1. How Deloitte helped Cameron Achieved Sales and Operations Planning (S&OP) Excellence at Cameron using SAP APO’s Characteristic-Based Planning
Vikas Dhand
Deloitte Consulting LLP

2. Contents Manufacturing Strategies Key planning challenges at Cameron
Cameron Overview
Manufacturing Strategies
Introduction to Manufacturing Strategies
Configure-To-Order (CTO)/ Variant Configuration Overview
Cameron complex manufacturing scenarios
Key planning challenges at Cameron
Sales & Operations Planning (S&OP)
What is S&OP
Why S&OP is important in MTO Environment
Key Business objective for S&OP
SAP APO Characteristic Based Planning
APO Characteristic Based Forecasting
How Deloitte Helped Improve S&OP Process at Cameron
Deloitte Solution Highlight- Overview & Detail
Deloitte Solution Highlight- Tool Limitation
Cameron’s Improved S&OP-Business Benefits
Wrap Up
Key Take Away
Question

3. In This Session … We will discuss how Deloitte helped Cameron…
Improve it’s S&OP process
Optimize forecasting and supply planning by leveraging SAP ECC & APO- Characteristic Based Planning (CBP)

4. Cameron International - Facts
Cameron is leading provider of flow equipment products, systems, and services to worldwide oil, gas, and process industries
Fortune 500 company
$8.05 billion in revenues in 2012
Leadership position in most of the market segment served
Leading aftermarket service provider
25,000+ employees in 300+ locations worldwide

5. Cameron International— Market Coverage
Cameron is in nine market segments, offering a full suite of products and services to the drilling, production, processing, refining, petrochemical and LNG, transmission and storage, and industrial segments of the industry.
Offshore Drilling
Offshore Production
Topsides Processing
Refining/
Petrochemical/LNG
Onshore Production
Subsea Production
Transmission/Storage
Industrial

6. Manufacturing Strategies at Cameron
Manufacturing strategy determines the degree of product customization that is offered to customers.
Delivery Lead Time
Make-to-Stock
(MTS)
Engineering
Procurement
Processing
Assembly
Shipping
Reduced Delivery Lead Time
Increased Product Customization
Delivery Lead Time
Engineering
Procurement
Processing
Assembly
Shipping
Assemble-to-Order
(ATO)
Delivery Lead Time
Engineering
Procurement
Processing
Configuration
Assembly
Shipping
Configure-To-Order
(CTO)
Delivery Lead Time
Engineering
Procurement
Processing
Assembly
Shipping
Make-to-Order
(MTO)
Delivery Lead Time
Engineering
Procurement
Processing
Assembly
Shipping
Engineer-to-Order (ETO)
Forecast-Driven
Customer Order-Driven
Key:
- 6 -

7. SAP CTO/Variant Configuration Overview
Class
Characteristic
Char Values
Object Dependencies
2 IN
Components (2 “ BODY ASSY, 2” STEM ASSY)
Valve Size
4 IN
Components (4” BODY ASSY, 4” STEM ASSY)
6 IN
Constraint - Invalid Combination
150 PSI
Ball Valves
Pressure Class
300 PSI
Operation (Machining = 24 Hrs, Workcenter = M1)
600 PSI
Operation (Machining = 48Hrs, Workcenter = M2)
Raised Face
Price = +$400
End Connection
Flat Face T3
Default if (Size = 2 IN and Pressure Class = 300)
Trim T7
Default if Pressure Class = 600
GB1
Yield Strength
GB3
Object Dependencies identify rules for :
Defaults/ Constraints
Pricing
BOM Components
Routing Operations
Configured Products are assigned to a Class.
Characteristics are used to define product features.
Characteristic Values identify options within product features.

8. Cameron Manufacturing Scenarios/Requirements
Single Product Line may be sold using multiple scenarios –
Make To Order VC - Dynamic BOM/ Routing
Engineering To Order VC - Order specific BOM/ Routing
Engineering To Order Finished Product
Used when customer requirements are too complex to handle with VC
Make To Order Product Variant
Low Sales Volume - Normally VC material but Material Variants defined for STO process
Make To Stock Product Variant
High Sales Volume - Material Variant functionality used to simplify BOM/ Routing maintenance
Business requirement was to be able to plan a product line at a characteristic level – irrespective of the manufacturing scenarios (KMAT/ FIN/ Material Variants) .

9. Key Planning Challenges at Cameron
Lack of collaborative planning. Master plans, production plans and production schedules change frequently.
Configured products have a very large number of features and options.
Sales history is not always a good predictor of future demand.
The same product lines are using a mix of Configurable Materials, Product Variants and one-off finished products (ETO).
Lack of clear visibility on capacity utilization, forecast accuracy, obsolete inventory, product-to-market time and transportation costs
New products regularly miss their launch dates.
Infinite planning & cumbersome capacity leveling.
Lack of long term visibility to the suppliers.
Product phase-outs are costly.
Inaccurate Financial Plans.

10. Case for an Improved S&OP Process at Cameron
Executive S&OP via PowerPoint and Spreadsheets
Manual forecast to suppliers
No simulations for supply capacity
Demand Planning on spreadsheets or non- existent
Market Indicators available but not identified for all Products
Forecast Models built only for 2 Product Lines
CRM- solid forecast data not available.
Supplier
ECC
Customer
Execution System – ECC

11. What is Sales & Operations Planning
Step 1: Demand Review
Step 2:
Supply Review
Step 4:
Executive S&OP Meeting
Step 3:
Pre- S&OP Meeting
S&OP Process
S&OP is a collaborative, cross-functional, periodic process to reach consensus and set future direction based on a single operating plan.
Strategic
Tactical
Operational
Executional
“What and who?”
Time horizon: multiyear or annual
Linkages to business or functional strategies
Sample output: service policies
“How much?”
Time horizon: quarterly or monthly
Forecast driven
Sample output: monthly production plan
“Where and when?”
Time horizon: weekly, daily, or shift
Order driven (internal or external)
Sample output: production schedule
“Do it”
Time horizon: real time
Order driven (internal/external)
Sample output: execution

12. Why S&OP is important in MTO Environment
Forecasting is critical even in MTO environment. Customers require custom-manufactured products in the shortest possible time
MTO products may have several sub-assemblies/components that are built/procured to a forecast in order to reduce overall lead times
MTS products are sold from inventory; MTO products are sold from capacity
Long-range capacity planning (long before customer order is received) is critical to ensure quick cycle times and on-time delivery

13. Key Business Objectives for S&OP at Cameron
For Variant Configured Product Lines (Configure-To-Order Manufacturing Strategy)
Able to plan a product line at a characteristic level – irrespective of the manufacturing scenarios (KMAT/ FIN/ Material Variants)
Plan for long lead shared components (manufactured or procured) before customer order is received
Validate manufacturing capacity based on Forecasted+ Actual
Demand
Increase involvement of Sales function in the Planning process.
Improve Forecast Accuracy

14. SAP APO- Characteristic Based Planning (CBP)
Product
Loc
Size
Pressure
End Conn
May
Jun
Jul
Aug
Sep
Oct
CAM_PLN
A311 2
150 RF 10 15
300 WE 20 30 4 40 60 80
120
Characteristics-Based Forecasting (CBF) is a powerful tool that enables you to forecast demand on both the product and the characteristics level.
In Characteristics-Based Forecasting, we can produce demand plans for combinations of characteristic values or for individual values. We generally restrict the characteristics used in CBF to those that are important for planning purposes.

15. How Deloitte Helped Improve S&OP Process at Cameron
Generate Baseline Forecast
Develop Unconstrained Sales Forecast
Validate Manufacturing Capacity
Develop Consensus Demand Plan
Release Consensus Demand Plan
Demand Planner
Demand Planner
Demand Planner/ MPS planner
MPS Planner
Consensus Demand Plan - One Number that drives Execution

16. Deloitte Solution Highlights - Overview
Manage History
(PKMAT+Char)
Statistical Fcst
Opportunity Fcst
Opportunity
Manual Fcst
Sales Fcst
Finite Capacity Planning
(Simulation Vers)
Adjustments to Sales Forecast
Consensus Fcst
Generate Dependent Demand
PIR Components
Planned Order
Forecast Consumption
MRP
ECC DP
CTM BI
CRM
APO BI
History Cube
Backup Cube
Transfer Cube
CBP Cube (Versioned)
Fcst Accuracy Report 1b 1c 1d 1e 1f 2 3b 3c
Sales Order CTO
Sales Order FIN
Sales Order PV 1a 4 5b 5c 6a 6b 6c
Component Planned / Production Order 5d 7a 7b 7c 3a 5a

17. Deloitte Solution Highlight-Configurable Planning Material
Sellable KMAT (SKMAT)
Planning KMAT (PKMAT)
Size
2’’, 4’’..
Trim
T3, T7…
End Connection
FF, RF..
Pressure Class
150 psi,
300 psi..
Yield Strength
N, GB1
GB5..
Wall Thickness
0.25”, 0.5”, 0.75” …
Stem Size
1.0, 1.5
2.0..
XXX
Size
2’’, 4’’,
6’’, 10’’, 12’’..
Trim
Standard T3
Standard T7.. …
End Connection
Flanged FF
Flanged RF..
Pressure Class
ASME150
ASME 300..
Yield Strength
N, GB1
GB5..
Using Planning KMAT simplifies forecasting & planning process
Planning KMAT material ‘holds’ forecasted demand
Planning KMAT allows us to consolidate history/ forecast at a characteristic level
Use of Planning KMAT material overcomes the limitations of CTM support for complex characteristics & object dependencies.
till the time customer demand is received. Once customer demand is received for the Sellable KMAT, forecast of the Planning KMAT is reduced.
across different finished product types (Sellable KMAT/ Product Variants/ FIN Materials) for the same product line.
by including only planning relevant characteristics, long lead components and critical work center routings.
- 17 -

18. Deloitte Solution Highlight- PKMAT Product Data Structure
Maintain representative critical sub-assemblies in Planning BOM
VC Material on Sales Order
Planning VC
SKMAT
Make-To-Order
PKMAT 10 10 20
Make-To-Stock
A11
A12
A13
A21
A22
A23 B1 B2 B3
A12
A23
VS = 16
PR = 600
EC = WE
TR = T31
YS = x60
VS = 16
PR = 600
EC = WE
TR = T30
YS = x60
VS = 16
PR = 600
EC = WE
TR = T3
YS = x60
VS = 16
PR = 600
EC = WE
TR = T31
YS = x61
VS = 16
PR = 600
EC = WE
TR = T30
YS = x61
VS = 16
PR = 600
EC = WE
TR = T3
YS = x61
VS = 16
PR = 600
EC = WE
YS = x60
VS = 16
PR = 600
EC = WE
YS = x61 30 50 70
120
140
160 40 60 80
130
Representative critical sub-assemblies
150
170 90
100
110 P1 P1 P1 P2 P2 P2 P3 P3 P3
PIR sent at this level to ECC from APO
Critical Component
Critical Operation

19. Deloitte Solution Highlight- Merge Order History
Consolidated Sales PKMAT Level
Product Variant
SKMAT
FIN Product
Custom interface to merge order history with matching configuration.
For VC scenario, sales order configuration is used.
For FIN type materials, Engineering classification (Class Type = 001) is used.
For Material Variants, material/plant level classification linked to configurable material is used.
Sales from distribution centers is consolidated into manufacturing plant.
History is first loaded into APO BI and then to Planning Book.
Standard BAPI for Planning Area upload does not work with CBP !

20. Deloitte Solution Highlights - Opportunity Forecasts
Sales history is not always a good predictor of future demand in MTO environments. Therefore, potential order pipeline (the sales funnel) visibility is key to Demand Planning.
Instead of simply specifying total anticipated pipeline demand, maintaining individual opportunities and related forecast quantities in DP allows the sales teams to provide forward-looking input to the demand planning process
Opportunities is integrated into DP from an Microsoft
Dynamics CRM system depending on the stage in the sales
funnel (Leads  Opportunities  Quotes  Orders)
The total of opportunity quantities is reflected in the Opportunity Key Figure in the Planning Book leveraging the same custom functionality developed to upload order history from ECC into APO.
Sales Funnel

21. Deloitte Solution Highlights - Forecast Consumption
Standard SAP Forecast Consumption could not be used because Forecast and Sales Orders are for different materials.
Net Sales Forecast is calculated using a BADI macro in DP = Sales Forecast – Order History. (BADI macro used instead of regular macro due to complexity of forward/ backward consumption requirement).
This avoids doubling of demand in CTM – Actual demand from Sales Orders will come from Production Orders/ Planned Orders for the Sellable KMAT.
Forward 1 bucket/ Backward 1 Bucket consumption - M1 M2 M3 M4 M5 M6 M7
Forecast 10 15 5
Sales Order
Net Forecast -5
Step1 (Without backward consumption)
Step 2(With backward/forward consumption)

22. Deloitte Solution Highlights - Capacity Validation
Net Unconstrained Sales Forecast released to Simulation Version.
CTM used for capacity validation and finite capacity planning. CTM explodes PDS of Planning KMAT based on object dependencies.
CTM can’t run on the Sellable KMAT (as CTM cannot evaluate complex object dependencies). Instead Planned Orders and Production Orders from ECC (generated by MRP) are CIF’ed to APO to generate work center load.
Review Demand requirement dates Vs Supply Receipt dates
Review aggregated and also product level

23. Deloitte Solution Highlight- Sales Forecast Adjustments
When CTM is run based on Unconstrained Sales Forecast, it may push out Planned Orders if capacity is not available
Planner reviews planned orders based on characteristic values.
Pegging Overview shows forecast pegged to planned orders. This helps planners identify which forecast cannot be met.

24. Deloitte Solution Highlights – Forecast Transfer to ECC
Demand Plan at characteristic level can’t be released to ECC directly. Instead consensus demand plan is exploded using CTM to determine component requirements (in a separate simulation version without considering resource constraints.)

25. Deloitte Solution Highlights – Forecast Accuracy
Forecast (Statistical/ Opportunity/ Sales/ Consensus) + History transferred to BI (different version every month).
Lagged Forecast Accuracy report developed at Product Line + Characteristic Level in BI. Metrics include - Forecast Error/ MSE/ MAPE, etc.
Since BI cannot handle an indefinite number of characteristics, update rules are used to translate classification characteristics (different for every product line) into 4 generic InfoObject characteristic name value pairs.

26. Deloitte Solution Highlight- Tool Limitation
Ref: SAP Note
There is no possibility to release any planning data from DP to SNP with the intention to use in SNP heuristic (Location; Multilevel; Network); SNP Optimizer. CBF planning results can only be used in CTM and PP/DS scenario.
Standard CBP functionality does not support Proportional Factor calculation (/SAPAPO/MC8V) at classification characteristic level.
For reference characteristics only components /SAPAPO/CULL_CFG_COMPONENT-QUANT and /SAPAPO/CULL_CFG_MODE_PROCEDURE-DURVAR are supported.
Complex select conditions are not supported. Multi-value characteristics are not supported.
PDS must come from an ERP system. The supply chain should not mix VC and CDP scenarios.
Multilevel Phantom and component duplication under phantom are not supported.

27. Cameron’s Improved S&OP-Business Benefits
Improved predictability and accuracy of customer commitments
Planning based on consensus across functions- “One Number”
Alignment between strategic plans and day-to-day activities
Greater visibility of demand and supply across the company- improved decision making (e.g. Make Vs. Buy)
Improved inventory management- Optimized inventory Levels
Predictable throughputs- Minimized “hockey stick” effect
Shortened customer lead times for MTO/CTO product lines
Improved Product Lifecycle Management process

28. Key Points to Take Home
Forecasting is critical even in MTO environments- MTO products are sold from capacity, just as MTS products are sold from inventory
In MTO environment, Sales history is not always a good predictor of future demand.
APO features allow sales function to provide input into the Demand Planning process at aggregate levels.
Identify key business roles and ownership to support the APO implementation
Engage business users early on to define scope of APO. Keep initial scope simple and ramp up over time. Crawl  Walk  Run!
Size your system adequately using SAP Quick Sizer.

29. Questions ?

30. Session Evaluations & Meeting Survey
Evaluate the education sessions immediately
Provide event feedback in the same survey
bit.ly/ASUG14

31. Appendix

32. APO CBP before & after SCM 7.0
CBP in APO (VC Scenario) – Before SCM 7.0
CBP in APO (VC Scenario) –SCM 7.0 Onward
Demand Plan level) of Configurable (KMAT) material released to Inactive Version.
PP/DS run explodes object dependencies and creates dependent demand of components. (SNP/ CTM is not supported)
Dependent Demand of components converted into Forecast of components in Active version (and/ or ECC PIR’s).
Forecast consumed by dependent demand of Sales Order in Active version.
Demand Plan level) of KMAT material released to Active Version.
CTM can be used for supply planning (in addition to PPDS)
Forecast of KMAT consumed by Sales Orders (with matching configuration).

33. What’s New in APO 7.0 Application Component-wise New Functionalities
New Functionality
Industry Coverage
Enhancement Details
APO
Characteristic-based Cross Location Planning
Mill, Industrial Machinery  & Components, Hi Tech, Chemicals, Pharma
Forecast Release and Consumption with Configuration
CTM with Characteristics-based Planning
New Cross Location Planning Heuristics (JJ_INT_SRC, SAP_PP_022)
Shelf Life Propagation across locations and production levels
Rescheduling of Operation at Block Changes 
Shipment Creation in TPVS based on planned Stock Transfers 
New Requirements Strategy 35 assigned to segment Characteristic-based Planning without Final Assembly
Planning with Configuration in MTS segment for non-DIMP clients

34. APO Characteristic Based Forecasting
APO supports two ‘configuration schemas’ [client level setting] :
Variant Configuration (VC) – Allows configuration only for sales orders. The VC-configuration scheme was developed mainly for the automotive and engineering industries and is closely linked to the variant configuration in R/3.
Characteristic Dependent Planning (CDP) - allows a configuration for other objects like forecast, planned orders, production orders and batches. The CDP configuration scheme was developed mainly for the mill industries.