SNP Optimization Training Slides

7/22/2019 SNP Optimization Training Slides

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Decision variables are unknown before the decision problem is solved. By solving the

optimization problem, a value is assigned to them.Types of variables in APO:

continuous variables these may take any floating-point values (real numbers) in a range,

e.g., the starting time for a certain job

discrete variables they can only take on integer values in a range, e.g., the numbers of

vehicles on a particular route

binary (0/1-) variablesthey represent binary decisions (e.g. setup decisions).

Finding a feasible solution may be difficult. The objective of SAP APO is to obtain the best

feasible (= optimal) solution of a decision problem or at least a good feasible solution for a

given run time.


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In many practical situation there is more than only one business goal. In case of conflicting

goals it is very difficult to weigh between these objectives. In APO the users can assigndifferent weights to each goal, and they can determine interactively the value of the weights

by using different scenarios.

Linear objective function: each variable is multiplied by a constant, and all terms are added

together. For example, if we are trying to determine the optimum product mix of two products,

A and B, which we denote as X1 and X2 respectively. If the profit margin derived from selling

one unit of A is $100 and that from B is $400, then the overall profit would be F(X) = 100 X1+ 400 X2 which is a straight line on a graph of X1 and X2.


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Variable-type constraints reflect the basic properties of the variables, e.g., non-negativity or integrality ofproduction volume. These constraints usually define the domains for the decision variables (search space).

Functional constraints describe the structural relationship of activities and resources. In allocatingresources to activities, the demand must not exceed the availability. Common constraints on resourcesinclude:

Market demand - The market demand ultimately determines the throughput of the firm. The marketplace

dictates the product lines and production plan. Loss of sales usually is caused by theplants inability tomeet customer requirements at the required time, price and quality. Examples of these constraints are

customer orders; sometimes there are different priorities between the customer orders. In SAP APO

Optimizer, we can either enforce the prioritization of customer orders strictly (i.e., a higher prioritizedcustomer order may not be delayed in favor of a lower prioritized customer order), or we can model

these constraints as soft constraints (i.e., penalize lateness according to priorities).

Material availabilityThis is a fundamental requirement for production. If a vendor delivers the rawmaterial late or the material is defective, the production process is disrupted.

Resource capacityMachines, tools and labor must be synchronized to ensure a smooth and timely flow

of the production process. Both capacity and materials may be soft constraints in the sense that extracapacity and materials can be purchased at an additional cost for mid- term planning. However, for short-

term detailed scheduling, these would be hard constraints. Which resource is a bottleneck may also

change over time as supply or demand changes. Infrastructure logisticsThese include temporal constraints and sequencing constraints in operations and

routing. For example: after the heat treatment, a part may have to wait for a period of time before the

next production step can be executed.


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Hnumber of hockey sticks per day (x1)

Cnumber of chess sets per day (x2)The region enclosed by the graphs of each constraint represent the feasible solution space

(yellow). The objective function (dotted lines) is plotted at different levels. It can be calculated as

x2 =-5/3 x1 + Z/4, where Z represents a fixed level for the objective value. Here, the objective

function values are 32 and 64, respectively.

In case of LPs the optimal solution always occurs at a vertex of the feasible region. Here, it is

reached for x1(H) = 24, x2(C) = 4, and a maximum profit of $64.


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Points in the interior of the feasible space cannot be optimal, because the levellines of the objective function may be moved to higher objective values.

Note that for some special cases the optimal solution may also occur in twovertices of the feasible region. Then, all convex combinations between thesetwo vertices are optimal solutions of the decision problem. In this case, thelevel line of the objective function is parallel to the critical restriction.In our example, this would be the case for the following objective function:

max x1 + 3 x2=> x2 =-1/3 x1 + Z/3

Then, we get several optimal solutions, e.g.

x1 = 24, x2 = 4 x1 = 12, x2 = 8 x1 = 6, x2 = 10 The objective function value is always Z = 36.

BACK-UP: SAP TUTOR: EXAMPLE_THEORY.SIM


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Constraints are transformed into =-restrictions by adding slack variables: x3,x4,x5.


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Idea of the simplex method:

Change of a basis variable (here: x3, x4,x5) with a non-basis variable in the way that- the objective value increases

- the values of the basic variables remain non-negative

Candidates for change of basic: non-basic variables with negative coefficient in

objective function row

Optimal solution found if all coefficients of the objective function are non-

negative

Starting Corner: Non-Basis-Variables: x1, x2 = 0


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1st Iteration:

1. Determination ofPivot Column: the variable with the lowest negative coefficient in the

objective function row is chosen, because the objective value increases most by taking this

variable in the basic (here: x2 with4 isPivot Column).In the sequel, the pivot column is denoted by q (here: q = 2)

2. Determination ofPivot Row: In general, the increase of a new basic variable leads to a

reduction of another basic variable, because otherwise the constraints would be violated.

Therefore, the increase of the new basic variable is limited by the condition that the values

of the other basic variables remain non-negative. The new basic variable can only be

increased until the value of one of the other variables is equal to zero. This variable will be

taken of the basic. For determining this bottleneck for all coefficients aiq > 0 of the pivot

column q the following quotients are calculated:

i= bi/ aiq for all rows i with a iq > 0

where bi describes the value of the coefficient in the row i of the solution column. Then,

the variable in the row p has to be taken of the basic for which the quotient i takes on the

lowest, non-negative value (here: 3 with a value of 10).

=>Pivot Element: a32 (row p=3, column q=2)

3. Pivot step: creation of the unit vector with a 1 in the pivot row, i.e. a*pq = 1.

4. Optimality criteria: optimal solution is found, if all coefficients in the row of the objective

function are non-negative. Otherwise go back to step 1 (choice of pivot column) -> notfulfilled => 2nd iteration necessary


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2nd Iteration

1. Determination ofPivot Column: q = 12. Determination ofPivot Row: p =2



4. Optimality criteria: not fulfilled => 3rd iteration necessary


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3rd Iteration

1. Determination ofPivot Column: q = 52. Determination ofPivot Row: p =1



4. Optimality criteria: fulfilled => optimal solution found


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shadow prices -> coefficients of slack variables in objective function

u3 = 2/6 => decreasing the first restriction (resource A) by one unit would lead to a reductionof the profit by $0.333

-> 4H + 6C < 119 => Profit = 63.667 ( with optimal solution: H = 23.5 and C =4.167)

u4 = 1/3 => decreasing the second restriction (resource B) by one unit would lead to a

reduction of the profit by $0.333

-> 2H + 6C < 71 => Profit = 63.667 ( with optimal solution: H = 24.5 and C =3.667)

u5 = 0 => third restriction (resource C) is not active

reduced costs -> coefficients of decision variables in objective function

since x1 and x2 are both larger than zero, the corresponding reduced costs are both equal to

zero

If reduced costs have a positive value, then they indicate how much the corresponding

coefficient in the objective function for this decision variable has to be increased in order to

get this variable into the basis.


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Simplex: algorithm which moves from one vertex of the polyhedron to a new vertex by

advancing along one edge at a timeInterior Point Method (IPM): algorithm which moves through the interior of the polyhedron.

Comparison:

1. The optimal solution to an LP problem will always lie on a vertex, i.e. on an extreme

point of the boundary of the feasible region

2. An algorithm which moves through the interior of a region must pay attention to the

fact that it does not leave the feasible region. Approaching the boundary of the feasible

region is penalized. This penalty is dynamically decreased in order to find a solution

on the boundary.

3. Interior point methods contain complicated mathematics and use advancedmathematical concepts. A large variety of IPMs has been developed. In linear

programming, IPMs are well suited especially for large, sparse problems. Here,

considerable computing-time gains can be achieved.


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B&B is a specific enumeration algorithm. Different from complete enumeration, only a

limited number of sub-problems has to be solved.The B&B algorithm works like a divide and conquer strategy. First, the linear relaxation of the

integer problem is solved. If the solution is integer, then this is also the optimal solution for

the problem. Otherwise, two new sub-problems are created by branching on a fractional

variable. The process is repeated until no further sub-problem can be created or all solutions

are integers or infeasible.

The determination of good bounds is important, so that the branching may be stopped early.


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Step 1: linear relaxation (e.g., with simplex method)

No feasible solution => MILP has no feasible solution => END

Optimal integer solution => END

Optimal, non-integer solution => step 2

objective function value of LP relaxation is upper bound for objective function value for MILP,

lower bound is `-oo`Step 2: Branching

Branching of a non-integer variable X, with g < X < g+1 and g as integer. The set of all solutions

is divided into two sub-sets: (a) solutions with X g

(b)solutions with X g+1

the other constraints remain unchanged.Step 3: Bounding

The two sub-problems of step 2 are solved. The objective function values of both problems are

upper bounds for the objective value of both sub-sets. If there are still non-integer variables which

have to be integers, then the branching must be continued. But: it is not necessary to analyze a

sub-problem if the bound of the sub-set is lower than the objective value of a feasible, integer solution

the sub-problem is not feasible

If all variables are integers, and if there is more than one sub-problem with a feasible integer

solution, then the optimal solution can be found by comparing the values of the objective

function.Step 4: Optimality test

The algorithm terminates if all branches are implicitly or explicitly analyzed. Then, either an

optimal solution has been found or the problem is infeasible.


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Abbreviation: SP = sub-problem; numbers according to sequence of solving the sub-problems


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SP5 ends, because integer solution is found.

SP6 ends, because objective function value (Z=14) is lower than the function value of SP5

(Z=15) which fulfills all constraints incl. discretisation and therefore is an lower bound for the

overall optimal solutions. Sub-Problems which have a lower objective function value as this

bound does not need to be investigated further.


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End of sub-problem 5: integer solution; new bound: Z = 15

End of sub-problem 6: objective function value of solution with Z = 14 falls below bound Z =15

Note: If there are (in at least one sub-set) variables that are not integer, then a criteria is

needed to determine which sub-problem should be branched. Within the Dakin-Algorithm the

`newest bound rule is usually applied. This means that the last generated sub-set which has

not been completely analyzed is branched. If the sub-sets are generated simultaneously, then

the sub-set with the higher upper bound is chosen.


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Cutting-Plane methods operates by starting in the same way as B&B but then moving towards a

solution by restricting the feasible region defined by the constraints of the relaxed problem.Pure cutting-plane algorithm tends to be slow to reach a solution => In APO: combination of

B&B and Cutting Plane Approach: Branch & Cut (B&C) algorithm.


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Branch and Cut (B&C) proceeds in steps which are similar to B&B, but at each node of the

tree rather than branching on a single variable, the algorithm appends cuts to the problemwhich cut off parts of the LP feasible region without cutting off any valid integer solution.

Standard Cuts:

Gomory

Flow

Cover

Disjunctive

......


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Constraints

Transportationtransportation resource

inbound/outbound handling resource

Productionproduction resource

storage resource

Procurementinbound handling resource

Product Constraintsminimal/fixed lot sizes

safety stock

shelf life

Decision Variables

production, transportation, procurement, storage quantities

deliveries (fulfilled demands)

capacity expansion


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Production/transportation/storage capacities: Limiting the maximum availability of

production/transportation/storage resources for each location.Handling capacity: Limiting the flow in and out of a location. The optimizer may consider

different handling resources for inbound and outbound.

Capacity calendar for resources: The capacity may change during the planning horizon

Break calendar: Breaks can be defined for each resource, e.g. weekends (not: downtimes)

Due dates/ deadlines/ maximum delay: Due dates are considered as asoft constraint, i.e.

lateness is only penalized in the objective function, whereas deadlines are hard constraints,

i.e. no delay is allowed. Deadlines may be modeled by limiting the maximal delay with 0.

Safety stock: Falling below safety stock is considered as a soft constraint, i.e. penalized in the

objective function.Additional:

Integer lot size: production or transportation lot size is not continuous sizeable.

Minimum lot size: some resources may only be used for large lot sizes.


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If setup times are small and a lot of products are on a resource per bucket, then a global

capacity reduction in the resource master data should be used instead of using fixed resourceconsumption in the PPMs of each product.


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If the average lot size (derived from demand data) is considerable larger than the minimum lot

size, then it is not necessary to model minimum lot sizes. For instance, with an average demandof 10 pieces, it makes actually no sense to use a minimum lot size of 1 piece for the SNP

Optimizer.


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Optimization from final stage in Supply Chain (customer level) to the first stage in the Supply

Chain (plant level).


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The optimizer has to run on a Windows Server machine. As of SCM 2007, Linux is also

supported.On a 32bit OS, the maximum main memory which can be used is 3 GB. (1 GB is reserved for

operating system).

On a 64bit OS, the only restriction is the physical memory size (swapping will greatly reduce

the performance).

The processor should always be as fast as possible. With the processor performance you can

directly influence the run time performance of the SNP optimizer. You have an almost linear

relation, i.e. if you have a run time of 3 hours when you use an 2 GHz processor, then you will

get a run time of only 2 hours, if you use an 3 GHz processor of the same type (for different

CPU types, the .Important note: 112403 (addressing 3 GB memory under Windows NT/2000/2003)

In case of problems with main memory (overflow), there are some specific parameters in

transaction copt10 available to solve these problems. Get in touch with development (via OSS

message) when you experience these kind of problems, so that development can recommend

which parameter should be set. However, there are upper bounds for the number of variables

and constraints for the optimizer. Benchmarks from customer cases will follow later.

How to get the sheet?

http://service.sap.com/scm

-> mySAP SCM technology -> Performance and ConfigurationRecommendations

-> System Requirements for the SAP APO 3.x and SAP SCM 4.x Optimizer


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For a FIXED run time with increasing model detail (i.e. complexity of the model), the gap

between optimal solution and computed solution by the optimizer increases. business will probably not accept these solutions, since they are too far away from the optimal

solution (model too detailed)

if the model is too rough, then the business will also not accept the solution, since the model

doesnt correspond to their business requirements.

In the optimal model detail region, the model is not too rough (i.e. it respects the important

business requirements) and not too detailed (i.e. it is still solvable in an acceptable run time).

Here, acceptable solutions (in terms of business requirements and run time) are found.

However, it might also be necessary to use e.g. decomposition techniques to get the right trade-

off between solution quality and run time and to reach business acceptability of the computed

solutions.


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The customer uses SNP and PP/DS. Nevertheless, they have used a very detailed time bucket

profile in SNP resulting in 82 planning periods and a huge amount of (discrete) variables andconstraints. It was not possible at all to solve this model in SNP. But since the customer uses

PP/DS for detailed scheduling, it actually didnt make sense to use such a detailed time bucket

profile and it was possible to convince him to use an aggregated time bucket profile with only 39

planning periods.

Additionally, the number of transportation lanes could have been reduced and the number of

discrete variables as well. It was not absolutely necessary to have minimum lot sizes and fixed

resource consumption, but, on the other hand, transportation lot sizes have been introduced. In

total, the number of discrete variables have been reduced.

Nevertheless, it is not possible to solve a problem with such a lot of (discrete) constraints and

variables without any decomposition technique. After testing a lot of problem instances with time

and product decomposition (with different parameter settings), the customer uses now product

decomposition. The total run time is 10 hours (including data collection and writing back of the

results), pure optimization time is set to 7 hours, since such an amount of time is available.

However, good results are already found after approx. 3 hours.


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General remark

cost values shouldnt differ too much range from 1 to 10^6 - 10^8 is o.k.

larger differences may lead to numerical problems

Central maintenance of costs

-> Master Data -> Supply Network Planning Master Data

-> Maintain Costs (Table)

-> Maintain Costs (Directory)

Purpose of cost profile:

global weighting of different cost factors

checking global impact of one cost factor on solution, e.g. what is the impact if capacityincreases are very expensive

cost values for all corresponding decision variables are changed

to increase/decrease specific values for, e.g., producing/transporting/storing a specific product,

the individual PPM/transportation/ storage costs in the master data have to be changed


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Costs for increasing production capacities are defined in the resource master data(quantities/rates-definition in capacity variants).

Costs for storage expansion are defined in the resource master (in the same manner as costs for

production expansion).

The costs functions can centrally be maintained in transaction /sapapo/snpcosf.


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Single-level costs are variable (quantity-dependent) costs which are incurred when using this

production process model in one bucket. For each execution of the PPM the single-level costsoccur. If, e.g., an PPM is executed 10 times in a bucket, and the single-level costs are equal to

5, then the PPM costs in this bucket are 50.

Multi-level costs are not relevant for the SNP optimizer. They are only used for the scheduling

heuristics in PP/DS.

Cost value in Input log: //ET_PROMO: PCOSTvariable cost of PPM


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The costs are calculated according to the cost function in the example as follows:

5 + 4*x for 0 < x < 10 15 + 3*(x-10) for 10 < x < 20

20 + 2*(x-20) for 20 < x < 30

25 + 1*(x-30) for 30 < x < 999.999.999,999

where x represents the number of PPM executions per bucket.

Note, that the upper bound of one segment is equal to the lower bound of the subsequent

segment. If the number of PPM executions is equal to these bounds, then the cheaper part of

the segment is chosen. If in our example x = 10, then the costs will be 15.

In general, the costs are computed as follows:

fix costs + variable costs * (x- from-value in cost function)If you want the optimizer to consider the cost function, you do not only have to maintain it in

the PPM, but you also have to activate the corresponding field in the optimization profile. If the

cost function for the PPM is used, the optimizer ignores the PPM costs.

Cost functions for transportation and procurement work exactly with the same logic.

Again, you have to use the discrete solver if the optimizer should consider the cost

functions.

Important note: continuous cost functions reduce the complexity for the optimizer!


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Cost Functions in Inputlog:

// ET_TRANCOS (Transport Cost Functions)

// ET_PRODCOS (Production Cost Functions)

// ET_PROCCOS (Procurement Cost Function)

with

ORIGN: smallest value

FIXCO: fixed cost

VARCO: variable cost

In example above:

// ET_PRODCOS

// PROID ORIGN FIXCOVARCO

< PPM name> 0,0000000000000E+00 1,0000000000000E+03 5,0000000000000E+00

< PPM name> 1,0000000000000E+02 1,5000000000000E+03 4,0000000000000E+00

< PPM name> 2,0000000000000E+02 1,9000000000000E+03 2,0000000000000E+00

< PPM name> 3,0000000000000E+02 2,1000000000000E+03 1,0000000000000E+00

cost = FIXCO + VARCO * (XORIGN)


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Transportation costs are calculated as follows:

Trans. costs describe the variable costs for transporting a product via a transportation laneper unit of measure.

Mns of Trsp Costs mean the distance-dependent costs of a Transport Method (per km).

The cost function can only be maintained in transaction /sapapo/snpcosf (or: SC Engineer ->

Goto -> Cost functions which leads to this transaction).

Transportation costs in Input log:

table ET_ARC:

TCTYP: variable transportation cost for the fleet

-> calculated as: Mns of Trsp Costs * Trsp distance

table ET_ARCMAT:TCOST: variable cost for the material at this lane = Trans. costs

table ET_TRANCOS: cost function for transportation


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Transportation Unit [TRAUNIT] is the unit maintained at the transportation resource and Base Unit ofMeasure [BMAT] is the unit of measure in the product master. Both have to be maintained

consistently, i.e. in the product master the conversion to the transportation unit has to be maintained.Here, both units are kg.

resulting transportation costs: 180/5000[BMAT/TRAUNIT]*2[APO-$/km]*672,576[km] +

180[BMAT]*100 [APO-$/TRAUNIT] = 18048,425 [APO-$]The first cost term describes the distance-dependent costs associated with the usage of a transportation

resource, the second term describes the quantity dependent costs of transporting the material.

If you dont have a transportation resource, then the first cost term will simply be multiplied with the

transported quantity, i.e. the default value is then 1 transportation unit [TRAUNIT]. In case of a

transportation resource this implies that the Mns of Trsp Costs associate with the complete utilization of

the resource, whereas in case of no transportation resource the Mns of Trsp Costs associate with the base

unit of measure of the product.The first cost term is bucket-dependent, the second one bucket-independent. This means, that the first

cost term is taken into account in each bucket. If, e.g., a transport starts in one bucket and ends in thenext buckets, then these costs will occur twice, whereas the quantity-dependent cost occur only once.

If you use a cost function, then you have to specify variable and fixed costs. Note, that both cost terms

are multiplied with the transportation distance. Example: If you have maintained fixed costs of 100APO-$ and variable costs of 10 APO-$, and a transportation distance of 950 km, then the fixed costs

are equal to 95000 APO-$ and the variable costs are equal to 9500 APO-$. You can check this in the

Input log of the optimizer, in table ET_TRANCOS. For calculating the cost of a transport, the variablecosts are multiplied with the transported quantity.


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Procurement costs in Input log: in table ET_LOCMAT:

FCOST: linear procurement cost

FPERF: flag: procurement permitted = 'X'


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Restricted use of shelf life in SNP. Please check notes 574321 (SNP in

general) and 579556 (SNP OPT).

The optimizer only considers shelf life when the flag Plng w/ shelf life is switched on in the

product master and a shelf life is maintained (Attributes Tab). Additionally, the flag Do Not

Take Shelf Life into Account must not be activated in the optimizer profile.

Shelf life violating costs in Input log: in table ET_LOCMAT:

WASFL: flag: shelf life penalty is active -> WASFL = 'X'

WASTE: shelf life: penalty for wasted quantity (same values as for procurement

costs FCOST)

STODU: shelf life: storage duration


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Restricted use of shelf life in SNP. Please check notes 574321 (SNP in general) and 579556

(SNP OPT).

Settings:

Do Not Take Shelf Life into Account: the optimizer does not take product shelf lives into

account during optimization, even if the corresponding master data are maintained.

Continue Using Expired Product: the optimizer takes into account the shelf life of

products that was specified in the product master. For expired products (those that have

passed their shelf life expiry date), the optimizer plans for the continued use of this product,

but calculates penalty costs for this continued use.

Dispose of Expired Product: the optimizer takes into account the shelf life of products that

was specified in the product master. For expired products (those that have passed their shelf

life expiry date), the optimizer plans for the disposal of this product, but calculates penaltycosts for this disposal.

Use Penalty Csts that are not Prod-Dep.: This indicator can be activated in connection

with either the Continue Using Expired Productindicator or theDispose of Expired Product

indicator. By default, the optimizer considers the location product procurement costs that

were defined on theProcurementtab page in the product master. If you want to enter and

define different costs that are not dependent on the product, you are able to do this in the

Penalty Costs: Not Product Dependentfield. In this instance, you must also activate the Use

Penalty Csts that are not Prod-Dep. indicator.


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Only the ending inventory for the bucket accrues an inventory cost. If you use cost per bucket, it

is harder to adjust the storage cost to balance with the safety stock penalty cost which iscalculated as a cost per day. In addition, using a storage cost per bucket does not account for

planning in different bucketse.g., weekly or monthly planning buckets.

For LPs, it is not necessary to maintain storage costs to get JIT behavior of the optimal

solution. It is ensured via an internal post processing in the optimizer that planned orders are

generated as late as possible to satisfy the demand. This post processing is not active for discrete

problems, i.e. storage costs have to be maintained to get this JIT result.

Cost Calculation:

Average Stock On Hand: the optimizer calculates the storage costs by multiplying the

following values together: Stock on hand at the end of the bucket (period)

Storage costs that have been defined for the location product in the product master (in theProcurementtab)

Number of days in the bucket

Stock on Hand at End of Period: the optimizer calculates the storage costs by multiplying

the stock on hand at the end of the bucket (period) with the storage costs that were defined for

the location product.

Storage costs in Input log: in table ET_LOCMAT

HCOST: storage costs

BUCFL: storage cost: multiply by bucket length = 'X'

-> if setting Average Stock On Handis chosen


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Prerequisite in location product master:

maintained safety stock (target safety stock level)

safety stock planning method

Take Period Length into Account:

the optimizer also takes the period length into account when calculating the penalty costs for

falling below the safety stock, which means that it multiplies the product from an absolute value

(or relative value) of the stock fallen below with both the penalty costs and the period length in

days. In case of weeklybuckets this would mean that the resulting penalty costs would be

multiplied with 7 if this checkbox is activated.

If this setting is not active, then the optimizer does not consider the number of days in a bucket (in

which inventory is below safety stock).


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Safety stock penalty is calculated as:

Take a Relative Deviation from the Safety Stock into Account: the optimizer calculates

penalty costs when the safety stock level is fallen below. To do this, it multiplies the percentageof the amount fallen below with the penalty costs that were defined in the product master (per

day).

calculation:

safety stock penalty cost * [(target safety stockinventory)/target safety stock] * (if required)

period length in days (number of days that inventory is below safety stock)

Take an Absolute Deviation from Safety Stock into Account: the optimizer calculates

penalty costs when the safety stock level is fallen below. It multiplies the absolute value of the

amount fallen below with the penalty costs that were defined in the product master (per day).

calculation:safety stock penalty cost * amount of stock below the desired safety stock * (if required) period

length in days (number of days that inventory is below safety stock)

Take Period Length into Account: the optimizer also takes the period length into account

when calculating the penalty costs for falling below the safety stock, which means that it

multiplies the product from an absolute value (or relative value) of the stock fallen below with

both the penalty costs and the period length in days. In case of the example above with weekly

buckets this would mean that the resulting penalty costs would be multiplied with 7 if this

checkbox is activated.

If this setting is not active, the optimizer takes into account the number of buckets in which

inventory is below safety stock.

Safety stock penalty costs in Input log: in table ET_LOCMAT:

SSPEN: penalty for not covering safety stock


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The penalty cost for not satisfying the demand is interpreted as cost per unit of demand not

satisfied.The penalty cost for delay is interpreted as cost per unit of demand per day of delay, e.g., if

you are working in monthly buckets, then the cost is multiplied by the number of calendar

days.

Late Delivery costs, maximum lateness and non delivery costs in Input log:

// ET_DEMCLTIM (Demand Class with Penalty for Delay/Not Delivering)

LAPEN: penalty for lateness

MAXLA: maximum lateness

NDPEN: penalty for not delivering

Demand data in Input log in table ET_DEMAND: DEMCL = demand classes:

1highest priority (customer demand)

2priority 2 (corr. forecasts)

3lowest priority (forecasts)


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Resource utilization cost is on the capacity variant 2 of the resource. Capacity variant 2 has to

be specified as the active variant of the resource. This works for mixed and bucket resources.For mixed resources, you have access to the capacity variant only if you explicitly enter the

bucketed capacities.

Resource utilization cost is interpreted as cost per unit of resource above the capacity variant

1. If you are only using capacity within capacity variant 1, then no costs are considered.

In APO 3.x you have to use capacity variant 1 and 2.

Costs for capacity expansion in Input log:

// ET_RESFAMC (Resource Family Calendar)

INCRF: upper bound for increasing production resource

INPEN: penalty per unitary volume for increasing capacity


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You have to maintain non-delivery or delay costs, otherwise there will be no production at all,

since the do-nothing-at-all solution will lead to minimal costs (=0).With storage costs you can control the JIT behavior of the system.


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Storage and shipping calendar are only used if the corresponding resources are not maintained.

If there is a storage resource, or a handling resources, respectively, then the calendar from thisresource will be used by the optimizer.

The same is valid for production resources.

The stock category group from the SNP Tab is not used by the SNP optimizer, and the

deployment settings are only used by the deployment optimizer.


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The shelf life parameters are on the product master. Shelf life parameter is product specific not

product-location specific (e.g., the same shelf life at the plant or at the DC). The optimizer onlyconsiders shelf life when the flag Plng w/ shelf life is switched on in the product master and a

shelf life value is maintained (Attributes Tab). Additionally the corresponding setting in the

optimizer profile has to be activated. Only the shelf life field is used by optimization, any of the

other fields (maturity, min/max shelf life) are not used.

In SNP, only optimization recognizes shelf life. Other SNP solvers and the interactive planning

tables do not recognize shelf life. It may be misleading to see stock in the interactive planning

table that is no longer useable. Please check notes 574321 (SNP in general) and 579556 (SNP

OPT) for more detailed explanations.

The shelf life costs occur in the bucket where the product has to be discarded due to exceededshelf life.

Shelf life parameters in Input log: in table ET_LOCMAT:

WASFL: flag: shelf life penalty is active -> WASFL = 'X'

WASTE: shelf life: penalty for wasted quantity

STODU: shelf life: storage duration


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Production storage capacity is a product specific maximum stock level at this location (per bucket).

The maximum lot size from the product location master is not considered during the optimizer run, but it is taken into

account for the creation of orders (in liveCache).

Example: maximum lot size in location-product master data = 500 optimizer creates an order of e.g. 2000 (also displayed in interactive planning table)

4 orders of 500 are shown in the detailed view / product view

Customizing change necessary; ta: SPRO -> APO Implementation Guide -> Advanced Planner

and Optimizer -> Supply Chain Planning -> Supply Network Planning (SNP) -> Global Settings

for SNP Optimizer: activate checkbox create orders, if more than one order should be created,

if necessary by location-product master data settings (for details see consultancy note 503222)To consider rounding value as lot size, the flag discretisation in the PPM has to be activated.

Additionally, the corresponding settings in the optimizer profile for discretization (min lot size, rounding value) and

production storage capacity (general constraints/capacity restrictions: maximum product-specific quantity stored) have

to be selected.Lot Size parameters in Input log:

in table ET_PROMO:

LOTSZ: rounding value or fixed lot size from location product

PMINQ: minimum production lot size (from PPM or location product)

Note: 'PMAXQ' in table ET_PROMO: maximum production lot size (from PPM)

in table ET_LOCMAT:

MAXST: maximum stock

MAXFL: flag: Constraint active -> MAXFL = 'X' (Optimization Profile)

in table ET_LOCPROD:

SAFTY: safety stock (Note: 'FPROD' in table ET_LOCPROD describes confirmed production)


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Within the Production Horizon (in days) / Stock Transfer Horizon (in days) the optimizer does not

plan production / transfers. That is, the optimizer does not create SNP planned orders /

distribution receipts within this horizon, but postpones production / distribution to the first daybeyond the specified production / stock transfer horizon.

Forecast horizon (in days) defines a horizon in calendar days when the forecast is not considered

as part of total demand. Actually, the optimizer does not respect the forecast horizon. If you want

it, apply note 412551. Note 443012 has also to be applied, if you are lower than SP17.

Horizons in Input log: table ET_PRODBND for production horizon, table ET_TRANBND for

stock transfer horizon.

It may happen that there is an entry in the input log even if no production / stock transfer horizon

is maintained: if you have e.g. weekly buckets, and you run the optimizer on the second day of the

week, then no creation of production/distribution orders in this week is possible, i.e. the horizonsare active. The same is true for table ET_PROCBND (see below).

Procurement parameters in Input log:

in table ET_LOCMAT:FPERF: procurement type

FCOST: linear procurement cost (variable)

SSPEN: penalty for not covering safety stock

HCOST: storage costs

in table ET_PROCCOS:procurement cost function

in table ET_PROCBND:planned delivery time => procurement bounds are set to 0.


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Maintenance in Product Location Master Data -> Procurement Tab, field: Procurement Type

E = In-house Production

x = maintained

- = not maintained

Consulting note: 510559.


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Maintenance in Product Location master data -> Procurement Tab, field: Procurement Type

F = External Procurement

x = maintained

- = not maintained


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Maintenance in Product Location master data -> Procurement Tab, field: Procurement Type

X = In-house Production or External ProcurementP = Procurement Planning: External

x = maintained

- = not maintained

* = maintenance not relevant for interpretation


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GR/GI parameters in Input log in table ET_LOCMAT: RECTI: goods receipt processing time

ISSTI: goods issue processing time

CONIN: consumption of input handling capacity (goods receipt) (if inbound handling

resource is maintained in location master data: Resources Tab)

CONOU: consumption of output handling capacity (goods issue) (if outbound handling

resource is maintained in location master data: Resources Tab)

CACON: storage capacity consumption (if storage resource is maintained in location

master data: Resources Tab)

Example: If the handling resource can handle 1000 kg per day, and you define the handling

capacity consumption as 10 kg per piece, the maximum rate is 100 pieces per day.

If you want to consider the GR time for in-house production, then go to APO customizing:

supply chain planning -> SNP -> basic settings -> maintain global SNP settings: HEU:

Planned Order GR = processing time.


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Handling and storage resources in Input log:

ET_LOC (Handling resources at a location)

LOCID: Location ID, RESIN: Handling resource ID for inbound INPEN: Penalty for increasing the handling capacity

MAXIN: Flag: Hard + soft constr. active -> MAXIN = 'X'

RESOU: Handling resource ID for outbound

OUPEN: Penalty for increasing the handling capacity

MAXOU: Flag: Hard + soft constr. active -> MAXOU = 'X'

ET_LOCC: Handling resource: normal capacity MAXHA: handling capacity

ET_LOCUC: Handling resource: additional capacity INCHA: upper bound for increasing the handling capacity

ET_SUBLOC (Storage resources of a sublocation) SUBID: Sublocation ID

INPEN: Penalty for increasing the capacity

MAXFL: Flag: Hard + soft constr. active -> MAXFL = 'X'

ET_SUBLOCC: Storage resource: normal capacity MAXSL: storage capacity

ET_SUBLOCUC: Storage resource: additional capacity INCSL: upper bound for increasing the storage capacity


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Over utilization, downtime settings, minimum utilization not recognized by SNP.

There is no cost associated with using the normal capacity. The cost assigned to the resource is thecost per unit of capacity used above the normal capacity (variant 2).

Details about Cross-Period Lot Sizingwill follow later.

Production resource in Input log:

ET_RESOURCE (Elementary production resources) RESID, RFAID: Resource ID and resource family ID

UNIVO: Unitary volume (=1.0, redundant entry)

ET_RESFAM (Production resources of a resource family) RFAID: Resource family ID

INPEN: Penalty per unitary volume for increasing capacity

MAXFL: Flag: Hard + soft constr. active -> MAXFL = 'X' (finite capacity)

DISCR: Flag for using discrete increase of resource-family

ET_RESC: Production resource: normal capacity (variant 1 or standard capacity) MAXRE: production capacity

ET_RESFAMC: Production resource: additional capacity (variant 2) INCRF: upper bound for increasing the production capacity

INPEN: penalty for increasing production capacity


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From an economical viewpoint it is sometimes sensible to require a minimum resource utilization even if thedemand is not sufficient. We use capacity variants to model the utilization, and the SNP optimizer then takes

them into account. If the utilization is below the minimum then the optimizer considers costs, i.e. the

minimum capacity is a soft constraint. The minimum, the normal and the maximum resource consumptionare realized as capacityvariants of a resource.

In the capacity view of SNP planning book three capacity variants are shown. The minimum resource

capacity will be shown in the new key figure SUPVAR3. The minimum resource capacity is computed bymacros according to the macros of the first two resource variants (SUPVAR1 and SUPVAR2).

The choice which product(s) are selected to load the resource up to the desired (minimum) capacity depends

on the overall cost situation. The decision is made in the way that the total costs are minimized.Calculation of costs

Non-utilization (minimum capacity) (minimum capacityused capacity) * costs for falling below minimum capacity

= (8

6) * 100 = 200

Resource utilization MIN(used capacity, normal capacity) * costs for used capacity = MIN(12,16) * 1 = 12

Increased utilization (maximum capacity) (used capacitynormal capacity) * costs for increased capacity = (22 16) * 10 = 60

+ Resource utilization costs = 16

=> total costs = 76


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SAP TUTOR: OPT_MIN_RES.SIM


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For further information refer to the PDS guide in the Manufacturing section of the servicemarketplace.


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The PPM duration is important for the finish date of an activity. Together with the bucketoffset, the PPM duration determines the material availability.

The optimizer takes the fixed resource consumption only in account when the corresponding

setting in the optimization profile is activated (discrete constraints: fixed consumptions: fixed

material and resource consumption).


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The minimum lot size is always the minimum lot size per BUCKET. This is also true for

minimum transportation lot sizes.

The lot size is always the lot size per BUCKET. This is also true for transportation lot sizes.The maximum lot size is always the maximum lot size per WORKING DAY. This is also true

for maximum transportation lot sizes.

Note that both, minimum and maximum lot size in the PPM, refer to the unit of measure of

the location product, not to the unit of measure of the output component in the PPM.

Note 503222 describes how order splitting can be activated. In APO 3.0, you have to use an

copt10 parameter, in APO 3.1 and 4.0, you can customize this.

Note that SNP heuristic and CTM only use the min./max. lot size from the location product

as min./max lot sizes. The min./max. lot size from the PPM only determines the validity ofan order.

Example: if there is only one PPM (or one means of transportation) with a min. PPM

(transportation) lot size of 5 and a max. PPM (transportation) lot size of 10 to produce

(transport) a specific product, and we have an order/demand of 2 or 20, respectively, then this

order/demand cannot be satisfied, since no valid procurement source exist to produce/procure

this product.


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Note: Rounding value, fixed lot size and Lot-for-Lot are only considered with selected

discretisation in PPM and corresponding setting in the optimization profile.

Minimum lot size is only considered if the corresponding setting in the optimization profile(discrete constraints) is selected.

Maximum lot size in location product master is only considered for the determination of the

planned production order, i.e. the produced quantity is split in planned production orders in

the size of the maximum lot size in the location product master (or less). In AP0 3.0 it is only

done, if all PPM of the selection have no "fixed resource consumption". If you don't want this

splitting in any case - it can be switched off (see note "503222 Info on Optimizer Production

Order Splitting").

Recommendation for maximum PPM lot size: If you have no specific "maximal lot size"

for your PPM, don't enter a value like 9999999999 in this field. This could lead to problems

in the floating point precision during optimization, i.e. it could provoke bad results and a

bad performance of the SNP optimizer. In this case we recommend a value that is around

10% - 20% higher than the maximum that could be produced in one day.

If a rounding value is used, then the maximum lot size should be a multiple of the rounding

value. Using the rounding value requires that Lot-for-Lot in the location product master is

activated.

Output quantity means the production (planned) as displayed in the interactive planning table.

The planned production in the optimizer log file describes the number of PPM executions and

it can be different.

Consulting Notes: 503294 (Info on Optimizer Production Lot Size), and 448986 (Info onOptimizer Lot Sizes, collection of related notes).


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Note:If fixed lot size is maintained in location product master, then it overwrites the

minimum lot size from PPM, i.e. PMINQ.


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In both cases, the minimum and maximum lot sizes are not relevant, e.g. PMINQ = 0, PMAQ= 1000.

Examples illustrates interpretation of rounding value and material consumption in PPM. Note

that in the first case the output rate (OUTIN) displayed in the input log is still equal to 7, but

internally, the optimizer calculates with an output rate of 11.


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Definition of Lot Size Profile for Transportation Lanes:Supply Network Planning-> Environment -> Current Settings ->Profiles-> Define Supply

Network Planning Lot Size Profiles (Transportation Lanes)

Example for discrete transportation method (= truck):

demand at a DC: Product A: 8 tons; Product B: 8 tons

capacity of a truck: 10 tons

solution without discretisation: transportation of 16 tons

solution with discretisation: planned transport of either

10 tons with 1 truck and 6 tons backlog for one of the products (for which

product a backlog is created, depends on the late delivery penalty and non-delivery costs).

Here transportation costs are incurred for one a full truck

or

8 tons for product A and 8 tons for product B (with 2 trucks). Here,

transportation costs are incurred for 2 full trucks.

The actual result depends on the cost and master data (late delivery penalty,

non-delivery costs, storage costs, warehouse capacities, and so on).

Consulting note: 511782


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To activate discrete transportation fleets, the corresponding setting in the optimizer profile has to

be selected (-> transports). Additionally, the checkbox DiscrTrMet has to be selected at the

corresponding transportation lane.To activate minimum lot sizes, the corresponding setting in the optimizer profile has to be

selected (-> Transp lots).

To activate the maximum lot size, the corresponding checkbox in the optimizer profile has to be

selected.

To get integer lot sizes, the following setting in transaction /sapapo/copt10 has to be entered:

Section: SNP

Name: DISCRETETRANSPORTLOTHORIZON

Switch: INTEGER Integer Column:

Capacity Consumption (TCONA) describes how much capacity is used by transporting the

material at this lane.

The available capacity can be found in table ET_FLEET, column TUNIA. The penalty costs for

increasing the capacity are displayed in column INPEN. MAXFL describes whether the capacity

is finite (-> X) or infinite.

Additional entries in Input log:

ET_FLEETC: Fleet resource: normal capacity MAXFL: capacity measured in trucks

ET_FLEETUC: Fleet resource: additional capacity INCFL: upper bound for increasing the fleet capacity


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Bucket-Offset (BO) controls the material availability in a bucket. Values between 0 and 1 can

be entered.Counterpart in optimization profile: rounding limit for production (value: 0-100%). But

bucket offset from PPM is leading, i.e. if you have entered a value there, the rounding limit

from the optimization profile is not considered.

Additional example: if this bucket offset factor is set to 0.5 and the optimizer is ran for

monthly buckets: If the duration of the operation is less than 15 days (half the month), the

material output from the operation will be available for the subsequent operation in the same

month. If the duration is more than half a month, the output will only be available in the next

monthly bucket. For the first scenario, the first and following operation will be scheduled in

the same month. But note that since the optimizer works in buckets, both operations will havea start date set to the first of the month - not scheduled in sequence.

Notes:

Breaks (e.g. weekends) extend the length of a PPM duration

If PPM exceeds the bucket length, then the number of days in the bucket in which the PPM

is finished, is relevant for the calculation.

Consultancy Note: 434197


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Definition of Quota Arrangements in Master Data (transaction: sapapo/scc_tq1).Optimizer Profile: Multiple quotas: either consider first or consider last

Defines how the Optimizer in Supply Network Planning (SNP) proceeds, if multiple quotaarrangements exist for a particular period: You define that the optimizer considers the first or lastquota arrangement in a period. The optimizer then ignores the remaining quota arrangementswithin this period.

Not maintained: treat as zero or ignore Defines how the optimizer in Supply Network Planning (SNP) proceeds if quota arrangements

have not been defined for all relevant sources of supply:

Treat as Zero: The optimizer does not plan any stock transfers from the sources of supply forwhich no quota arrangements have been defined.Ignore: The optimizer plans stock transfers from all relevant sources of supply. Quotaarrangements are considered for those sources of supply where they have been defined.

Product-Group Assignment in Product Master data (Tab: Properties 2)Quota arrangement constrains the total receipt of all products within the group at the givenlocation only.


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All three methods arrive at an optimal solution. The main difference in the application of these

methods may be the runtime. There is no general rule known for selecting the best method fora given problem (except testing each one of them). A good measure for the application is

benchmarking on a test scenario because the optimal choice of the method depends mainly on

the structure of the supply chain and less on the given input data. Therefore, in a productive

environment, daily benchmarking is not necessary.


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Window size defines the number of periods which are fixed after each single run. In the example

above, it is set equal to 3.

Standard settings / Default values: window size is defined by the user (should be as large as possible)

overlap = window size/2 (non integer numbers are rounded down)

aggregated = remaining periods are aggregated to one bucket

Example: 52 buckets, window size = 26=> delta = 13, aggregated = 1

=> problem with 40 buckets is considered in the first optimization run

With transaction /sapapo/copt10 you can change these standard settings (Section:

SNP_TIMEDECO)

parameter MinOverlap defines the overlap/delta (in the example above it is equal to 3) parameter DiscreteOverlap defines the discrete overlap (which is equal to 2 in the example).

It has to be defined smaller than or equal to MinOverlap. If not, it will be set equal to Min

Overlap.

parameter AggregationWindowSize defines how many of the remaining buckets are

aggregated into one bucket (in Example = 4)

SWITCH for all parameters: Integer

Dont change default values without talking to development/expert optimization

consulting.


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DEMO


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planning window defined by the material flow: consider all resources, intermediate materials andactivities which could be used to produce a given product

reduction of complexity by considering only a subset of the materials as well as only the subset

of other entities that have to be considered for these materials (e.g. resources, transportation

lanes)

after each run for a sub-problem, capacities are reduced according to the individual solution, and

these solutions are aggregated iteratively to a consistent solution for the whole problem

window size represents the %-value of the model size which are considered in one sub-problem

(single LP run)

gliding window approach => overlaps between sub-problems (only a part of the solution of a

specific sub-problem stays fixed)


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Column 1 and 2 are defined by the user, taken from the prioritization profileColumn 3 results from the Bill of Materials in the PPM (or PDS). From this

BOM we get the atomic sub-problems. Sub-problem 1 is resulting e.g. from the fact that P2 and P3 are input components to

produce the output component P1.

The other sub-problems result from similar relationships.


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Step 1:Column 4, i.e. the priority for the product decomposition, is then derived from the

priority of the sub-problems.


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Step 2:

For sub-problems with DIFFERENT priority, there is definitely NO overlap (step 2).


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Step 2:

For sub-problems with DIFFERENT priority, there is definitely NO overlap (step 2).

Step 3:

For sub-problems with the SAME priority, the standard sorting is used. E.g. if a

partitioning parameter (window size) of 99% is used, all sub-problems are solved

together, if a window size of 0% is used, they are all treated separately.


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The iteration limit (number of improvements) should only be used for test purpose: specify an

iteration limit of 1. Solve the discrete model without a runtime limit with full search. Thatwill give you an estimate, how long the optimizer will take to find the first feasible

solution.

Note: changes in the model and/or changes in the demand pattern and quantities may

implicate a big difference for the time to solve the model!

Disable the iteration limit (set it to 0) and use a reasonable runtime limit instead (take the time

you gathered during the tests with the iteration limit =1 into account and multiply it by e.g.

2) and solve the model.


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Example: Take the Minimum PPM Lot Size into Account

If you have chosen the discrete optimization method, you can specify in this field that you want

the optimizer to take into account the minimum lot size (that was defined in the productionprocess model (PPM)) when running the PPM.

You can define the horizon for which you want this discrete constraint to be considered. You can

either define a specific horizon (in days or weeks, for example), or specify whether the discrete

constraint should be considered across all daily buckets, or across all daily and weekly buckets,

and so on, based on the buckets defined in the planning buckets profile. The discretization

horizon starts from today's date, even if the planning horizon starts in the future or the past.

Dependencies

You must activate Discrete Optimization in the optimizer profile header data.

You must define the minimum lot size in the PPM.

If a larger minimum lot size has been defined in the location product master, the optimizer

takes into account the value from the location product master, even if this indicator is

activated.


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Detailed discretization for different restrictions for daily, weekly or monthly buckets

(analogously for quarterly or yearly buckets)If SNP - time buckets consist of weeks and days is chosen as detailed discretization, then in the

optimization run this choice is ignored and there is no discretization at all. Analogously for

monthly SNP bucket sizes: if days or weeks are activated in detailed discretization, then there

are no discrete variables.

Discretization in Input log:

DISCM Discretization algorithm: K, P or V

DITER Discr.: Maximum number of iterations

DLAUF Discr.: Maximum runtime in seconds

DTRUN Discr.: Rounding-limit transportat. variables

DPRUN Discr.: Rounding-limit production variables

DISRF End bucket for increasing prod.res. discretely

DISRZ End bucket for using fix prod.res. consumption

DISTR End bucket for using discrete fleet on lanes

DISPR End bucket for using a discrete PPM

TRLOS End bucket for using transport lots of material

PRLOS End bucket for using production lots

COSTR End bucket for using transport cost function

COSPD End bucket for using production cost function COSPC End bucket to use procurement cost function


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Examples:

10 D => first 8 buckets are discrete, since days 8-10 fall in the first weekly buckets;afterwards no discretization

3 Z => first 21 days are discrete; afterwards no discretization

7 Z => first 49 days are discrete; afterwards no discretization

3 I => first 3 months are discrete; afterwards no discretization


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With strict prioritization you can assign the priority ofsafety stockto one of the three demand

classes. This is only possible for LPs.


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Calculation of profit simply means to multiply the fulfilled demands of all products with the

corresponding non-delivery costs and subtracting the costs from this value.Example: 1 Product with non-delivery costs of 10,000 APO-$ and a demand of 100. The costs for

producing, transporting and storing this product is 50,000 APO-$ (this is the result which you will

get if calculation of profit is not activated). Then, the profit is equal to 1,000,000-50,000 =

950,000 APO-$.


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Average Stock On Hand

the optimizer calculates the storage costs during planning, by multiplying the following valuestogether:

Stock on hand at the end of the bucket (period)

Storage costs that have been defined for the location product in the product master (in the

Procurementtab page)

Number of days in the bucket

Stock on Hand at End of Period

the optimizer calculates the storage costs during planning, by multiplying the stock on hand at the

end of the bucket (period) with the storage costs that were defined for the location product on the

Procurementtab page in the product master.


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SAP TUTOR: OPT_INCREMENTAL_DEPENDENT_DEMAND.SIM


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Result:

planned distribution receipt/demand at DC-03/Plant-03

planned production at Plant-03


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Dependent Demand cannot be fulfilled due to production/stock transfer horizon for input

components (8 weeks).


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If pseudo-hard is activated, then the optimizer does consider shortages on thenon-selected input product. However, the resulting negative stock on hand in a

bucket is set to zero. There is no further consideration of this backlog in

subsequent periods.


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By creating a purchase requisition, the optimizer doesnt recognize that there isa source available in the APO model. Additionally, (potential) resource

constraints will not be considered, if a purchase requisition is created.


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Consideration of Priorities

You can define priorities from 1-4 for three different priority classes of the demand and the safetystock. 1 is the highest,4 the lowest priority. You can also assign the same priorities for two or more

priority classes. The standard setting is that all priority classes and the safety stock have the same priority.Consideration of Priority of Location Products

If you set this indicator, the system considers not only the priority for the priority classes of thedemand, but also the priority of the location products. You define this priority in the master data of thelocation product on the SNP 2 tab page

To simplify this combination of both priority types, you must also subdivide the product prioritiesinto three product classes with A, B and C.

In addition, you can set the Consider Priority of Location Products indicator so that the system considers the priority ofproducts. The system considers the product priority together with the demand priority. You can define which priority is moreimportant and should be considered by the system first, by setting thePrio.of Loc.Prod. more important than Prio. of

Prio.Classes indicator.Consideration of Procurement Priority of PPMs/PDS

If you set this indicator, the system considers the procurement priorities of production process models(PPMs) or production data structures (PDS). You define this priority in the master data for PPMs orPDS.

Consideration of Procurement Prio.of Transportation Lanes

If you set this indicator, the system considers the procurement priorities of transportation lanes. Youdefine this priority in the transportation lane master data in the Product-Specific Transportsection.

Consideration of Costs of Products without Input Products

To calculate the product value, the system automatically accepts the value 1 as the value of rawproducts (products without input products). If you set this indicator, the system bases the calculation

on the actual storage costs instead. You define the storage costs in the master data of the locationproduct on theProcurementtab page.


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Optimizer should not transport or produce without demand

No transport should be activated due to cheaper target location cost No production should be activated due to cheaper output location-product cost No transport should start to save storage cost on the truck No production should start to save storage cost by production in progress


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Consistency Check Mode

No Checks:No checks are made. We do not recommend that you choose this option.

Switching off the check function does not improve performance and can even lead totermination of the optimizer, if there are data errors.

Maximum Number of Messages per Consistency Check

restricts number of messages that are to be issued per consistency check.

Once this maximum number is reached, the system displays an additional message specifying

how many more messages from this consistency check are suppressed (not shown).

Example: You have restricted the messages per consistency check to 25. Your model contains

30 production process models for which you have not maintained costs. You receive 25

messages: "Production model PA at location LA: are costs ok?" (number 149) plus another

message "5 messages with number 149 have been suppressed".Write All Log Data

If you activate this indicator, the log data for an optimization run is stored. You can view this

data in the Optimizer Log Data transaction (Supply Network Planning -> Reporting ->

Optimizer Log Data).

If you do not activate this indicator, you will notice an improvement in performance. However,

it is then only possible to view the SNP optimizer messages directly after the SNP optimization

run. Upon leaving the transaction, these messages are lost permanently. Note:

In Customizing for Supply Network Planning in theMaintain Global Settings for the SNP OptimizerIMG activity, you must

enter a positive value in theNumber of log entries field, to make it possible for log entries to be stored. If you deactivate the indicator, the SNP optimizer is not able to take into account any SNP optimization bound profiles.

Additionally, you cannot release the corrected demand forecast from Demand Planning to Supply Network Planning


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During optimization, various constraints are taken into consideration, e.g., the capacity constraints as defined in

resources. However, sometimes more flexible definition of constraints is required. For example, the quantity of

parts that can be supplied by a vendor may vary from period to period. In such cases, we need to use time series

key figures to define restrictions.

In APO 4.0, there are 4 additional time series key figures for production, procurement, storage, and

transportation upper bound. These key figures (9APRODUB, 9APROCUB, 9ASTORAGEUB, and

9ATRANUB) can be used to define time bucket dependent constraints for production, procurement,

transportation, and storage.

You can specify the time-based key figures in the standard SNP planning book 9ATSOPT with data view

OPT_TSB (standard SNP planning area: 9ASNP04) or in a planning book that you created yourself. The main

grid of the data view is the same as that in the SNP94(1) view of SNP standard planning book 9ASNP94. The

second grid of this data view include key figures 9APRODUB, 9APROCUB, 9ASTORAGEUB, and

9ATRANUB.

Zero-Indicator indicates whether an entry in the upper bound KF should be interpreted as 0 (enter

then an e.g. 1 in the corresponding column) or whether it is empty (ignored).Only relevant upper bounds are displayed during interactive planning. For example, if location product is

selected, only procurement and storage upper bounds are displayed; if PPM is selected, only production upper

bound is displayed; if transportation lane is selected, only transportation upper bound is displayed.

The SNP optimizer then takes these constraints into consideration when calculating the optimal solutions.

The Ignore Time-Based Constraints Indicator in the Optimizer Profile should be set if you want to use an

SNP optimization bound profile for the SNP optimization run, for example. If you use the time-based

constraints alongside the bound from the optimization bound profile, the two types of constraints might cancel

each other out. If this occurs, the SNP optimizer would not be able to find a feasible solution.


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Requirement: For each location-product combination a time-dependent maximum and a minimumstock level can be used. If these levels are violated penalty costs should be taken into account within

the SNP optimization.For the time-based minimum stock level (which is actually a safety stock requirement) the existingkey figure SAFTY can be used. Additionally, you can define a key figure in order to model the costs

of violating the minimum stock level. Moreover, you can specify the stock upper bound and any

penalty costs for violating this upper bound as time-based key figures.This can be done in the standard SNP planning book 9ATSOPT (standard SNP planning area:

9ASNP04) or in a planning book that you created yourself.

In the Optimizer Profile (Extended Settings Tab) it is possible to distinguish whether the time-basedmaximum stock level is a soft or a (pseudo-)hard constraint. This indicator is used to specify how the

SNP optimizer takes into account a time-based stock upper bound that you may have set in interactive

Supply Network Planning. You have the following options: If you set the indicator, the optimizer regards the stock upper bound as a soft constraint, which

means it can be violated with incurring penalty costs. You define the penalty costs in a time-based

key figure in interactive Supply Network Planning. If you do not set the indicator, the optimizer regards the stock upper bound as a pseudo-hard

constraint, which means that it can be violated, but will incur infinitely high penalty costs.


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Mass maintenance of key figures: transaction /SAPAPO/TSKEYFMAIN

Optimizer Profile -> Extended Settings: Checkbox Ignore Time Based Constraints must

NOT be activated to consider receipt bounds.


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The optimization bound profile is an optional profile that you can use for subsequent

optimization runs.

Using the resulting values for all decision variables (production quantity, transportation

quantity, procurement quantity, storage quantity or delivery quantity) from an initial

optimization run as your basis, you can set maximum and minimum limits for each bucket to

recalculate particular decision variables in subsequent optimization runs.


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Period - Choose the type of period (day, week or month) for which the optimization should

be valid.

Number- Enter the number of periods for which the optimization should be valid.

Upper Limit- Set this flag, if the maximum percentage of the decision variable should be

taken into account.

Deviation (+%)Enter a percentage to recalculate maximum deviation of all decision

variables.

Lower LimitSet this flag, if the minimum percentage of the decision variable should be

taken into account.

Deviation (-%)Enter a percentage to recalculate the minimum deviation of all decision

variables.

Basic valueChoose a basic value (average, maximum or minimum) if the resulting value

for the decision variable in a particular bucket from the initial optimization run was zero.

The basic value is derived from the previous optimization run. Depending on your settings, the

basic value is derived from either the bucket with the largest or smallest quantity of the

decision variable (MAX/MIN), or from the average of all buckets that contain non-zero values

for the relevant decision variable (AVG).


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Prerequisite: Executing the SNP optimizer in the background: /sapapo/snpop

Quotations can be checked with transaction: /sapapo/scc_tq1validity date of quotas = end time of planned transports

Inbound quotas are used for SNP Heuristics, outbound quotas are used for Deployment

Heuristic.


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execution of the optimizer in the background

usage of a data view

definition of planning start and end datestart dates before the start date of the data view are shifted to the start date of the data view

end dates in the field "Planning end date" after the end date of the data view are shifted to the end

date of the data view.

If the planning start date" is not at the start of a bucket, it is shifted to the start of the bucket in

which it falls, meaning that the SNP optimizer takes the complete bucket into account. The same is

true for the planning end date": it is always shifted to the end of the bucket in which it falls.

The "Stock on hand" quantity is taken from the initial column, or if the planning start date is greater

than the start date of the data view from the column before. This is the only value that is considered.

If you need to consider orders that fall outside of this "data view" horizon, you must choose or createa data view that has a horizon to suit your optimization needs. Negative quantities or backlogs are

not transferred to the SNP optimizer. The reason for this is that the origin of these quantities is not

known. For example, it is not known whether this quantity is from dependent demand, a forecast, or

a sales order. The age of the backlog is also not known and whether it would be allowed to fulfill the

forecast or the sales order.


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If you are running the optimizer, it is always useful to read the optimizer log files.

Log files: Supply Network Planning -> Reporting -> Optimizer Log

/sapapo/snpoplog

Consulting notes: 454194, 509732 -> general information abou

SNP Optimization Training Slides

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