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Source : SAP
Pricing and Revenue Optimization: A Manufacturing Perspective
Business-to-business (B2B) is also known as :
B2B Solutions,
Business-To-Consumer,
B2B Software,
B2B Trade Portal,
B2B Marketing,
B2B Database Marketing,
B2C,
B2B Market Segmentation,
Strategic B2B Planning,
Business To Business Integration,
B2B E-Commerce,
Business Processes,
B2B Technology,
B2B Marketing Solutions
CONTENTS
- Executive Summary
- Optimization Perspectives
- Current State: Profit Management by Pricing and Revenue Optimization
- Pricing and Revenue Optimization Defined
- Demand Curve: The Impact of Pricing
- Pricing and Revenue Optimization Drivers
- Real-World Examples of Industry Applications
- A. Airline Industry
- B. Retail Industry
- Challenges with Pricing and Revenue Optimization
- A. Application in a B2C Environment
- B. Application in a B2B Environment
- A View of the SAP Solution
- A. Strategic-Level Pricing
- B. Tactical-Level Pricing
- C. Execution-Level Pricing
- Key SAP Solution Components Supporting Profit Management
EXECUTIVE SUMMARY
Pricing and revenue optimization is the process of intelligently
using a combination of market, customer, product,
promotion, and supply-and-demand data to improve business
margins by either increasing unit prices or increasing gross
revenues.
This pricing and revenue optimization seeks intelligent tradeoffs
among various competing objectives consistent with an
organization's business strategy. The area of pricing and
revenue optimization is fast being recognized as having the
capability to leverage optimization technology to help businesses
grow margins significantly. Studies by some of the
world's best management-consulting companies have shown
that intelligent pricing can add a 15% to 50% incremental
margin to the business bottom line and can significantly
enhance revenue.
The majority of point solutions available today work in very
localized environments and fail to have a wide industry application.
SAP, as a provider of world-class, enterprise-level solutions,
recognizes that the process of pricing and revenue optimization
needs to be managed as an end-to-end, closed-loop
process monitored across three time-phased stages: the strategic-
pricing stage, the tactical-pricing stage, and the executionpricing
stage. As a result, SAP continues to develop and
improve on a series of integrated applications to provide
customers with the best solution for profit management by
pricing and revenue optimization.
OPTIMIZATION PERSPECTIVES
Optimization as a technology has been around for years. However,
some significant changes in the past decade have brought
optimization to the forefront as a critical business differentiator.
In the early 1990s, computing power had an exponential takeoff,
and it became possible to run complex algorithm routines
far faster than before. This computing power was a critical
enabler for quickly solving complex optimization algorithms.
At the same time, optimization algorithms moved from the
academic corridors into the industrial domain, as companies
began to realize that optimization could bring substantial
benefits when used effectively. Most important, the adoption of
the Internet in the late 1990s as a mainstream businesscommunications
vehicle increased information velocity and
volume far beyond the capabilities of the "back of envelope"
and spreadsheet techniques commonly in use.
Optimization technology today makes it possible to examine
thousands of variables and solve complex mathematical problems
in ever-shortening time frames and deliver results in minutes
and hours, rather than days and weeks. Optimization as a
process enables the determination of the best possible utilization
of resources (that is, people, time, processes, vehicles,
equipment, raw materials, supplies, and capacity) needed to
achieve a desired result. The result sought could be either (1)
the minimization of cost, process time, or resources, or (2) the
maximization of price, profit, throughput, service levels, or
revenues. In some cases, the result could be an optimum
balance between the two.
Once embedded in software, optimization technology provides
the engine that ensures adherence to business rules (or constraints)
and quickly and accurately solves complex problems.
These problems can range from optimizing tactical operations
to help manage operation costs to dynamically optimizing
profits and revenue to determine the best price to charge
customers.
CURRENT STATE: PROFIT
MANAGEMENT BY PRICING
AND REVENUE OPTIMIZATION
The term profit has its origins in the Latin word profectus,
which means advancement or improvement. There are only
two ways companies can achieve profectus: (1) Raise the top line
by managing prices to increase sales, or (2) lower the expense
line by reducing costs.
Raising the top line and increasing profits by managing prices is
a challenge. Price, without a doubt, is the single most critical
driver for managing profits. Yet pricing continues to be one of
the least understood profit levers. This is because of the sheer
number of variables that need to be factored into the pricing
decision, the lack of a single point price ownership within
organizations, the inability to have timely visibility into market
dynamics, and the lack of an integrated tool that supports
complex disaggregated pricing processes.
Lowering the expense line by reducing costs has been more
successful than raising the top line by increasing prices. With a
back-end internal endeavor relating closely to supply chain
and manufacturing efficiencies, the aim is to optimize a set of
variables, most of which are under company control. However,
despite its success, some significant challenges remain.
Although aggregated costs on a company-wide level are typically
easy to determine, costs at a single product or customer
level continue to be difficult to extract. Tools that help model
processes and related cost structures to simulate profitability
and the utilization of activity-based costing management
(ABCM) methods can assist by accurately allocating realized
costs across the supply chain by process, by product, and by
customer. Used appropriately, this process can yield significant
returns. On the basis of an analysis done by John Bermudez of
AMR Research in 2000, planning and scheduling modules that
depend on optimization technology have generated 30% to
300% return on investment within many companies that have
used these technologies.
PRICING AND REVENUE
OPTIMIZATION DEFINED
Pricing and revenue optimization is the process of intelligently
using a combination of market, customer, product, promotion,
and business segmentation data to improve business
margins by either increasing unit prices or increasing gross revenues.
Pricing and revenue optimization seeks the best trade-off
among various competing objectives consistent with an organization's
business strategy.
Objectives of this process include maximizing category revenue,
identifying promotions that drive business traffic, identifying
loss leaders, and increasing the share of wallet in target customer
segments.
The process brings structure to an environment in which
prices are generally based on heuristics, straightforward target
cost, instinct, or other internal business rules that lead to
overpricing, undercharging, and inconsistent procedures for
managing pricing.
DEMAND CURVE:
THE IMPACT OF PRICING
Confronted with the combined complexities of the shrinking
times to market and smaller order volumes, the proliferation of
SKUs and product configurations, the accelerated commodification
of products, and the increasing distribution channel
complexity, companies today are realizing that the pricing lever
needs to be far better managed in order to drive revenue and
profits. These complexities create a delicate and challenging
balance between underpricing and overpricing. Underpricing is
a direct loss of margin that would have flowed straight to the
bottom line; overpricing leads to a loss of market share that
affects both current and future margins. The demand curve in
Figure 1 that identifies revenue captured and revenue lost indicates
that companies need to be able to make optimal pricing
decisions across product life cycles to continually drive demand
toward the most profitable product mix. Without the ability to
manage pricing across a product and market life cycle, companies
will continue to incur revenue and margin leakage.
Pricing too low initially can cause stock outs across the supply
chain and can create margin erosion by losing customers who
are less price sensitive and would have paid a higher price than
offered. On the other hand, pricing too high, especially as a
product matures, can lead to lower revenue and excess inventory
buildup that needs to be later discounted or written off as
obsolete inventory. These factors make understanding the value
equation and the elasticity of products fundamental to effective
pricing strategy. (Elasticity is defined as the volume change
given a 1% price change.) However, most companies do not have
sufficient information about the real demand elasticity of their
products and thereby continue to struggle with identifying and
isolating segments with differing price elasticity that could
allow them to bring intelligence into pricing practices.
In terms of solutions available, most pricing and revenue management
applications today are not attached to the demand
curve dynamics. Current pricing approaches vary widely and
use a mix of inefficient pricing tools that lack any standardization,
such as spreadsheets, price books, custom applications,
and disparate information systems. Since demand management
covers a broad scope, including key item pricing, category price
management, promotional pricing, markdowns and discounts,
competitive pricing, and multichannel pricing, integrating the
pricing solutions into a unified system that provides rapid access
to accurate demand-supply transactional data and links into
supply chain management solutions is crucial.
Integration is the key because of the complexity of pricing
processes and the strong relationship between supply chain
efficiency and pricing. Although some vendors today offer
stand-alone pricing and revenue optimization solutions, these
solutions by themselves have limited usability unless integrated
with enterprise supply chain, demand, and cost management
solutions.
PRICING AND REVENUE
OPTIMIZATION DRIVERS
Contribution margins drive profitability. As a result, increasing
the total contribution margin is the driving force behind this
new focus on pricing and revenue optimization. Pricing and
revenue optimization today is at a stage similar to that of supply
chain optimization a decade ago. Supply chain optimization
produced a significant leap forward in managing expenses and
implementing competitive pricing strategies focused on reduced
costs. However, the potential to benefit from pricing arbitrage
was not addressed. Pricing arbitrage is the concept of buying
something at one price and selling it at another, taking advantage
of the imbalance between the two prices. Now pricing and
revenue optimization can benefit from this arbitrage opportunity
to increase overall contribution margins and leverage the
elasticity-contribution margin relationship to identify customer
segments along the price-elasticity scale.
As shown in Figure 2, this opportunity is identified by the building
of a mathematical model that plots the contribution margin
against the break-even elasticity, which is defined as the point
where any volume change due to price change has no effect on
the margin.
If the actual demand elasticity for the product falls below the
break-even elasticity curve, raising the price will drive higher
profits, even accounting for the related volume reductions.
Conversely, if the actual elasticity falls above the curve, lowering
the price will drive greater demand, which in turn will drive
higher revenues and higher total margins.
The advances in computing power and the availability of
real-time data allow such sophisticated mathematical and
statistical models to be quickly run at very granular levels if
the systems are integrated and the appropriate pricing processes
are in place.
REAL-WORLD EXAMPLES OF
INDUSTRY APPLICATIONS
Most of the current applications relating to pricing and revenue
optimization are in industries that operate more in a businessto-
consumer environment. Two of the key industries that have
been using some form of this optimization are the airlines
(and other service industries such as hotels and car rentals)
and retail.
A. Airline Industry
For those familiar with pricing and revenue optimization, the
first industry that comes to mind is the airline industry. Airlines
have been spectacularly successful in leveraging optimization
techniques. It is widely known that large airlines earn an additional
$1 billion each year by optimizing fare mix, overbooking,
upgrading, and balancing capacities. Peter Belobaba, whose optimization
strategies most airlines use, estimated that airlines that
leverage optimization have a 10% revenue and corresponding
profit advantage compared with those airlines that do not.
Although some vendors have been pitching that the airlines'
optimization solution is closely applicable to other industries, it
is essential to note that what the airline industry is actually
doing is yield management. This means that all the airlines
are doing is allocating inventory on the basis of the forecasted
expiration to different fixed points (that is, inventory is allocated
on the basis of price). This method yields increased revenues
by selling the same product at different prices to different customers,
but it does not consider costs, optimize profitability, or
determine the optimal price points themselves.
Thus far, using this method in other industries has been mostly
unsuccessful, especially in those industries with networked
supply chains and environments where mass customization,
make to order, make to stock, and configure to order are standard
practices.
B. Retail Industry
The retail industry has also experienced limited success with
pricing and revenue optimization. However, the utility of solutions
as stand-alone products has been limited. Most of the
retail applications available today have been associated with
markdown pricing (that is, discounting products). Two different
models have emerged to illustrate the differences in the retail
industry approach:
- Extended life-cycle products
For products that have alternatives available in the market
and can be compared in terms of common attributes, including
price, it has been possible to improve sales and pricing by
grouping them with products with similar attributes. To maximize
the performance of the product categories, customers
have been exposed to product groupings, substitutions, and
cross-sell promotions.
- Short life-cycle products
For products that have alternatives that are hard to find, the
goal is to first optimize the product's performance in meeting
the retailer's objectives while the item is in the market. Next,
the product is priced along its life cycle to retire the item in
the market by the target date while zeroing out the inventory.
Given the complexities involved, the extent to which pricing
and revenue optimization has been successfully leveraged has
varied significantly among different industries. Figure 3 provides
a simple framework, along with some examples, for identifying
industries that are amenable to pricing optimization.
The greater the similarity of the product offering and the
greater the proximity to the end consumer, the easier it is to
build holistic optimization models with pricing optimization
as the key objective.
This is not to say that pricing and revenue optimization is not
applicable to other industries. Instead, the essential point is that
either the need for pricing and revenue optimization in these
industries is limited or more pragmatic price execution and
price visibility options may be available, especially because
determination of some type of elasticity models to optimize
item prices within a category can be difficult.
CHALLENGES WITH PRICING
AND REVENUE OPTIMIZATION
The key challenge with pricing and revenue optimization is
how to better understand the dynamics of the complex system
relations and find feasible solutions that can be constantly
tweaked and, when necessary, fed to the back-end supply
chain that manages the demand planning, finite planning,
manufacturing, and fulfillment process.
As optimization technologies continue to be incorporated into
pricing and revenue management, the integration of enterprise
data, supply chain data from multiple trading partners, and revenue
management analytics is necessary to align with enterprise
demand creation and fulfillment activities. Unless fully integrated
with the supply chain management solutions, the pricing
and revenue management solutions will have limited usage.
In addition, it is essential to keep in mind the challenges that
exist while applying pricing optimization within a business-toconsumer
(B2C) environment versus a business-to-business
(B2B) environment.
A. Application in a B2C Environment
In a B2C environment, price typically is only one of several
marketing control variables affecting demand, depending on
the industry in which the company competes. And price is
not necessarily the most important factor. In addition to price,
market share and volume depend on variables such as product
availability, fulfillment and replenishment capabilities, promotion
and advertisement effectiveness, store and distribution center
location, supplier capability, logistics capability, and sales
force effectiveness. The combination of these variables leads to
an exponential increase in complexity and quickly overloads the
intuitive decision-making process.
One example of the scale of pricing complexity is the pricing
in a retail store. Figure 4 shows the many factors that need to
be considered while organizations are developing a pricing
strategy.
Research shows that individual consumers generally remember
a maximum of 180 to 200 item prices ' mostly consumables.
Part of the purpose in creating price-sensitive classifications is to
ensure that items that are priced for image are truly priced to be
competitive with the market leader. Depending on the number
of merchandise categories, most retailers should not define
more than 1% to 2% of total SKUs as price sensitive (some retailers
define only 50 to 100 items as price sensitive). These SKUs, in
turn, rarely generate more than 10% to 15% of revenue or 3% to
7% of gross margin.
In other words, the profit risk of pricing these items aggressively
is minimal, and, in some cases, the increase in unit volume may
be offset by a breakeven or increase in gross margin dollars.
Also, these margin sacrifices can be frequently offset by higher
prices on items that are priced for profit (also called blind
items).
However, most retailers have little understanding of the many
dynamic factors that impact such intelligent pricing. Therefore,
despite the numerous ongoing initiatives to apply price optimizations
in a B2C environment, most of these to date have
not been highly successful, and margins continue to leak away.
The key challenges for pricing and revenue optimization in a
B2C environment are:
- Low visibility and integration
Lack of integration and visibility of item-level pricing
decisions with category, fulfillment, purchasing, or financial
planning
- Uncertain understanding of elasticity
Unscientific attempts to understand demand elasticity at
levels of granularity that are far too dynamic and complex to
be executed
- Lack of analytics
No systematic analysis of pricing decisions to reflect crosselasticity
within demand groups of like items or to allow
proactive identification of trends, which means pricing
decisions are driven with inexact information
- Poor dependency understanding
Inability to decompose sales forecasts supporting markdown
timing and markdown depth into product life cycle, seasonality,
and price elasticity to net out the impact of price reductions
on sales revenue and margin
While some success has been found in specific areas with sophisticated
point solutions that optimize prices, the examples are
usually too customized and too localized. Bayesian inference
techniques are available to predefine pricing structures and
leverage information across locations and product attributes
to shrink data outliers to average values and then use crosselasticity
to tweak prices among similar products. This helps in
a demand shift to higher net profit items. However, these
pricing practices, while mathematically sound, depend on the
visibility of store-level activity-based costing at the item level,
which most retailers are hard pressed to identify.
B. Application in a B2B Environment
The more tiers in a supply chain, the more complex the optimization
requirement is. The airline, car rental, and hotel
industries are all examples of industries where the distance
between the supplier and end customer is minimal. Also facilitating
optimization is the fact that each of these industries has
relatively few unique product offerings. However, in manufacturing
environments that have a multitiered supplier and customer
environment, holistic optimization to determine optimal
price points is far more complex. The most practical solution,
therefore, has been to have local optimums determined at the
most complex business intersections.
For example, if shop-floor scheduling is the most complex operation,
the optimization focus should be on maximizing profitable
throughput or on minimizing resources on the shop floor.
Attempting to add other variables to optimize in addition to the
shop-floor operations would provide possibly feasible but
unpractical solutions in most cases. Some of the main pricing
challenges in a B2B environment are:
- Long-term sales contract
The majority of the sales in a B2B environment are based on
relatively long-term contracts and permanent relationships
with well-defined pricing agreements.
- High switching costs
Switching suppliers and customers in a B2B environment is
far more difficult and expensive than in a B2C environment.
- Fragmented price ownership
Pricing ownership is split among different functional organizations,
which means updating policies and consistent
practices are not well managed.
- Difficult price reconciliation
Large differences between the list price and the invoice price
because of elements such as terms, freight, rebates,
promotions, and chargebacks make visibility into B2B pricing
difficult.
Rather than optimization, the key in a B2B environment is to
understand price visibility. The current methodology for pricing
in a B2B environment is to start with a list price and then
track discounts to arrive at the invoice price. However, ignoring
details of discounts that were incorporated into the list price
prevents a proper understanding of profitability and margins on
deals and also hinders good segmentation analysis. Providing
visibility into the individual discounts and expenses between the
list price and invoice price can help determine the most profitable
orders that need to be promised and can provide the foundation
to focus on the management of key price elements
impacting transactions sliced by region, by product, by age, by
elasticity, and by sales representative.
A VIEW OF THE SAP® SOLUTION
Optimizing pricing and revenue is important but is secondary
to the objective of a well-aligned demand management process.
The optimization of prices based on the elasticity models need
not always be the solution. Constructing the pricing and revenue
optimization algorithms has never been the issue. The challenge
instead is accurately incorporating different variables into
the pricing equation and arriving at an optimal price for the
market and customer conditions being modeled.
SAP believes that profit management by pricing and revenue
optimization is a process that needs to be first streamlined to
obtain visibility across the demand management and supply
management operations. The solution to address the pricing
process may be part of a single solution suite or may reside in
different solution suites that could include supply chain management
(SCM), customer relationship management (CRM),
product life-cycle management (PLM), or existing enterprise
resource planning (ERP) applications. Having an end-to-end
integrated process that leverages accurate supply-and-demand
information and that links the three principal pricing stages '
strategic, tactical, and execution ' backed by strong analytics,
is the key.
As shown in Figure 5, SAP's position on pricing optimization is
based on understanding these key pricing stages that need to be
managed to successfully streamline the pricing process. Based
on the classification of the pricing strategies, the solutions that
form the building blocks for profit management by pricing and
revenue optimization are seen in Figure 6.
A. Strategic-Level Pricing
At the strategic level, pricing and revenue optimization is part
of a longer-term picture. Strategic pricing is typically done once
or twice a year. Necessary decisions include markets to be pursued,
products needed to position against competition, appropriate
channels (direct, indirect, or electronic) for products and
markets, operations alignment to support strategy, and product
life-cycle and portfolio profitability analysis. Strategic pricing
decisions must address issues within the enterprise and the marketplace.
Such decisions are not based solely on quantitative
metrics (such as market size, prices, and sales projections) that
lend themselves well to automation. These decisions must also
address intangible, qualitative considerations such as client
goodwill, competitive positioning, brand value, and market
awareness.
Strategic pricing offerings from SAP address the areas previously
mentioned. Strategic pricing can reference a balanced scorecard
methodology to access decision support details and employs
value-driver trees to align strategic goals with business operations.
By leveraging business planning and simulation, performance
measurement, business consolidation, and stakeholder
relationship management capabilities, strategic pricing can
address the full life cycle of profit management.
B. Tactical-Level Pricing
At the tactical level, developing basic prices (such as list price)
and pricing programs that leverage the decisions made in the
strategic-planning phase is the objective. Performed quarterly,
tactical-level pricing considers expected supply-and-demand
balances and production capacity availability to help facilitate
setting list and catalog prices by market/customer segment, and
it evaluates special promotions strategy and marketing programs
(for example, volume discounts, substitutes and bundles,
and special offers). Traditional demand-planning tools forecast
demand using raw historical volume (such as sales and
shipments), and the tactical level tools then leverage the
demand patterns to project future sales on the basis of proposed
variations in price.
Other significant objectives of profit management at this level
include the ability to determine the demand-supply match and
cost of manufacturing products, provide services, and plan
profitable campaigns. The adaptive pricing and campaign optimization
solutions enable this.
C. Execution-Level Pricing
At the execution level, the objective is to develop short-term
and very specific prices and pricing programs organized by
product, customer, market, and time. This process is generally
performed every one to four weeks. The inputs for this process
come from tactical-level pricing. Regular and promotional pricing
to meet the margin and revenue goals is done at this level,
as is promotion management, which is essential to achieve sales
goals and objectives. Multiple scenarios are tested and evaluated
to identify the optimal cases that meet or exceed targets (such as
volume and revenue) while maximizing profitability.
Capabilities and functionalities supporting promotional planning,
profitable to promise, and available to promise are all execution-
level programs that can be leveraged to identify the most
profitable orders to fulfill and to set quantity discounts that are
based on inventory available and planned by simulating various
price points and the related impact on volumes and margins.
SAP's promotion and planning solutions can help define
campaigns according to higher sales or brand management
objectives. Promotions linking the mySAP&8482; Customer Relationship
Management (mySAP CRM) and mySAP Supply Chain
Management (mySAP SCM) solutions ensure that sufficient
demand is generated to exhaust existing product supplies,
thereby creating a process by which profitability can be modeled
and ultimately predicted. Promotions optimization supports
promotional pricing so that the combined value of real additional
supply chain costs and the associated benefits are optimized.
This optimization enhances the margin visibility in the
promotion planning process, which is otherwise mainly driven
only by strategic sales and marketing goals.
Also, SAP's available-to-promise and capable-to-promise capabilities
within mySAP SCM ensure availability of product and
enable fast and efficient order promising based on customer and
product hierarchies. The demand fulfillment capabilities allow
customer orders to be committed on the basis of supply chain
constraints and constrained capacity that is allocated to the
most profitable customers or the most important customers,
which are frequently a mix of the two. Ensuring the availability
of components and resources that can be delivered on time, on
the basis of future production schedules, is also possible.
The profitable-to-promise solution extracts information about
customers and margins and analyzes the cost and potential
delivery alternatives from an activity-based costing standpoint
before promising an order, thus respecting the margin
constraints that may have been imposed.
After following through the three stages of pricing, it is essential
to get end-to-end visibility to the performance data across the
value chain to close the pricing loop. The foundation of all the
applications is the SAP® Manufacturing solution that connects
the plant floor to the business warehouse solution. The solution
provides the business intelligence analytics capability to
examine relevant data to support profitability decisions. Support
is also available for manipulating commonly sold vendor
databases, including data segmenting to identify specific sales
goals and objectives.
KEY SAP SOLUTION
COMPONENTS SUPPORTING
PROFIT MANAGEMENT
The key SAP applications that support profitability management
across the strategic, tactical, and execution phases
include:
- SAP Manufacturing
The SAP Manufacturing solution enables companies to react
at the "speed of their business" by providing business context
for manufacturing data and exceptions to close the loop
between the factory and the enterprise.
- Profitable to promise
The available-to-promise capabilities are extended by considering
the initial cost of components required to manufacture
products and the cost for substitution locations of components.
- Campaign optimization
Campaign planning solutions define campaigns according to
higher sales or brand management objectives. Costs are taken
into account in the form of lost or gained revenue and campaign
spending cost. Promotion planning across mySAP CRM
and mySAP SCM ensures sufficient supply to drive promotions.
Campaign optimization supports promotion pricing so
that the combined value of additional supply chain cost and
gained benefits is optimized.
- Adaptive pricing engine
The adaptive pricing engine supports a price-based demandand-
supply matching on a tactical level. Using price-elasticity
functions derived from historical customer behavior, product
prices are adjusted to keep demand optimally adjusted to
given supply. Goods with a varying value during their
product life cycle, such as computer chips, food, or fashion,
especially need regularly adjusted prices so they are not overpriced
(lost sales) or underpriced (lost revenue). This is also
applicable during product phaseout or phase-in.
- Strategic pricing
Focusing on determining list prices for products, including
discount structures for special customer segments, this application
takes into account the buying behavior of customers in
different segments, as well as value-based pricing approaches.
These applications are built on a number of analytical
building blocks that in turn are built on the SAP Business
Information Warehouse (SAP BW) component, which includes
the following:
- Measure builder
Builds measure catalogs in a businesslike fashion, including
business content (delivered set of business measures for financials,
CRM, SCM, and human resources)
- Balanced scorecard
Makes strategies operational through translation into
strategic objectives, targets for qualitative and quantitative
measures, and resource allocation via strategic action
program initiatives
- Reporting
Reports on the basis of the measure or other key performance
indicators defined, which allows for analysis of current business
performance and influences decision making in the
operational, tactical, and strategic pricing applications
- Planning and simulation
Enables strategic and operative planning in areas such as
financials statement planning, sales planning, and profit
planning
- Activity-based costing and management
Models and simulates clearing models for cost and profitability
management, visualizes settlements along the entire
value chain, and creates cost transparency for the different
processes in the enterprise
- Customer segmentation capabilities
Defines and analyzes customer segments, which
are part of the mySAP CRM analytics solution
- Price-elasticity determination
Determines price-elasticity curves on the basis of the historical
data of changing customer behavior due to changes in
price and other parameters, which then can be used for simulating
the effects of different pricing strategies on the supply
chain and on the bottom-line profitability of the business