Service Parts Planning | Westernacher Consulting

Whitepaper
Service Parts Planning with
SAP SCMTM
Enable the planning capabilities
of your after-sales business
By:
Version:
Ute Messmer
1.1
Service Parts Planning with SAP SCM
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Contents
1
2
3
Introduction .................................................................... 3
Planning Process Overview ........................................... 4
Planning Features .......................................................... 5
3.1
3.2
3.3
3.4
3.5
3.6
4
5
Modeling the service parts distribution network (supply
chain design) ..................................................................... 5
Capture and manage demand history and forecast
demands (demand planning)............................................. 6
Determine optimal stock levels for ensuring high product
availability by minimal costs (inventory planning).............. 7
Define the overall procurement plan (supply planning) ..... 9
Deploy the goods within the network and balance excess
and need (distribution planning) ...................................... 10
Interchangeability of products ......................................... 12
System Landscape for the Service Parts Planning
Environment ................................................................. 14
Summary ..................................................................... 16
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1
Introduction
The after-sales market is a constantly growing business with attractive future profit potential.
Manufacturing companies make 25-55% of their profit with service (parts), although the latter only
contribute 10-25% to their revenues. Therefore today’s aftermarket business carries significantly
higher profit margins than the initial sale.
Within this market environment, effective service parts management is gaining more and more
strategic importance and becomes a main factor for companies aspiring to improve customer
satisfaction, customer’s brand loyalty and repurchase of products. Service parts management has
also turned into a key differentiator in competition with other companies.
Effective service parts management must strike a balance between high service levels and
increasing logistics costs. The key challenge is having the right part, at the right time at the right
location to fulfill the service requests without having huge inventory buffers or managing excess
stock. The service parts business is defined by the following attributes:
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Increasing number of service parts for all active and old products (esp. for long warranty
items)
High number of line items with small quantities
High number of product storage locations and extensive distribution network
High inventory buffers
Complex product-interchangeability
Time critical deliveries
Irregular and unpredictable demand
Right stocking decision for the critical mix of slow and fast movers
Intricate optimization of supply alternative (make or buy, repair, substitution)
Therefore supply chain management for service parts requires another focus than the supply chain
management for finished goods.
This article will primarily discuss the planning process within service parts management and how it
can be handled efficiently with SAP Advanced Planner and Optimizer (APO) which is part of SAP’s
supply chain management module SAP SCM. Service Parts Planning (SPP) was introduced with
SAP SCM 5.0. It relies on completely new planning functions to cover the special requirements for
service parts and cannot be seen as a close derivative of the existing planning functionalities of
Demand Planning (DP) or Supply Network Planning (SNP) in SAP APO.
Supply chain execution for service parts such as procurement, warehousing, fulfillment and
transportation is not part of the scope for this article.
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2
Planning Process Overview
The target of the service part planning process is to determine the required inventory levels at the
customer facing locations to
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meet the target service level,
plan procurement
and plan the replenishment needs for the customer facing locations.
Service Parts Planning with SAP offers a variety of planning features that support this target. It
offers transparency throughout the service parts supply chain from the appearance of demand to
the delivery of the service product. The following process flow helps to get a better understanding
of SAP’s planning sequence for service parts:
Monitoring and Analysis
Planning Process Sequence
During Supply Chain Design, the distribution network is modeled by so-called bills of distribution
(BODs).
Within Demand Planning, the demand history of the parts is captured and depending on that, the
forecast is done with the help of extended and sophisticated forecast models. Forecast services
cover the whole product life cycle from phase-in planning to product interchangeability to phase-out
planning.
Inventory Planning deals with the determination of economic order quantities (EOQ) in
combination with safety stock. It also makes decisions about stocking or de-stocking of products
and helps to identify surplus quantities in the network.
The results from the demand and inventory planning processes serve as input into Supply
Planning. Supply planning determines the distribution requirements as procurement proposals.
After the approval process, the proposals are handed over to the execution side.
Finally Distribution Planning decides about the deployment of the received inventory along the
distribution network. Inventory balancing is used when receiving does not cover the requirements
in customer facing locations.
The result of the planning process should guarantee that customer facing locations have sufficiant
inventory to fulfill customer requirements.
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3
Planning Features
3.1 Supply Chain Design - Modeling the service parts distribution network
An important element of the service parts planning is the bill of distribution (BOD). It reflects the
standard distribution path of the service part and serves as basis for most planning activities.
A BOD always consists of one or more entry locations and can contain so-called parent locations
and child locations. The entry location defines the location to which the supplier delivers the parts.
The location from which the customer is served is called a customer facing location. Parent
locations ship to other locations that are then called children. For locations that ship directly to the
customer and also to other location(s), a virtual child needs to be introduced. All data related to
customers as e.g. forecast, safety stock, etc. is then assigned to the virtual child. As a result, stock
transfers will be created from the physical to the logical location in order to cover the demand.
Example: The supply chain for a service part looks like:
Supply chain example
This supply structure transferred in a BOD results in:
Entry location
Child location
to Stuttgart
Parent
location to
New York and
Vancouver
Virtual child
location
Berlin, New York, Vancouver, Hannover and Virtual Frankfurt are customer facing locations
Bill of distribution
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The assignment of a product to a BOD can change over time. Data relevant to planning will then
be rerouted to the new BOD. The planning process will also consider any future BODs. As the
distribution hierarchy is firmly predefined by the BOD, the planning process can run without a time
consuming supply source determination.
The concept of virtual consolidation locations is used to group physical locations with relatively
small demand into one logical location within the BOD. Although this aggregates the data for
demand and replenishment planning, the physical locations are yet deployed according to their real
demand.
SPP is also able to deal with subcontractors, so-called contract packagers (CP). A contract
packager packs and repacks the goods (e.g. from containers to pallets) for the warehouses within
the BOD. In case of contract packaging, the CP needs to be assigned to a specific location within
the BOD. The assignment is done in the ERP1 system. When using SAP Extended Warehouse
Management (EWM) at the subcontractor’s warehouse, there is no loss of information about
inventory and goods movements for the service parts planning process.
3.2 Demand Planning - Capture and manage demand history and forecast demands
The demand history forms the basis for further forecasting and planning processes. Historical
demand data is usually captured from SAP Customer Relationship Management (CRM) or SAP
ERP. During this upload process, the data is reorganized according to certain events such as e.g.
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a future BOD becomes valid for a product,
a supersession (product replacement rule) is created for a product,
stocking or de-stocking decisions have changed for a product.
The system aggregates the demand for the product in different time buckets (weeks, months, fiscal
periods) along the BOD for each stockholding and customer facing location. The system allows
manual adjustments to the raw data (sales orders from SAP CRM or SAP ERP) and to the
aggregated demand data. The aggregated demand history serves as input into the forecasting
process. This process is divided into the following steps:
1. Aggregation of demand along the BOD (as part of demand capture and management
process).
2. Calculation of forecast at all customer facing and parent stockholding locations by use of a
suitable statistical model.
3. Disaggregation of statistical forecast of the entry location along the BOD down to the
customer facing locations. This disaggregation is done in proportion to the statistical
forecast of the respective child locations.
4. Either the statistical forecast or the disaggregated forecast can now be used for inventory
and supply planning. Usually the disaggregated forecast is used as it has a broader
database which delivers better results in statistical models.
The forecast service contains a huge number of forecast models. The selection of the appropriate
model is based on the analysis of the demand history types as e.g. constant, seasonal, sporadic
demand with or without trend. Before each forecast run, the forecast model can be evaluated and
changed where required.
1
Enterprise Resource Planning
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The following table provides an overview of the forecast models for SPP:
Forecast model
First Order Exponential Smoothing
Second Order Exponential Smoothing
Moving Average
Linear Regression
Seasonal Trend Model
Seasonal Trend Model with fixed
Periods
Intermittent Model
Dynamic Moving Average
Declining Demand
Applicable for
Products with small demand data history;
Used as default model for forecasting
Products with assumed trend
All kind of products when a very simple
model is desired
Products with trend
Seasonal products with less than 24
months demand history
Seasonal products
Products with sporadic demand
Slow movers
Products with decreasing demand (end of
life period)
Forecast service also offers scenarios for phase-in and phase-out of a product in order to support
the whole life cycle planning. Thereby, the forecast calculation for new and end-of-life products
refers to the demand history of reference products which the user can define within a product
group.
3.3 Inventory Planning - Determine optimal stock levels to ensure high product
availability by having minimal costs
The inventory planning process determines the required safety stock and the optimal order
quantity of a service part for a certain location. The overall aim is to ensure optimal service levels
to the customer whilst keeping the inventory and purchasing costs as low as possible. During this
process
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stocking and de-stocking decisions are made,
the economic order quantity (EOQ) in combination with safety stock is calculated and
surplus and obsolete quantities are identified.
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The decision whether a service part is stocked or not stocked usually depends on the demand and
the procurement costs. A simple example for a decision rule for stocking and de-stocking is
provided below:
Example for a stocking and de-stocking decision
The concept of a combined determination of an EOQ and safety stock helps to optimize inventory
and procurement costs plus the targeted service level. The procedure is always starting with the
calculation of the EOQ. It is obvious that an increasing order quantity reduces the number of
purchase orders, which also means a reduction in ordering costs. At the same time, the average
inventory and the related costs will increase. The EOQ calculation tries to determine the order
quantity with the minimal total costs (ordering + inventory costs).
Determination of EOQ
The amount of safety stock is dependent on the defined service level and the reliability of the
forecast. The higher the forecast error, the higher the safety stock must be, to ensure the defined
product availability. Other influencing factors are the replenishment lead time and the order
quantity. Long lead times and small order quantities requires a higher safety stock. SAP Service
Parts Planning uses sophisticated statistical methods to determine the required safety stock in
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order to supply within a defined service level. Once determined, EOQ and safety stock are used in
the replenishment planning to calculate the procurement requirements.
Another feature of the inventory planning service is the identification of surplus quantities in the
distribution network. The stock in a location plus all stock in transit for a service part within its BOD
that exceeds the probable demand for a certain time frame are called surplus stock. Surplus stock
can be removed (scrapped) by an interactive approval process.
3.4 Supply Planning - Define the overall procurement plan
The so-called distribution requirements planning (DRP) determines the required replenishment
quantities considering the output from the demand and inventory planning process. It is a multistaged procedure that ultimately generates procurement proposals for the entry location(s) of the
BOD. To determine the proposals, a net demand calculation is performed for all stock holding
locations on each level of the BOD. This net demand is then shifted to the next higher level with
consideration of the relevant lead times (e.g. goods issue time, transportation duration, goods
receipt time). To get a balanced planning, DRP also generates stock transfer proposals to cover
the net demand at the child locations.
The following picture shows a simplified view of the net demand calculation process (rounding is
disregarded):
Simplified calculation of the net demand
Goods receipts at the customer facing locations (CL1 and CL2) are stock transfer orders coming
from the parent location (PL). Same applies to the receipts at the mid-level location (PL): receipts
are represented by stock transfer orders coming from the entry location EL. Purchase orders resp.
delivery schedules form receipts for the entry location.
The main demand driver at the customer facing locations CL1 and CL2 is the forecast. Customer
orders are only relevant as demand elements, when they are dated in the future. Demand for the
mid-level location PL is mainly represented by stock transfer orders plus stock transfer order
proposals to its child locations CL1 and CL2. These ‘planned stock transfers’ should cover the net
demand at the child locations. Demand for the entry location EL is also generated by stock transfer
orders plus stock transfer order proposals to its child location PL. These proposals satisfy the net
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demand at the mid level location PL. Finally, the net demand for the entry location EL is covered by
procurement proposals (schedule lines or purchase requisitions).
The net demand at each level is always rounded according to EOQ, additionally considering other
factors such as e.g. pack sizes.
At the end of the DRP process, procurement proposals need to be approved and handed over to
the execution side where they are transferred into purchase orders resp. delivery schedules of a
scheduling agreement. Stock transfer proposals which were generated during the DRP run are not
released to the execution system. The latter are recalculated in the subsequent deployment
process and afterwards approved and executed.
DRP also supports the concept of consolidated ordering2 for virtual locations. Demands and
inventories of the individual locations are aggregated and a consolidated net demand is
determined for the virtual location.
Supplier shutdowns can also be integrated in the distribution requirements planning. The
demand will then be sourced earlier.
3.5 Deploy the goods within the network and balance excess and need (distribution
planning)
3.5.1
Deplyoment Process
The purpose of the deployment process is the distribution of goods within the BOD. It is always
performed from one level of the BOD to the level below. For deployment, it has to be ensured that
a demand is available on the child location and a distributable quantity is available for the parent
location. If the BOD contains more than two levels, the BOD is split into sub-BODs and several
deployment runs are performed.
Required deployment runs for a BOD
There are two ways of doing deployment, the so-called push deployment and the pull
deployment. When a service part is flagged for push deployment, the deployment is triggered by a
goods receipt at the parent location. That means that the goods are not stored at the parent
location but immediately sent to the child location. This leads to a faster replenishment within the
distribution network and it is often applied to fast movers. Pull deployment is triggered by a
demand at the child location. This is the common way deployment runs are performed.
2
See also chapter ‚Modeling the service parts distribution network‘.
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The results of the deployment process are stock transfer proposals from all parent locations to their
child locations. A key differentiator between the deployment in service parts planning and supply
network planning for sales products in APO is that the deployment process creates no stock
transfer proposals for futures dates. This means that stock transfers are always due today.
The way stock transfer proposals are determined is as follows:
1. The available3 quantity for distribution is calculated for the parent location.
2. The net demand for the child locations during their replenishment lead time is determined.
3. If the net demand (during the replenishment lead time) is lower than the available quantity
at the parent location, the system performs a fair-share distribution4 for the remaining
deployment quantity.
4. If the net demand (during the replenishment lead time) is higher than the available quantity,
the demand is divided into prioritized tiers (according to the type of demand) and
sequenced. This should ensure that demand with high priority is covered first. Within a tier
bucket, the available quantity is also distributed according to a fair share-distribution.
The deployment service offers a certain flexibility. In case the real customer demand often differs
from the actual forecast, it could make sense to retain a portion of the deployable quantity at the
parent location. This allows the planner to react short-term to changing demand situations in the
child locations. The way this can be triggered, is either
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limiting the available deployment quantity at the parent location by constraining the time
horizon for the consideration of confirmed expected receipts or
limiting the supply quantity to the child locations by setting a supply limit.
Another beneficial feature within the deployment process is the possibility to trigger express
(expedited) shipments. This could make sense in case a demand can be covered in time through
the selection of a faster but more expensive means of transportation.
3.5.2
Balancing Excess and Needs
While deployment is distributing the goods along the defined BOD, inventory balancing can bypass the existing paths in case of a need at a location that cannot be supplied in time via the
regular transportation lanes. This planning step can ally to the deployment process in case the
latter fails to satisfy all demands.
Inventory balancing can only be performed within a defined inventory balancing area which
contains those locations that are allowed to supply via the predefined by-passes. Usually the
shortage and the excess of a service part are calculated for all locations within the inventory
balancing area and the imbalance is adjusted if possible. However each adjustment is preceded by
a cost and benefit analysis for the ‘exceptional’ stock transfer.
3
Inventory (pull) resp. goods receipt quantity (push) minus confirmed customer orders
Fair share distribution means that the remaining quantity for deployment at the parent location is distributed in proportion to the
demands of the child locations.
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3.6 Interchangeability of products
The replacement of a service part by another is called supersession. SPP supports (1:1),
(1:many) and (many:1) supersession. These supersession types can be enhanced to complex
replacement strategies using conjunction (AND) resp. disjunction (OR).
Examples for replacement strategies:
Replacement strategy
(1:1) supersession
Description
Product A will be replaced by product B
Partial (1: 1) supersession
Product A will be replaced by product B.
But A will also continued to be sold.
(1:many) supersession (AND link)
Model A consists of product B and product C.
A should not longer be sold but only B and C
as standalone products
(1:many) supersession (OR link)
Product A is part of model B and model C.
A should be replaced by product D in model B
and product E in model C
(Many:1) supersession (AND link)
Product A, B and C should be sold together in
future as product D.
(Many:1) supersession (OR link)
Model A and model B contain different
components for the same function.
C is part of model A and D is part of model B.
In future E will replace C and D.
(1:1) supersession chain
Product A will be replaced by product B in Dec,
12th 2010 and product B by product C in Jan,
20th 2011.
Supersession of remanufactured products
A new product should take on a proportion of
the demand from a remanufactured product.
This should only be done in case the supplier’s
remanufacturing capacity is not sufficient to
satisfy the complete demand.
The supersession service in the SPP module calculates the key dates for planning with
supersession. The process starts with the input of the pending obsolescence date (POD). It
represents the earliest date the successor product can be used. Starting from the POD, the system
determines the date when the remaining stock of the predecessor is used up (stock exhausting
date). Doing a backward calculation starting from the stock exhausting date, supersession service
determines the date when the planning for the successor product has to start. When the latter is
reached the demand history of the predecessor product is copied to the demand history of the
successor product. This is then the basis for forecasting, EOQ and safety stock calculation.
In an ideal situation, the stock of the predecessor product will be sufficient to cover its demand and
also the supply for the successor product will already be available to satisfy its requirements. In
case of an imbalance, DRP is able to generate substitution orders to cover the demand of the
successor product with supply of the predecessor and vice versa.
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If a substitution strategy exists for remanufactured products, DRP will also create substitution
orders (already for the new product that has not yet been used) to solve the mismatch between
demand and remanufacturing capacity at the supplier side.
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4
System Landscape for the Service Parts Planning
Environment
Let us now have a look at the system architecture surrounding Service Parts Planning. SPP, as an
element of the SAP Service Parts Management (SPM) solution, is integrated into SAP APO. SAP
APO itself is a component of SAP SCM. The full-blown SPM solution requires a complex system
architecture consisting of the following components:
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SAP Customer Relationship Management (CRM) including Order Management
SAP Supply Chain Management (SCM) including SAP Advanced Planner and Optimizer
(APO), SAP Supply Network Collaboration (SNC) and SAP Extended Warehouse
Management (EWM)
SAP Enterprise Resource Planning (ERP) including Advanced Shipping Notification (ASN)
verification and delivery processing
SAP Business Warehouse (BI)
SAP Process Integration (PI)5
SAP NetWeaver
System Architecture for SAP Service Parts Management
5
Formerly known as Exchange Infrastructure (XI)
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When focusing purely on the planning functionality for service parts, a reduced system landscape
will work as well since the planning is completely captured in SAP APO. Integration with SAP ERP
is required to incorporate master data and demand history and for the execution.
The picture for a minimized setup to run SPP looks like this:
Reduced landscape for Service Parts Planning
Based on the fact that SPP is an exception driven process (due to the high number of SKU6’s) and
that planning runs are mainly performed in the background, process monitoring and analysis are
extremely important. Monitoring and controlling is done with the help of the so-called shortage
monitor and alert monitor. These tools report any kind of problem or imbalances within the supply
chain. Shortage and alert monitors are part of SAP Supply Network Collaboration7 (SNC).
Performance measures towards the customer, within the supply chain and from the supplier to the
manufacturer are included in SAP Business Information Warehouse (BI).
6
7
Stock Keeping Unit
Supply Network Collaboration was introduced with SAP SCM release 5.1. It is replacing the Inventory Collaboration Hub (ICH).
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5
Summary
After having learned about the specific planning functionalities that are offered by SAP’s Service
Parts Planning solution, the following picture should bring the elements together into their
chronological order:
Service parts planning process overview
SAP’s integrated planning approach encompasses the complexity and specialties of the service
parts planning environment and provides a comprehensive solution that can deal with all aspects
of the business. It optimally matches demand with supply and automates the different planning
activities. In the long run, it will enable companies to reduce stockholding costs, improve service
levels and shorten delivery times through an improved planning accuracy and increase revenues
through higher customer retention and brand loyalty.
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References
Dickersbach, J. Th.:
Service Parts Planning with mySAP SCM – Processes, Structures and Functions.
Springer-Verlag Berlin Heidelberg 2007
SAP Online Help for SPP in SAP SCM 2007:
http://help.sap.com/saphelp_scm2007/helpdata/en/a3/7ed240caeb752ae10000000a155106/frameset.htm
Spare Parts Management – An IT Automation Perspective
http://www.infosys.com/industries/resources/white-papers/automating-the-spare-parts-management-function.pdf
Westernacher & Partner Unternehmensberatung AG:
Service Parts Management.
http://www.westernacher.com/wup/dcms/sites/wp2/de/beratung/service_parts_management.html
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Contact and Information
For information on our services, please email us with your
inquiries and let us know how we can best contact you:
[email protected]
Westernacher Business Management Consulting AG
Im Schuhmachergewann 6 · 69123 Heidelberg · Germany
phone: +49 6221 187 62-0
Westernacher & Partner Consulting Limited
72 New Bond Street, London, W1S 1RR, UK
phone: +44 870 383 0272
Westernacher Consulting China Co., Ltd.
1701 Beijing West Road, Fortune Gate, Suite 2901
Shanghai 200040 · P.R.C.
phone: +86 21 6288 6370
Westernacher & Partner Consulting, Inc.
8 Grove Street, Suite 200 · Wellesley, MA 02482 · U.S.A.
phone: +1 781 283 5777
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Author
Ute Messmer studied industrial engineering with focus on information technology and operations research at the
University of Karlsruhe, Germany. Before joining Westernacher and becoming a consultant, she worked for Hewlett Packard,
Germany in several supply chain operation, design and management functions.

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