer to R/3.
  38. 38. APO - R/3 – BW landscape BW 1 BW BW BW Development Development Production Production 9 11 6 4 APO APO 2 APO APO APO APO Development Development QA QA Production Production 10 3 8 5 R/3 R/3 R/3 R/3 R/3 R/3 Development Development QA QA Production Production 7 R/3 R/3 Transports Sand Box Sand Box Data Flow
  39. 39. How we use the system … technical aspects Single Planning Area Storage bucket periodicity – week & fiscal month Single Master Planning Object Structure Characteristics = 15 Key Figures = 21 Characteristic combinations = 27,014 Planning Book = 40 Macros in use = approx 40
  40. 40. How we use the system … Planning Books / Data Views : A planning book was created for each Product Manager and Logistics Manager. (quantity 25) Configuration assigns a planning book to a user id and restricts what they can get into. This allows us to easily separate and identify each product manager and their selections. It allows us to define alerts differently for each product manager. It makes adding or changing an existing macro more difficult to roll out. Time will tell if this is a good decision. We have planning books / data views (quantity 15) that we use in processing macros to prepare the data.
  41. 41. How we use the system … Monthly forecast process Characteristics Key Figures Product number Sales order history quantity Market Segment number Invoiced sales Product Family number Previous consensus forecast Product Manager number Profit Center Actual sales order bookings Forecast Tier / Mfg Strategy Batch statistical forecast Ship to number Product Manager adjustments Sold to number Adjusted Product Manager forecast Customer Market Segment Sales forecast Country Adjusted Logistics forecast Region Consensus forecast Sales District Ship from location
  42. 42. Product Managers monthly data view
  43. 43. How we use the system … Weekly Logistics forecast process Characteristics Key Figures Product number Sales order history quantity Market Segment number Previous Logistics forecast Product Family number Actual sales order bookings Product Manager number Adjusted Product Manager forecast Profit Center Batch statistical forecast Forecast Tier / Mfg Strategy Ship to number Customer Forecast Sold to number Logistics adjustments Customer Market Segment Adjusted Logistics forecast Country Region Sales District Ship from location
  44. 44. Logistics weekly data view
  45. 45. APO-DP Implementation details … Business Warehouse and APO • dependency • complexity • reporting using BEX • learning curve
  46. 46. APO-DP Implementation details … Phase 1 accomplishments • Established two forecast processes • Established a data flow from R/3 to BW to APO to BW • Setup the APO-DP tool and generated forecasts by the Go-live date of March 3rd. • Trained 25 end users on both the process and the system • Transferred the forecast to R/3 • Moved the BW and APO systems to the Hosting vendor with minimal downtime and impact to the business.
  47. 47. APO-DP Implementation details … Mid project fears and concerns … • Resistance to forecasting from the business as a whole. Management says we will do it, but PM level is still resisting. • First use of BW in a productive environment – any prior BW efforts were only in a development project. Building real cubes with real data and ensuring accuracy of the data was challenging. • Entegris knew very little about APO & BW and their dependencies. Moving data from system to system gets easier to understand, but timing and dependencies continue to challenge us. • Very small team keeps internal knowledge base concentrated in a few individuals. • Decisions and timing of the hosting move impacted hardware size, performance & client availability. Initial system was in-house and only a Dev to QA landscape. Hardware was undersized and performance was poor. We went live on the APO-QA system and BW-Dev system.
  48. 48. APO-DP Implementation details … Phase 2 May-03 till July-03 Forecast for Fiscal Year Budget Planning, including Capacity Evaluation in the R/3 sandbox system. Develop forecast in APO-DP for all products using a separate planning area and multiple versions. Project Team IT Project Lead/Business Analyst ( 1 ) Demand Management Business Project Lead ( 1 ) Bristlecone Consultants ( 1 ) IT Business Warehouse Analyst ( 1 ) Phase 2 accomplishments – created another planning area used to generate the forecast for all non-tier 1010 products. Added the values from tier 1010 to generate a comprehensive forecast of all products. Transferred the forecast to a R/3 sandbox system and performed rough capacity evaluation for 18 months.
  49. 49. BW APO APOODSSF Historical Revenue & Units (BUoM) Invoiced History Sales Org 1000 Historical Revenue & Units (EA) Units Conversion Hist Revenue Converted Units Hist inv. Sales (BUoM) Hist Revenue (ZV_EA) Hist inv. Sales (BUoM) Hist inv. Sales (EA) (APOASPFV) Converted Units Planning Area Hist Revenue (Conversion) Hist inv. Sales (BUoM) Hist inv. Sales (EA) (ZFV_EA) Reports ASP (EA) (ZFV_BW_EA) (MATNRASP5) Planning Area (APO) (ZQ_PA) Tier 1010 APO Forecast Planning Forecast Versions Version Units Versions Area (APO) Sales History Version Units (ZFV_PA) Version Units (ZV_BW) (APOFVER) APO Info Sales Changes APOODSIN Sales Order Hist. Cube Profit Center changes (ZFV_PC) Sales history Master Data (Z_FV_IC) Master Data A Flat file based on Sales Excel sheet
  50. 50. APO-DP Implementation details … Phase 3 June-03 till Sept-03 Incorporate “sales units” from the newly developed Sales Forecast Process to the existing Monthly Forecast Process. Project Team IT Project Lead/Business Analyst ( 1.5 ) Demand Management Business Project Lead ( .5 ) Bristlecone Consultants ( 1 ) IT Business Warehouse Analyst ( 1 ) Phase 3 accomplishments – the Sales Forecast values are transferred from the front-end system to BW, then to APO resulted in integrating the Sales & Unit processes. The Sales forecast values appear in the planning book where there was previously blank spaces. Changes to the characteristics were also made.
  51. 51. BW APO A Historical Revenue BW Consensus Revenue (Consensus Units * ASP + Other Revenue) Revenue Revenue BW Revenue History (ZQ_SALES) APO BW Structures Final Revenue (Adjusted) Revenue Planning Area (Sales) Sales History Revenue APO (ZQ_MSALES) Consensus Revenue (ZQ_APO_SA) Final Revenue APO Units Consensus Units Consensus Units + + (ZQ_BW_F) Planning Area (APO) Master Data (ZQ_PA) Master Data A APO Info Frontend Cube Data Sales Unit Forecast Sales history (ZQ_FRONT) (ZQ_IC) Sales Unit Forecast (FE) Frontend data via BW A
  52. 52. APO-DP Implementation details … Long term project fears and concerns … • Resistance to forecasting from the business. We still have varying degrees of participation and support. Management is pushing use of the process but we still have several Product Managers who barely participate. • Entegris knew very little about APO & BW and their dependencies. Moving data between systems is stable. Need more development of BW-BEX reporting skills outside of the BW team. • Very small team keeps internal knowledge base concentrated in a few individuals.
  53. 53. Six months later… • Unified global forecasting process is established and adhered to • APO tool is performing well, although still requires significant technical support • Some inventory reductions have been achieved, although still far from the two-year goals • Forecast accuracy has improved, but not as much as we would like
  54. 54. Future direction • Stabilize APO-BW data transfer • Continue to work towards targeted inventory and lead time reductions • Investigate/prepare for APO Supply Network Planning
  55. 55. 2003 Supply Chain Management Forum Thank You For Attending! Please remember to complete and return your evaluation form following this session. Mike_beller@entegris.com Joan_ellison@entegris.com Chandra.mohan@bcone.com Session Code: 710

×