SAP Bucher Meissner

Automatic Parameter Configuration

for Inventory Management in SAP ERP/APO

David Bucher and Joern Meissner

Department of Management Science, Lancaster University Management School, United Kingdom.

[email protected], [email protected]

August 20, 2010

Abstract

Companies today generally hold several thousands SKUs (stock-keeping units) in

stock. With an ever increasing trend towards highly customized products, the

number of SKUs held by companies is likely to increase even more in the future.

For each of the SKUs held in stock, a decision has to be made on how to configure

each module of the SKU’s inventory system. Doing this for each SKU individually

seems to be an unfeasible task, given the large numbers of SKUs held in stock

by most companies. Therefore, these companies configure inventory systems not

on a SKU-basis but on a group basis, where the groups are usually determined

using an ABC/XYZ analysis. The configuration using such a rough group basis

for several thousand SKUs does not allow for individually customized inventory

systems and therefore might not exploit the implemented inventory methods in

a software package in an optimal way. In this research project, decision systems

allowing an automated inventory system configuration in SAP ERP and SAP APO

at the SKU-level are developed. The SAP ERP corporate software package is the

market leader for Enterprise Resource Planning systems, offering a wide range of

inventory methods for the relevant inventory system modules. The advanced plan-

ning and scheduling system of SAP, Advanced Planning and Optimization (APO),

provides additional inventory methods that can be chosen for the configuration in

the inventory system modules.

Keywords: material resource planning, SAP ERP/APO, inventory management, pa-

rameter configuration

Please reference this paper as:

David Bucher, Joern Meissner. Automatic Parameter Configuration for Inventory Manage-ment in SAP ERP/APO. Working Paper (available at http://www.meiss.com), LancasterUniversity Management School, 2010.

BibTEX and plain text references are available for download here:http://www.meiss.com/en/publications/inventory-parameter-configuration-sap.html

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Bucher and Meissner: Automatic parameter configuration in SAP ERP/APO 1

1 Introduction

Efficient material planning and control forms one of the most important competitive

factors in many industries. High amounts of tied up working capital may account for

a substantial cost factor of a product, which may severely affect the profit margin.

Today, due to an ever increasing rate of customized products, companies often carry

ten or hundred of thousands SKUs (stock-keeping units) in stock, making inventory

control a burdensome task. Since the beginning of the last century, planning and con-

trol of materials represents one of the main areas in operations research. Numerous

methods for efficient inventory control have been developed. Several of these methods

have found their way into practice, mostly through corporate ERP software. SAP Ger-

many, the world-market leader for corporate software, implemented over 200 methods

for inventory control in their backbone transaction system SAP ERP and the advanced

planning and scheduling system SAP APO (SAP SCM) . The implemented methods are

drawn from operations research as well as from the best practices in several industries.

The large amount of SKUs held in stock and the complexity of configuring an inventory

planning and control system for a single SKU, given the numerous methods provided,

seem to make inventory planning at the SKU level impossible. Thousands of inven-

tory control systems would have to be defined and maintained manually. Therefore,

common practice is to configure inventory control systems for inventory groups, which

are formed mainly based on an ABC/XYZ classification. Furthermore, not all provided

methods are considered by the inventory planner, as it is very difficult to keep overview

of the 200 available methods and to know how and when to apply them. Thus, current

practice in companies often does not result in efficient material planning and control as

group-wise planning does not account for individual requirements of a SKU regarding

the configuration of the inventory control system and further, the provided methods in

a company’s material planning software are not exploited adequately.

The goal of this research is to show how an automated decision system can help

individually configure adequate inventory control systems for each SKU in stock, con-

sidering all the provided methods in SAP ERP and SAP APO. The general academic in-

ventory control process will be outlined and compared to the inventory control process

implemented in SAP. Five important parameters, which have to be configured for the

SAP inventory control system, are identified and presented. These include ‘MRP strat-

egy’ , ‘MRP procedure’, ‘Forecasting’, ‘Safety stock planning’ and ‘Lot-sizing’, whereas

‘Forecasting’ will not be discussed in this work, as a decision system for this parame-

ter is already in place in SAP. For the remaining four parameters in SAP, implemented

methods for their configuration are discussed, and decision systems are developed.


The criteria used for the decision systems of each parameter are mainly based on in-

formation stored in the SAP Material Master Data; however, some criteria draw on ad-

ditional information, which then has to also be maintained in the Master Data. The use

of the criteria, the interdependence of the decision systems developed for each param-

eter and potential implementation are discussed in the subsequent chapter. Finally, a

conclusion is drawn, and potential future research projects are outlined.

2 Material Planning Process

The process of material planning and control involves several planning steps. In the

following, these steps are outlined from a general operations research perspective and

then compared to the inventory planning and control process, which forms the basis

for inventory planning and control in SAP-ERP/APO. The complexity and interaction

between the planning steps will be pointed out.

2.1 Material Planning Process in Operations Research

The architecture of a material-planning and control process depends on the type of ma-

terial that is to be planned. In particular, whether the material planning is conducted

in the context of a single-echelon or a multi-echelon environment. To define a general-

ized planning process, a multi-echelon material planning process will be outlined. The

multi-echelon material planning process usually follows a general MRP (Material Re-

quirements Plannning) process, which was developed by IBM in the early 1960s (Hopp

and Spearman (2001)). Before the MRP paradigm was first introduced, each echelon in

an inventory or production setting was planned and controlled as a single-echelon, so

called consumption-based inventory control, and therefore planned disregarding the

direct connection of the materials described by the BOM (Bill of Material). The MRP

method makes use of the direct connections of lower-level items to the final product,

and so distinguishes between independent (market) demand/requirements for the final

products and dependent (internal) material requirements. However, a single-echelon,

consumption-based inventory control might be preferable to an MRP approach when

demand for a SKU is good to forecast, no forecast is conducted, and the control costs

shall be kept at a minimum (e.g. KANBAN). Therefore, both approaches, MRP and

consumption-based inventory planning are valid concepts for inventory planning and

can also be combined within the BOM. In the following both approaches, the general

MRP process and the consumption-based inventory planning, will be outlined.


MRP process

The MRP process starts with generating the system’s input data, which are the inde-

pendent demands for all final products as well as all low-level materials with direct

market demand (e.g. spare parts). The independent demands are generated in the Mas-

ter Production Schedule (MPS) ; further discussion about master production schedul-

ing in advanced planning and scheduling systems can be found in Stadtler and Kilger

(2007). To determine the independent demands, either only customer orders are col-

lected or customer orders and additional forecast values are added. The prerequisite

for a Make-To-Order policy for final products, where only customer orders are col-

lected and no forecast has to be conducted, is that the accepted delivery time by the

customer is greater or equal to the actual lead time of the product. In many situations,

however, the lead time is greater than the accepted delivery time, thus a mixed Make-

To-Order/Make-To-Stock policy has to be applied by splitting the BOM in two or more

parts and conducting MRP runs for each BOM part separately, including the gross re-

quirements determination. This is usually done by a forecast which has to be conducted

to plan prior to the actual customer orders as discussed in Silver et al. (1998). Forecasts

can be conducted either on qualitative or quantitative (historical demand data) infor-

mation. Finally, the forecasted and, if available, the already received customer orders

are added to the Master Production Schedule. The following figure shows a produc-

tion situation where the maximum accepted lead time by the customer (seven days) is

greater than the lead time of the whole BOM (14 days). Thus, the BOM has to be split

at the lowest possible level to allow for the lowest possible inventory holding cost; this

level represents the interface between MTO and MTS (push/pull interface).

For the top level SKUs of each BOM part, the independent demands and customer

orders are determined and fixed in the Master Production Schedule for the planning

period. Once the independent demands are planned in the MPS, the MRP procedure

is launched. For each material, starting from top in the BOM, the following steps are

conducted (Hopp and Spearman (2001)):

1. Net requirements calculation: Determine the net requirement by subtracting on-

hand inventory and outstanding orders from the gross requirement. The gross re-

quirement equals the determined requirement from the MPS for the top level BOM

materials and through BOM explosion for the lower level materials.

2. Lot-sizing: Divide the net requirements in appropriate lot sizes for production or

procurement.


Final Product

Sub‐assembly 1Component 1

Supplier 1 Component 2 Sub‐assembly 2

Raw material 1 Component 3 Component 4

Raw material 2 Raw material 3

Lead time: delivery

(2days, acc. 2days)

Lead time: Production

(2days, acc. 4days)

Lead time: Procurement

(1days, acc. 7days)


(1day, acc. 5days)


(3days, acc. 8days)Lead time: Production

(1day, acc. 6days)

Supplier 2


(1day, acc. 7days)


(2days, acc. 10days)


(3days, acc. 11days)

Supplier 3 Supplier 4


(1day, acc. 11days)


(1day, acc. 14days)

MTO‐MTS Interface

MPS: Determine independent demand + customer

MPS: Determine independent demand + customer

Market Demand

Customer accepted lead time

(7 days)

Figure 1: Example of a BOM splitting when production lead time is greater than the

customer accepted lead time

3. Time phasing: Determine start dates for production and order dates for procure-

ment given the due dates of the net requirements and the lead times.

4. BOM explosion: Use the start dates, lot sizes and the BOM to generate gross require-

ments of any required components at the next lower level.

5. Iteration: Repeat these steps until all levels are processed.

For an appropriate design of an MRP system, decisions have to be made in several areas

(Figure 2):

1. MRP strategy: Given basic information such as the BOM structure, lead times, ac-

cepted lead time by the customer, production requirements, capacity restrictions

etc., BOMs might have to be split and then an appropriate MRP strategy (MTS, MTO

or a combination) has to be chosen.


Net

requirement

calculation

Lot‐sizing

Scheduling

BOM

explosion

Independent demand:

Forecasting

Customer orders

Independent gross

requirements

Net requirements

Inventory on‐stock

Outstanding orders

Reservations

Gross requirements

Through BOM explosion for lower‐level items

Lot‐size procedure

Start of production/order date Due date

Lead time

Capacity restrictions

Determination of gross

requirements for immediate

predecessors in BOM

Splitting of net requirements in lots

Schedule lots on machines or for procurement

Iteration to

next lower item

in BOM

Demand

planning

Figure 2: The MRP process

2. Forecasting: If a forecast has to be conducted, an appropriate forecasting method

has to be chosen, including qualitative or quantitative methods.

3. Safety stock: Additionally to the forecast, a safety stock usually has to be planned

to assure a set target service level. For products that experience independent de-

mand, this safety stock has to cover demand variability, lead time variability as well

as additional uncertainties that might arise. For lower level items, no safety stock

is planned; however, if there is considerable lead time variability or other variabil-

ity, a safety stock is planned to account for these uncertainties. The choice of the

safety stock method can have substantial impact on the inventory performance of

the whole MRP system.

4. Inventory policy: How the net requirement is determined and when to set an order

(type of stock review policy) is determined by the chosen inventory policy.

5. Lot-sizing: The net requirements are split into lot sizes to assure feasible lot sizes

(consider minimum, maximum and fix lot size restrictions) as well as to balance

order versus inventory holding costs to reduce overall inventory cost.

6. Job scheduling and capacity management: In a production context the orders have

to be planned in a feasible and cost-optimal sequence for each machine to allow for

low overall production and inventory costs.


Consumption-based material planning process

The consumption-based inventory planning process is simpler than the MRP process,

as interrelations between SKUs based on the BOM are ignored. Each SKU is treated

independently from the others. Thus, a forecast has to be conducted for each SKU

with a lead time greater than the accepted market lead time. If the lead time is shorter

than the accepted lead time by the market, then no inventory is held except for safety

stocks to cover lead time variability. Production or procurement only takes place when

internal or external orders arrive. This approach is usually chosen when the inventory

planning and control costs will be kept at a minimum.

The following figure shows a simple consumption based inventory planning process:

Net requirement

calculation

Lot‐sizing

Scheduling

Ordering

Independent demand:

Forecasting

Customer orders

Gross requirements

Net requirements

Inventory on‐stock

Outstanding orders

Reservations

Gross requirements

Lot‐size procedure

Start of production/order date Due date

Lead time

Capacity restrictions

Set internal or external orders

Splitting of net requirements in lots

Schedule lots on machines or for procurement

Demand planning

Internal orders

Figure 3: The consumption-based process

Depending on the lead time compared to the market accepted lead time, a forecast

has to be conducted and/or internal orders and customer orders are considered. Then

the net requirement calculation follows, then lot-sizing, scheduling and the placing

of internal and external orders. The connection between BOM-related SKUs is only

accounted for by orders; there is no central planning.

The following section discusses how the inventory planning and control process is

implemented in SAP ERP and SAP APO.

2.2 Material Planning Process in SAP

Inventory planning and control in SAP can be conducted using the transaction based

ERP software of SAP, SAP ERP or for more functions, the planning system SAP APO

(now SAP SCM). When both systems are available, combinations of the modules in both


systems can be used for the inventory planning as well. The following figure shows

the inventory planning and control modules in SAP ERP and SAP APO; the systems can

communicate with each other (data transfer) via the CIF (Core Interface) system.

ERPLIS

(Logistics Information System)

Program Planning

(Sales and Distribution(?))

Flexible Planning

(Standard SOP as special case)

SCM (APO)BW

(Business Warehouse)

Global Availability Check

(gATP)

Demand Planning

(DP)

CIF(Core Interface)

ERP: Place Orders (order confirmation and monitoring) :

Manufacturing and Procurement

Demand planning

(Gross requirements)

Quantity Planning

(Net requirements

+

Lot‐sizing)

Material Requirements Planning

(MRP module)

Scheduling +Capacity

requirem. planning

Supply Network Planning

(SNP)

Production Planning and Detailed

Scheduling (PP/DS)

Capacity Requirements Planning

(CRP module (? Or in MRP‐module?))

Figure 4: The Material Planning Process in SAP ERP and APO

The material planning process in SAP is very similar to the general MRP process as out-

lined in the previous section. The first step is the determination of the gross require-

ments for all BOM top level SKUs (program planning or Master Production Schedule). As

already mentioned, if consumption-based inventory planning is chosen, then the gross

requirements must be determined in the first step as well, disregarding their position

in the BOM. In SAP, the gross requirements are determined by using forecasts and/or

customer orders. The forecasts can be conducted in the SAP ERP module ‘Flexible Plan-

ning’ with ‘Standard SOP’ (Sales and Operations Planning) being a special method of

‘Flexible Planning’; the forecasts are based on historic demand data, which is retrieved

from the Logistics Information System (LIS) . In SAP APO the forecasts are conducted

in the Demand Planning (DP) module , which draws the input data from the internal

Business Warehouse (BW) . This module offers a much wider range of methods and

functionalities than SAP ERP does. The dependent demands, i.e. the customer orders,

are recorded in the Sales and Distribution area of SAP ERP (as orders are transactions).

Once the gross requirements are determined, a global Available-To-Promise (gATP) can

be conducted in SAP APO on the sales order. The ATP function implemented in SAP ERP


can do a single-level check, however SAP APO offers more functionalities such as multi-

level ATP checks, ATP checks across the whole Supply Chain Network and rule-based

checks; further information about the ATP functionalities can be found in Gulyassy

et al. (2009). Another intermediate step between demand planning and the MRP run is

to use the SNP (Supply Network Planing) module in SAP APO to assign and plan the inde-

pendent demand requirements and already received sales orders to production plants

and procurement and to take stock transfers into account Dickersbach (2009). Thus,

the primary demand is allocated to a production plant or procurement (i.e. supplier).

Now, for quantity planning, the MRP run can be conducted in the ERP MRP module for

each plant. Through BOM explosion the gross requirements are determined for all BOM

levels; additionally lot-sizing and scheduling disregarding capacity restrictions can be

conducted. The quantity planning can also be performed in SAP APO with additional

functions. Scheduling and capacity requirements planning can be executed either in

SAP ERP or SAP APO. If a finite production plan is to be determined, then the capacity

requirements planning in SAP APO offers considerably more functions including the

capacity planning table, detailed scheduling heuristics and an optimizer system using

CPLEX, which is presented by Kallrath and Maindl (2006) in detail. Finally, order conver-

sion and monitoring (processing of external procurement and manufacturing orders)

is conducted, whereas orders for external procurements are executed in the SAP ERP

module Materials Management (MM) which is in the Procurement area of SAP ERP, and

orders for internal production are scheduled and monitored in the SAP ERP module

Production Planning (PP) which is in the Production area of SAP ERP.

In the presented modules that make up the material planning process in SAP, several

parameters have to be set for each material or material groups to allow for an adequate

planning and cost-efficient material planning process. Important parameters in SAP

will be presented in the next section.

2.3 Important Parameters in SAP ERP and APO to conduct the Material Plan-

ning Process

In the previous sections, the structure of the material planning process has been out-

lined, and the SAP modules in SAP ERP and SAP APO, with their basic function for the

material planning process, have been presented. In the concerned SAP material plan-

ning modules several parameters have to be set for each material or material group to

design the material planning process. For each parameter setting there is a choice of

numerous methods implemented in SAP ERP and SAP APO. The choice of the parameter

setting determines the customer service level (CSL) and the inventory cost. Therefore,

choosing the right setting when running SAP as material planning and control system


is the most important part of material planning. It has already been stressed that due

to the high number of materials to be planned in a company, the high number of pa-

rameters to be set and the numerous methods that can be chosen for each parameter

make the parameter setting a very difficult task. In companies usually two problems

arise. First, due to the high number of materials to be planned one parameter setting is

usually applied for material groups consisting of several hundreds or thousands SKUs.

The special features of a SKU are therefore not always accounted for, as an individu-

alized material planning process for each SKU is not generated. The second problem

that often arises is that the material planner does not exploit the whole potential of the

methods implemented in SAP as some of the functionalities and application of several

methods might not be familiar to the planner. Thus many methods, which might lead

to better results in terms of CSL and inventory costs, are often ignored when setting the

parameters for material planning. In the following, five important parameters for the

material planning process are presented and four of them will be considered for the

development of an automated decision systems for the parameter setting in Section 3.

1. Parameter: MRP strategy

In SAP the MRP strategy parameter for a single material, material group or a whole

or part of a BOM represents the basic structure about how the material planning pro-

cess is conducted. This is a long-term parameter setting, as the name ‘MRP strategy’

suggests, and the basis for the subsequent parameter settings. Therefore, it is very

important to set this parameter in an economically sensible way. As Hoppe (2006)

states, the main decision when setting this parameter is to decide about whether to

use a customer-independent make-to-stock (MTS) planning strategy, meaning that

production and procurement for all materials takes place before demand occurs.

Choosing an MTS strategy affects the choice of the forecasting parameter as a fore-

cast is needed for the market demand or possibly a make-to-order (MTO) planning

strategy is selected with a sales-order-related production and procurement as or-

ders are only submitted once market demand is observed, i.e. customer orders

are received. Using an MTO strategy implies that no forecast is needed as mate-

rial planning is only based on received customer orders (only dependent demand is

considered, no independent demand is planned).

In practice, mixed strategies, combinations of MTS and MTO strategies, are often ap-

plied. The materials on the higher levels of the BOM are planned based on customer

orders to allow for customization and low inventory costs; however, lower BOM level

materials are planned before customer orders arise (MTS) to shorten the total lead

time of the final product. Thus, when setting the MRP strategy parameter to a mixed


strategy, it must be decided on which BOM level to place the MTO-MTS interface

(customer order decoupling point). This concept of BOM splitting has already been

discussed in Section 2.1. Several more decisions have to be made when setting the

MRP strategy parameter; these will be discussed in detail in Section 3.

2. Parameter: MRP procedure

For the material planning process, it has to be decided which basic procedure in SAP

shall be used. As mentioned before, the MRP procedure (deterministic, plan-driven

procedure) or the consumption-based procedure can be used. A special form of the

MRP procedure is used by the Master Production Scheduling (MPS) procedure, where

the first master schedule items are planned with special care in a first planning run

to determine the MPS. This generates the dependent requirements for the BOM levels

directly under the MPS planning level. Material planning below these levels are not

conducted in this planning run; once the MPS run is conducted, an MRP planning run

is conducted for the planning of the remaining lower-level items. As can be seen, the

right setting of the parameter MRP procedure highly depends on the setting of the

MRP strategy parameter. There is a strong relationship between the production type

(MTO, MTS and mixed strategies) and the applicability of an MRP procedure (MRP,

MPS and consumption-based planning). Additional decisions have to made when

choosing the parameter setting for MRP procedures; these will be outlined in detail

in Section 3.

3. Parameter: Demand planning and forecasting

As mentioned before, in an MRP process, demand planning only takes place for the

top-level materials of each BOM (whereas BOMs can be split). Additionally, demand

planning is conducted for all consumption-based materials; however, consumption-

based demand planning usually follows a simple reorder policy. There are three

combinations of demands that can be planned: (customer-order) independent re-

quirements, customer order requirements, or both, as discussed by Hoppe (2006).

Once the parameters for MRP strategy and MRP procedure are set, it can be deter-

mined from these parameters which type of the three demand planning methods

has to be conducted for each material. Demand planning for lower BOM-level ma-

terials is not conducted explicitly when using the MRP or MPS/MRP procedure, as

these dependent demands are determined through the BOM explosion in the MRP

process.

When only dependent demands are planned (usually implying a strict MTO strategy

is in place), no forecast has to be conducted. Only customer orders are consid-

ered for production and procurement, which are retrieved from SAP-ERP as already


explained. However, if only independent demands are planned (implying a strict

MTS strategy) or independent and dependent demands have to be considered for

the gross demand planning, then a forecast has to be conducted to estimate future

demand (independent demand). SAP-ERP and SAP-APO offer numerous quantitative

and qualitative forecast methods. The main concern of the Demand Planning and

Forecasting parameter is therefore the choice of an adequate forecast method. As

Gulyassy et al. (2009) point out, in SAP APO a decision system is already in place to

support the material planner in choosing a good forecasting method. Therefore, a

new decision system is not developed for this parameter in Section 3.

4. Parameter: Safety stock

Safety stock planning is an important part of material planning when variability in

terms of demand variability and/or lead time variability occurs. So, whether a safety

stock has to be planned or not depends on whether a material experiences uncertain

demand and/or uncertain lead times. Safety stock for uncertain demand has to be

carried only when a forecast is conducted for a material, that is, when it is (partly)

planned based on independent demand requirements. Materials for which no fore-

cast is conducted, no safety stock has to be planned explicitly to cover demand un-

certainties. Through BOM explosion, required safety stocks are already implemented

in the upper-level materials, which experience uncertain market demand. However,

lead time variability states another source of uncertainty, which all materials can

be subject to. Thus, a (usually relatively small) safety stock or safety time to cover

lead-time variability of a material might have to be planned.

Safety stock planning is a very important tool to achieve a set target customer service

level (CSL), and so the choice of the safety stock planning method, which is the

setting of the safety stock parameter in SAP, has a big impact on the service that is

delivered to the customer. Additionally, the cost aspect of holding safety stock also

is a critical aspect when setting the safety stock parameter as safety stock holding

cost can become a substantial part of the total inventory holding cost. It is therefore

important to balance holding cost and the service level in an economically sensible

way.

In SAP-ERP and SAP-APO, dynamic (time dependent) and static (time independent)

safety stock procedures can be chosen. Further, the methods can be divided into

time-range of coverage methods and order cycle period methods and combinations

of these. As will be discussed in Section 3, there is a strong interdependence be-

tween the safety stock and the lot-sizing parameter as both parameters have critical

impacts on the inventory costs.


5. Parameter: Lot-sizing:

Once the net requirements, including the safety stock, are determined for a material,

the net requirement will be split or merged into lots. There are two reasons for

the splitting or merging into lots: in the academic inventory research the balancing

of the order or setup cost and inventory holding cost is the focus of lot-sizing;

additionally capacity restrictions and material flow as well as additional lot size

constraints often given by the supplier (minimum, maximum or fixed lot size) or

due to technical reasons (delivery in pallets) have to be considered for the planning

and scheduling.

In SAP, numerous methods for lot-sizing are available. These can be divided into

static lot-sizing methods, periodic lot-sizing methods and cost optimizing lot-sizing

methods. Further, functionalities such as additional long-term (periodic) lot size

planning, final lot size for the last order of discontinued materials and lot size split-

ting and overlapping are available and will be discussed in detail in the following

section.

The presented parameters include the most important decision areas in the context of

material planning, such as MTS versus MTO, consumption-based vs demand-driven and

the standard modules of a (single-echelon) inventory system forecasting, safety stock

and lot-sizing. The setting of these parameters to derive an adequate material plan-

ning system can be a complex task as the parameters are interrelated and numerous

settings are available for each parameter. In the following section a decision system for

each parameter will be presented to support the material planner in designing an ade-

quate material planning system and/or allow for an automated setting for each material

(when using SAP-ERP and SAP-APO). As a decision support system for the forecasting

parameter setting is already in place in SAP-APO, the forecasting parameter is not con-

sidered in the following.


3 Decision Systems for the Material Planning Parameters

In the following decision systems are presented that allow an automated configura-

tion/selection of methods for each parameter.

3.1 MRP Strategy

In the following, the decision system for the MRP strategies available in SAP ERP and

SAP APO is presented. Four main decision levels have been identified:

1. Production type: The principal decision of the MRP strategy is the choice of the

production type; in SAP it can be chosen from the five types MTO (Make-To-Order),

MTS (Make-To-Stock), ATO (Assemble-To-Order), ETO (Engineer-To-Order) and STO

(Service-To-Order). This strategic (long-term) decision about how to plan the mate-

rial is usually derived from the nature of the industry, customer expectations and

production settings. The production type is therefore seen as a criterion itself.

2. Product type: The type of product gives important insights about the position in

the BOM of the material; the decision system differentiates several product types

including customer specific products, configurable materials, general final products,

assemblies and phantom assemblies, components, projects, parts of projects, serial

products and services.

3. Planning of configurable materials: There are three basic types of planning for con-

figurable materials offered in SAP: material variant, characteristics pre-planning and

configurable material.

4. Planning level in BOM: The choice of the planning level in the BOM that will be used

to determine the requirements of a material is an important decision. It determines

the interface of MTS and MTO (customer order decoupling point) in the BOM and

therefore the stock-levels as well as the forecast accuracy, which is the main driver of

the safety stock level. The planning level is chosen based mainly on the comparison

of the lead time with the accepted lead time by the market to allow a stocking level

on the lowest possible level in the BOM (implying a low stock value and therefore

low holding costs).

The decision system covers 28 MRP strategies of SAP ERP and SAP APO that are fre-

quently used. The four decision levels give a first overview about the types and dif-

ferences of the MRP strategies. The principle criterion for the choice of MRP strategy

is the ‘production type’ that is used. Further, the ‘product type’ as a criterion gives


important insights for the strategic material planning decisions. The MRP strategy sets

the strategic (long-term) outline in which a material is planned. This involves several

important implications about the further material planning process, including whether

stocks have to be held at all and at which stage in the BOM, the determination of the

pull/push interface in the BOM as well as which level is used for demand planning.

The choice of an adequate MRP strategy is crucial for an efficient material planning

process. The numerous possible configurations of the ‘MRP strategy’ parameter (choice

of MRP strategy), however, make it difficult to keep overview when deciding on the

setting. The following figure shows the decision system.


YES

NO

YES

NO

NO

YES

YES

NO

YES

NO

YES

NO

NO

NO

YES

YES

YES

YES

NO

NO

YES

NO

YES

NO

YES

NO

YES

YES

NO

YES

NO

YES

NO

NO

YES

Com

mon

fina

lpr

oduc

tCo

nfigu

rabl

em

ater

ial

Cust

omiz

edpr

oduc

tion

Hig

h nu

mbe

r of

vari

ants

(>x)

?

Mat

eria

l var

iant

Ass

embl

y /

phan

tom

ass

embl

yCo

mpo

nent

BO

M s

ucce

ssor

bet

ter

to f

orec

ast?

BO

M s

ucce

ssor

bet

ter

to f

orec

ast?

Plan

ning

on

succ

esso

r le

vel

Confi

gura

ble

mat

eria

l

no p

lann

ing

Char

acte

rist

ics

pre-

plan

ning

Plan

ning

on

asse

mbl

y le

vel

Plan

ning

on

succ

esso

r le

vel

Plan

ning

on

com

pone

nt le

vel

Prep

lann

ing

onco

mpo

nent

leve

l

Pre

-pla

nn

ing

Tota

l LT

final

pro

d >

acc

LT

Prep

lann

ing

No

prep

lann

ing

No

plan

ning

mat

eria

lPr

epla

nwit

hpl

anni

ng m

at.

52, P

repl

anni

ng w

ith-

out

final

ass

embl

y or

cust

omiz

ed p

rod.

89, A

ssem

bly

wit

hch

arac

teri

stic

s pr

e-pl

anni

ng

A -

, B-i

tem

?Pr

epla

nnin

g w

ith

plan

ning

mat

eria

lN

o pl

anni

ngm

ater

ial

prep

lann

ing

no p

repl

anni

ng

Prep

lann

ing

wit

hpl

anni

ng m

ater

ial

No

plan

ning

mat

eria

l

56,C

hara

cter

isti

cspr

epla

nnin

g w

ith

seco

ndar

y de

man

d

25, M

TOpr

oduc

tion

for

confi

gura

ble

mat

eria

ls

65, P

repl

anni

ngm

ater

ial v

aria

nt w

ith

plan

ning

mat

eria

l

55, P

repl

anni

ngm

ater

ial v

aria

nt w

ith-

out

final

ass

embl

y

26, M

TOpr

oduc

tion

for

mat

eria

lva

rian

t

60, P

repl

anni

ngw

ith

plan

ning

mat

eria

l

50, P

repl

anni

ngw

itho

utfin

al a

ssem

bly

20, K

EF (M

TOpr

oduc

tion

)63

, Pre

plan

ning

wit

hpl

anni

ng m

ater

ial w

ith-

out

cust

omiz

ed p

rod.

74, P

repl

anni

ng a

tas

sem

bly

leve

l wit

hout

final

pro

duct

ass

embl

y

59, P

repl

anni

ng a

tPh

anto

m a

ssem

bly

leve

l

Plan

ning

as

'Cus

-to

miz

ed p

rodu

ctio

n'P

lan

nin

g m

ate

ria

l

• Se

vera

l fina

l pro

duct

s w

ith

com

mon

par

tsP

lan

nin

g m

ate

ria

l

• Se

vera

l fina

l pro

duct

s w

ith

com

mon

par

ts

Pre

-pla

nn

ing

Tota

l LT

final

pro

d >

acc

. LT

Pla

nn

ing

ma

teri

al

• Se

vera

l fina

l pro

duct

s w

ith

com

mon

par

ts

Prep

lann

ing

No

prep

lann

ing

Pre

-pla

nn

ing

Tota

l LT

final

pro

d >

acc

. LT

Prim

ary

dem

and

know

n/de

term

inab

le?

Prod

cuti

on o

f pl

anne

d am

ount

,d

isre

gard

ing

the

stoc

k le

vel

Prod

ucti

on s

moo

thin

g im

port

ant

Pla

nn

ing

ma

teri

al

• Se

vera

l fina

l pro

duct

s w

ith

com

mon

par

ts

Pre

-pla

nn

ing

Tota

l LT

final

pro

duct

> a

cc. L

T51

, Pre

-pla

nnin

g w

itho

ut fi

nal

asse

mbl

y (P

roje

ct a

ccou

ntin

g)

61, P

re-p

lann

ing

wit

h pl

an.

mat

eria

l (Pr

ojec

t ac

coun

ting

)

85, A

ssem

bly

wit

h ne

twor

k m

ap

(Pro

ject

)

Succ

esso

r pl

anne

d w

ith

anon

ymou

spr

oduc

tion

+ is

bet

ter

to f

orec

ast

Succ

esso

r pl

anne

d w

ith

anon

ymou

spr

oduc

tion

+ is

bet

ter

to f

orec

ast

10, a

nony

mou

s M

TSpr

od./

Net

pla

nnin

g11

, ano

nym

ous

MTS

prod

./Gro

ss p

lann

ing

Com

mon

fina

lpr

oduc

tA

ssem

bly

/ph

anto

m a

ssem

bly

Com

pone

nt

S-T-

O

A-T

-O

Serv

ice

Vari

ant

Part

of

proj

ect

Proj

ect

Seri

al p

rodu

ct

Fina

l pro

duct

or

asse

mbl

y

86, C

onfig

urat

ion

&as

sem

bly

orde

r

81, A

ssem

bly

wit

hse

ries

pro

duct

ion

82, A

ssem

bly

wit

hpr

oduc

tion

ord

er

84, S

ervi

ce o

rder

Smal

l am

ount

for

'Sal

e-fr

om-s

tock

'

70, P

repl

anni

ng o

nas

sem

bly

leve

l

Plan

ning

on

asse

mbl

y le

vel

M-T

-S

E-T-

O

M-T

-O

Pre-

plan

ning

no p

re-p

lann

ing

21, P

roje

ctac

coun

ting

pre-

plan

ning

wit

hpl

anni

ng m

ater

ial

no p

lann

ing

mat

eria

l

Plan

ning

on

succ

esso

r le

vel

40, P

repl

anni

ngw

ith

final

ass

embl

yco

nsum

ptio

n-ba

sed,

Kan

ban

Plan

ning

on

com

pone

nt le

vel

30, L

ot p

rodu

ctio

n

Plan

ning

on

Succ

esso

r le

vel

STO

(Serv

ice-T

o-O

rder)

:

• Se

rvic

eE

TO (

En

gin

eer-

To-O

rder:

• Pr

ojec

t or

par

t of

pro

ject

(pro

ject

acc

ount

ing)

MTS

(M

ak

e-T

o-S

tock

):

• A

nony

mou

s pl

anni

ng (p

rocu

rem

ent

and

prod

ucti

on in

depe

nden

t fr

om

cu

stom

er o

rder

s)•

AN

D/O

R: L

ead

tim

e >

acc

epte

d le

ad t

ime

ATO

(A

ssem

ble

-To

-Ord

er)

:

• N

umer

ous

final

pro

duct

s ar

e pr

oduc

ed f

rom

com

mon

pre

-pla

nnin

g

(com

mon

ass

embl

ies

and

com

pone

nts)

.•

Use

, whe

n 's

tati

stic

ss'

, 'ra

nge

of c

over

age'

or

'com

bina

tion

'

not

appl

icab

le.

MTO

:

• O

rder

dep

ende

nt p

rodu

ctio

n an

d pr

ocur

emen

t

Figure 5: Configuration of the SAP MRP Strategy Parameter

For better viewability, this Figure is available as a separate download at:




3.2 MRP Procedure

The MRP procedure defines how orders are initiated. The decision system comprises

30 MRP procedures that are implemented in SAP ERP and SAP APO. The MRP procedure

is highly interrelated with the MRP strategy and also strongly influences the structure

of the material planning process. Important decision levels are:

1. Demand-driven (MRP) versus consumption-based: The principal decision when set-

ting the MRP procedure is whether a demand-driven or a consumption-based proce-

dure shall be applied. In general, demand-driven MRP procedures are preferable to

consumption-based procedures as forecasts are only conducted for a few BOM lev-

els and only a few BOM levels need to carry stock to account for forecast errors and

lead time variability. However, when lead time restrictions are in place due to mar-

ket expectations, consumption-based procedures might have to be applied for the

lower-level BOM items. Further reasons for using a consumption-based procedure

are the smoothing of the production to allow for a smoothed capacity utilization

and the use of a simplified order system such as KANBAN when material planning

and control costs shall be kept at a minimum.

2. Periodic versus continuous review: The choice of a periodic or continuous inven-

tory review policy has to be made in the MRP procedure module as well. In general,

a continuous policy is preferable to a periodic policy as it allows for more flexibility

and immediate reaction on current requirements, as no or little time elapses between

an under-stocking situation and a subsequent order. However, a realtime review of

stock levels may arise higher control costs or might not be implementable due to

organizational restrictions.

3. MRP with Master Production Schedule: In the context of demand-driven material

planning, demand planning for critical or important materials can be conducted in a

first step, the Master Production Scheduling. This first demand planning step allows

the planning of the demand of the master schedule materials for a fixed planning

horizon with the benefit of decreased planning nervousness in the MRP system.

4. Supplier managed inventory: If a customer’s inventory shall be replenished by the

supplier, SAP offers ‘replenishment’ MRP procedures allowing a consumption-based

replenishment planning for the customer. For external customers, the SAP VMI (Ven-

dor Managed Inventory) module can be used. For demand-driven material planning,

the SMI (Supplier Managed Inventory) can be chosen.

The decision system for the choice of an adequate MRP procedure in SAP ERP and SAP

APO is presented in Figure 6.


R1

wit

h PD

Dem

and-

driv

en(M

RP)

Dem

and-

driv

en(M

RP)

MR

P w

ith

Mas

ter

Sche

dule

item

s

SMI -

dem

and-

driv

en

SM

P4(p

erio

dic

)P2

(per

iod

ic)

P3(p

erio

dic

)P1

(per

iod

ic)

P4(c

onti

nuou

s)P3

(con

tinu

ous)

P2(c

onti

nuou

s)P1

(con

tinu

ous)

M4

RR

RS

RF

RP

V1

V2

VB

VM

VI

RE

M2

M3

M1

Set

and

fix o

rder

pro

posa

l Set

and

fix

orde

r pr

opos

al m

anua

l con

trol

Set

and

fix o

rder

pro

posa

l Set

and

fix

orde

r pr

opos

al m

anua

l con

trol

Set

and

fix o

rder

pro

posa

l au

tmat

ical

ly w

itho

ut m

anua

l con

trol

Set

and

fix o

rder

pro

posa

l au

tmat

ical

ly w

itho

ut m

anua

l con

trol

Gen

erat

e or

der

prop

osal

s au

tom

atic

ally

? (g

ood

fore

cast

qua

lity)

PDCo

ntin

uous

rev

iew

Peri

odic

rev

iew

peri

odic

sto

chas

tic

polic

y (s

, S),

VV

Del

iver

y rh

ythm

de-

pend

s on

day

of

orde

rpe

riod

ic r

eord

er p

oint

po

licy

(T,s

,S),

R2

Peri

odic

wit

hde

liver

y rh

ythm

Peri

odic

pol

icy

wid

th r

ange

of

cove

rage

pro

file

Cont

inuo

us r

evie

w(r

eord

er p

oint

pol

icy)

Peri

odic

rev

iew

Inte

rnal

cus

tom

erEx

tern

al c

usto

mer

Rep

leni

shm

ent

polic

yCo

nsum

ptio

n-ba

sed

(nor

mal

)

VS

Sais

onal

pla

nnin

g

Cons

umpt

ion-

base

dN

o d

ispo

siti

on

BO

M e

xplo

sion

No

dis

posi

tion

X0,

X1

ND

Gen

erat

e or

der

prop

osal

s au

tom

atic

ally

? (g

ood

fore

cast

qua

lity)

Plan

ning

wit

h fix

ing

hori

zon:

high

pla

nnin

g ne

rvou

snes

s ov

er B

OM

Co

nti

nu

ou

s re

vie

w a

pp

lica

ble

• te

chni

cal a

nd o

rgan

izat

iona

l•

Del

iver

y at

any

dat

e po

ssib

le, n

o fix

ed in

terv

als?

• A

or

B it

ems

only

, if

high

con

trol

cos

ts

MR

P w

ith

Ma

ster

Sch

ed

ule

ite

ms

(cri

tica

l it

em

s)

• M

ater

ial h

as s

igni

fican

t in

fluen

ce o

n pr

oduc

tion

per

form

ance

• O

R p

rodu

ctio

n te

chno

logy

hig

hly

influ

ence

s th

e pr

oduc

tion

pro

cess

• O

R h

igh

shar

e on

ove

rall

turn

over

(A-i

tem

) AN

D t

op le

vel B

OM

item

Dem

an

d-d

riven

(M

RP,

pla

n-b

ase

d, d

ete

rmin

isti

c) v

s co

nsu

mp

tio

n-b

ase

d d

isp

osi

tio

n

• LT

+ L

T of

BO

M-s

ucce

ssor

s <

acc

. LT

• Pr

oduc

tion

sm

ooth

ing

and

stra

tegi

c st

ocki

ng a

re n

ot p

rim

ary

goal

s of

mat

eria

l pla

nnin

g

Co

nd

uct

ion

of

dis

po

siti

on

(M

RP

pro

ced

ure

, M

RP

po

licy

)

• M

ater

ial n

ot c

riti

cal a

nd n

o de

man

d (d

urin

g la

st x

mon

ths)

• O

R: M

inim

al p

lann

ing

effo

rt r

eque

sted

(pla

nnin

g w

ith

BO

M-s

ucce

ssor

thr

ough

BO

M e

xplo

sion

): C-

item

Co

nti

nu

ou

s re

vie

w a

pp

lica

ble

• te

chni

cal a

nd o

rgan

izat

iona

l•

Del

iver

y at

any

dat

e po

ssib

le, n

o fix

ed in

terv

als?

• A

or

B it

ems

only

, if

high

con

trol

cos

ts

Co

nti

nu

ou

s re

vie

w a

pp

lica

ble

• te

chni

cal a

nd o

rgan

izat

iona

l•

Del

iver

y at

any

dat

e po

ssib

le, n

o fix

ed in

terv

als?

• A

or

B it

ems

only

, if

high

con

trol

cos

ts

Rep

len

ish

men

t p

oli

cy

Mat

eria

l pla

nnin

g co

nduc

ted

for

cust

omer

Plan

-dri

ven

+ p

erio

dic

rev

iew

P1

(M

1 w

ith

MP

S i

tem

s):

• O

rder

pro

posa

ls w

ithi

n th

e fix

ing

hori

zon

get

fixed

(but

no

new

pro

posa

ls)

P3

: (M

3 w

ith

MP

S i

tem

s)

• no

t au

tom

atic

fixi

ng o

f or

der

prop

osal

s•

nece

ssar

y pr

opos

als

are

shif

ted

to t

he e

nd o

f

the

fixin

g ho

rizo

n P

4 (

M4

wit

h M

PS i

tem

s)

• O

rder

pro

posa

ls a

re n

ot g

ener

ated

aut

omat

ical

y•

A s

hort

age

situ

atio

n is

not

bal

ance

d

P2

(M

2 w

ith

MP

S i

tem

s)

• O

rder

pro

posa

ls a

re fi

xed

wit

hing

fixi

ng h

oriz

on•

No

auto

mat

ic s

etti

ng o

f ne

w o

rder

pro

posa

ls•

A s

hort

age

situ

atio

n in

fixi

ng h

oriz

on is

not

bala

nced

aut

omat

ical

ly

Plan

ning

wit

h fix

ing

hori

zon:

high

pla

nnin

g ne

rvou

snes

s ov

er B

OM

Set

and

fix o

rder

pro

posa

l au

tmat

ical

ly w

itho

ut m

anua

l con

trol

Set

and

fix o

rder

pro

posa

l au

tmat

ical

ly w

itho

ut m

anua

l con

trol

M0:

No

auto

mat

ic fi

x-in

g of

ord

er p

ropo

sals

Dyn

amic

tar

get

stoc

k le

vel (

A o

r Y,

Z it

em)

Supp

lier

man

ages

stoc

k m

ore

effic

ient

ly?

Sais

onal

pro

duct

?Pl

anni

ng a

ccor

din

gto

BO

M e

xplo

sion

Dyn

amic

tar

get

stoc

kle

vel (

A-

or Y

,Z it

em)

Cons

ider

ext

erna

lre

quir

emen

tsN

o co

nsid

erat

ion

of e

xt. r

equi

rem

.

Cons

ider

ext

erna

lre

quir

emen

ts(R

eser

vati

ons

etc.

not

adeq

uate

lyco

nsid

ered

in f

ore-

cast

and

saf

ety

stoc

k)

• co

nsta

nt d

eman

d

(X-i

tem

)•

B-/

C-it

ems

• O

rder

cos

t re

lati

vely

high

• va

r. de

man

d

(Y, Z

item

)•

A-i

tem

s•

orde

ring

cos

ts

re

lati

ve lo

w

• de

liver

y sc

hedu

le in

plac

e•

rela

tive

ly s

tabl

e av

-

erag

e da

ily d

eman

d

(X-i

tem

) •

use

dyna

mic

ss

(R1)

Use

of

VM

I-m

odul

e

Inte

rnal

or

exte

rnal

cust

omer

Gen

erat

e or

der

prop

osal

s au

tom

atic

ally

? (g

ood

fore

cast

qua

lity)

Plan

ning

wit

h fix

ing

hori

zon:

high

pla

nnin

g ne

rvou

snes

s ov

er B

OM

Plan

-dri

ven

and

cont

inuo

us r

evie

w

Set

reor

der

poin

t au

tom

atic

ally

good

for

ecas

t qu

alit

y

Set

reor

der

poin

t au

tom

atic

ally

good

for

ecas

t qu

alit

y

NO

YES

YES

NO

NO

YES

NO

Co

nC

on

Co

nC

on

Co

nC

on

Co

nC

on

NO

YES

YES

NO

YES

NO

YES

NO

YES

YES

NO

YES

NO

YES

NO

YES

NO

YES

NO

YES

NO

YES

NO

YES

NO

YES

NO

YES

NO

YES

NO

YES

NO

YES

NO

YES

NO

YES

NO

NO

YES

YES

NO

YES

NO

Figure 6: Configuration of the SAP MRP Procedure Parameter





3.3 Safety Stock

The setting of the safety stock parameter has a high influence on the resulting inventory

cost. The MRP strategy and MRP procedure design the material planning process for

each material. Given the structured planning process, a safety stock method needs to

be chosen that leads to minimal costs. The influence on the design and flow of the

material planning process of the safety stock parameter is low. The safety stock and

lot sizing parameters should therefore be chosen with a strong focus on cost factors.

Besides costs, the setting of an adequate safety stock method is also of high importance

to achieve a specified service level. In the presented decision system, four important

decision levels can be identified.

1. Static versus dynamic safety stock: The principal differentiation of safety stock meth-

ods is between static (time-independent) and dynamic (time-dependent) safety stocks.

Generally, dynamic safety stocks lead to a better inventory performance in terms of

costs and service level as the safety stock is determined based on current require-

ments. The planning effort, however, is usually higher for the material planner as

dynamic safety stock methods need steady maintenance. An important exception is

the statistical safety stock methods, which can be run in an automated way. Static

safety stock methods may be used when the additional planning effort for dynamic

methods can not be justified due to low holding costs or a poor service level when

the material is of lower importance. Static safety stock methods are preferable for

lower value materials and low demand and lead time variability, e.g. in a KANBAN

system. Dynamic methods are preferable for higher value materials and for materi-

als with higher demand and lead time variability.

2. Range of coverage versus order cycle safety stock: In SAP, alternatively to the com-

mon order cycle safety stock, a range of coverage safety stock can be selected. This

method is preferable when safety stock is held mainly for the protection against lead

time variability, as it is measured in safety time.

3. Statistical safety stock methods: As mentioned before, statistical safety stock meth-

ods are automated dynamic methods and therefore require limited planning main-

tenance. In SAP, the Normal distribution and the Poisson distribution (in the Spare

Parts Planning module (SPP) ) are implemented. Based on a target service level, the

required safety stock is determined by the system. The alpha service level or the

beta service level can be chosen as service level measures. As stated by Tempelmeier

(2006), the alpha service level, as an event oriented measure, should be chosen when

stock-out costs have mainly fix cost elements, whereas the beta service level, as an


amount oriented measure, should be used when the stock-out costs have mainly

variable cost elements.

4. Position in product life cycle: The position of a material in its product life cycle is

important for the safety stock level, as new, old and established products require

different planning. Additionally, materials with seasonal demand require special

treatment.

The decision system considers 14 safety stock methods implemented in SAP.


NO

NO

YES

NO

YES

YES

YES

NO

YES NO YES NO

Position in the product life cycle

Range of coverageplanning

fixed reorderpoint (ERP)

SB (APO, PP/DS)

SM (APO)SZ (APO) MM (APO) Dynamic ssplanning (ERP)

MB (APO) MZ Range of coverage (ERP)

Combination(max{RoC,ss})

Order cycle ss('normaler' ss)

Planning in context ofproduction planning

Continuous review applicable

• technical and organizational• Delivery at any date possible, no fixed intervals?

Stockout mainly cause stockout amount independent costs (fix costs, e.g. expedition)

Stockout mainly cause stockout amount independent costs (fix costs, e.g. expedition)

Order cycle ss('normal' ss)

Range of coverageplanning

Combination(max{RoC,ss})

Statistic ss

Static (fixed) safety stock Dynamic safety stock

Continuous ss

AS (alpha-SL) BS (beta-SL)AT (alpha-SL) BT (beta-SL)

Periodic ss

No safety stock planning

Established material Old/phase-out materialNew/phase-in material(no demand history)

No safety stock planning:

If MTS strategy:

• Low demand variability (and low lead time variability)If MTO strategy:

• Low lead time variability

• OR: L/non-critical part (inventory holding costs too - high due to volume and non-critical part)

Range of coverage planning:

• X-part (low daily demand variability)• AND: periodic/calendar-based ss-planning preferred(i.e. focus on precting against lead time variability); in particular when demand-driven and protecion against lead time variability necessary.Combination of Max{Range of coverage and order cycle ss}:

• same conditions as 'range of coverage planning'• AND: medium/high variability of daily demand (oder cycle ss gets adapted to range of coverage period and then compared to the 'SZ' ss)Order cycle safety stock ('normal' ss):

• Protection against demand and/or lead time variability• Use, when 'range of coverage' or 'combination' not applicable (as these methods make use of more information and therefore generally lead to a more adequate ss)

Statistic ss-planning (according to target service level):

• X or Y-part (coefficient of variation of demand <= 0,5)• Protection agains demand and lead time variability• Minimal planning effortRange of coverage planning:

• X-part (low daily demand variability)• AND: periodic/calendar-based ss-planning preferred (i.e. focus on precting against lead time variability); in particular when demand-driven and protecion against lead time variability necessary.Combination of Max{Range of coverage and order cycle ss}:

• same conditions as 'range of coverage planning'• AND: medium/high variability of daily demand (oder cycle ss gets adapted to range of coverage period and then compared to the 'MZ' ss)Order cycle safety stock ('normal' ss):

• Protection against demand and/or lead time variability• Use, when 'statistic ss', 'range of coverage' or 'combination' not applicable.

Saisonal material?

Static or dynamic ss?

Static(fixed) ss (low planning effort, time-independent):• C/N and C/M-parts• B/X/N/non-critical, B/X/M/non-critical parats

Dynamic ss higher planning effort (except for 'statistic ss'), time-dependant,lower costs, higher target service level achievement):• A-parts• B/Y, B/Z and B/X/L and B/X/critical

}}} }

Figure 7: Configuration of the SAP Safety Stock Parameter





3.4 Lot-sizing

The lot size parameter determines, given the structured (or designed) material planning

process, how net requirements shall be combined into orders. As the safety stock

parameter, the lot sizing parameter has a strong cost focus. The principal idea of lot

sizing is to balance ordering cost with inventory holding cost and therefore decrease

overall inventory cost. The main decision levels of the lot size parameter are:

1. Static, periodic or optimizing lot size: The principal decision for the lot size param-

eter setting is to decide on whether to use a static, periodic or optimizing lot size

method. The ‘optimizing lot size’ methods are generally preferable as they offer a

scientific optimal method and are well established and tested in practice. However,

when cost factors (i.e. holding cost and ordering cost) are not available or demand

variability is too high, a periodic or static method has to be chosen. The periodic

length for a lot size method can be chosen either in common time units, e.g. daily or

weekly, or according to the accounting period or planning period. ’Replenishment

up to maximum level’ is a special form of the static lot size, which is to be applied

when storage space is limited, e.g. tanks or silos, and the inventory holding costs per

material unit are negligible compared to the order cost. Further, this lot size method

is applicable for low value materials with a relatively constant demand (also for KAN-

BAN planning). All materials, for which the optimizing methods, periodic methods

and ‘replenishment up to maximum level’ are not applicable, are planned according

to the exact lot size method, where only the set lot restrictions are considered and

no additional required consolidation is conducted (i.e. no additional balancing of

ordering and holding costs). The remaining materials are usually mid and low items

with highly variable demand. For the mid-value items, a frequent manual update of

the lot size by the material planner is recommended.

2. Position in product life cycle: As presented in the safety stock decision system,

new, old, and seasonal materials need special treatment.

3. Planning with lot splitting and overlapping: SAP offers the ‘splitting and overlap-

ping’ function for all lot size methods, which is recommended when lots are large

relative to the available capacity; production planning of split lot sizes increases

flexibility, throughput and WIP (work-in-process) as batch sizes are decreased.

4. Long-term periodic lot size planning: In some cases production planning or the sup-

plier require the quotation of long-term lot sizes in order to conduct capacity plan-

ning, material pre-planning and reservations. For this case, SAP offers the additional

use of a long-term lot size planning method. For the long-term lot size planning, all


periodic lot size methods are available. The period length is chosen based on the

requirements of the production, supplier, controlling or any other addressees.

The following lot size decision system considers 16 methods implemented in SAP ERP

and SAP APO.


NO

NO

NO

YES

YES

YES

YES

YES

NO

NO

NO

YES

NO

YES

NO

YES

Lot

size

wit

h sp

litti

ngan

d ov

erla

ppin

g

Split

ting

of

big

lots

(big

lot,

sm

all c

apac

ity)

Split

ting

of

big

lots

(big

lot,

sm

all c

apac

ity)

FK: F

ix lo

t si

ze f

orcu

stom

er s

peci

fic o

rder

Lot

size

wit

h sp

litti

ngan

d ov

erla

ppin

g

EX/E

S/ZX

:ex

act

lot

size

Lot

size

wit

h sp

litti

ngan

d ov

erla

ppin

g

Split

ting

of

big

lots

(big

lot,

sm

all c

apac

ity)

Split

ting

of

big

lots

(big

lot,

sm

all c

apac

ity)

Ho

url

y l

ot

size

– A

/X it

em–

OR

: ver

y lim

ited

sto

r-

age

and

L or

N-i

tem

– O

R: v

ery

high

rat

e of

ob

sole

scen

ce

(<

1day

)+

low

ord

er/s

etup

cos

t+

ver

y sh

ort

lead

tim

e

(<1d

ay)

Sh

ift

lot

size

– A

/X, B

/X it

em–

OR

: ver

y lim

ited

sto

r-

age

and

L or

N-i

tem

– O

R: v

ery

high

rat

e of

obso

lesc

ence

(<2d

ays)

+ lo

w o

rder

/set

up c

ost

+ v

ery

shor

t le

ad t

ime

(<

2d)

Da

ily l

ot

size

– A

/X,A

/Y,B

/X it

em–

OR

: lim

ited

sto

rage

and

L-it

em–

OR

: hig

h ra

te o

f

obso

lesc

ence

(7

days

)+

low

ord

er/s

etup

cos

t+

sho

rt le

ad t

ime(

<7d

)

Week

ly l

ot

size

– A

/Z,B

/X,C

/X it

em–

OR

: lim

ited

sto

rage

and

L-it

em–

OR

: hig

h ra

te o

f

obso

lesc

ence

(<4w

ks)

+ lo

w o

rder

/set

up c

ost

+ s

hort

lead

tim

e

(<4w

ks)

2-w

eek

ly l

ot

size

– B

/Y,C

/Y it

em–

OR

: lim

ited

sto

rage

and

L-it

em–

OR

: mod

erat

e ra

te

of o

bsol

esce

nce

(<

8wks

)+

mod

erat

e or

der/

se

tup

cost

+ m

oder

ate

LT (<

8wks

)

Mo

nth

ly l

ot

size

– B

/Z,C

/Z it

em–

OR

: low

rat

e of

obso

lesc

ence

(>8w

ks)

+ lo

w o

rder

/set

up c

ost

+ lo

ng le

ad t

ime

(>

8wks

)

Qu

art

erl

y l

ot

size

(an

alo

g t

o P

PS

pla

nn

ing

ca

len

da

r)

– C/

Z it

em–

Coor

din

atio

n w

ith

pr

oduc

tion

pla

nnin

g/

item

onl

y ne

eded

for

one

prod

ucti

on r

un

Fle

xib

le p

eri

od

(an

alo

g t

o p

ost

ing

peri

od

)

– Co

ord

inat

ion

wit

h

acco

unti

ng s

yste

m–

Whe

n m

ater

ial c

ost

ne

ed t

o be

det

er-

m

ined

wit

hin

the

po

stin

g pe

riod

(e.g

.

for

proj

ects

)

HB

: Rep

leni

shm

ent

unti

l max

leve

l

Lot

size

wit

h sp

litti

ngan

d ov

erla

ppin

g

Hou

rly

lot

size

Lot

size

wit

h sp

litti

ngan

d ov

erla

ppin

g

Split

ting

of

big

lots

(big

lot,

sm

all c

apac

ity)

Split

ting

of

big

lots

(big

lot,

sm

all c

apac

ity)

Shif

t lo

t si

ze

Lot

size

wit

h sp

litti

ngan

d ov

erla

ppin

g

TB: D

aily

lot

size

Lot

size

wit

h sp

litti

ngan

d ov

erla

ppin

g

Split

ting

of

big

lots

(big

lot,

sm

all c

apac

ity)

Split

ting

of

big

lots

(big

lot,

sm

all c

apac

ity)

WB

: Wee

kly

lot

size

No

long

-ter

mpl

anni

ng

Peri

odic

Stat

ic

Lot

size

wit

h sp

litti

ngan

d ov

erla

ppin

g

W2:

2-w

eekl

y lo

t si

ze

Lot

size

wit

h sp

litti

ngan

d ov

erla

ppin

g

Split

ting

of

big

lots

(big

lot,

sm

all c

apac

ity)

Split

ting

of

big

lots

(big

lot,

sm

all c

apac

ity)

MB

: Mon

thly

lot

size

Lot

size

wit

h sp

litti

ngan

d ov

erla

ppin

g

PK: P

erio

d ac

cord

ing

to p

lann

ing

cale

nder

Lot

size

wit

h sp

litti

ngan

d ov

erla

ppin

g

Split

ting

of

big

lots

(big

lot,

sm

all c

apac

ity)

Split

ting

of

big

lots

(big

lot,

sm

all c

apac

ity)

PK: P

erio

d ac

cord

ing#

to p

lann

ing

cale

nder

Lot

size

wit

h sp

litti

ngan

d ov

erla

ppin

g

SP: P

art

peri

odba

lanc

ing

Lot

size

wit

h sp

litti

ngan

d ov

erla

ppin

g

Split

ting

of

big

lots

(big

lot,

sm

all c

apac

ity)

Split

ting

of

big

lots

(big

lot,

sm

all c

apac

ity)

GR

: Gro

ff r

eord

erpr

oced

ure

Lot

size

wit

h sp

litti

ngan

d ov

erla

ppin

g

DY:

Dyn

amic

lot

size

cre

atio

nW

I: Le

ast

unit

cos

tpr

oced

ure

Exac

t fin

al lo

t-si

ze

Opt

imiz

ing

'Rep

leni

shm

ent

to m

axim

um le

vel'

• hi

gh o

rder

cos

ts, n

eglig

ible

hol

din

g co

st,

lim

ited

sto

ckro

om•

AN

D/O

R t

echn

ical

req

uire

men

t, e

.g. s

ilo, t

ank

Add

itio

nal l

ong-

term

lot

size

pla

nnin

g•

Whe

n pr

oduc

tion

pla

nnin

g or

sup

plie

r ne

ed lo

ng-t

erm

est

imat

es

Choi

ce o

f pe

riod

leng

th f

or w

hich

the

req

uire

men

ts a

re c

ombi

ned

to o

ne lo

t si

ze |

E.g.

spe

cific

atio

n by

sup

plie

r,pr

oduc

tion

pla

nnin

g, c

ontr

ollin

g/bu

dget

pla

nnin

g fo

r ad

vanc

ed p

lann

ing

and

rese

rvat

ion

Ch

oic

e o

f p

eri

od

len

gth

for

whi

ch t

he r

equi

rem

ents

are

com

bine

d to

one

lot

size

(if p

re-s

peci

fied

deliv

ery

peri

od (e

.g. b

y su

pplie

r) ,

corr

espo

ndin

g pe

riod

leng

th t

o be

cho

sen;

oth

erw

ise

give

n cr

iter

ia a

pply

)

Dai

ly lo

t si

zeW

eekl

y lo

t si

ze2-

wee

kly

lot

size

Mon

thly

lot

size

Peri

od a

nalo

g to

acco

unti

ng p

erio

dPe

riod

acc

ord

ing

topl

anni

ng c

alen

der

X-it

em

B-i

tem

Proc

urem

ent

mat

eria

l(+

dis

coun

t an

d su

pply

stru

ctur

e im

port

ant)

New

/pha

se-i

n m

ater

ial

(no

dem

and

hist

ory)

FX/F

S: F

ix lo

t si

ze•

Can

be e

nter

ed a

s fix

lot

size

res

tric

tion

Esta

blis

hed

mat

eria

l

Sais

onal

mat

eria

l

Cust

omer

-spe

cific

Po

siti

on

in

pro

du

ct l

ife c

ycl

e

Ap

pli

cati

on

of

ad

dit

ion

al

lot

size

meth

od

:

• L-

part

s an

d lim

ited

sto

ckro

om•

AN

D: Z

-par

ts a

nd n

ot d

eman

d-dr

iven

• A

ND

: no

sais

onal

mat

eria

l (Z-

cate

gori

zati

on v

alid

e va

lid o

ver

all p

erio

ds)

• A

ND

: NO

'rep

leni

shm

ent

to m

axim

um le

vel'

(hol

din

g co

sts

are

not

negl

igib

le c

ompa

red

to o

rder

cos

ts)

Op

tim

izin

g m

eth

od

• A

or

B it

em•

Dem

and-

driv

en O

R M

TO•

Cons

umpt

ion-

base

d O

R d

eman

d pl

anni

ng w

ith

fore

cast

for

BO

M t

op-l

evel

and

X,Y

item

• In

vent

ory

hold

ing

cost

and

ord

er c

ost

know

n•

No

plan

ning

in fi

xed

orde

r-/d

eliv

ery-

inte

rval

s O

R t

his

is n

ot f

ocus

of

the

lot-

size

pla

nnin

g•

If li

mit

ed s

tock

room

: no

Y/L

part

sE

lse:

Peri

od

ic m

eth

od

• D

eman

d-dr

iven

OR

MTO

• Co

nsum

ptio

n-ba

sed

or d

eman

d pl

anni

ng w

ith

fore

cast

for

BO

M t

op-l

evel

and

X, Y

item

• A

ND

/OR

A-i

tem

(inc

lud

ing

A/Z

-ite

ms)

• A

ND

/OR

: Pla

nnin

g in

fixe

d or

der-

/del

iver

y-in

terv

als

(incl

udin

g Z-

item

s)E

lse:

Sta

tic

meth

od

• 'R

eple

nish

men

t to

max

imum

leve

l' (h

igh

orde

r co

sts,

neg

ligib

le h

old

ing

cost

, lim

ited

sto

ckro

om a

nd/o

r

tech

nica

l req

uire

men

t, e

.g. s

ilo, t

ank)

• R

emai

ning

mat

eria

ls: '

EX'(e

xact

lot

size

); in

par

ticu

lar

for

B/Z

and

C/Z

item

s, w

hen

cons

umpt

ion-

base

d

or B

OM

top

-lev

el a

nd f

orec

ast

is c

ondu

cted

En

ter

lot

size

rest

rict

ion

s

• M

in lo

t si

ze (e

.g. q

uote

d by

pro

duct

ion

or s

uppl

ier)

• M

ax lo

t si

ze (e

.g. q

uote

d by

pro

duct

ion,

sup

plie

r or

inve

ntor

y)•

Fix

lot

size

(tec

hnic

al r

estr

icit

ons:

tan

ks, p

alet

tes,

box

es)

Old

/pha

se-o

ut m

ater

ial

• La

st o

rder

LL p

erio

dic

Figure 8: Configuration of the SAP Lot Sizing Parameter





The four presented decision systems give a structured overview of the available inven-

tory methods in SAP for each parameter. As these parameters are complex and linked

to one another, decisions taken in one parameter might affect decisions in following

parameters. Further, same or similar criteria are used in the different decision systems,

which might give rise to a simplification or merging of the decision systems to one sys-

tem. Interrelations and further insights will be discussed and outlined in the following

chapter.

4 Analysis of the Decision Systems

The complexity of the decision systems, their interrelations and applied criteria will be

discussed in the following.

4.1 Interrelations of the Decision Systems and Overview of Decision Criteria

In the decision systems for the four material planning parameters, 51 criteria are used.

The following table shows which criteria are used for which parameter. Some of the cri-

teria are used in combination with each other for a decision in the system, which leads

to this relatively high number of applied criteria. As indicated in the table, twelve crite-

ria were identified as being of major importance, including the common ABC, XYZ and

LMN (large, medium and small volume item) analysis as well as inventory cost factors

such as holding cost and order cost. Further, the criterion ‘criticality’ implies that the

material plays a critical role in the production process or for the final product or that

the material has a critical supply chain. Critical materials might require special treat-

ment when determining the safety stock; further the criticality can be accounted for

already in the material planning layout of the MRP process, the MRP strategy. Further

materials which require special treatment are ‘old’ and ‘new’ materials, which are iden-

tified by the criterion ‘position in product life cycle’, as well as materials with seasonal

demand.


Criteria Classification fuction MRP strategy MRP procedure Safety Stock Lot SizingCriteria Classification fuction MRP strategy MRP procedure Safety Stock Lot Sizing

ABC classification x x xABC classification x x x

XYZ classification x x xXYZ classification x x x

LMN classification x xteri

a XYZ classification x x x

LMN classification x x

crit

eri

a

LMN classification x x

Lead time variability x

t cr

ite

r

Lead time variability x

Criticality (of material) x x

ue

nt

crit

e


Position in product life cycle x xqu

en

t cr


Position in product life cycle x x

po

rta

nt/

fre

qu

en

Position in product life cycle x x

Saisonal material x x x

po

rta

nt/

fre

qu

en

Saisonal material x x x

Forecast quality x

po

rta

nt/

fre

Forecast quality x

Required planning effort (low) x x

po

rta

nt/

fr

Forecast quality x


po

rta

nt/

fr


Continuous vs periodic review x x

Imp

ort

an

t/fr

Continuous vs periodic review x x

Order cost x x

Imp

ort

an

t/fr

Order cost x x

Holding cost x

Im

Order cost x x

Holding cost xHolding cost x

Production type STO, ETO, MTS, ATO, MTO xProduction type STO, ETO, MTS, ATO, MTO x

Customized product, configurabel material, commonCustomized product, configurabel material, common

Product type

Customized product, configurabel material, common

final product, (phantom) assembly, component, (part of) x

teri

a Product type final product, (phantom) assembly, component, (part of)

project, service

x

ite

ria

project, service

BOM successor better to forecast BOM planning level x cri

teri

a

BOM successor better to forecast BOM planning level x

eg

y c

rite

ri

BOM successor better to forecast BOM planning level x

Total LT of final product > acc. LT Conduct preplanning x

ate

gy

cr

Total LT of final product > acc. LT Conduct preplanning x

Several final products with common

tra

teg

y

Several final products with common

partsUsing planning material x

RP

str

ate

gy

partsUsing planning material x

RP

str

at

parts

Configurabel materials

MR

P s

Configurabel materials

High number of variants (>x)? Material variant vs characteristics planning x

MR

P s

High number of variants (>x)? Material variant vs characteristics planning x

MTO for configurable materials vs characteristics

M

Primary demand known/ determinableMTO for configurable materials vs characteristics

xPrimary demand known/ determinableMTO for configurable materials vs characteristics

preplanning with secondary demandx

preplanning with secondary demand

Material not critical xMaterial not critical x

No demand during last x months xConduct disposition

No demand during last x months xConduct disposition

No demand during last x months x

LT + LT of BOM successors > acc. LT xLT + LT of BOM successors > acc. LT x

Production smoothing and strategic Demand‐driven vs consumption based dispositionProduction smoothing and strategic

stocking are not primary goalsx

Demand‐driven vs consumption based disposition

stocking are not primary goalsx

stocking are not primary goals

Demand‐driven MRPDemand‐driven MRP

Supplier manages stock more efficiently Supplier managed inventory xSupplier manages stock more efficiently Supplier managed inventory x

Material has significant influence onx

Material has significant influence on

production performancex

production performancex

A‐item (High share on overall turnover) xMRP with Master Schedule itemsA‐item (High share on overall turnover) xMRP with Master Schedule items

Production technology highly influencesProduction technology highly influences

the production processx

teri

a

the production processx

crit

eri

a

the production process

Continuous review feasable (technicalx x

e c

rite

ri

Continuous review feasable (technical

and organizational)x x

ure

cri

teri

and organizational)x x

du

re c

r

A and B items only (if high control costs) x xContinuous vs periodic review

roce

du

re

A and B items only (if high control costs) x xContinuous vs periodic review

RP

pro

ced

Delivery at any date possible, no fixed

RP

pro

ce

Delivery at any date possible, no fixed

intervals?x xR

P p

roce

intervals?x x

MR

P p

intervals?

High planning nervousness Planning with fixed horizon x

MR

P p

High planning nervousness Planning with fixed horizon x

Automatic order proposals are of goodAutomatic order proposals are of good

qualityUse automatic order proposals x

qualityUse automatic order proposals x

quality

Fix order proposals automatically Fix order proposals automatically without manualx

Fix order proposals automatically

without manual control

Fix order proposals automatically without manual

controlx

without manual control controlx

Consumption‐basedConsumption‐basedConsumption‐based

Material planning conducted forReplenishment policy (Vendor managed inventory) x

Material planning conducted for

customerReplenishment policy (Vendor managed inventory) x

customerReplenishment policy (Vendor managed inventory) x

Internal or external customer External: use VMI module xInternal or external customer External: use VMI module xInternal or external customer External: use VMI module x

Reservations etc not adequatelyConsider external requirements x

Reservations etc not adequately

considered in forecast and ssConsider external requirements x

considered in forecast and ssConsider external requirements x

Delivery rythm important (planning in Disposition with range of coverage or delivery rythm /Delivery rythm important (planning in Disposition with range of coverage or delivery rythm /x x

Delivery rythm important (planning in

fixed order/delivery intervals)

Disposition with range of coverage or delivery rythm /

range of coverage lot sizex x

fixed order/delivery intervals) range of coverage lot size

Planning in context of productionPlanning in context of productionuse APO PP/DS module x

tock

Planning in context of production

planninguse APO PP/DS module x

sto

ck

planning

Stockouts mainly cause stockout amount

Sa

fety

sto

ck

Stockouts mainly cause stockout amount

independent costs (fix costs, e.g. Alpha vs beta service level x

Sa

fety

s

independent costs (fix costs, e.g. Alpha vs beta service level x

Sa

fety

independent costs (fix costs, e.g.

Expedition)

Alpha vs beta service level x

Sa

fety

Expedition)

Customized material Customer specific lot size xCustomized material Customer specific lot size x

Lot size restrictions min, max, fix lot size restrictions xLot size restrictions min, max, fix lot size restrictions xLot size restrictions min, max, fix lot size restrictions x

Restricted stockroom focus of planning LMN analysis considered xria Restricted stockroom focus of planning LMN analysis considered x

cri

teri

a Restricted stockroom focus of planning LMN analysis considered x

technical lot size requirement silo, tank ‐ replenishment to maximum level x cri

teri

a

technical lot size requirement silo, tank ‐ replenishment to maximum level x

ze c

rite

technical lot size requirement silo, tank ‐ replenishment to maximum level x

Procurement material WI: Least unit cost procedure x

siz

e c

ri

Procurement material WI: Least unit cost procedure x

Relatively small capacity compared to lot

Lot

size

Relatively small capacity compared to lot

sizeSplitting of big lots xLo

t si

sizeSplitting of big lots xLo

t

size

Production/supplier requires long‐termAdditional long‐term lot size planning x

Production/supplier requires long‐term

lot size planningAdditional long‐term lot size planning x

lot size planningAdditional long‐term lot size planning x

Figure 9: Criteria Overview


The MRP strategy sets up the material planning structure in which the planning pro-

cess will take place. This parameter mainly draws on given conditions, involving minor

managerial decisions and therefore makes use of only eight criteria, seven of which are

used for this parameter exclusively. This implies a rather low dependance on other pa-

rameters; however, the following parameter decisions heavily depend on the decisions

undertaken in the MRP strategy. For example, the BOM level chosen as the planning

level will have an impact on the forecast quality, as materials in the BOM might experi-

ence different demand variability.

The decision system for the MRP procedure parameter represents the most complex

system, involving 22 criteria, 12 of which are used exclusively for that parameter. This

is due to the function of this parameter, which is filling the planning framework given

by the MRP strategy with detailed planning and control procedures, e.g. decisions about

applying SMI or VMI, using a Master Production Schedule, a continuous or periodic re-

view policy or fixed planning horizons. This multitude of decisions to design a detailed

material planning process leads to a high number of criteria used. The MRP procedure

parameter is influenced by decisions taken in the MRP strategy module. Further, it has

seven criteria in common with the safety stock module, which suggests that similar

decisions have to be made in both modules. As the MRP strategy and MRP procedure

parameters together form the (detailed) process design of the material planning run,

both strongly influence the safety stock and lot-sizing parameters.

The safety stock and lot sizing parameter have, unlike the planning process de-

signing MRP strategy and MRP procedure parameters, a stronger immediate cost focus.

While assuring a high service level, the goal in these two parameters is to minimize

inventory costs. The ABC- and LMN-analysis are therefore important criteria in both de-

cision systems. The lot-sizing decision system directly draws on the order and holding

costs, which is a sign of the very strong cost focus of the lot size parameter. The safety

stock parameter needs to assure a determined service level, and therefore, criteria rep-

resenting the level of demand and lead-time variability are important. Both parameters

have to be configured given the detailed material planning process designed by the two

preceding parameters, showing their high dependency on these two parameters. The

safety stock and lot-sizing parameters follow similar goals and hence, are similar in

structure and criteria used; the dependencies between these two parameters, however,

are limited.

4.2 Implementation of the Decision System in SAP ERP/APO

Before planning an implementation of the decision systems in SAP, these systems

should be tested in the context of various industries and supply chain designs to as-


sure that the systems configure adequate material planning systems for a wide range

of different materials. Next, the additional information cost incurred by providing the

necessary input data for the decision systems needs to be evaluated and compared

to the potential cost savings of having the material planning systems configured au-

tomatically by the system. As discussed in the previous section, 51 criteria need to

be applied for the conduction of all four decision systems. That implies that a lot of

information has to be stored and maintained in the Material Master for the decision

systems to work. The following table gives insights about which information needs to

be maintained for which criteria.


Da

taC

rite

ria

Dra

win

g f

rom

oth

er

crit

eri

a

Co

mp

uls

ory

da

ta e

ntr

ies

cost

/va

lue

pe

r u

nit

AB

C c

lass

ific

ati

on

C‐i

tem

: lo

w p

lan

nin

g e

ffo

rt r

eq

uir

ed

X‐i

tem

: g

oo

d f

ore

cast

qu

ali

ty (

or

use

MA

PE

)

pri

ma

ryd

em

an

dk

no

wn

(MT

O)

/d

ete

rmin

ab

le(g

oo

dfo

reca

stq

ua

lity

=X

‐

ite

m (

or

use

MA

PE

))

au

tom

ati

co

rde

rp

rop

osa

lsa

reo

fg

oo

dq

ua

lity

(go

od

fore

cast

qu

ali

ty(X

‐ite

m

or

MA

PE

))

fix

ord

er

pro

po

sals

au

tom

ati

call

yw

ith

ou

tm

an

ua

lco

ntr

ol

(au

tom

ati

co

rde

r

pro

po

sals

are

of

go

od

qu

ali

ty (

go

od

fo

reca

st q

ua

lity

(X

‐ite

m o

r M

AP

E))

)

no

de

ma

nd

du

rin

g l

ast

x m

on

ths

sea

son

al

ma

teri

al

(se

aso

na

l d

em

an

d p

att

ern

)

po

siti

on

in

pro

du

ct l

ife

cy

cle

(d

em

an

d i

ncr

ea

se,

sta

gn

ati

on

, fa

ll)

his

tori

cal

lea

d t

ime

s

(ma

rke

t) a

cce

pte

d l

ea

d t

ime

BO

M s

ucc

ess

or

be

tte

r to

fo

reca

st (

in c

om

bin

ati

on

wit

h f

ore

cast

qu

ali

ty c

rite

rio

n)

sev

era

l fi

na

l p

rod

uct

s w

ith

co

mm

on

pa

rts

pro

du

ctio

n o

r p

rocu

rem

en

t

pro

du

ctio

n t

yp

ep

rod

uct

ion

ty

pe

pro

du

ct t

yp

ep

rod

uct

ty

pe

con

tin

uo

us

rev

iew

fe

asa

ble

(te

chn

ica

l a

nd

org

an

iza

tio

na

l)

Aa

nd

Bit

em

so

nly

(if

hig

hco

ntr

ol

cost

s(e

.g.

ma

nu

al

con

tro

ln

ece

ssa

ry/n

oe

lect

ron

icsy

ste

min

pla

ce))

Da

ta e

ntr

ies

on

ly i

f re

lev

an

t/a

va

ila

ble

lot

size

re

stri

ctio

ns

tech

nic

al

lot

size

re

qu

ire

me

nt

(av

era

ge

) o

rde

r co

sto

rde

r co

st

inv

en

tory

ho

ldin

g c

ost

(ra

te)

ho

ldin

g c

ost

LMN

cla

ssif

ica

tio

n

rest

rict

ed

sto

ckro

om

fo

cus

of

pla

nn

ing

('y

es'

, w

he

n s

tora

ge

sp

ace

re

qu

ire

me

nt

da

ta e

nte

red

)

Ev

alu

ati

on

:cr

itic

ali

tyo

fm

ate

ria

l(m

ate

ria

lh

as

sig

nif

ica

nt

infl

ue

nce

on

pro

du

ctio

n

pe

rfo

rma

nce

, p

rod

uct

ion

te

chn

olo

gy

hig

hly

in

flu

en

ces

the

pro

du

ctio

n p

roce

ss e

tc.)

crit

ica

l m

ate

ria

l

de

liv

ery

sch

ed

ule

(if

av

ail

ab

le)

fixe

d d

eli

ve

ry s

che

du

le

nu

mb

er

of

va

ria

nts

hig

h n

um

be

r o

f v

ari

an

ts (

> x

, e

.g.

50

or

wh

at

is n

ot

ma

na

ga

ble

ma

nu

all

y)?

pro

du

ctio

nsm

oo

thin

gim

po

rta

nt

(te

chn

ica

lre

qu

ire

me

nts

),e

.g.

hig

hp

rod

uct

ion

sta

rtin

g

cost

pro

du

ctio

n s

mo

oth

ing

an

d s

tra

teg

ic s

tock

ing

are

pri

ma

ry g

oa

ls

Ev

alu

ati

on

: su

pp

lie

r m

an

ag

es

sto

ck m

ore

eff

icie

ntl

ysu

pp

lie

r m

an

ag

es

sto

ck m

ore

eff

icie

ntl

y

Ev

alu

ati

on

: h

igh

pla

nn

ing

ne

rvo

usn

ess

ov

er

BO

M l

ev

els

hig

h p

lan

nin

g n

erv

ou

sne

ss

ma

teri

al

pla

nn

ing

co

nd

uct

ed

fo

r cu

sto

me

r

inte

rna

l o

r e

xte

rna

l cu

sto

me

r

Ev

alu

ati

on

:re

serv

ati

on

sn

ot

ad

eq

ua

tely

con

sid

ere

din

fore

cast

an

dsa

fety

sto

ck(r

eg

ula

r,

exp

ect

ed

de

ma

nd

or

rath

er

irre

gu

lar,

un

exp

ect

ed

de

ma

nd

(e

.g.

ne

w c

ust

om

er)

)re

serv

ati

on

s e

tc n

ot

ad

eq

ua

tely

co

nsi

de

red

in

fo

reca

st a

nd

ss

Ev

alu

ati

on

: st

ock

ou

ts c

au

se m

ain

ly a

mo

un

t in

de

pe

nd

en

t (f

ixe

d)

cost

s (i

f k

no

wn

)st

ock

ou

ts m

ain

ly c

au

se s

tock

ou

t a

mo

un

t in

de

pe

nd

en

t co

sts

(fix

co

sts,

e.g

. e

xpe

dit

ion

)

cap

aci

ty [

pe

r ti

me

un

it]

rela

tiv

ely

sm

all

ca

pa

city

co

mp

are

d t

o l

ot

size

(lo

t si

ze/c

ap

aci

ty[p

er

tim

e u

nit

] >

x)

lon

g‐t

erm

lo

t si

ze r

eq

uir

em

en

t in

pla

ce a

nd

pe

rio

d l

en

gth

pro

du

ctio

n/s

up

pli

er

req

uir

es

lon

g‐t

erm

lo

t si

ze p

lan

nin

g

pla

nn

ing

wit

hfi

xed

pla

nn

ing

ho

rizo

n:

ma

nu

al

con

tro

lo

fo

rde

rse

ttin

gn

ece

ssa

rya

nd

po

ssib

le?

(a

uto

ma

tic

ord

er

sett

ing

in

ad

eq

ua

te,

en

ou

gh

sta

ff a

nd

A o

r C

ite

m)

fix

ord

er

pro

po

sals

au

tom

ati

call

yw

ith

ou

tm

an

ua

lco

ntr

ol

(au

tom

ati

co

rde

rp

rop

osa

lsa

reo

fg

oo

d

qu

ali

ty (

go

od

fo

reca

st q

ua

lity

(X

‐ite

m o

r M

AP

E))

)

Ev

alu

ati

on

: m

ate

ria

l p

lan

nin

g c

on

du

cte

d f

or

cust

om

er

(in

tern

al

or

ext

ern

al)

lot

size

re

stri

ctio

ns

(if

in p

lace

) +

te

chn

ica

l lo

t si

ze (

ma

x re

ple

nis

hm

en

t le

ve

l)

his

tori

cal

de

ma

nd

da

ta

XY

Z c

lass

ific

ati

on

lea

d t

ime

va

ria

bil

ity

AN

D

tota

l LT

of

fin

al

pro

du

ct >

acc

. LT

BO

M

Ev

alu

ati

on

: co

nti

nu

ou

s re

vie

w f

ea

sab

le (

or:

ele

ctro

nic

in

ve

nto

ry c

on

tro

l in

pla

ce)

sto

rag

e s

pa

ce r

eq

uir

em

en

t (i

f re

stri

cte

d s

tock

roo

m o

f im

po

rta

nce

)

Figure 10: Data Overview


Only eight data entries are compulsory to maintain in the Material Master file for

each SKU. Additional 16 data entries can be maintained to allow for a wider range of

methods to chose from in the decision systems. Out of the 16 data entries, only relevant

information in the specific context of a company’s inventory and supply chain has to

be stored; the decision about how much information to maintain can be made on a SKU

basis. Thus, the information requirement can be controlled and kept to a reasonable

level for each SKU or SKU group. Further, some decisions can be excluded from the

decision systems and be set manually to avoid an overwhelming data maintenance

effort, e.g. the decision about splitting or not splitting a lot size when the lot size is

relatively large compared to the available capacity might be a decision to be done along

the operations run.

Some of the 8 compulsory data entries are usually already maintained in SAP, such

as ‘value per unit’, ‘historical demand’, ‘historical lead times’ or at least an estimate

of the average lead time, and the BOM. Further, some of the optional data entries are

usually maintained in SAP as well, such as ‘lot size restrictions’, ‘order cost’, ‘inventory

holding cost’ and ‘delivery schedule’. Thus, the additional data requirements needed

for applying the decision systems might cause limited effort.

The implementation of the decision systems in SAP ERP or SAP APO as an add-on

(monitor) tool might be a promising solution to apply the results of this work effec-

tively in SAP for the everyday business. In order to reduce the required effort of an

implementation, the decision systems can also be implemented individually. This will

incur less work and cost and allow the use of the decision system for the parameters in

which the most support is needed by the material planners. As mentioned before, such

a decision system for the configuration of the forecast parameter is already in place in

SAP. Further, it is possible to exclude some detailed decisions from the decision sys-

tems to allow for a rough configuration of the parameter only, leaving some details for

manual (group-wise) configuration. This also reduces the data maintenance effort as

mentioned earlier.

5 Conclusion and Further Research

The developed decision systems for the configuration of the MRP strategy, MRP proce-

dure, safety stock and lot size parameters in SAP ERP and SAP APO offer high potentials

of saving cost and the time of the material planner. Further, the systems are the first

to categorize the available methods implemented in SAP ERP/APO for each parame-

ter; the decision systems give valuable insights about which inventory methods are

implemented and what their functionalities are. The implementation of the systems


can be flexible as they can be applied independently from each other, and several de-

cision steps within each system could be excluded and left for manual configuration

to reduce implementation and data maintenance efforts. In the literature, no similar

decision systems can be found, making the presented ones the first approach for an

automated configuration of the parameters in SAP.

Next steps of this research project could be to implement the decision systems

in a software tool and run tests on empirical data. Considering inventories and whole

supply chains from various industries can show if the configuration systems deliver ad-

equate configurations of the parameters. Further, the implemented inventory methods

in SAP ERP/APO can be examined to identify missing methods or areas with poten-

tial improvement. For example, only the Normal distribution can be applied for the

parametric safety stock planning; however, current literature in inventory management

suggests that the distinguished application of a wide range of statistical distributions

for safety stock planning might lead to substantial cost savings and increase of the

service level.

6 Acknowledgements

We would like to thank Marc Hoppe, head of the Supply Chain Management Consulting

Unit of SAP AG, for his valuable support during this project. We would also like to

thank Professor Hartmut Stadtler, University of Hamburg, for comments on an early

draft of this paper that have improved the presentation substantially.


References

Dickersbach, Joerg Thomas. 2009. Supply Chain Management with APO: Structures,

Modelling Approaches and Implementation of SAP SCM . Springer Publisher, Berlin.

Gulyassy, Ferenc, Marc Hoppe, Martin Isermann, Oliver Koehler. 2009. Materials Plan-

ning with SAP. SAP Press by Galileo Press, Bonn.

Hopp, Wallace J., Mark L. Spearman. 2001. Factory Physics. Irwin McGraw-Hill, New

York.

Hoppe, Marc. 2006. Inventory Optimization with SAP. SAP Press by Galileo Press, Bonn.

Kallrath, Josef, Thomas I. Maindl. 2006. Real Optimization with SAP APO. Springer

Publisher, Berlin.

Silver, Edward A., David F. Pyke, Rein Peterson. 1998. Inventory Management and Pro-

duction Planning and Scheduling. John Wiley and Sons, New York.

Stadtler, Hartmut, Christoph Kilger, eds. 2007. Supply Chain Management and Ad-

vanced Planning: Concepts, Models, Software, and Case Studies. Springer Publisher,

Berlin.

Tempelmeier, Horst. 2006. Bestandsmanagement in Supply Chains. 2nd Edition. Books

on Demand GmbH, Norderstedt, Germany.

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