sap service parts planning (spp) - whitepaper_en

Service Parts Planning with SAP SCM

TM

Enable the planning capabilities of your after-sales business

By: Ute Messmer

Version: 1.1

Whitepaper

Service Parts Planning with SAP SCM Page 2 of 17

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Contents

1 Introduction .................................................................... 3

2 Planning Process Overview ........................................... 4

3 Planning Features .......................................................... 5

3.1 Modeling the service parts distribution network (supply chain design) ..................................................................... 5

3.2 Capture and manage demand history and forecast demands (demand planning) ............................................. 6

3.3 Determine optimal stock levels for ensuring high product availability by minimal costs (inventory planning).............. 7

3.4 Define the overall procurement plan (supply planning) ..... 9

3.5 Deploy the goods within the network and balance excess and need (distribution planning) ...................................... 10

3.6 Interchangeability of products ......................................... 12

4 System Landscape for the Service Parts Planning Environment ................................................................. 14

5 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 todays 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, customers 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:

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 SAPs 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

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 SAPs planning sequence for service parts:

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.

Monitoring and Analysis


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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:

Bill of distribution

Entry location

Child location to Stuttgart

Parent location to New York and Vancouver

Berlin, New York, Vancouver, Hannover and Virtual Frankfurt are customer facing locations

Virtual child location


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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 subcontractors 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.

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 Applicable for

First Order Exponential Smoothing Products with small demand data history; Used as default model for forecasting

Second Order Exponential Smoothing Products with assumed trend

Moving Average All kind of products when a very simple model is desired

Linear Regression Products with trend

Seasonal Trend Model Seasonal products with less than 24 months demand history

Seasonal Trend Model with fixed Periods

Seasonal products

Intermittent Model Products with sporadic demand

Dynamic Moving Average Slow movers

Declining Demand 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

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 multi-staged 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

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 by-pass 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 4 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 Description

(1:1) supersession 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 suppliers 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:

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 SKU6s) 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 Stock Keeping Unit

7 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 SAPs Service Parts Planning solution, the following picture should bring the elements together into their chronological order:

Service parts planning process overview

SAPs 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

i 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

i 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.

sap service parts planning (spp) - whitepaper_en

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