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


Key Concerns

Solution Review in SAP

Key Areas of Improvements in SAP PP/DS

Key Learnings

Agenda

© National Geographic image

DuPont is a Science CompanyWe 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 Lead Times

Differentiated service levels

DuPont’s Transformational Approach For Digital DuPont

About DuPont




Key Concerns



Key Learnings

Agenda

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


About DuPont




Key Concerns



Key Learnings

Agenda

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

About DuPont




Key Concerns



Key Learnings

Agenda

Major Scheduling Challenges discussed are:

Dealing with Uncertainties

Setup optimization

Production Quantity Constraints

Multiple Stage Synchronization

Resource (Machine) Constraints


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 OffsDetailed 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.

About DuPont




Key Concerns



Key Learnings

Agenda

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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Documents

SC544_544_Scheduling Challenges in PPDS