SC544_544_Scheduling Challenges in PPDS

Scheduling Challenges in Process Industry &
Solution Using PP/DS in SAP Software: DuPont
Case Study
Chintesh Chopkar Kevin Bunn
Bristlecone DuPont
SESSION CODE: SC544

At the end of this session, you should be able to understand:
 Scheduling Challenges in Process Industry
 Ways to address scheduling challenges using SAP PP/DS
 Key Areas of Improvements required in SAP PP/DS
Learning Points

 About DuPont
 Detailed Scheduling in Integrated Business Planning
 Differences between Process and Discrete Manufacturing
 Scheduling Challenges in Process Industry
 Key Concerns
 Solution Review in SAP
 Key Areas of Improvements in SAP PP/DS
 Key Learnings
Agenda

© National Geographic image
DuPont is a Science Company
We work collaboratively to find sustainable, innovative, market-driven
solutions to solve some of the world’s biggest challenges, making lives
better, safer, and healthier for people everywhere.
Our Purpose

We are applying our science to find solutions to some really BIG
challenges…
FOOD ENERGY PROTECTION
FEEDING THE
WORLD
REDUCING OUR
DEPENDENCE ON
FOSSIL FUELS
KEEPING PEOPLE & THE
ENVIRONMENT SAFE
About DuPont

+
+
Improve
Customer
Experience
Reduce
Cost to Serve
Reduce
Inventory
From …
 High inventory
 Misaligned policies
 Non-productive inventory
 High complexity
 Manual interventions
 Numerous work around
 Significant expediting
 Inconsistent delivery
performance
 Difficulty meeting promises
 Significant churn and effort
To …
 Automated, streamlined,
rules based processes
 Reduced manufacturing
costs
 Reduced expediting costs
 Advantaged working
capital productivity
 Aligned E2E performance
 Capital available to re-
invest
 Reliable Supply
 Competitive LeadTimes
 Differentiated service
levels
DuPont’s Transformational Approach For Digital DuPont

 To determine what to make, when to make, on which equipment to make
in a production facility
 Aims to maximize the efficiency of the operation at lowest possible costs
 The major inputs for Detailed Scheduling are:
 Master Production Schedule
 Demand Forecasts
 Sales
 Manufacturing
 Distribution
 Purchasing
What is Detailed Scheduling?
Goals of Detailed Scheduling

Detailed Scheduling in Integrated Business Planning

Chemical, Food, and Beverage manufacturing processes are different from
automobile production!
Process Manufacturing vs. Discrete Manufacturing
Process Industry Discrete Industry
Production Product is manufactured by mixing,
blending and transforming chemicals,
liquids or food stuffs
Product is manufactured by assembly
of distinct items
Breaks or Stoppages Difficult and costly to Start & Stop.
Dependent on Production Sequence.
Easy to Start and Stop.
Independent of Production sequence.
Shortage of Input
Component
More Flexibility in altering production
lot size (above minimum) to continue
production
If sufficient quantity of components is
not available to assemble then
production must be halted.
Inventory Mainly maintained at Finished
Product level
Lot of Work-In-Progress inventory
Example Pharma, Food, Chemicals Automobile, High Tech

Material Flows are different in Process Industry and Discrete Industry requiring
different approach in Planning, Scheduling and Execution.
Process Manufacturing vs. Discrete Manufacturing
Discrete Industry
Forecast
Process Industry
Forecast Forecast Forecast

Major Scheduling Challenges discussed are:
 Dealing with Uncertainties
 Setup optimization
 Production Quantity Constraints
 Multiple Stage Synchronization
 Resource (Machine) Constraints
Scheduling Challenges in Process Industry

Changes in Detailed Schedule are inevitable.
 Customers cancels or changes orders
 Suppliers have problems and miss delivery dates
 Machines break down or new machines are added, changing capacity
 Processes create more scrap than expected.
 Key Concerns:
 How to keep detailed schedule in sync
with planning and execution?
 How to balance competing goals?
 How to deal with uncertainties?
Dealing with Uncertainties in Detailed Scheduling

Scheduling Horizons – Aid to Decision Making
 Scheduling Horizons define artificial time boundaries used to separate periods
for planning or scheduling purposes.
 Used to establish policies to stabilize Detailed Schedule and Planning
 Frozen Horizon: Changes to DS are kept to a
minimum to avoid disruption to the production
schedule.
 Slushy Horizon: Changes to the detailed
schedule along with minor manual quantity
adjustment allowed.
 Liquid Horizon: Free to make changes in
detailed schedule as long as the schedule
remains within the production plan constraints

Horizon Purpose Where How
Frozen Schedule changes kept to a
minimum
Resource Detailed Scheduling Firming
Heuristics
Slushy Order in Slushy Horizon are
not changed by SNP
Product
Master
SNP Horizon Field
PP/DS Conversion of SNP Orders to
PP/DS Orders
Product
Master
PP/DS Horizon Field
Liquid MPS creates new orders to
respond to demand changes
Product
Master
Deletion job to delete the
orders beyond slushy horizon
Solution in SAP

Scope for improvements in SAP PP/DS
 If production is running behind, then detailed schedule must be updated to reflect
the new expected completion times. Current detailed scheduling heuristics does
not allow adjustment of backlog orders containing different phases within
operation.
Backlog Order

 A transition between products on a machine can incur a high cost or loss of large
amount of production time in setup.
 Sequence of manufacturing product influences production costs, setup times and
inventory levels and the goal is to balance the costs against inventory levels
while meeting demand.
Scheduling Challenge: Setup Optimization
 Key Concerns: How to create
a detailed schedule which
 Reduces setup cost
 Reduces setup time
 Meets customer demand
 Minimizes Inventory

Solution in SAP PP/DS
 SAP provides PPDS Optimization tool which can consider various trade offs
such as setup cost, setup time, delays and demand priority
 PP/DS Optimizer can create a schedule considering competing goals
 Automatic pickup of setup time based on predecessor product being
manufactured is supported in Detailed Scheduling Board
PP/DS Optimization profile for
Scheduling Trade Offs
Detailed Scheduling Board showing setup transition

Scope for Improvements in SAP
 Detailed Schedule prepared considering various trade offs is not aligned with
SNP planning as SNP optimization supports setup sequencing to a limited
extent.
 Transition materials produced during setup transition in process industry are not
supported in PP/DS.
 Modelling of costs in PP/DS Optimization to achieve the desired schedule is
challenging.
Sequencing of products at
regular interval.
A C B B A C SNP Orders
A A B B C C DS Order
(Current)
A B C A B C DS Order
( Expected)
Week 2
Modeling of PP/DS Optimization Costs is challening
Week 2
Week 2
Week 1
Week 1
Week 1

 Many factors affect quantity of product that can be produced at one time (Batch
Size, minimum campaign size, etc.)
 Process Industry have a lot of variation of production processes.
Key Concerns
 Batch Size: Consider minimum and maximum capacity requirements and align
with package size (increments of standard package).
 Intermittent Output: Product is produced in a continuous process with output at
regular time intervals. (e.g. 100 Gal every Hr)
 Minimum Input Quantity: A Product is produced in a continuous process and
order can not be started unless a minimum quantity (e.g. 200 Gal) of component
is available.
 Campaign lengths: Creating a campaign considering inter-dependency of semi-
finished product and finished product. (e.g. If semi-finished product order size
increased to meet lot size requirement then adjust the finished product order.)
Scheduling Challenge: Production Quantity Constraints

Intermittent Output & Minimum Input Component Quantity
 Finished Good is produced in a continuous process.
 Finished Good order can not be started unless a minimum quantity (e.g. 200
Gal) of Semi-Finished Good is available.
 SFG is produced in an intermittent process with output at regular time intervals

 SAP PP/DS supports scheduling of
continuous production with intermittent
output.
 PP/DS Optimizer can be used for
scheduling of continuous production.
A10111000: Raw Material
B10111000: Semi-Finished
D10111000: Finished
After Scheduling main order output quantity is
spilt equivalent to required intermittent output.
FG Orders are scheduled based on continuous
production considering availability of minimum
input component quantity.

Scope for Improvements in SAP
Lot Size integration of Detailed Scheduling with SNP:
 In SNP optimization, its not possible to address lot size constraints if the input
data volume is large.
 Plan generated in SNP may be feasible but not always executable.
 If lot size is addressed in detailed scheduling, it may create infeasible plan
downstream.
 GR time is not supported in continuous output production which create
inconsistencies in execution and planning.

Scheduling Challenge: Multiple Stage Synchronization
 The conversion of raw materials to finished products often consists of multiple
distinct manufacturing stages, either within a single site or across multiple
sites.
 When planning and scheduling these operations, synchronization between
stages and across multiple assets within the same stage must occur.
Key Concerns
 There is a scheduling dependencies between different levels of the BOM.
 Ideally, such products should be the part of the same manufacturing order but
due to the need to sell them differently, separate manufacturing orders are
required.
 Schedule operations on different orders in a way that they are a part of the
same manufacturing order.

Synchronization within order is supported
 First two operations start together but finish at
different times. Third operation starts 2 hours after
the start of the second operation
Scope for Improvement in SAP PP/DS
Synchronization across order is not supported:
 Adjust the finished product orders to meet lot size
requirement of semi-finished products
 If finished good order is rescheduled, then
reschedule semi-finished good order.
 Maintain a minimum and maximum duration
between finished good order and semi-finished good
order.
Scheduling Challenge: Multiple Stage Synchronization

 In order to generate feasible schedules and plans, it is important to understand
the capacity of the resources used in production.
Key Concerns
 Capacity / Rates / BOM changes over time
 Machine Downtime
 Resources to process multiple orders/products:
 Resource Network
 Labor Availability
Scheduling Challenge: Resource Constraints

Resources to Process multiple orders/products
 Example: Oven/Dryer
 Used for drying of multiple products.
 Can only be loaded or unloaded all at once.
 Should be filled with Products that require the
same temperature and duration.
Solution in SAP
 Resource can be defined to process multiple products
at the same time.
 Synchronization on the resource is activated to
process products with similar properties together.
 Products are grouped together so that they can be
heated/processed at the same time
Oven with Multiple
Sections

Alternate Resources
 Production rates may be limited due to available capacity of
machines.
 In such cases, production capacity is increased by the addition
of multiple machines.
 Asset Schedulers should have the flexibility to schedule production
on these multiple machines.
 A specific machine is preferable due to factors like higher
production rate, ease of operation, proximity to work area, etc.
Solution in SAP
 Alternate resource can be added in master data.
 Resource priorities and differential production rates can be
achieved using development
 Optimizer will try to schedule the order on preferred resource
(resource with high priority).
 If capacity is not available on preferred resource, then order is
scheduled on alternate resource (resource with low priority).
Before Scheduling
After Scheduling

Resource Network
 Manufacturing a product requires several steps
 Limited set of successor resources can be used
for the successor operation
Solution in SAP
 An additional master data called as Resource
Network can be used to define allowed
physical connections between resources.
 Resource Network can be used to define the
flow within an order or across orders
 PP/DS Optimizer can remove capacity overload
while respecting Resource Network constraints.

Labor Constraint
 Labor: Limited availability of labor to run
multiple resources.
 Specific Time: Requirement of Labor at the
start or end of operation.
 A shared labor resource can be mapped as a
secondary resource and can be added to a
specific operation or phase.
 PP/DS Optimization considers the capacity of
secondary resource while scheduling on
primary resource.

Scope for improvements
 SAP design to support alternate resources
is not consistent in SNP and PP/DS.
 Results in maintenance of additional
master data to support alternate resource
functionality in SNP and PP/DS .
 Creates inconsistency in planning and
scheduling.
In PP/DS, alternate resources should be a part of the
same production data structure support alternate
resource selection in case of capacity overload.
In SNP, alternate resources within the same PDS
is not supported. Separate production data
structure is required in order to ensure that
capacity overload can be resolved.

 Scheduling Processes in Process industry are complex due to the
nature of manufacturing processes.
 Standard SAP PP/DS solution delivers value to meet scheduling
requirement in process industry
 SAP should improve integration of PP/DS with other modules to
better suit process industry scheduling requirements.
Key Learnings

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SESSION CODE: SC544
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SC544_544_Scheduling Challenges in PPDS

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