deploy ement sap apo snp deployment

8/10/2019 Deploy Ement sap apo snp deployment

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Deployment After production is complete, deployment determines which demands can be fulfilled by the existing supply. If there

are insufficient quantities available to fulfill the demand or the quantities available exceed the demand, deployment

makes adjustments to the plan created by the SNP run.

The deployment run generates deployment stock transfers based on the SNP stock transfers that were created during

the SNP run. The Transport Load Builder (TLB) then uses these deployment stock transfers to create transport loads,

thus generating TLB shipments .

NoteYou can run deployment from the interactive planning table in Supply Network Planning (on the SAP Easy

Access screen, choose Supply Network Planning Planning Interactive Supply Network Planning ), or as a

background job (on the SAP Easy Access screen, choose Supply Network Planning Planning Supply Network

Planning in the Background Deployment or Deployment Optimization).

PrerequisitesYou cannot use deployment for stock transfers involving storage location MRP areas. For more information,

seeStock Transfer with Storage Location MRP Areas .

Features

Deployment HeuristicThe deployment heuristic creates a distribution plan for one product at one location. If the available quantities are

insufficient to fulfill the demand, the system uses fair share rules to determine the distribution plan. However, if

there is more supply than is necessary to meet demands, the system uses push rules to determine the distribution

plan. You define fair share and push rules in the SNP deployment profile. For more information, see DeploymentHeuristic .

Real-Time Deployment

This variant of the deployment heuristic is used to execute deployment based on the most recent results from the

SNP run. Here, the system considers the current demand situation at the destination locations by first executing an

SNP run between the source location and its associated destination locations before the actual deployment run.

When using fair share rule B (see Deployment Profiles ), it is particularly important that distribution is based on the

most exact calculation of target and planned stock levels possible.
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Deployment Optimization

The deployment optimizer creates a distribution plan for all the products you chose in all the chosen locations of the

supply chain model. In the main, the deployment optimizer bases its decisions on all the costs defined in the supplychain model (such as transportation and storage costs), as does the SNP optimizer. It also takes into account the

following factors: Fair share rules and push rules Constraints such as transportation capacity, storage capacity, and transportation lot sizes.

You make most of the deployment optimization settings in the SNP deployment optimizer profile (see Deployment

Optimizer Profiles ). For more information, see Deployment Optimization .

Deployment Heuristic Purpose

The deployment heuristic creates a distribution plan for one product at one location of the supply chain model.

Once production is complete, the system first checks what product quantities are actually available at the source

locations (locations where there is stock). The sum of these product quantities is known as the available-to-deploy (ATD) quantity. The system then determines how the ATD quantity is to be distributed to destination

locations (locations where there is demand). The system considers the various distribution rules if the available

product quantities exceed or fall below the demand (fair share and push rules). You can set these rules in the

SNP deployment profile or on the SNP 2 tab page of the location product master. Deployment takes into

account a number of deployment horizons that you also define in the location product master.

Distribution Rules Fair Share Rules If demand exceeds supply, the system can use fair share rules to calculate deployment using the available-to-deploy

(ATD) quantity. Various methods use fair share rules to assign a limited amount of available product to sources of

demand. The following rules are available:

Fair Share Rule A: Proportional Distribution Based on Demands

The objective of fair share rule A is to distribute the stock proportionally to all demand locations according to

planned distribution demand.
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Fair Share Rule B: Proportional Distribution Based on Target Stock

The objective of fair share rule B is to raise the stock levels at all demand locations to approximately the same

percentage of target stock level. The percentage at each destination location is defined as the deployment-

relevant stock (=stock on hand SNP stock transfers) divided by the target stock level. If the deployment-

relevant stock, is negative the system first attempts to raise the stock level at all destination locations up to

zero. The system then attempts to raise the stock level at all destination locations to the same target stock level

percentage.


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Fair Share Rule C: Percentage Distribution Based on Quota Arrangements

The objective of fair share rule C is to distribute the stock according to quota arrangements at the demand

locations. To apply rule C, you have to define outbound quota arrangements for source location products in the

Supply Chain Engineer.

Fair Share Rule D: Distribution Based on Distribution Priority

The objective of fair share rule D is to distribute stock according to priorities that you defined for the outbound

transportation lanes of the source location (distribution priority). At the onset of a fair share situation, the

system attempts to fulfill all of the current dates demands until the ATD quantity is exhausted. For example,you have three outbound transportation lanes to your destination locations (A, B, and C), each withcorresponding priorities (1, 2, and 3). Your ATD quantity is 150 pieces and the required quantity in each

location is 100 pieces. If you choose fair share rule D, destination location A receives a quantity of 100 pieces

over the transportation lane with priority 1. Destination location B receives a quantity of 50 pieces over the

transportation lane with priority 2, and destination location C receives nothing over the transportation lane with

priority 3.

Push Rules SNP only uses push rules to calculate deployment if the ATD quantity covers the demand. The following rules are

available:

Pull Distribution

Deployment fulfills all of the demand within the pull deployment horizon (for definition, see below). Products

are distributed according to the due date specified at the demand locations. The system does not distribute anysupply to the demand source in advance of the demand date.

Pull/Push Distribution

The system immediately distributes all supply to the demand locations (ignoring the demand dates specified at

the demand locations) to fulfill all demands within the pull deployment horizon.


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Push Distribution by Demand

The system immediately distributes the entire supply for the entire planning horizon to the demand locations to

fulfill all demands. The pull deployment horizon is ignored.

Push Distribution by Quota Arrangement

The system immediately distributes the supply according to the quota arrangements defined for the demand

location. The demand situation at destination locations is ignored. Push Distribution Taking the Safety Stock Horizon into Account

The system confirms planned issues that are to be covered by safety stock at the source location if thedifference between the demand and deploy date is smaller than the safety stock horizon (see below). This

means, the system will only fall below the safety stock level that you defined on the Lot Size tab page of the

location product master if the demand to be fulfilled is in the safety stock horizon. Note that the safety stock

horizon moves forward (rolls) with planning.

The following example illustrates the three push rules: Pull distribution, pull/push distribution,

andpush distribution by demands.

Pull distribution - A quantity of 200 is distributed to the distribution centers on every day within

the pull horizon where there is existing demand (four days into the future).

Pull/Push - A quantity of 200 pieces is distributed on the first day, when supply amounts to 200

pieces. On the second day, a quantity of 600 pieces is distributed. Although supply is 1000 pieces,the demand within the pull horizon is for only 600 pieces, so only 600 pieces are distributed.

Push distribution by demands - 200 pieces are distributed on the first day when supply amounts

to 200 pieces; on the second day, when the supply is 1000, 1000 pieces are distributed. Since the

demand in the system is 1400, all the supply can be distributed. A demand of 200 at the end of the planning horizon is left unfulfilled due to insufficient supply within the push horizon. If the

demand in the system had been 800, only 800 would be distributed on the second day.


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If you set the Push not allowed indicator on the SNP tab page of the location master (only

possible in active version), the available supply is distributed according to the daily demand at the

demand locations only.

Deployment Horizons The deployment heuristic takes into account the following four different deployment horizons (that you define ontheSNP2 tab page of the location product master):

Pull deployment horizon : This horizon refers to the period of time over which deployment takes into account

the planned distribution demand. The horizon starts from todays date.

During the deployment run, the system attempts to fulfill all distribution demands within this horizon.

Distribution begins on the first day for which distribution demands exist in the system and ends on the last day

of the pull deployment horizon.

The pull deployment horizon is also used during push distribution. In this instance, it specifies whether the

demand is to be fulfilled immediately (pull/push distribution) or according to the due date (pull distribution). It

puts a limitation on the date by which SNP stock transfers are to be considered as relevant for deployment.Within this horizon, deployment only fulfills planned demand that has been confirmed.

Push deployment horizon : This horizon refers to the period of time over which deployment takes into account

the receipts defined in the ATD receipt category group of the location master. The horizon starts from todays

date.

If push distribution has been specified (if the distribution demand is smaller than the supply and stock on hand),

this horizon determines whether stock on hand is to be distributed before the distribution demand due date

according to the push rule specified. Only stock on hand within the push deployment horizon is taken into

account for push deployment (deployment before the actual demand date).

Safety stock horizon : This horizon is only used during push rule Push distribution taking the safety stock

horizon into account (for an explanation, see below).

SNP checking horizon : Deployment uses this horizon to calculate the quantity available for distributing to

demand locations. It puts a limitation on the quantity that is available within the push deployment horizon.

Within the SNP checking horizon, deployment calculates the ATD quantity for a period by adding up the ATD

receipts from the current and preceding periods, and subtracting all the ATD issues within the SNP checking

horizon.

Running the Deployment Heuristic Use After production planning is complete and the system knows what is actually to be produced (this information is

stored automatically in liveCache), the deployment run generates deployment stock transfers .

Prerequisites You have maintained category groups in Customizing for Supply Network Planning (Maintain Category

Groups). You then have to assign these category groups to a location in the location master data (the SNPtab page). (Default settings: ATD receipts ATR, all categories that describe demand; ATD issues ATI, allcategories that describe supply.)
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Master Data Setup for Deployment Heuristic

You have maintained the relevant deployment heuristic profiles . You have run the SNP heuristic, SNP optimizer, supply and demand propagation, or Capable-to-Match (CTM)

Procedure From the SAP Easy Access screen, choose

Supply Network Planning

Planning

Supply Network

Planning in the Background Deployment. The Supply Network Planning: Deploymentscreen appears. 1. 1. Enter the planning book and data view. 2. 2. Enter an SNP planning profile that you defined in the Customizing. This contains basis settings for the SNP

planning procedures. If you do not specify a profile, the system uses the default profile activated in the

Customizing. 3. 3. Enter a parallel processing profile that you defined in the Customizing for SNP. This determines how the

background jobs are divided into parallel processes. 4. 4. In the Deployment Horizon in Days field, enter the number of days you want the system to take into

account during its deployment calculations. 5. 5. Enter a selection profile in which you stored your selections in interactive Supply Network Planning or

manually select planning data. When selecting this data manually, enter the planning version, location (or rangeof locations), and product (or range of products) that you want to plan.

6. 6. In the Destination Locations field, you can limit deployment of supply to specific destination locations.You may wish to use this option if there are high priority destination locations, such as VMI customers. You

should run deployment for important destination locations first. 7. 7. Choose one of the following options to indicate how you wish the system to proceed with the SNP stock

transfers after the deployment run: Select Do not change if you only want to run a simulation (so that no deployment stock transfers are

created). Select Reduce if you want the system to create deployment stock transfers and reduce the SNP stock

transfers accordingly.

Select Delete if you want the system to create deployment stock transfers and delete all the SNP stocktransfers from within the planning period, irrespective of whether or not they were able to be confirmed by

the deployment run. 8. 8. Set the Real-Time Deployment indicator, if you wish the system to take into account the current demand

situation at the destination locations during the deployment heuristic run instead of the results from the lastSNP run. Enter a Deployment Destinatn Location if you want the demand of only this destination location to beconsidered during real-time deployment.

For real-time deployment, you can specify that you want already existing deployment stock transfers to be

deleted and replanned during this planning run. The following options are available: Select Replan All if you want the system to delete all deployment stock transfers that exist from the

selected source location to the selected destination locations and to replan them on the basis of the current

demand situation. Select Replan Non-Fixed if you want the system to delete all non-fixed deployment stock transfers that

exist from the selected source location to the selected destination locations and replan them on the basis ofthe current demand situation.
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Select Do Not Delete if you do not want the system to delete any existing deployment stock transfers.The demand situation at the destination locations is used as a basis for creating new deployment stock

transfers (taking into account the confirmed distribution receipt). 9. 9. Make settings for the application log generated in the planning run. In the log availability field, enter the

number of days you want the log to be saved for. The default value is 30 days.

You define whether the log contains detailed information, such as data for the deployment stock transferscreated, and whether this is displayed directly at the end of the planning run. You can also display the log afterthe heuristic run by choosing Display Logs. You can also specify whether you want a spool list to be generated as well as the application log, and whether

you want this to be displayed automatically at the end of the planning run.

Set the No Message for Missing Destination Loc. indicator if you do not want the following message toappear: No destination locations found for material &1, location &2. This message is frequently displayed(particularly when multiple products are selected) if the system does not determine a destination location for a

source location product during the deployment run. 10. 10. Set the Do Not Use Alerts indicator if you do not wish to use the Alert Monitor and want to improve

performance of the deployment run.

11. 11. Choose Execute.

Result After the deployment run, the system automatically converts SNP stock transfers into deployment stock transfers,

which are used for transportation planning in the Transport Load Builder (TLB).

Deployment Heuristic Profiles Use The following table describes the profiles used by the deployment heuristic. You can define these profiles partly inthe location product master and partly in the Customizing or in the current settings for Supply Network Planning(SNP). For more information, see the Implementation Guide (IMG) or the field-level help (F1 help).

Profile Use in Deployment Run

Planning calendar(time streams)

Specifies the planning calendars the system uses to determine when you can produce,transport, and so on.

SNP demand profile(product master)

Specifies how the system calculates demand. You define the pull deployment horizon inthis profile.

SNP supply profile(product master)

Specifies how the system calculates supply. You define th e push deployment horizon inthis profile.

SNP deployment Specifies the logic used by the system to distribute the available supply to the demand
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You define constraints and distribution rules in the SNP deployment optimizer profile . In this profile, you can

also define additional parameters for deployment optimization, such as decomposition methods for improving

runtime.

The aim of the deployment optimizer is to find the most cost-effective plan from all feasible plans (plans that

consider all the specified constraints).

Distribution Rules Supply Shortage If the deployment optimizer detects that the ATD quantity falls below the demand at the destination locations, it

applies the supply shortage rule you defined in the SNP deployment optimizer profile. The following rules are

available:

Distribution Based on Lowest Costs

When calculating the product quantities to be distributed, the deployment optimizer only considers the costs

defined in the supply chain model and the constraints specified in the SNP deployment optimizer profile. The

calculated distribution plan will be the plan with the lowest costs. With this option, it is possible that the demand at one location will not be covered because the demand at

another location can be covered at less cost.

Fair Share Distribution by Demands

The deployment optimizers main objective is to distribute the ATD quantities evenly, according to thedemands (sales orders and forecasts) at the destination locations.

When distributing the calculated quantities, the system also considers the costs defined in the supply chain

model. For example, if transportation costs are high, the optimizer can decide not to distribute the allocated

amount directly to a demand location, but rather to make a detour through another location. If the calculated

fair share quantities cannot be distributed to individual locations due to identified constraints, these quantities

are distributed to other destination locations on the basis of costs.

Earliest Delivery

When used in conjunction with the Fair Share by Demands option, you can specify that, in fair share

distribution, the deployment optimizer is to completely fulfill the earliest demands if possible, before planningthe fulfillment of later demands. If you do not choose this option, the optimizer will attempt to distribute the

ATD quantities evenly over the demands in various periods.

Supply Surplus If the deployment optimizer detects that the ATD quantity exceeds the demand from the destination locations, it

applies the supply surplus rule you defined in the SNP deployment optimizer profile. The following rules are

available:

Distribution Based on Lowest Costs When calculating the product quantities to be distributed, the deployment optimizer only considers the costs

defined in the supply chain model and the constraints specified in the SNP deployment optimizer

profile.However, as a rule, the system does fulfil l all demands at the destination location, since high non-

delivery costs prevent a demand from not being fulfilled. The optimizer distributes excess stock to the lowestcost locations.This does not necessarily have to be a location where there is demand.

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The deployment optimizers main objective is to distribute the ATD quantities evenly, according to demands at

the destination locations. Since the ATD quantity exceeds the demand quantity, the calculated percentage, in

contrast to the fair share situation, is over 100% (the destination locations may receive a quantity that exceeds

their demand).

As with fair share distribution, when distributing calculated quantities, the system also considers the costs

defined in the supply chain model and, if necessary, accepts detours in the supply chain, if this means that thetotal cost can be reduced.

Earliest Delivery

When used in conjunction with the option Push Distribution by Demand, you can specify that, in push

distribution, the deployment optimizer is to distribute all available ATD quantities to destination locations at

the earliest demand time. If you do not choose this option, the optimizer will attempt to distribute the ATD

quantities evenly over the demands in various periods.

Consideration of Costs The deployment optimizer considers the following costs defined in the supply chain model:

Storage and transportation costs Costs for increasing storage, transportation and handling capacity

Safety stock penalty

Late delivery penalty

Non-delivery penalty

You use the SNP cost profile to set the relative importance of different cost types.

Consideration of Constraints The optimizer considers that a plan is feasible when it satisfies all the supply chain model constraints that you set in

the SNP deployment optimizer profile. A feasible solution might involve due date or safety stock constraint

violations. Due dates and safety stocks are soft constraints (constraints to which you assign violation costs). Theoptimizer only proposes a plan that will violate soft constraints if, according to the costs specified in the system, it is

the most cost-effective plan.

The optimizer uses the linear programming method to take account of all planning-problem-related factorssimultaneously within one optimal solution. As more constraints are activated, the optimization problem becomes

more complex, which usually increases the time required to solve the problem. As a rule, you should run

optimization as a background job.

The optimizer makes a distinction between continuous linear optimization problems and discrete optimization

problems.

Linear Optimization You can choose one of the three following methods from the SNP deployment optimizer profile to solve continuous

linear deployment optimization problems:

Primal simplex method

Dual simplex method

Interior point method


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All three methods arrive at an optimal solution. Runtime could be the main influencing factor when deciding which

of these methods to use. However, there is no general rule for selecting the best method for a given problem (apart

from to test each method individually). A good way of assessing the application is to do a benchmarking based on a

test scenario. This is because the optimal choice of method depends more on the structure of the supply chain and

less on the input data. Therefore, in a productive environment, daily benchmarking is not necessary.

Discrete Optimization A problem is not continuous (and is therefore discrete) for the deployment optimizer when the model contains:

Discrete (integer value) lot sizes for transportation

Discrete means of transport

Minimum lot size for transportation

Piecewise linear cost functions for transportation

Storage, transportation and handling capacity

If you want the optimizer to consider any of the above constraints, you must use one of the discrete optimization

methods from the SNP deployment optimizer profile.

The piecewise linear cost function that you can define in master data makes a distinction between the convex cost

function (cost per unit increases for higher volumes; for modeling overtime or night shifts for instance) and

theconcave cost function (cost per unit decreases for higher volumes; for modeling freight rates for instance).

Convex cost functions do not complicate the planning problem and can be solved efficiently. However, they can also

be modeled using alternative modes without using piecewise linear cost functions.

In contrast, concave piecewise linear cost functions cannot be solved by an LP solver but only by using

discretization methods (mixed integer linear programming). If piecewise linear functions are modeled but the

optimizer is run without discretization or the discretization horizon is smaller than the planning horizon, the

optimizer takes into account the linear cost function defined in addition to the piecewise linear cost function.

The discrete optimization method cannot be used with strict prioritization (see below).

Using the discrete optimization method can significantly increase runtime requirements. Note that

Supply Network Planning is a medium-term planning function and its focus should not be on

solving integer problems (that is, using the discrete optimization method).

Decomposition The primary focus of decomposition is on reducing the runtime and memory requirements of optimization.

Decomposition may also represent the only way for the deployment optimizer to find a feasible solution in the event

of large discrete problems.

Decomposition is a flexible tool for the user to balance the tradeoff between optimization quality and requiredruntime. When runtime is unrestricted, the optimizer usually provides a better (optimal) solution without

decomposition; however, when a fixed runtime has been specified, using decomposition could assist the optimizer to

find a better or, in fact, feasible solution.


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The decomposition methods available for the deployment optimizer are detailed below. The time, product, and

internal deployment decomposition methods can be used in conjunction with the linear and discrete optimization

methods. It is only advisable to use resource decomposition in conjunction with discrete optimization.

Time decomposition speeds up the solution process by dividing the source problem into a series of partial

problems. These sub-problems are then solved in sequence. Product decomposition speeds up the solution process by forming product groups. The complete model solves

one product group at a time according to the window size chosen. The rule of thumb is as follows: The smaller

the window size, the less time it will take to find a solution, but the larger the window size, the better the

quality of the solution found.

Resource decomposition speeds up the solution process by analyzing the material flow and basic optimizer

decisions about transportation and storage determine a resource sequence. The optimizer can then create sub-

problems for the individual resources, which are solved in sequence. The optimizer makes decisions in every

sub-problem that cause the resource to be loaded.

It is particularly advisable to use resource decomposition if the resources are always loaded in a similar

sequence. Resource decomposition does not reduce memory requirements. If you would like to reduce memory

requirements, you could use time decomposition alongside this decomposition method. Resource

decomposition cannot be used in conjunction with product decomposition or strict prioritization.

Internal deployment decomposition can speed up the solution process by dividing the optimization problem

into the smallest partial problems possible. These partial problems are then solved sequentially. It is particularly

advisable to use this decomposition method if the supply chain model has a particular problem structure. For

example, is the model does not contain capacitiy restrictions, the products can be distributed independently indeployment, which can help reduce runtime.

You can also use the SNP priority profile to define priorities for product and resource decomposition, that is, you

can change the sequence in which the optimizer groups and plans products and resources in partial problems. For

more information, see the SNP Priority Profile section in Decomposition (SNP optimizer documentation).

Prioritization The deployment optimizer can differentiate between the priority of sales orders and forecast demand. With strict

prioritization, sales orders always have priority 1, the corrected demand forecast priority 5, and the demand forecast

priority 6. Within every priority class, the system uses all available cost information to determine the final solution.If you are using cost-based prioritization, the optimizer uses penalty cost information from the product master data

(on the SNP1 tab page) to determine the optimal solution.

Other Considerations The deployment optimization run results do not include pegging orders back to the original individual

requirements because requirements are bucketed. The deployment optimizer considers the entire capacity and the entire alternative capacity that is globally

available (at all locations).

In the event of a capacity overload, the deployment optimizer, depending on the system settings, either does not provide a solution or increases the capacity based on a penalty cost calculation.

The deployment optimizer considers the shelf life of a product in a restricted way (see the information about the

SNP optimizer in stock planning , which is also applicable to the deployment optimizer).
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The deployment optimizer considers the three following deployment horizons: Pull deployment horizon , push

deployment horizon , and SNP checking horizon . You define these in the deployment optimizer profile.

Running Deployment Optimization

Use After production planning is complete and the system knows what is actually to be produced (this information isstored automatically in liveCache), the deployment optimization run generates deployment stock transfers . You canrun deployment optimization in the background and in interactive Supply Network Planning.

Prerequisites You have maintained category groups in Customizing for Supply Network Planning (Maintain Category

Groups). You then have to assign these category groups to a location in the location master data (the SNPtab page). (Default settings: ATD receipts - ATI, all categories that describe demand; ATD issues - ATR, all

categories that describe supply.)

Master Data Setup for the Deployment Optimizer

You have maintained the relevant deployment optimizer profiles You have run the SNP heuristic, SNP optimizer, supply and demand propagation, or Capable-to-Match (CTM)

Procedure To Run Deployment Optimization in the Background:

From the SAP APO Easy Access menu, choose Supply Network Planning Planning Supply Network Planning in the Background Deployment Optimization.

12. 1. Enter the planning book and data view.

13. 2. Enter an SNP planning profile that you defined in the Customizing. This contains basis settings for the SNP

planning procedures. If you do not specify a profile, the system uses the default profile activated in the

Customizing. 14. 3. Enter a parallel processing profile that you defined in the Customizing for SNP. This determines how the

background jobs are divided into parallel processes. 15. 4. Enter a selection profile in which you stored your selections in interactive Supply Network Planning or

manually select planning data. When selecting this data manually, enter the planning version, level ID, product(or range of products), and the location (or range of locations) that you want to plan.

16. 5. Specify the start and end date that you want the system to consider for planning. This entry is optional. If

you do not enter a start and end date, the system uses the planning buckets profile that was specified in the data

view. Enter the SNP deployment optimizer profile to be used for deployment optimizer calculations. To define

a deployment optimizer profile, choose Supply Network Planning

Environment

Current

Settings Profiles Define SNP Deployment Optimizer Profiles. Specify the cost profile that you want to be used for deployment optimizer calculations. To maintain a

cost profile, choose Supply Network Planning Environment Current Settings Profiles DefineSNP Cost Profiles.

17. 6. If required, specify the optimization bound profile that you want to use for the deployment optimizer

calculations. To maintain an optimization bound profile, choose Supply NetworkPlanning Environment Current Settings Profiles Define SNP Optimization Bound Profiles. In the
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accompanying field, you can still specify or choose a previously executed optimization run that you want the profile specified in theOptimization Bound Profile field to reference.

18. 7. Choose Execute.

To Run Deployment Optimization in Interactive Supply Network Planning: From the SAP Easy Access screen, choose Supply Network Planning Planning Interactive Supply

Network Planning. 19. 1. Choose Deployment Optimizer. The Deployment Optimizer window appears.

The pushbutton for accessing the deployment optimizer is displayed on the interactive desktop automatically if

you are using the standard SNP planning book SNP94. However, if you create your own planning books and

wish to use the deployment optimizer in interactive planning, you must specify that you want this pushbutton to be displayed. You do this in the Design mode of the interactive planning desktop.

20. 2. On the Optimization tab page in the Deployment Optimizer window, you can choose one of your predefined deployment optimizer profiles, cost profiles, and optimization bound profiles. By choosing theicons , , and , you branch to the maintenance screens for the deployment optimizer profiles, cost

profiles, and optimization bound profiles to make changes to existing profiles, for example.

21. 3. Choose to start the optimization run. The current solution resulting costs determined by the optimizer are displayed in the Current Solution area ofthe Optimization tab page. The individual steps of the optimization process are displayed in the Status areaduring the optimization run and information about intermediary solutions is displayed in the Solution Run area.In addition to this, messages about the current optimization run are displayed in the Messages area. You can also display the products that were selected for this optimization run by choosing . Choose to

branch to the Supply Network Planning: Optimizer log file screen. From there, you can decide which type/formof log to display (the message log, input log, or results in a text file for example.)

22. 4. Choose the Solutions tab page to view information about the previous optimization runs. If you select asolution in the right-hand screen area, the data corresponding to that solution appears in the left-hand area.

23. 5. Choose the Messages tab page to view messages about the previous optimization runs. If you select anoptimization run in the left-hand screen area, the messages corresponding to that optimization run appear in the

right-hand area.

Result After the deployment optimization run, the system automatically converts SNP stock transfers into deploymentstock transfers, which are used for transportation planning in the Transport Load Builder (TLB).

Deployment Optimizer Profiles Use The following table describes the profiles used by the deployment optimizer. To access each profile individually,choose Supply Network Planning Environment Current Settings Profiles from the SAP Easy Accessscreen. You can also define the profiles in the Customizing for Supply Network Planning (SNP). For moreinformation, see the Implementation Guide (IMG) or the field-level help (F1 help).
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SNP deploymentoptimizer profile

In this profile, you choose which optimization method you want to use during theoptimization run (linear optimization or discrete optimization) and which constraints youwant the system to take into account. Although almost identical to the SNP optimizer

profile, this profile only contains parameters that are relevant for the deploymentoptimizer. It also contains a tab page where you can define deployment-specific

parameters (distribution rules and deployment horizons).

SNP cost profile In this profile, you assign a weighting to different cost elements in the objective function(thus determining how the costs relate to one another).

We recommend that you only change the standard settings for test purposes whenmodeling. To avoid undesired side-effects, you should not change the default setting of1.0 in the production system if possible. This setting corresponds to the costs entered incost maintenance.

SNP lot size profile(transportationlanes)

In this profile, you define minimum and maximum lot sizes for the shipment. You thenspecify this profile for one specific product in the Product-Specific Means ofTransportsection of the transportation lane. You can thus define minimum and maximumtransportation lot sizes for specific products.

If you wish to make shipments in integer multiples of a transportation lot size only, youcan also define the transportation lot size as a rounding value in this profile.

To enable the deployment optimizer to take into account the minimum lot sizes androunding values defined, you have to choose the discrete optimization method in the SNPdeployment optimizer profile.

SNP optimization bound profile

If you want to perform a new planning run after a deployment optimizer planning run,you use this profile to improve planning stability by restricting possible decision variabledeviations from the previous optimization plan. For example, you can permit smallerdeviations at the start of the planning horizon and then increase these towards the end ofthe horizon to avoid too many last-minute planning changes.

Your new plan does not have to be based on the directly preceding optimization run, youcan also choose earlier runs.

SNP planning

profile In this profile, you can make basic settings for the various SNP planning procedures, such

as heuristic, optimizer, deployment heuristic, deployment optimizer, and Transport LoadBuilder (TLB).

The SNP planning profile that you activate in the SNP Customizing under MaintainGlobal SNP Settings applies globally for all SNP planning procedures. For some planning

procedures, you can overwrite the settings of the active profile by entering another SNP planning profile during execution of planning in the background.


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Parallel processing

profile

You use this profile to define how background jobs are divided in parallel processes. Youcan specify the number of concurrent parallel processes, the number of objects per

processing block, and the server group. In each case, you define the profile for one

specific application function, such as the deployment optimizer.

Product Interchangeability in Deployment Use The deployment heuristic and the deployment optimizer take into account SNP product substitution orders that werecreated during heuristic-based or optimization-based planning in Supply Network Planning (SNP). In this way, you

can continue to perform the product interchangeability that you planned in the SNP planning run in deployment too.

Prerequisites You have executed an SNP heuristic run or optimization run during which SNP product substitution orders were

created. For more information, see Product Interchangeability in Supply Network Planning .

The prerequisites described under Product Interchangeability in Supply Network Planning are also valid for

product interchangeability in deployment. This includes defining product substitution data in the master datafor product interchangeability.

You can define the interchangeability rules for the deployment heuristic at both the source location and the

destination location. Since the deployment optimizer creates a distribution plan for all selected products in all

selected locations in the supply chain model, you must define the rules for the deployment optimizer at each

location that is relevant to product interchangeability. As is the case for the SNP optimizer, you must have set the Supersession Chain and Form-Fit-Function

Class indicators for the deployment optimizer on the General Constraints tab page in the deployment optimizer

profile .

You have defined the fair share rule A (Proportional Distribution Based on Demands) or one of the pushrules,Pull, Pull/Push, or Push by Demands as the distribution rules for the deployment heuristic in the product

master data. The other distribution rules are not supported.

Features Deployment confirms the SNP product substitution orders and creates deployment orders or SNP product

substitution orders in the destination location. Deployment creates deployment stock transfers for the products

that replace the discontinued products. The system deletes or reduces the corresponding SNP product substitution orders and stock transfers.

Activities 24. 1. You perform a deployment heuristic run or a deployment optimization run. You use the standard planning

book 9ASNP_PS with data view PROD_SUBST or your own planning book that you based on the standard

planning book. You also set the Add Products from Supersession Chainsindicator.
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25. 2. The system takes into account the product substitution data that has been defined for product

interchangeability in the master data and creates, alongside the deployment stock transfers, deployment product

substitution orders in the Substitution Demand (Confirmed) and Substitution Receipt (Confirmed) key figures,

if required.

26. 3. You display the results in interactive Supply Network Planning.

Example The following figure shows the result of a deployment heuristic run that was performed on the basis of an SNP

heuristic run while taking into account the product interchangeability data:

In the example, it is assumed that product A can be fully interchanged with product B (AB). There is a demand

for 100 pieces of product A at the distribution center. There is a stock of 60 pieces of product A and 40 pieces of

product B at the plant. Since the stock of 60 pieces of A at the plant cannot cover the entire demand for A at the

distribution center, the demand is partially covered by the stock of B. The system proceeds as follows when

planning:

SNP Heuristic (Destination Location):

The system creates a substitution receipt (in other words, an SNP product substitution order) for A (from

B) for 100 pieces. The system creates an SNP stock transfer for B for 100 pieces.

SNP Heuristic (Source Location):

The system creates a substitution receipt for B (from A) for 60 pieces.

Deployment Heuristic:

The system creates a deployment stock transfer for A for 60 pieces as well as a deployment stock

transfer for B for 40 pieces.


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The system creates an SNP product substitution order at the destination location for A for 60 pieces

(from B).

Monitoring TLB and Deployment Alerts You can use the Alert Monitor to monitor alerts for the Transport Load Builder and for Deployment in Supply

Network Planning (SNP). To do this, you define a TLB alert profile containing a user-specific selection of alerts that

are relevant for your planning area.

FeaturesThe following table shows you the alert types available, and a brief description of each one.

NoteFor all the alerts except Invalid Delivery Date, you can enter priority variants based on critical values.

TLB/deployment

alerts

Description

Fair share (general) The fair share percentage is determined according to the rule specified in the deployment heuristic profile

(SNP2 tab in the location product master). The system triggers this alert if the percentages to deploy to meet

product demand fall below the percentages specified in the master data.

Fair share

(customers only)

Same as above, except in this case fair share distribution refers only to customers instead of locations. (for

example, VMI customers)

Invalid shipment When the shipment does not fulfill the parameters for the capacity of the means of transport (such as volume,

weight, and pallet positions) defined in the TLB profile , the system generates an alert.

Invalid delivery

dates

You active this alert using the /SAPAPO/TLB_CHK_DATE BAdI.

Activities1. Select the alert types that are relevant to you, and define threshold values where required.2. Select the objects (products, transportation lanes, means of transport) for which the system should determine

alerts.3. If necessary, assign the TLB alert profile to your overall alert profile (see Overall Alert Profile ), your SCC User

Profile, or your user in SAP Supply Chain Management (SAP SCM) from the SAP Easy Access screen by

selecting Advance Planning and Optimization Supply Network Planning Environment Current

Settings Assign Planners to Alert Profiles .

RecommendationFor performance reasons, we recommend restricting the number of alerts to be displayed to a minimum.
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Deployment Optimization Purpose

The deployment optimizer creates a distribution plan for all products you selected in all selected locations of

your supply chain model. Once production is complete, the system first checks what product quantities areactually available at the source locations (locations where there is stock). The sum of these product quantities is

known as the available-to-deploy (ATD) quantity. The system then determines how the ATD quantity is to be

distributed to destination locations (locations where there is demand). To do this, the optimizer considers the

following factors:

Distribution rules (such as fair share and push rules) if the available product quantities exceed or fall below the

demand.

All costs defined in the supply chain model, such as transportation costs, storage costs, and penalties for non-

delivery.

Constraints such as transportation capacity, storage capacity, and transportation lot sizes.

You define constraints and distribution rules in the SNP deployment optimizer profile . In this profile, you can

also define additional parameters for deployment optimization, such as decomposition methods for improving

runtime.

The aim of the deployment optimizer is to find the most cost-effective plan from all feasible plans (plans that

consider all the specified constraints).

Distribution Rules Supply Shortage If the deployment optimizer detects that the ATD quantity falls below the demand at the destination locations, it

applies the supply shortage rule you defined in the SNP deployment optimizer profile. The following rules are

available:



defined in the supply chain model and the constraints specified in the SNP deployment optimizer profile. The

calculated distribution plan will be the plan with the lowest costs.

With this option, it is possible that the demand at one location will not be covered because the demand at

another location can be covered at less cost.

Fair Share Distribution by Demands

The deployment optimizers main objective is to distribute the ATD quantities evenly, according to the

demands (sales orders and forecasts) at the destination locations.

When distributing the calculated quantities, the system also considers the costs defined in the supply chainmodel. For example, if transportation costs are high, the optimizer can decide not to distribute the allocated

amount directly to a demand location, but rather to make a detour through another location. If the calculated

fair share quantities cannot be distributed to individual locations due to identified constraints, these quantities

are distributed to other destination locations on the basis of costs.

Earliest Delivery

When used in conjunction with the Fair Share by Demands option, you can specify that, in fair share

distribution, the deployment optimizer is to completely fulfill the earliest demands if possible, before planning


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the fulfillment of later demands. If you do not choose this option, the optimizer will attempt to distribute the

ATD quantities evenly over the demands in various periods.

Supply Surplus If the deployment optimizer detects that the ATD quantity exceeds the demand from the destination locations, it

applies the supply surplus rule you defined in the SNP deployment optimizer profile. The following rules are

available:



defined in the supply chain model and the constraints specified in the SNP deployment optimizer profile.However, as a rule, the system does fulfil l all demands at the destination location, since high non-

delivery costs prevent a demand from not being fulfilled. The optimizer distributes excess stock to the lowest

cost locations.This does not necessarily have to be a location where there is demand.


The deployment optimizers main objective is to distribute the ATD quantities evenly, according to demands at

the destination locations. Since the ATD quantity exceeds the demand quantity, the calculated percentage, in

contrast to the fair share situation, is over 100% (the destination locations may receive a quantity that exceeds

their demand).

As with fair share distribution, when distributing calculated quantities, the system also considers the costs

defined in the supply chain model and, if necessary, accepts detours in the supply chain, if this means that thetotal cost can be reduced.

Earliest Delivery

When used in conjunction with the option Push Distribution by Demand, you can specify that, in push

distribution, the deployment optimizer is to distribute all available ATD quantities to destination locations at

the earliest demand time. If you do not choose this option, the optimizer will attempt to distribute the ATD

quantities evenly over the demands in various periods.

Consideration of Costs The deployment optimizer considers the following costs defined in the supply chain model:

Storage and transportation costs

Costs for increasing storage, transportation and handling capacity

Safety stock penalty

Late delivery penalty

Non-delivery penalty

You use the SNP cost profile to set the relative importance of different cost types.

Consideration of Constraints The optimizer considers that a plan is feasible when it satisfies all the supply chain model constraints that you set in

the SNP deployment optimizer profile. A feasible solution might involve due date or safety stock constraint

violations. Due dates and safety stocks are soft constraints (constraints to which you assign violation costs). The

optimizer only proposes a plan that will violate soft constraints if, according to the costs specified in the system, it is

the most cost-effective plan.


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The optimizer uses the linear programming method to take account of all planning-problem-related factors

simultaneously within one optimal solution. As more constraints are activated, the optimization problem becomes

more complex, which usually increases the time required to solve the problem. As a rule, you should run

optimization as a background job.

The optimizer makes a distinction between continuous linear optimization problems and discrete optimization problems.

Linear Optimization You can choose one of the three following methods from the SNP deployment optimizer profile to solve continuous

linear deployment optimization problems:

Primal simplex method

Dual simplex method

Interior point method

All three methods arrive at an optimal solution. Runtime could be the main influencing factor when deciding which

of these methods to use. However, there is no general rule for selecting the best method for a given problem (apartfrom to test each method individually). A good way of assessing the application is to do a benchmarking based on a

test scenario. This is because the optimal choice of method depends more on the structure of the supply chain and

less on the input data. Therefore, in a productive environment, daily benchmarking is not necessary.

Discrete Optimization A problem is not continuous (and is therefore discrete) for the deployment optimizer when the model contains:

Discrete (integer value) lot sizes for transportation

Discrete means of transport

Minimum lot size for transportation

Piecewise linear cost functions for transportation Storage, transportation and handling capacity

If you want the optimizer to consider any of the above constraints, you must use one of the discrete optimization

methods from the SNP deployment optimizer profile.

The piecewise linear cost function that you can define in master data makes a distinction between the convex cost

function (cost per unit increases for higher volumes; for modeling overtime or night shifts for instance) and

theconcave cost function (cost per unit decreases for higher volumes; for modeling freight rates for instance).

Convex cost functions do not complicate the planning problem and can be solved efficiently. However, they can also

be modeled using alternative modes without using piecewise linear cost functions.

In contrast, concave piecewise linear cost functions cannot be solved by an LP solver but only by using

discretization methods (mixed integer linear programming). If piecewise linear functions are modeled but theoptimizer is run without discretization or the discretization horizon is smaller than the planning horizon, the

optimizer takes into account the linear cost function defined in addition to the piecewise linear cost function.

The discrete optimization method cannot be used with strict prioritization (see below).


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Using the discrete optimization method can significantly increase runtime requirements. Note that

Supply Network Planning is a medium-term planning function and its focus should not be on

solving integer problems (that is, using the discrete optimization method).

Decomposition The primary focus of decomposition is on reducing the runtime and memory requirements of optimization.

Decomposition may also represent the only way for the deployment optimizer to find a feasible solution in the event

of large discrete problems.

Decomposition is a flexible tool for the user to balance the tradeoff between optimization quality and required

runtime. When runtime is unrestricted, the optimizer usually provides a better (optimal) solution without

decomposition; however, when a fixed runtime has been specified, using decomposition could assist the optimizer to

find a better or, in fact, feasible solution.

The decomposition methods available for the deployment optimizer are detailed below. The time, product, and

internal deployment decomposition methods can be used in conjunction with the linear and discrete optimization

methods. It is only advisable to use resource decomposition in conjunction with discrete optimization.

Time decomposition speeds up the solution process by dividing the source problem into a series of partial

problems. These sub-problems are then solved in sequence.

Product decomposition speeds up the solution process by forming product groups. The complete model solves

one product group at a time according to the window size chosen. The rule of thumb is as follows: The smaller

the window size, the less time it will take to find a solution, but the larger the window size, the better the

quality of the solution found.

Resource decomposition speeds up the solution process by analyzing the material flow and basic optimizer

decisions about transportation and storage determine a resource sequence. The optimizer can then create sub-

problems for the individual resources, which are solved in sequence. The optimizer makes decisions in every

sub-problem that cause the resource to be loaded. It is particularly advisable to use resource decomposition if the resources are always loaded in a similar

sequence. Resource decomposition does not reduce memory requirements. If you would like to reduce memory

requirements, you could use time decomposition alongside this decomposition method. Resource

decomposition cannot be used in conjunction with product decomposition or strict prioritization.

Internal deployment decomposition can speed up the solution process by dividing the optimization problem

into the smallest partial problems possible. These partial problems are then solved sequentially. It is particularly

advisable to use this decomposition method if the supply chain model has a particular problem structure. Forexample, is the model does not contain capacitiy restrictions, the products can be distributed independently in

deployment, which can help reduce runtime.

You can also use the SNP priority profile to define priorities for product and resource decomposition, that is, you

can change the sequence in which the optimizer groups and plans products and resources in partial problems. For

more information, see the SNP Priority Profile section in Decomposition (SNP optimizer documentation).

Prioritization The deployment optimizer can differentiate between the priority of sales orders and forecast demand. With strict

prioritization, sales orders always have priority 1, the corrected demand forecast priority 5, and the demand forecast

priority 6. Within every priority class, the system uses all available cost information to determine the final solution.
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If you are using cost-based prioritization, the optimizer uses penalty cost information from the product master data

(on the SNP1 tab page) to determine the optimal solution.

Other Considerations The deployment optimization run results do not include pegging orders back to the original individual

requirements because requirements are bucketed.

The deployment optimizer considers the entire capacity and the entire alternative capacity that is globally

available (at all locations).

In the event of a capacity overload, the deployment optimizer, depending on the system settings, either does not

provide a solution or increases the capacity based on a penalty cost calculation.

The deployment optimizer considers the shelf life of a product in a restricted way (see the information about the

SNP optimizer in stock planning , which is also applicable to the deployment optimizer).

The deployment optimizer considers the three following deployment horizons: Pull deployment horizon , push

deployment horizon , and SNP checking horizon . You define these in the deployment optimizer profile.

See also: For more information, see the SNP optimizer documentation that is, to a large extent, the same as the deployment

optimizer documentation (see for example, Application Examples for the SNP Optimizer ).

See also:

Deployment Optimizer Profiles

Running Deployment Optimization
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