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SAP SCM Solution Roadmap
DEMAND
PLANNING & FORECASTING
MAIN PROCESS: Demand Planning is often the starting point of the entire
Supply Chain Planning process and delivers the anticipated customer demand on a
finished product level. This can be used by following planning and execution
scenarios. Demand Planning is basically a toolkit consisting of statistical
forecasting techniques, life cycle management, promotion planning, data anaysis
and calculation tools, and gives visibility to all levels of detail.
Demand Planning & Forecasting
Statistical or univariate forecasting predicts future demand based on
historical data. Unlike causal forecasting, other factors are not taken into
account.
Univariate forecasting provides methods that recognize the basic time
series patterns as a basis for the forecast:
The system can automatically identify the optimal method for any item
to be forecasted. Different error measures are calculated by the system and
alert planners if user-defined limits are exceeded.
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Multiple linear regression (MLR) enables you to include causal variables
(such as climatic conditions, price, and advertising) in the forecasting
process. MLR investigates the historical influence of these variables on
demand to produce a forecast. You can set up different scenarios for causal
variables to simulate possible developments and thus identify possible risks
and opportunities.
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The underlying objective is to take advantage of the strengths of
each method to create a single "one number" forecast. By combining
the forecasts, the business analyst aims to develop the best forecast
possible. The composite forecasts of several methods have been proven to
out-perform the individual forecasts of any of those methods used to generate
the composite.
However, the planner can also let the system automatically select the
individual forecast that delivers the lowest statistical error.
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A product's life cycle consists of different phases: launch, growth,
maturity, and discontinuation.
You can represent the launch, growth, and discontinuation phases by
using so-called phase-in and phase-out profiles. A phase-in profile mimics
the upward sales curve that you expect the product to display during its
launch and growth phases, whereas a phase-out profile mimics the downward
sales curve that you expect the product to display during its discontinuation
phase.
For new products, it is proven practice to use historical data of
corresponding products (such as the predecessors) as the basis for
forecasting. This can be done using what is known as like-modeling.
Using the central maintenance instance for intechangeability
relationships, the above profiles can be generated and assigned
automatically. Life cycle planning is taken into consideration during
statistical forecasting and works on detailed and aggregated level.
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In Demand Planning, you can plan promotions or other special events
separately.
You can use promotion planning to record either one-off events such
as the millennium, or repeated events such as quarterly advertising
campaigns. Other examples of promotions are trade fairs, trade discounts,
dealer allowances, product displays, coupons, contests, free-standing inserts,
as well as non-sales-related events such as competitors' activities, market
intelligence, upward/downward economic trends, strikes, and hurricanes.
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Demand Planning should include all available information about
historical sales, budgets, strategic company plans, or sales targets. This
data can come from different sources and can be transferred from any source
to InfoCubes in SAP Business Intelligence (SAP BI). From there, the data can
be read directly or transferred first to the liveCache to improve
performance.
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All Demand Planning data is available on the Web to include internal
or external partners in the planning process. This ensures that all partners
agree on the defined quantities, horizons and conditions.
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Macros enable any kind of calculation on the planning grid. Planners
can define them using a simple macro language in the macro builder, which has
an easy-to-use interface.
Macros can be executed during background processing and on the
planning grid. In particular, they are used to combine different types of
information, derive dependent measures, or calculate alerts based on any
check. More sophisticated macros can even add new planning logic. This
increases the flexibility and strength of the application.
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As well as planning demand for a product, you can also forecast
dependent demand at different planning levels by exploding bills of material.
This is of importance in situations where you need to plan the demand for a
bundle of products that is sold, in a promotion activity, for example.
For example, this can be used for a kit that consists of several
finished products (that can also be sold separately). Planning demand for the
kit generates dependent demand that can be combined with the independent
demand for the single products. The overall demand by product can then be
used for supply, production, and procurement planning.
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In SAP APO Demand Planning, you can create a forecast based on the
characteristics of configurable end products. For example, in the case of a
car, you can plan the characteristics color, engine, and air-conditioning.
Moreover, you can forecast the demand for a combination of several characteristics,
and thus take into account the mutual interdependency of the demand for these
characteristics.
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The final consensus demand plan can be transferred to subseqeuent
planning or execution processes:
•
to SAP BI for reporting and archiving, or
integation to any other systems
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to supply or production planning in SAP APO
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to SAP R/3 or SAP ERP for e.g. Materials
Requirement Planning (MRP)
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Customer Forecast management is used to receive and analyze incoming
customer forecast data and make the necessary adjustments before releasing it
to Demand Planning for downstream planning. An analysis of forecasts
enables the vendor to sense tendencies and changes in customer demand and
integrate this information into replenishment planning. Customer
Forecast Management ensures higher responsiveness to fluctuations in
demand and also contributes to the prevention of stock-outs.
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Inventory Management
MAIN PROCESS
Safety stock is the quantity of additional stock procured and/or held to satisfy unexpectedly high demand. Safety stock planning within Supply Network Planning allows you to meet a service level while creating a minimum amount of safety stock throughout your entire supply chain for all intermediate and finished products at their respective locations.
Increasing Revenue
· Improve order fill rate
Lowering Working Capital
·
Improve capacity utilization
·
Increase inventory turns
· Lower work-in-process inventory
Managing Fixed Assets & Resources
· Improve physical inventory process
The easiest safety stock planning method is
to define a fixed safety stock or the safety days’ supply required in
any stocking location for a material. The system applies these settings in a
static or time-dependent way, and calculates the resulting safety stock
automatically. The safety stock is then considered during subsequent planning
runs.
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Advanced
safety stock methods can calculate and consider the variability on the demand
and supply sides. Furthermore, simulations of the service level and the
forecast error can be performed. The service level can be defined in two
ways:
1. As the share of periods with complete deliveries compared to all periods (alpha service level) 2. As the share of quantity delivered in time compared to overall requested quantities (beta service level) Furthermore, the system supports the reorder cycle (that is, reorders can be placed in special periods only, for example, every 4 weeks) and reorder point (that is, reorders are placed if stock falls below a minimum level) strategies. |
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SAP Enterprise Inventory Optimization by
SmartOps is a solution that enables companies to pursue perfect product
availability while significantly reducing inventory and working
capital. The solution provides a comprehensive, enterprise-scale
process for optimizing, managing, and monitoring inventory stocking levels
for every finished product and raw material component at every stocking
location in a multi-tier distribution or manufacturing supply chain.
Features include the following:
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Multistage modeling approach calculates the relationships
among inventories, service levels, capacity, and costs across all stocking
locations and stages across different types of supply chains within
organizations
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Coordinates capacity, inventory, demand, lead time, and
product availability variables to calculate how much inventory should be
carried by item, location and time period
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Planners can set and manage targets such as safety stocks more
frequently at granular level, supporting lean processes
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Customers can realize savings from improved customer service
levels, lower inventory and working capital reduction
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Accurately tracks and streamlines inventory positions
throughout order to cash value chain, using advanced algorithms
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SUPPLY NETWORK PLANNING
MAIN PROCESS
Supply Network Planning integrates purchasing, production, distribution (of demands), and transportation so that comprehensive mid-term to long-term tactical planning and sourcing decisions can be simulated and performed on the basis of a single, global consistent model.
The heuristic is used as part of a
repair-based planning process consisting of the heuristic, capacity leveling,
and deployment. The heuristic process considers each planning location
sequentially and determines sourcing requirements. It sums up all
requirements for a given material in the location into one requirement for the
period. The heuristic determines the valid sources of supply and quantities
based on pre-defined percentages for each source of supply (quota
arrangements).
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The SNP optimizer offers cost-based
planning. This means that it searches through all feasible plans to try
to find the most cost-effective one.
Total costs refer to the following:
1.
Production,
procurement, storage, and transportation costs.
2.
Costs
for increasing the production capacity, storage capacity, transportation
capacity, and handling capacity.
3.
Costs
for falling below the safety stock level.
4.
Costs
for delayed delivery.
5.
Stockout
(or shortfall quantity) costs.
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Capable-To-Match (CTM) planning uses
constraint-based heuristics to conduct multi-site checks of production
capacities and transportation capabilities based on predefined supply
categories and demand priorities. CTM can also consider characteristics
during planning which is especially important for industries with
customizable products like e.g. Mill or Machinery & Components. Shelf
Life restrictions are respected in CTM as well. The aim of the CTM planning
run is to propose a feasible solution for fulfilling demands.
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The subcontracting process is a kind of
outsourcing of manufacturing facilities to a third-party called the
subcontractor. The manufacturer provides the subcontractor with components
and the subcontractor uses these components and supplies the finished product
to the manufacturer.
Subcontracting scheduling agreements can be created as
external procurement relationships and you can plan ordered finished products
and subcontracting components at the subcontractor (vendor) location for the
SNP heuristic as well as for Production Planning and Detailed Scheduling
(PP/DS). This gives you access to information about subcontracting stock at
subcontractor locations. Third-party order processing can also be used if you
want an external supplier to provide you with the components for a finished
product that is to be manufactured by a subcontractor, rather than providing
the components yourself from your own plant.
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Scheduling agreements define a fixed amount
of quantity to be delivered from a supplier to a manufacturer over a longer
period (for example, a year). The SNP heuristic can use this information (transferred
from SAP R/3, for example) to plan the schedule lines and to create the
releases. Using collaborative supply planning, the supplier can confirm these
quantities before they are transferred to the execution system (for example,
SAP R/3).
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Aggregated planning can be performed, by defining
hierarchies for products and locations to be taken into account by both the
SNP heuristic, capacity leveling and optimizer.
Disaggregation logic ensures that plans are
consistent at all levels of aggregation. Extended drilldown options enable an
easy displaying of the aggregated planning results. This type of planning
will reduce the planning complexity as well as improve the system
performance.
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DISTRIBUTION PLANNING
MAIN PROCESS
The
distribution planning process is used to determine which demands can be
fulfilled by the existing supply elements. The deployment run generates
deployment stock transfers based on the SNP or PP/DS stock transfers that
were created during the SNP run. The PP/DS deployment also considers
characteristics during planning. The Transport Load Builder (TLB) then
uses these deployment stock transfers to create transport loads based on
predefined constraints of the means of transports. Responsive Replenishment:
You can use this business process to plan responsive replenishment based on the
results of the Responsive Demand Planning process. Responsive Replenishment
Planning plans the optimal shipments to customer locations. During this process
the netting takes place, transport loads are created, and finally the orders are
created. Promotion and baseline demands can be planned independently.
The distribution planning process is used
to determine which demands can be fulfilled by the existing supply elements.
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 based on predefind constrainst of the means of transports.
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You can use this business process to plan
responsive replenishment based on the results of the Responsive Demand
Planning process. Responsive Replenishment Planning plans the optimal
shipments to customer locations. During this process the netting takes place,
transport loads are created, and finally the orders are created. Promotion
and baseline demands can be planned independently.
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The aim of vendor-managed inventory (VMI) is to
integrate key customers in supply chain planning. The customer regularly
sends stock and sales data to the vendor, and based on that information, the vendor determines replenishment
requirements for the customer. It improves the vendor's access to the actual
customer requirements, and also enables the vendor to make informed decisions about how to distribute goods for
different customers. This ensures improved customer service, lower
transportation costs, less inventory, and lower sales costs.
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SERVICE PARTS PLANNING
MAIN PROCESS
Service Parts
Planning delivers integrated planning capabilities to the service parts supply
chain. While it considers the specific aspects of the after-market and provides
extended capabilities for handling the large parts volumes present in an
after-market supply chain, it also delivers a tight integration of the
end-to-end planning process with other processes, such as Supply Network
Collaboration, Procurement, Warehousing, and Order Fulfillment.
For detailed
information on Service Parts Planning please click here to review SAP
Service and Asset Management
Lowering
Working Capital
· Increase inventory turns
· Reduce material and
component obsolescence
· Reduce out-of-stock
situation
Reducing
Operating Costs & Increasing Efficiency
· Reduced service parts
inventory levels across the service parts network
Improving
Customer Service
· Better service levels
· Improve forecast accuracy
Strategic Supply Chain Design includes the definition
of all elements of the supply chain.
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Parts Demand Planning offers a set of
capabilities that allow to capture historical demand, model that demand
according to the structure of the service parts supply chain, and perform
forecasts for future demand. Demand planning is done in an adaptive fashion
which continuously analyses past forecast performance and adjusts forecast
models and parameters accordingly. It considers the various stages of the
parts life cycle, including parts introduction, parts supersession, and a
longterm forecast.
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Parts Inventory Planning determines where
to stock which product and how much of it - balancing maximum service levels
with lowest inventory costs.
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Parts Supply Planning supports tactical net
requirements planning based on parts requirements throughout the service
network.
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Parts Distribution Planning distributes
available parts within the network via push or pull deployment and inventory
balancing.
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Parts Monitoring provides visibility of all
planning related processes and includes follow-up activities, e.g. using
- Shortage Management
- Service Parts Planning Cockpit
- Product Detail Information
- Service Parts Planning Alert Monitor
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DEMAND PLANNING IN MS EXCEL
MAIN PROCESS
Demand
planning has typically been the domain of a small group of highly specialized
individuals who manage the planning and forecasting process for an entire
organization or its divisions, product groups, or regions. The SAP® Supply
Chain Management application (SAP SCM) provides the perfect toolset for these
highly skilled and experienced users. Demand-planning software within the
application offers a complete range of advanced functions for data analysis,
multilevel planning, life-cycle management, forecasting, and promotion
planning. In addition, SAP SCM supports collaboration and supply chain
partnerships such as vendor-managed inventory and collaborative planning,
forecasting, and replenishment.
Used with a
fully configured SAP demand-planning application in the back end, Duet offers a
comprehensive array of interactive planning functions for creating,
controlling, and modifying demand plans or forecasts. Duet links the advanced
demand-planning features of SAP SCM with the familiar and flexible interface of
Microsoft Excel. Together, Duet and SAP SCM support users’ needs for comprehensive interactive functions
that are flexible, intuitive, and easy to use. In addition, users have the
option to work offline allowing, for example, a sales representative to enter
forecast data while on the road.
When working online, planners can use
preconfigured planning sheets to load data from SAP SCM, analyze, modify, and
add data as required and save the changes back in SAP SCM. They can use the standard
spreadsheet functions in Microsoft Excel and additionally an action pane to
manage selections or switch between
pre-assigned selections in order to navigate to the relevant data.
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If users want to work offline while visiting clients or other business
partners, for example they can load the data into the planning
sheet while you are still online and connected to SAP SCM.
Planners can use the standard
spreadsheet functions in Microsoft Excel to add rows or columns and carry out
calculations. They can use macros and formulas created in Excel to fill out
cells in the planning sheet or make calculations and reference data from
other worksheets in their calculations.
The next time back online and connected to
SAP SCM, planners simply save any changes made while offline to the SAP
back-end application.
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