The document discusses a webinar on IBP Response & Supply focusing on time series supply planning and optimization. The webinar agenda includes presentations on the need for feasible and optimal supply response in S&OP, an introduction to SAP IBP Response & Supply, and a showcase of the solution. It is moderated by Puru Goyal from GitaCloud and includes speakers from SAP and GitaCloud.

1. © 2017 GitaCloud, Inc. All Rights Reserved.
IBP Response & Supply Webinar: Time Series Supply Planning
25th July 2017

2. © 2017 GitaCloud, Inc. All Rights Reserved.
Moderator Introduction, Housekeeping
• Welcome to this webinar focused on IBP Response & Supply: Time Series Supply Planning & Optimization
• This webinar is brought to you jointly by SAP and GitaCloud
• Housekeeping Items:
• Questions & Answers: Type your questions into chat section at any time during the webinar
• All attendee microphones will stay muted
• This webinar is being recorded and will be available later on SAP website and GitaCloud website
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Webinar Moderator: Puru Goyal – Consultant, GitaCloud
 Data Scientist and IBP Consultant at GitaCloud
 Comes from IIT Kanpur, India (Gold Medalist); graduate from UC Berkeley
 Specializes in Demand & Supply Optimization using Advanced (Prescriptive) Analytics
 Recently led a Statistical Forecasting IBP client engagement, where he was able to cut Forecast Error by > 500%

3. © 2017 GitaCloud, Inc. All Rights Reserved.
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Our Speakers
Jay Foster – SCM Solution Management, SAP America
 Director, Solution Management at SAP America
 Solution Owner for SAP IBP for S&OP
 20+ years in leadership positions as CIO, SVP of IT, VP of Operations, and VP of Sales
Ashutosh Bansal – Founder & CEO, GitaCloud
 23+ years enabling Business Transformation at 25+ Fortune 500 companies in US
 SCM/IBP SME: Ashutosh has led sales & delivery for multiple IBP engagements
 ex-SAP: where Ashutosh played High-Tech Industry Principal and Services Sales Executive roles
 Ashutosh blogs actively on IBP topics at www.GitaCloud.com/blog and LinkedIn (7K+ followers)
Navin Kumar Prasannam – Solution Advisor – Digital Supply Chain, SAP
 Navin is a Presales Solution Advisor with focus on Digital Supply Chain
 Navin having graduated from the SAP Presales Academy in California, USA
 Worked with a reputed CPG company in India prior to joining SAP
 Navin’s educational background combines engineering and business management

4. © 2017 GitaCloud, Inc. All Rights Reserved.
Agenda
Need for Feasible & Optimal
Supply Response in S&OP
SAP IBP Response & Supply
Showcase
Wrap-up
10 min
Kick-off
5 min 15 min 15 min
Puru Goyal Ashutosh Bansal Navin Kumar Prasannam All
4
SAP IBP Response & Supply
Introduction
15 min
Jay Foster

5. © 2017 GitaCloud, Inc. All Rights Reserved.
Agenda
Need for Feasible & Optimal
Supply Response in S&OP
SAP IBP Response & Supply
Showcase
Wrap-up
10 min
Kick-off
5 min 15 min 15 min
Puru Goyal Ashutosh Bansal All
5
SAP IBP Response & Supply
Introduction
15 min
Jay Foster Navin Kumar Prasannam

6. © 2017 GitaCloud, Inc. All Rights Reserved.
Supply Chain Excellence: Winners and Losers
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Source: Supply Chains To Admire 2016 and 2017 Reports from Supply Chain Insights

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Polymer Manufacturers: Competitive Landscape
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1. Handful of large, vertically integrated players provide 80% of polymer demand globally.
2. Profit is hurt by increased feedstock prices (linked to price of crude oil).
3. Cost increases cannot be passed to customers in the short term.
4. Extremely competitive price sensitive market.
5. Commodity product. Quality / Delivery reliability are assumed by customers.
6. Make To Stock. Customers expect instantaneous delivery.
Source: Kadipasaoglu, S., Captain J. and James, M ‘Polymer supply chain management’, Int. J. Logistics Systems and Management, Vol. 4, No. 2, pp.233–253.

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Polymer Manufacturers: Supply Chains
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1. Feedstock Supplier and Polymer Manufacturer belong to the same group of companies.
2. Plants typically located closer to Raw Materials than customers (near pipelines supplying feedstock).
3. Regional Supply chains: plants, DCs in a Region serve demand in that region.
4. 3-6 plants; 2-10 lines per plant
5. 250-1000 customers. Customers have multiple ship-to locations.
6. Distribution networks vary by region. Rail in North America, Sea in Middle East, Trucks in Europe.
7. North America: Plants have limited storage capacity, polymer is stored in railcars, then shipped to 10-
60 bulk terminals (DCs). Shipment Lead Time from Plan to DC: 10-14 days.
8. Long transit times (14 days) + lengthy production campaigns (30 days) = High Inventory Needed at DC
Source: Kadipasaoglu, S., Captain J. and James, M ‘Polymer supply chain management’, Int. J. Logistics Systems and Management, Vol. 4, No. 2, pp.233–253.

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Polymer Manufacturers: Manufacturing Wheels
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1. Hybrid of Batch and Continuous Processing
2. Capital intensive large plants need to operate at full capacity given
slim margins. OEE is a critical metric.
3. 10-30 campaigns in a cycle (firm production sequence). 5-30 main
products  50-400 end products  even more SKUs.
4. Product transitions produce lower grade material for a while, which
sells at a discount.
5. Lines are dedicated to campaigns.
Source: Kadipasaoglu, S., Captain J. and James, M ‘Polymer supply chain management’, Int. J. Logistics Systems and Management, Vol. 4, No. 2, pp.233–253.

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Polymer Manufacturers: Integrated Business Planning
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1. Long lead time to make/ship product. Customers demand instant delivery. Volatile demand (10-
20% random fluctuations). High forecast variability  high safety stock needed at DCs.
2. Multiple packaging options (bulk, box, bag) create high number of SKUs. Large number of
customer ship-to locations in a region. Complex distribution network (1000s of Product/Locations).
3. Plants need to run at full capacity with minimum transitions. Breaking campaigns is not desirable.
Schedule updates can change the plan up to 30%.
4. Optimization can be to minimize cost or maximize margin. Minimum lot size constraints need to be
modeled. Transition time and lower-grade transition material needs to be modeled.
Source: Kadipasaoglu, S., Captain J. and James, M ‘Polymer supply chain management’, Int. J. Logistics Systems and Management, Vol. 4, No. 2, pp.233–253.

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Polymer Manufacturers: Case for an IBP Solution to deliver Margin Optimization
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1. Complex & volatile demand & raw material pricing environment.
2. Need to forecast market demand, optimize DC inventory, plan manufacturing resources, and Raw
Material buys optimally with minimal latency in the process.
3. High Production Line Utilization and minimal transitions are key given margin hit for transition
materials.
4. Profit Optimization is key given slim profit margins.
5. This requires an integrated planning system that stretches from Demand, Inventory, Supply,
Response, to Supplier & Customer collaboration. This system needs to optimize margin and be able
to optimize on what-if demand / pricing scenarios.
Source: Kadipasaoglu, S., Captain J. and James, M ‘Polymer supply chain management’, Int. J. Logistics Systems and Management, Vol. 4, No. 2, pp.233–253.

12. © 2017 GitaCloud, Inc. All Rights Reserved.
Agenda
Need for Feasible & Optimal
Supply Response in S&OP
SAP IBP Response & Supply
Showcase
Wrap-up
10 min
Kick-off
5 min 15 min 15 min
Puru Goyal Ashutosh Bansal All
12
SAP IBP Response & Supply
Introduction
15 min
Jay Foster Navin Kumar Prasannam

13. PUBLIC
Jay Foster, SAP, Director, Solution Management
July 25, 2017
SAP Time Series Supply Planning Overview
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SAP Integrated Business Planning for Response &
Supply
Fast, flexible supply planning supporting a variety of approaches, suitable for
many industries, including:
 Support of tactical (time series) supply planning in the context of S&OP
 Unconstrained heuristics or constrained optimization
 What-if analysis
 Support of operational supply planning (orders)
 Creates supply orders (planned orders, purchase req., distribution req.)
 Generates allocations to feed to live ATP process
 Constrained priority rules-driven planning, optimization based (roadmap) and
unconstrained heuristic (roadmap)
 What-if analysis
 New order data store and tight integration with ERP
 Support of response planning (orders)
 Adjust create supply orders, and reschedule sales orders
 What-if analysis
 New order data store and tight integration with ERP
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Tactical Planning using Heuristic or
OptimizationCreate advanced supply planning simulations for
S&OP based on forecasts, orders, and inventory or
safety stock targets
 Utilize either heuristic or optimization algorithms
to develop an unconstrained or constrained
supply plan
 Development of rough cut capacity plan
 Multi level sourcing determination for both
distribution and Bills of Material
 Meet optimized inventory targets set by IBP for
inventory
 Gain visibility for projected stock or shortages at
relevant levels of aggregation
 Scenario planning capabilities

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Tactical Planning Use Cases
Supply Simulation
Collaboration
Supply Overview
Rough Cut Capacity Planning
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Tactical Planning – Optimizer Example
 The objective of the Optimizer is to minimize the total costs or maximize total profit of the supply plan.
 Optimization is performed via a mathematical model using Mixed Integer Linear Programming (MILP).
 The output is an optimal and feasible Times Series plan that takes into consideration modeling constraints.
DC1
DC2
Plant 1
Plant 2
Customer1
Customer2
Customer Transport costs Transport costs
Customer Transport costs
Fixed Production Procurement Cost
Production Procurement Rate
Inventory Holding Cost Rate
Safety Stock Violation Rate
Inventory Holding Cost Rate
Safety Stock Violation RatePrice
Price
Transport costs
Fixed Production Procurement Cost
Production Procurement Rate
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Tactical Planning – Optimizer Constraint Types
Soft Constraints
 Should be satisfied, violations are penalized in the objective
function
 Examples:
 Variable/rate costs for transports, production,
external receipts
 Non-delivery costs for customer demands (rate)
 Fix costs for transports, production and external
receipts
 Inventory holding costs (rate) and safety stock
violation costs (rate)
Pseudo-hard constraints
 Soft constraints with a very high penalty cost (usually higher than the
sum of all other costs)
 Gets satisfied if possible, but still allows for a solution if not possible
 Examples:
 Manual adjusted values (internal to the optimizer, very high
cost if violated)
Hard Constraints
 Must be satisfied, no solution if not possible
 Examples:
 Max stock
 Minimum and Maximum lot-sizes (unless
overwritten by adjusted values)
 Resource Capacities
 Stock Balance
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Customer Adoption Since Fall 2016
 Consumer Products (7)
 Industrial Manufacturing (7)
 Oil & Gas/Chemical (4)
 Hi-Tech (3)
 Automotive (3)
 Other (5)
29 companies subscribing
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20. © 2017 GitaCloud, Inc. All Rights Reserved.
Agenda
Need for Feasible & Optimal
Supply Response in S&OP
SAP IBP Response & Supply
Showcase
Wrap-up
10 min
Kick-off
5 min 15 min 15 min
Puru Goyal Ashutosh Bansal All
20
SAP IBP Response & Supply
Introduction
15 min
Jay Foster Navin Kumar Prasannam

21. © 2017 GitaCloud, Inc. All Rights Reserved.
IBP Response & Supply Showcase
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Feasible & Optimal Supply Planning in Action…

22. © 2017 GitaCloud, Inc. All Rights Reserved.
Agenda
Need for Feasible & Optimal
Supply Response in S&OP
SAP IBP Response & Supply
Showcase
Wrap-up
10 min
Kick-off
5 min 15 min 15 min
Puru Goyal Ashutosh Bansal All
22
SAP IBP Response & Supply
Introduction
15 min
Jay Foster Navin Kumar Prasannam

23. © 2017 GitaCloud, Inc. All Rights Reserved.
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SAP IBP Response & Supply
Workshop
July 29th – Aug 6th, 2017
SAP IBP Response & Supply hands-on 4 day
Workshop
• Link to Register
Scope:
• SAP IBP Supply Overview
• Supply Heuristic, Supply Optimizer
• SAP IBP Response Overview
• Response Heuristic
GitaCloud: SAP IBP Upcoming Workshops
SAP IBP Demand & Inventory
Workshop
September 16th – September 24th, 2017
SAP IBP Sales & Operations Planning
Workshop
August 19th – Aug 27th, 2017
SAP IBP Sales & Operations Planning S&OP
hands-on 4 day Workshop
• Link to Register
Scope:
• SAP IBP Platform Overview
• SAP IBP Basic & Advanced Configuration
• S&OP Capabilities: Planning & Analytics
• Mock Implementation
SAP IBP Demand & Inventory hands-on 4
day Workshop
• Link to Register
Scope:
• SAP IBP Demand: Statistical
Forecasting, Demand Planning,
Demand Sensing
• SAP IBP Inventory: Single Stage
Inventory Optimization, Multi-Stage
Inventory Optimization
visit www.gitacloud.com/products to learn more

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Wrap-up: Q&A
IBP Response & Supply optimizes Net Profit Margin
Rules Based Planning
Cost Based Optimization

25. © 2017 GitaCloud, Inc. All Rights Reserved.
Ashutosh Bansal
Founder & CEO, GitaCloud
+1-925-519-5965
6200 Stoneridge Mall Road, 3rd Floor, Pleasanton, CA 94588, USA
ashutosh@gitacloud.com
www.gitacloud.com