IBP Implementation Analysis

SAP IBP USE CASE SCENERIOS

Presenter: Ayan Bishnu

2

WHAT IS BEING COVERED

Pain Areas Addressed with IBP (High Level).

IBP Landscape Architecture (High Level).

XYZ Inc Use Case Scenario for IBP Demand Planning.

ABC Group Use Case Scenario for IBP Demand.

ABC Group Use Case Scenario for IBP Inventory.

Wrap Up.

AREAS ADDRESSED WITH IBP ( A HAWK EYE VIEW )

Operational Validation Demand Validation

Demand and Supply Review S&OP Meeting

IBP PLANNING ARCHITECTURE

Use of IBP for demand (demand sensing) with APO (DP and SNP)

XYZ INC IMPLEMENTATION

THE CHALLENGE

Highly Manual & Time consuming Process

Financial Planning not integrated in S&OP

Inefficient data visualization

THE SOLUTION

SAP IBP solution implementation leveraging supply optimizer

Integrated with SAP APO, SAP BI, COPA systems

THE BENEFIT

Excel Based User Interface

Effective KPI tracking

Integrated with multiple stakeholders

Quick what-if Analysis

XYZ INC DEMAND PLANNING FLOW

Step1:Gather & cleanse historic

data

Run or schedule thestatistical forecasting job for background

processing

Solve potentialissues or

inconsistencies within the

forecast and overwrite the

created values manually if

needed

Use the DemandPlan that is

generated in SAP APO DP for further

processingin APO SNP

Load historic salesdata and use SAP BW functionality & to correct outliers or substitute missing

values

Define the way theforecast should be

calculated, parameters,

algorithms, etc.

Step2:Simulate &

select forecast models

Step3:Run mid / long term

forecasting

Step4:Review alerts & adjust

forecast

Step5:Reuse

forecast for other

process

XYZ INC PAIN AREAS IN DEMAND PLANNING

Step1:Gather & cleanse

historic data

Step2:Simulate &



forecasting


forecast

Step5:Reuse

forecast for other

process

1) Multiple data sources.2) Issue with Integration.3) Data Accuracy.

1) No S&OP process in APO.2) Limited Disaggregation capabilities.3) Limited WHAT IF simulations.

1) Limited alert based actions.2) Manual adjustments based on gut fill.3) Demand Sensing not available in APO.

LOADING HISTORICAL DATA

Step2:Simulate &


Load historic sales information(e.g. Confirmed QTY, Delivered QTY, etc) via the HANA Cloud

Integration (HCI)

Load historic sales information(e.g. Sales Orders, Confirmed QTY, Delivered QRY, etc) via the WebUI

Step1:

Gather & cleanse historic

data


CLEANSING HISTORICAL DATA

Step2:Simulate &



forecasting


forecast

Interactive charts provides better visualization to users for the

changes done in the historical data.

Excel based UI provides better flexibility to planners to perform

data cleansing

Step1:

Gather & cleanse historic

data


SAP Integrated Business Planning for sales and operations

Create the optimal business plan to drive revenue growth and increase market share

Effectively balance demand and supply and attain financial targets

Increase speed and agility of planning and drive most profitable responses

Improve forecast accuracy and on-time delivery across all levels

Deliver a cross departmental sales and operations plan balancing the impact on inventory, service levels and profitability

Step2:Simulate &



WHAT-IF SCENARIO MODELING

• Tactically review monthly supply imbalances at a facility and conduct gap closure

• New forward warehouse and assess cost/margin impacts• New products and associated prospective customer demand, with cost and

revenue impacts• New customer and associated demand to support revenue scenarios• Alternative cost and pricing inputs• Compare multiple scenarios

Step2:Simulate &



BETTER FORECASTING METHODS

FeaturesDemand sensing algorithms (short termforecasting)

Statistical Methods (mid- / long-term forecasting) Pre-Processing algorithms Time series algorithms Regression based methods

Integration with ERP and APO

Exception management

Fiori Apps and Excel as a planning front-end

Data model: Key figures and attributes

Key figure calculations

Aggregation/Disaggregation rulesUser management and authorizations

Pre-Processing algorithms: Substitute missing values Outlier correction with interquartile range test and variance

test

Time series algorithms: Simple moving average Weighted moving average Single exponential smoothing Double exponential smoothing Triple exponential smoothing Automated triple exponential smoothing with parameter

optimization 1st order exponential smoothing with adaptive alpha Croston’s method for intermittent demand

Combination of these algorithms(similar to composite forecasting in APO)

orPick the best

Regression based algorithms: Multiple linear regression (MLR)

More flexibility & options in terms of Statistical forecasting & regression

based methods

MANAGING FORECAST MODELS

RUNNING FORECASTING IN EXCEL

IBP SUPPLY CHAIN CONTROL TOWER

Supply Chain MonitoringEnable supply chain professionals tonavigate, analyze and profitably manage the end-to-end supply chain in real-time

Integrated Business Planning (IBP)

••

Increase end-to-end visibilityIncrease on-time delivery performance tocustomer

Increased forecast accuracy. More accurate sales evolution reporting

•

• Increase supply chain agility and reducesupply chain cost

User ExperienceIBP for sales & operations

IBP for demand IBP for supply IBP for inventoryIBP for response

SAP HANA Platform

Supply Chain Control Tower

Step4:Review alerts & adjust forecast


ADVANCED ALERT MANAGEMENT

• Alerts that were raised for statistical forecasts can be monitored via the Monitor or Alerts app. Those are dependent on the user’s or company’s alert definition and thresholds.

• Based on alerts, from concerned departments, the mid-to long-term demand forecast can then be manually adjusted via the SAP IBP add-in for Microsoft Excel.

• The outcome is a consensus demand plan that acts as the final mid-to long-term demand forecast, as agreed between the different departments.



DEMAND SENSING FOR OPTIMAL BLENDS & BETTER PREDICTION

Weekly AdjustmentsOpen Order datacorrelationcorrections Output

Optimal blend by lag patternsBias Tracking

Controls

Intelligent forecast consumption logic

Optimal weightingand patternrecognition

Daily DisaggregationDaily outputs for executionsystems

Aggregated weekly or monthly values for planning and reporting

Bias and variability calculations

•

Forecast patterns•

Shipment patterns

•

Demand Signaldata patterns



DEMAND SENSING USING EXCEL

Navigation for demand Sensing Issues from the Fiori Launchpad

ABC GROUP - PROTOTYPE DRIVEN ENGAGEMENT

OBJECTIVE

Evaluation: Transition from SAP APO to SAP IBP

Integrated with SAP APO (transition phase), SAP BPC, SAP ECC

ENVISIONED SOLUTION

SAP IBP solution implementation leveraging IBP Demand, Response &

Supply, Supply Chain Control Tower

SOLUTION BENEFITS

Tight integration between logistical and

financial planning

Increased collaboration and progress tracking

Ability to balance demand supply in a

user friendly way

Enable greater planning flexibility:

Rapid scenario analysis

ABC GROUP DEMAND PLANNING FLOW

Step1:Gather & cleanse historic

data

Run or schedule thestatistical forecasting job for background

processing

Solve potentialissues or

inconsistencies within the

forecast and overwrite the

created values manually if

needed

Use the DemandPlan that is

generated in SAP APO DP for further

processingin APO SNP

Load historic salesdata and use SAP BW functionality & to correct outliers or substitute missing

values

Define the way theforecast should be

calculated, parameters,

algorithms, etc.

Step2:Simulate &



forecasting


forecast

Step5:Reuse

forecast for other

process

APTERGROUP PAIN AREAS IN DEMAND PLANNING

Step1:Gather & cleanse

historic data

Step2:Simulate &



forecasting


forecast

Step5:Reuse

forecast for other

process




ABC GROUP PRESENT SUPPLY PROCESS FLOW

Step1:Inputs / PIR

from Demand module

• Update Resource Capacity

• Update other constraints for optimization

• Identify Resource overloads

• Identify other constraints thresholds

• Solve potential issues or overloads within the generated plan.

• manual Planner Intervention for correction.

• Use the agreed supply plan that is generated in SAP APO SNP for further processing in APO Deployment / TLB.

• Forecasted demand from Demand Planning

• Target inventory requirements (Master data)

• On hand inventory including stocks in transit

• Execute net demand planning

• Unconstrained production plan.

Step2:Net

Demand Planning

(Unconstrained Plan)

Step3:Capacity leveling /

Optimization


supply plan

Step5:Reuse

supply plan for other process

ABC GROUP PAIN AREAS IN SUPPLY PLANNING

Step1:Inputs /

PIR from Demand module

Step2:Net

Demand Planning

(Unconstrained Plan)


Optimization


supply plan

Step5:Reuse

supply plan for other process

1) High or uncontrolled inventory levels.2) Inadequate customer service levels or inventory

availability.3) Multiple planning and inventory target setting

processes.

1) How often can we plan production?2) Do we have to order in specific batch sizes?3) Are supplies commonly on time, early, or late?4) What are the bottlenecks ?

Already addressed in IBP Demand Planning

Already addressed in IBP SCCT

SAP IBP FOR INVENTORY OPTIMIZATION

Optimize inventory targets to increase service levels, considering supply chain uncertainties

• Improve customer service levels• Maximize the efficiency of inventory

and working capital

• Standardize planning processes for inventory targets

• Improve planner productivity (planning time reduced by 2 days)

• Reduce production and distribution costs (approx. 2.6 %**)

Achieving the right balance between inventory and service levels

Step2:Net Demand

Planning (Unconstrain

ed Plan)

1) High or uncontrolled inventory levels.2) Inadequate customer service levels or inventory availability.3) Multiple planning and inventory target setting processes.

INTEGRATED INVENTORY KPI DASHBOARD

Step2:Net Demand


ed Plan)


REPRESENTATIVE IMAGE

INVENTORY BUILT UP SCENERIO

Build inventory0

As we are already throttling at 100% capacity is not

possible to build inventory

1

Setting inventory targets based on available capacity2

DEMO SCENARIO

Step2:Net Demand


ed Plan)


SUPPLY SHORTAGE SCENERIO

• Business Event: Demand Loss or Production/Capacity Reduction

Production loss leading to drop in fulfilment

1

Production loss of 300 TPD0

DEMO SCENARIO

SAP IBP FOR SUPPLY OPTIMIZATION

Create advanced supply planning simulations for S&OP based on forecasts, orders, and inventory or safety stock targets

• Simulate either constrained or unconstrained production and distribution plans, using heuristics or optimization based algorithms

• Multi level sourcing determination for both distribution and Bills of Material

• Development of rough cut capacity plan in a times series bucketed supply plan

• Simulation capabilities for scenario planning


Optimization


PRODUCTION / RESOURCE VIEW

• Analyze Production Goods Receipt and Issue in Daily Buckets

• Ability to Review Resource Capacities and Consumption

WAREHOUSE VIEW

• Analyze Warehouse Goods Issue and Receipt along with Inventory Targets and Projections

SOURCING VIEW

• Ability to Track Sources Among Locations and Between Customer and Locations


Optimization


OPTIMIZER COST VIEW

• Ability to build ahead only runner items as there is more certainty of demand


Optimization


IBP SUPPLY CHAIN CONTROL TOWER

Supply Chain Monitoring

Enable supply chain professionals tonavigate, analyze and profitably manage the end-to-end supply chain in real-time

Integrated Business Planning (IBP)

••

Increase end-to-end visibilityIncrease on-time delivery performance tocustomer

Decrease overall inventory levels while reducing risk

•

• Increase supply chain agility and reducesupply chain cost

User ExperienceIBP for sales & operations

IBP for demand IBP for supply IBP for inventoryIBP for response

SAP HANA Platform

Supply Chain Control Tower


supply plan

1) No end-to-end visibility2) No on-time delivery performance3) High inventory levels

ADVANCED ALERT MANAGEMENT

• Alerts that were raised for stock outage or low stock can be monitored via the Monitor or Alerts app. Those are dependent on the user’s or company’s alert definition and thresholds.

• Based on alerts, from concerned departments, the mid-to long-term plan can then be adjusted via the SAP IBP add-in for Microsoft Excel.

• The outcome is a moderately constrained supply plan that takes into consideration, overall S&OP, inventory policies at all level of the supply chain.

IBP PLANNING OUTCOME

IBP will be used in parallel with existing APO solution

APO Responsibilities IBP ResponsibilitiesCustomer sourcing optimization

Demand and supply planning including order generation Production scheduling anddeployment planning

Integration with ECC

S&OP support tool, facilitateexecution and data visualization

What-if scenario modeling, including revenue and cost analysisAggregate product line supply planning

Tenets for parallel solutions:Supply optimization results in IBP must be as closely aligned to APO SNPoptimization results as possible:

Relative cost structure for optimization must be aligned

Master data for supply chain network must be aligned

IBP JOURNEY – OVERALL IMPRESSIONS

• User interface – both Excel and Web UI easier to learn than other traditional SAP applications

• Merging supply chain, commercial and financial information can be challenging• Business needs to agree on fundamentals of how the data

aligns between each group

• Level of detail required by all groups needs to be considered

up front to ensure proper design and configuration

IBP JOURNEY – OVERALL IMPRESSIONS

• Data integration always takes extra time and refinement

• Data modeling differences between IBP and APO to overcome• Some learning curve with aggregating/disaggregating

planning viewswithin

• Data management can be underestimated –

••

Need to consider full lifecycle of data in the design

IBP is really both a planning tool and a reporting/analytics tool all in one

IBP JOURNEY – EARLY SUCCESSES

• Excessive downtime issue was discovered and corrected within minutes inIBP. This allowed business to move forward in addressing capacity questionsand building a scenario.

Able to make quick changes to demand and see impact to supply which has saved hours of work for the Demand Planner.

Completed several what-if analysis scenarios which provided very realistic output on impact to gross profit and results shared in Executive review process.Provided decision support on a project factoring in things like a newlocation, new demand, and sourcing changes.

As proficiency continues to build expect to do scenarios and gross profit/margin analysis much more efficiently than in the past.

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“All in all we are feeling pretty excited about what we will be able to accomplish.” {Operational Planning Manager in XYZ INC}

CLOSING

Questions ?

Documents

IBP Implementation Analysis