U.S. patent application number 10/498685 was filed with the patent office on 2005-07-07 for profit optimization.
Invention is credited to Schierholt, Hans Karsten.
Application Number | 20050149377 10/498685 |
Document ID | / |
Family ID | 26835517 |
Filed Date | 2005-07-07 |
United States Patent
Application |
20050149377 |
Kind Code |
A1 |
Schierholt, Hans Karsten |
July 7, 2005 |
Profit optimization
Abstract
Profit optimization methods and systems for a supply chain are
described. An implementation of the technique includes determining
the initial cost of components required to manufacture a product,
dynamically determining the cost for substitution of at least one
product component, dynamically determining the location of at least
one substitute component, and manufacturing the product for the
lowest cost based on the results of the cost of substittuion and
substitute component location determinations. At least one of the
cost of substitute components and the component locations may be
determined at or near the time of manufacture.
Inventors: |
Schierholt, Hans Karsten;
(Montreal, CA) |
Correspondence
Address: |
BLAKELY SOKOLOFF TAYLOR & ZAFMAN
12400 WILSHIRE BOULEVARD
SEVENTH FLOOR
LOS ANGELES
CA
90025-1030
US
|
Family ID: |
26835517 |
Appl. No.: |
10/498685 |
Filed: |
January 24, 2005 |
PCT Filed: |
December 12, 2002 |
PCT NO: |
PCT/IB02/05810 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10498685 |
Jan 24, 2005 |
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10137713 |
Apr 30, 2002 |
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60340364 |
Dec 13, 2001 |
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60340364 |
Dec 13, 2001 |
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Current U.S.
Class: |
705/400 |
Current CPC
Class: |
G06Q 10/06 20130101;
G06Q 10/06375 20130101; G06Q 30/0283 20130101; G06Q 10/06312
20130101 |
Class at
Publication: |
705/010 |
International
Class: |
G06F 017/60 |
Claims
1. A profit optimization method comprising: determining the initial
cost of components required to manufacture a product; dynamically
determining the cost for substitution of at least one product
component; dynamically determining the location of at least one
substitute component; and manufacturing the product for the lowest
cost based on the results of the cost of substitution and
substitute component location determinations.
2. The method of claim 1 wherein at least one of the cost of
substitute components and the component locations are determined at
or near the time of manufacture.
3. An article comprising a computer-readable medium that stores
executable instructions for causing a computer system to: determine
the initial cost of components required to manufacture a product;
dynamically determine the cost for substitution of at least one
product component; dynamically determine the location of at least
one substitute component; and generate an output indicating how to
manufacture the product for the lowest cost based on the results of
the cost of substitution and substitute component location
determinations.
4. The article of claim 3, further comprising instructions for
causing the computer to determine at least one of the cost of
substitute components and the component locations at or near the
time of manufacture.
5. A profit optimization system comprising: at least one database
storage unit; and at least one processor coupled to the storage
unit, wherein the processor is operable to: determine the initial
cost of components required to manufacture a product; dynamically
determine the cost for substitution of at least one product
component; dynamically determine the location of at least one
substitute component; and generate instructions to manufacture the
product for the lowest cost based on the results of the cost of
substitution and substitute component location determinations.
6. A profit optimization method comprising: defining a set of
manufacturing rules based on customer segments; allocating critical
product components according to preferred customer segments;
allocating manufacturing capacity according to the preferred
customer segments; and manufacturing the product.
7. The method of claim 6 wherein the product components and
manufacturing capacity are allocated according to a feasibility
analysis.
8. An article comprising a computer-readable medium that stores
executable instructions for causing a computer system to: define a
set of manufacturing rules based on customer segments; allocate
critical product components according to preferred customer
segments; allocate manufacturing capacity according to the
preferred customer segments; and generate instructions to
manufacture the product.
9. The article of claim 8 further comprising instruction to cause
the computer system to allocate the product components and
manufacturing capacity according to a feasibility analysis.
10. A profit optimization system comprising: at least one database
storage unit; and at least one processor coupled to the storage
unit, wherein the processor is operable to: define a set of
manufacturing rules based on customer segments; allocate critical
product components according to preferred customer segments;
allocate manufacturing capacity according to the preferred customer
segments; and generate instructions to manufacture the product.
11. A profit optimization method comprising: monitoring product
demand and allocation reservations for product components;
comparing the product demand and component allocation reservations
with demand forecasts at predetermined intervals; dynamically
assigning component allocations for preferred customer segments
according to a comparison of the forecasted and monitored demand;
and manufacturing the product for the preferred customer segments
before manufacturing the product for other customer segments.
12. The method of claim 11 wherein preferred customer segments are
determined according to predefined rules.
13. The method of claim 11 wherein monitoring product demand and
allocation reservations is conducted in regular intervals.
14. The method of claim 11 further comprising alerting a user when
a component allocation is changed.
15. An article comprising a computer-readable medium that stores
executable instructions for causing a computer system to: monitor
product demand and allocation reservations for product components;
compare the product demand and component allocation reservations
with demand forecasts at predetermined intervals; dynamically
assign component allocations for preferred customer segments
according to a comparison of the forecasted and monitored demand;
and generate instructions to manufacture the product for the
preferred customer segments before manufacturing the product for
other customer segments.
16. The article of claim 15 further comprising instructions to
cause the computer system to determine customer segments according
to predefined rules.
17. The article of claim 15 further comprising instructions to
cause the computer system to monitor product demand and allocation
reservations in regular intervals. The article of claim 15 further
comprising instructions to cause the computer system to alert a
user when a component allocation is changed.
18. A profit optimization system comprising: at least one database
storage unit; and at least one processor coupled to the storage
unit, wherein the processor is operable to: monitor product demand
and allocation reservations for product components; compare the
product demand and component allocation reservations with demand
forecasts at predetermined intervals; dynamically assign component
allocations for preferred customer segments according to a
comparison of the forecasted and monitored demand; and generate
instructions to manufacture the product for the preferred customer
segments before manufacturing the product for other customer
segments.
19. A profit optimization method comprising: dynamically monitoring
product demand and component allocation reservations; comparing the
monitored product demand and component allocation reservations to a
forecasted demand; offering customers at least one less expensive
substitute component of the product in place of a requested
component; and manufacturing the product for a first price if the
substitute component is accepted, or manufacturing the product for
a second, higher price if the substitute component is not
accepted.
20. The method of claim 19, wherein price changes are imposed in
fixed increments.
21. The method of claim 19 wherein price changes are determined
according to price sensitivity functions.
22. The method of claim 19 wherein at least one customer offer
includes an optimal price based on a price elasticity value and
cannibalization effects.
23. An article comprising a computer-readable medium that stores
executable instructions for causing a computer system to:
dynamically monitor product demand and component allocation
reservations; compare the monitored product demand and component
allocation reservations to a forecasted demand; generate a customer
offer including at least one less expensive substitute component of
the product in place of a requested component; and generate
instructions to manufacture the product for a first price if the
substitute component is accepted, or to manufacture the product for
a second, higher price if the substitute component is not
accepted.
24. The article of claim 23 further comprising instructions to
impose price changes in fixed increments.
25. The article of claim 23 further comprising instructions to
determine price changes according to price sensitivity
functions.
26. The article of claim 23 further comprising instructions to
wherein offer an optimal price based on a price elasticity value
and cannibalization effects.
27. A profit optimization system comprising: at least one database
storage unit; and at least one processor coupled to the storage
unit, wherein the processor is operable to: dynamically monitor
product demand and component allocation reservations; compare the
monitored product demand and component allocation reservations to a
forecasted demand; generate a customer offer including at least one
less expensive substitute component of the product in place of a
requested component; and generate instructions to manufacture the
product for a first price if the substitute component is accepted,
or to manufacture the product for a second, higher price if the
substitute component is not accepted.
28. A profit optimization method comprising: determining a product
manufacturing cost value; comparing the current price of the
product to the cost value and calculating a contribution margin;
comparing the contribution margin to a desired target range for a
particular customer segment; and adjusting at least one of the
product price and a product configuration if the contribution
margin is outside the target range.
29. The method of claim 28 wherein the product manufacturing cost
value includes at least one of an assembly cost, available
components with known purchase cost, location substitution cost,
substitute component costs, and urgent missing supplies cost.
30. The method of claim 28 further comprising increasing the
product price if the contribution margin is below the target
range.
31. The method of claim 28 further comprising updating the product
manufacturing cost value on a regular basis.
32. An article comprising a computer-readable medium that stores
executable instructions for causing a computer system to: determine
a product manufacturing cost value; compare the current price of
the product to the cost value and calculating a contribution
margin; compare the contribution margin to a desired target range
for a particular customer segment; and adjust at least one of the
product price and a product configuration if the contribution
margin is outside the target range.
33. The article of claim 32 further comprising instructions to
include at least one of an assembly cost, available components with
known purchase cost, location substitution cost, substitute
component costs, and urgent missing supplies cost in the product
manufacturing cost value.
34. The article of claim 32 further comprising instructions to
increase the product price if the contribution margin is below the
target range.
35. The article of claim 32 further comprising instructions to
update the product manufacturing cost value on a regular basis.
36. A profit optimization system comprising: at least one database
storage unit; and at least one processor coupled to the storage
unit, wherein the processor is operable to: determine a product
manufacturing cost value; compare the current price of the product
to the cost value and calculating a contribution margin; compare
the contribution margin to a desired target range for a particular
customer segment; and adjust at least one of the product price and
a product configuration if the contribution margin is outside the
target range.
37. A profit optimization method comprising: determining a
contribution margin for each product order; calculating prices for
different product configurations such that product demand will be
met for preferred customer segments; presenting different product
configurations at the calculated prices to customers; and
manufacturing the products selected by the customers.
38. The method of claim 37 wherein the different product
configurations and prices include at least one of the original
product configuration, a plurality of different component
substitutions, and lead time considerations.
39. An article comprising a computer-readable medium that stores
executable instructions for causing a computer system to: determine
a contribution margin for each product order; calculate prices for
different product configurations such that product demand will be
met for preferred customer segments; present different product
configurations at the calculated prices to customers; and
manufacture the products selected by the customers.
40. The article of claim 39 further comprising instructions to
generate product prices for at least one of the original product
configuration, a plurality of different component substitutions,
and lead time considerations.
41. A profit optimization system comprising: at least one database
storage unit; and at least one processor coupled to the storage
unit, wherein the processor is operable to: determine a
contribution margin for each product order; calculate prices for
different product configurations such that product demand will be
met for preferred customer segments; present different product
configurations at the calculated prices to customers; and
manufacture the products selected by the customers.
42. A method for optimizing profit comprising: determining the
margin amount of an original customer product order; identifying at
least one potential bundled product package containing more than
the requested product order that would contribute to overall
profits; generating a probability value equal to the likelihood
that the customer would accept a bundled product package at a
special price; and offering at least one bundled product package at
the special price to the customer if the probability value is
greater than a predetermined value.
43. The method of claim 42 wherein the special price is a discount
price that is determined according to a pricing and discount
strategy.
44. The method of claim 43 wherein the pricing and discount
strategy includes deriving at least a portion of an order-specific
price of a product bundle offering using customer price elasticity
functions.
45. The method of claim 42 further comprising offering special
discount prices for at least one specific product bundle depending
on the price elasticity of the additional product offering.
46. The method of claim 42 wherein at least one potential bundled
product package would optimally contribute to overall profits.
47. An article comprising a computer-readable medium that stores
executable instructions for causing a computer system to: determine
the margin amount of an original customer product order; identify
at least one potential bundled product package containing more than
the requested product order that would contribute to overall
profits; generate a probability value equal to the likelihood that
the customer would accept a bundled product package at a special
price; and offer at least one bundled product package at the
special price to the customer if the probability value is greater
than a predetermined value.
48. The article of claim 47 further comprising instructions to
determine the special price according to a pricing and discount
strategy.
49. The article of claim 48 wherein instructions to determine the
pricing and discount strategy include instructions for deriving at
least a portion of an order-specific price of a product bundle
offering using customer price elasticity functions.
50. The article of claim 47 further comprising instructions to
offer special discount prices for at least one specific product
bundle depending on the price elasticity of the additional product
offering.
51. The article of claim 47 further comprising instructions to
ensure that at least one potential bundled product package
optimally contributes to overall profits.
52. A profit optimization system comprising: at least one database
storage unit; and at least one processor coupled to the storage
unit, wherein the processor is operable to: determine the margin
amount of an original customer product order; identify at least one
potential bundled product package containing more than the
requested product order that would contribute to overall profits;
generate a probability value equal to the likelihood that the
customer would accept a bundled product package at a special price;
and offer at least one bundled product package at the special price
to the customer if the probability value is greater than a
predetermined value.
Description
TECHNICAL FIELD
[0001] This invention relates to optimizing profits in a supply
chain, and more particularly to methods and systems for applying
adaptive pricing techniques to product manufacturing.
BACKGROUND
[0002] A supply chain is a network of facilities and distribution
options that performs the functions of procuring materials,
transforming the materials into semi-finished and finished
products, and distributing the finished products to customers.
Supply chain management (SCM) is a business policy that aims to
improve all activities along the supply chain. SCM results in
improved integration and visibility within individual companies, as
well as flexibility across supply and demand environments.
[0003] Product manufacturers strive to maximize profits by charging
the greatest price possible for the product, and by optimally
matching the supply of products to the demand. However, real world
issues often combine to present problems that must be overcome in
order to maximize profits. For example, certain types of products,
such as computers and fashion items, lose value over time. In
addition, supply chain capacity is typically flexible only within
certain bounds. Further, customer or product segmentation potential
varies by industry. Moreover, a manufacturers' share of variable
costs is higher than that of service industries, product demand may
be variable, and contracts may limit price adjustments.
[0004] SAP AG and SAP America, Inc. provide supply chain management
solutions for product manufacturers to help them reach their goals.
Some of the solutions are based on the mySAP.com e-business
platform (see www.sap.com for further information). One of the
building blocks of the e-business platform is the SAP R/3 component
that provides enterprise resource planning functionality. The SAP
R/3 product includes a Web Application Server ("Web AS"), an R/3
core, and various R/3 extensions.
[0005] The SCM Extensions of R/3 deal with various planning,
coordination, execution, and optimization issues that are
associated with a supply chain. It would be beneficial to provide a
web-based or on-line system that optimizes the alignment of
variable customer demand and existing supply capabilities to
optimize profits.
SUMMARY
[0006] Profit optimization methods, articles and systems for a
supply chain and a demand chain are presented. An implementation of
the technique includes determining the initial cost of components
required to manufacture a product, dynamically determining the cost
for substitution of at least one product component, dynamically
determining the location of at least one substitute component, and
manufacturing the product for the lowest cost based on the results
of the cost of substitution and substitute component location
determinations. At least one of the cost of substitute components
and the component locations may be determined at or near the time
of manufacture.
[0007] In another implementation, the method includes defining a
set of manufacturing rules based on customer segments, allocating
critical product components according to preferred customer
segments, allocating manufacturing capacity according to the
preferred customer segments, and manufacturing the product. The
method may include allocating the product components and
manufacturing capacity according to a feasibility analysis.
[0008] In yet another implementation, the technique includes
monitoring product demand and allocation reservations for product
components, comparing the product demand and component allocation
reservations with demand forecasts at predetermined intervals,
dynamically assigning component allocations for preferred customer
segments according to a comparison of the forecasted and monitored
demand, and manufacturing the product for the preferred customer
segments before manufacturing the product for other customer
segments.
[0009] This implementation may include one or more of the following
features. Preferred customer segments may be determined according
to predefined rules. Monitoring of product demand and allocation
reservations may be conducted in regular intervals. A user may be
alerted when a component allocation is changed.
[0010] Another profit optimization method includes dynamically
monitoring product demand and component allocation reservations,
comparing the monitored product demand and component allocation
reservations to a forecasted demand, offering customers at least
one less expensive substitute component of the product in place of
a requested component, and manufacturing the product for a first
price if the substitute component is accepted, or manufacturing the
product for a second, higher price if the substitute component is
not accepted.
[0011] This profit optimization method may include one or more of
the following features. The Price changes may be imposed in fixed
increments. Price changes may be determined according to price
sensitivity functions. At least one customer offer may include an
optimal price based on a price elasticity value and cannibalization
effects.
[0012] Yet another profit optimization technique includes
determining a product manufacturing cost value, comparing the
current price of the product to the cost value and calculating a
contribution margin, comparing the contribution margin to a desired
target range for a particular customer segment, and adjusting at
least one of the product price and a product configuration if the
contribution margin is outside the target range. The product
manufacturing cost value may include at least one of an assembly
cost, available components with known purchase cost, location
substitution cost, substitute component costs, and urgent missing
supplies cost. The product price may be increased if the
contribution margin is below the target range. The product
manufacturing cost value may be updated on a regular basis.
[0013] A further implementation of a profit optimization technique
includes determining a contribution margin for each product order,
calculating prices for different product configurations such that
product demand will be met for preferred customer segments,
presenting different product configurations at the calculated
prices to customers, and manufacturing the products selected by the
customers.
[0014] This implementation may include one or more of the following
features. The different product configurations and prices may
include at least one of the original product configuration, a
plurality of different component substitutions, and lead time
considerations.
[0015] In yet another implementation, a profit optimizing method
includes determining the margin amount of an original customer
product order, identifying at least one potential bundled product
package containing more than the requested product order that would
contribute to overall profits, generating a probability value equal
to the likelihood that the customer would accept a bundled product
package at a special price, and offering at least one bundled
product package at the special price to the customer if the
probability value is greater than a predetermined value.
[0016] This implementation may include one or more of the following
features. The special price may be a discount price that is
determined according to a pricing and discount strategy. The
pricing and discount strategy may include deriving at least a
portion of an order-specific price of a product bundle offering
using customer price elasticity functions. Special discount prices
may be offered for at least one specific product bundle depending
on the price elasticity of the additional product offering. At
least one potential bundled product package may be offered that
would optimally contribute to overall profits.
[0017] The above techniques may all be embodied in an article
comprising a computer-readable medium that stores executable
instructions for causing a computer system to operate according to
the invention as described herein. Moreover, the techniques could
all be utilized in a system that may include at least one database
storage unit and at least one processor coupled to the storage
unit, wherein the processor is operable to operate according to the
invention as described herein.
[0018] A manufacturer can benefit from utilizing the profit
optimization techniques according to the invention by moving from a
cost-based to a profit-based supply chain decision-making model. In
particular, a manufacturer can use the disclosed techniques to
analyze monetary impacts of decision choices on profit. The
analytic capabilities allow the manufacturer to determine the cost
and the profit of various resource utilization choices, and certain
price differentiation choices may become apparent that allow higher
average profitability of goods. The techniques also determine how
best to use scarce product component resources to achieve maximal
profit, and how to get the best possible price from customers. In
an implementation, a manufacturer can use price sensitivities to
determine optimum prices. Further, the techniques permit the
valuation of cross sensitivities and cannibalization effects
between different products of the same product family, between
different product families, and between a company and its
competitors.
[0019] The details of one or more embodiments of the invention are
set forth in the accompanying drawings and the description below.
Other features, objects, and advantages of the invention will be
apparent from the description and drawings, and from the
claims.
DESCRIPTION OF DRAWINGS
[0020] FIG. 1 is a simplified block diagram illustrating the
building blocks used for optimizing profit.
[0021] FIG. 2A is a simplified block diagram illustrating the
interrelationships between operations in an activity-based
management and supply chain performance management process.
[0022] FIGS. 2B to 2E are graphs to illustrate the concept of
adaptive pricing in the value life cycle of a product.
[0023] FIG. 3 is a simplified block diagram of an implementation of
a system for optimizing profit for a manufacturing supply chain
according to the invention.
[0024] FIG. 4 is a flowchart of an implementation of a profit
optimization method.
[0025] FIG. 5 illustrates a method for optimizing profit based on a
dynamic prioritization of product components.
[0026] FIG. 6 illustrates a method for optimizing profit based on a
dynamic price-driven model of demand.
[0027] FIG. 7 illustrates a profit optimization method wherein
component costs are considered during a profitable-to-promise
check.
[0028] FIG. 8 illustrates a profit optimization method wherein a
product price is determined during the profitable-to-promise
check
[0029] FIG. 9 illustrates a profit optimization method wherein spot
bundle pricing is offered to customers.
[0030] Like reference symbols in the various drawings indicate like
elements.
DETAILED DESCRIPTION
[0031] The profit optimization techniques described herein function
to align variable customer demand and existing supply capabilities.
It has been recognized that mismatches of demand and supply are not
strictly a Supply Chain Management (SCM) problem or a strict
product pricing problem. The profit optimization techniques
described below use analytical information about cost of supply and
benefit of revenue as a basis for profit-based decision-making
support. The techniques extend revenue management approaches
(wherein the optimal pricing of existing goods takes into account
customer buying behavior) and SCM capabilities (the guaranteed and
cost-optimized supply of goods to within certain manufacturing and
distribution constraints) in a comprehensive manner. In particular,
the profit optimization techniques introduce adaptive pricing
techniques into a manufacturing environment.
[0032] FIG. 1 is a simplified block diagram 10 to illustrate the
building blocks of the technique for optimizing profit. An
analytical foundation 20 includes a measure builder 21 which is
capable of analyzing performance metrics, at least one reporting
engine 22, a planning and simulation engine 23, an activity based
costing application 24, a customer segmentation application 25 and
price elasticity determination application 26. These analytical
applications gather any required operational data from a data
warehouse 27, although some other database, such as a
manufacturer's proprietary database, could be utilized. The data
warehouse typically contains mainly historical data, such as
average sales price for a product, customer discount information,
various alternate product configurations and price variations, and
the like. Also included is an operational management layer 30
having tactical and operational profit optimization applications
that may be industry specific and/or process specific. The
operational management layer may include an adaptive pricing engine
32 (to provide price-driven demand and supply matching), a campaign
optimization engine 34 (to factor in promotion pricing), and a
profitable-to-promise engine 36 (to factor in spot pricing).
Lastly, a strategy management layer 40 includes strategic profit
optimization applications (which may be industry or process
specific) such as an investment decision support engine 42 and a
strategic pricing engine 44 (list pricing).
[0033] All of the profit optimization applications utilize
analytical data, which may be generated by analytical Supply Chain
Management (SCM) applications, analytical Customer Relationship
Management (CRM) applications, and Financial Analytics applications
currently available from SAP AG and SAP America, Inc. A significant
part of profit optimization is the ability to determine the cost of
manufacturing products in an adaptive supply chain network because
the cost of production changes as the state of the supply chain
varies. It is thus necessary not only to determine the cost of
resources, such as materials, machines and/or human resources, but
it is important to also determine the full cost of production
processes.
[0034] FIG. 2A is a simplified block diagram 50 illustrating the
interrelationships between operations in an activity-based
management and supply chain performance management process. The
horizontal blocks within oval 59 pertain to manufacturing supply
chain performance. Measures 52 link performance objectives 51 to
the activities or processes 53 that are taking place. The vertical
blocks within oval 58 concern cost analysis considerations for the
product. Cost objectives 54 in view of activity cost assignments 55
influence the activities or processes 53, and the available
resources 56 and resource cost assignment considerations 57 also
influence the activities or processes 53. The performance and cost
consideration ovals 58 and 59 intersect at, and drive, the
activities and processes 53 pertaining to the manufacture of a
product. In general, manufacturers typically only consider
performance goals (oval 59) or only consider costs (oval 58) when
manufacturing a product. The techniques described herein marry
considerations from both realms to permit a manufacturing company
to determine the best combination of factors to use to obtain
optimal results (optimal profits). The process 50 could be
presented as an online analytical processing technique to permit a
company to determine the best mix of assignments of resource cost
to activities or processes, as well as the activity cost
assignments to cost objects. Such a supply chain performance
management system is capable of evaluating key performance
indicators for processes along the supply chain input for decision
making in profit optimization applications. With both instruments
and the content of supply chain processes based on a supply chain
operations model or some other company-customized supply chain
process model, a company is capable of determining product and
production cost in an adaptively changing supply chain network.
[0035] FIGS. 2B to 2E are graphs to illustrate the concept of
adaptive pricing in the value life cycle of a product. FIG. 2B
includes a value to time graph 60 and an associated product volume
to price graph 62. The graph 60 shows first market entry 61 of the
product wherein the product has a high value, and the graph 62
shows a sales volume and price point 63 indicating that the optimal
price point in this example is where the volume is quite low and
the product price is fairly high. FIG. 2C illustrates the same
value to time graph 60 at a point 64 wherein the first competitor
enters the market. In this case the product value is lower, and as
shown in graph 62 of FIG. 2C at point 65 the optimal price has been
lowered and the sales volume is higher. The graph 60 of FIG. 2D
illustrates the product life cycle wherein many competitors have
entered the market, and at point 66 the product value has
diminished. Consequently, as shown in graph 62, at point 67, the
product price has been reduced again and the sales volume has
increased. The graph 60 of FIG. 2E shows that at some later time at
point 68 the market is saturated and the product value is low so
that the product price, as shown in graph 62, has been lowered
again. At this time, however, the sales volume has not increased
and may even decrease so that the value of manufacturing this
product has diminished. Thus, FIGS. 2B-2E illustrate how adaptive
pricing in the value life cycle of a product can be used to
increase or maintain profits. In particular, a manufacturer adjusts
the price of a product by accepting different price elasticity
functions at different phases of the product life cycle to increase
or at least maintain profits.
[0036] FIG. 3 is a simplified block diagram of an implementation of
a computerized or on-line system 80 for optimizing profit for a
manufacturing supply chain. A programmable computer or server 82
may be configured to run one or more application programs to
process data and provide results in response to requests from
client computers 83, 84, 85, 86 and 87. The server 82 may be
connected to a manufacturing supply chain monitor device 88, a
database 90 and a data storage unit 92. The computer or server 82
and the client computers 83-87 may be any general purpose
programmable computer, such as IBM-type personal computers or
Apple-type computers. Alternately, the client computers may be any
type of portable electronic data device capable of sending and
receiving data, such as a personal digital assistant (PDA). The
computer or server 82, client computers 83-87, database 90, and
data storage unit 92 may all be in different locations, and may
communicate via a network connection, via the internet 94 and/or
over wireless connections or other communication links. Authorized
manufacturing employees can utilize client computers to access the
server 82 over direct or wireless connections via the internet 94,
or by direct or wireless network connections, as shown. The server
82 may contain various application programs, which are utilized by
the authorized employees to determine how best to optimize profits
under various circumstances and utilizing various techniques which
are described in detail below.
[0037] In an implementation, a profitable-to-promise application
extends the available-to-promise (ATP) capabilities of a supply
chain by considering the initial cost of components required to
manufacture a product, as well as dynamically considering the cost
for substitution of components or locations where components are
drawn from. In particular, the cost of substitution of components
or products, and their locations, are determined at or close to the
time of manufacture. Such timely information permits accurate
determinations to be made so that the product can be manufactured
and sold at a profitable margin.
[0038] There are different levels of implementation and
sophistication of such a profitable-to-promise solution. The
various levels and aspects are listed and described below in order
of increasing complexity.
[0039] In an implementation, profitability is ensured by using
available-to-promise rules (ATP rules) defined on the basis of
customer segments. Material components and capacities are allocated
specifically to preferred customer segments such as high value
customer segments. The ATP rules ensure that expensive service or
product or location substitutions are only taken into account for
high value customers during an ATP check. The ATP check is an
initial analysis of available products and their locations to
determine the feasibility of various substitution possibilities. A
profit-oriented ATP check may be made with the existing SAP APO
product, but it does not take into account costing information.
[0040] FIG. 4 is a flowchart 100 of an implementation of a profit
optimization method. Rules to follow for profitability concerning
product components and manufacturing capacity are defined 102 based
on the customer segments. The customer segmentation application 25
may be used to obtain customer segmentation data. Critical
components needed to manufacture the product are then allocated 104
to the high-value or preferred customer segments, and capacity is
allocated 106 to these customer segments. The product is then
manufactured 108. Manufacture of a product in this manner ensures
that profitability goals are achieved.
[0041] Another implementation keeps cost low for product and
component location substitution in less profitable customer
segments. In particular, FIG. 5 illustrates a method 150 for
optimizing profit based on a dynamic prioritization of product
components.
[0042] The demand and allocation reservations for products and
components are monitored 154 continuously and, in predetermined
intervals such as days or hours, compared 156 with the demand
forecast. Under the assumption that the forecasted demand leads
only to inevitable product and location substitutions, a deviation
from the forecasted demand patterns would result in higher cost.
The assignments of preferred customer segments to certain
prioritized material allocations are dynamically changed 156
according to the comparison of forecasted and monitored demand. The
product is then manufactured 158 for preferred customer segments
before manufacturing for other customer segments. This ensures that
the high valued or preferred customer groups will receive the
requested material in time, while lower priority groups (customer
segments with little expected profit) have to draw on material
allocations or components at later periods, or are rejected if no
substitutions are available. (The preferred customer segments may
be identified according to the respective rules defined as pointed
out above for profit-oriented ATP-checks).
[0043] In order for the dynamic prioritization of product
components technique to operate successfully, it is necessary to
monitor the consumption of allocation in a given period. This may
be done on a component level in the ATP, or such information can be
stored in a data warehouse 27 or other database. A monitor (monitor
agent) must be implemented capable of calculating the expected
consumption rate, or capable of triggering the adjustment of
selection values based on remaining component allocation levels
which depends on how consumption rates are forecasted. For example,
if the consumption rate is a linear function, then the expected
consumption rate could be calculated by average techniques or
linear regression. In addition, this monitor must determine new
selection priorities based on the above information in regular time
intervals. The new set of selection priorities will then avoid
certain substitutions to largely consumed location products, or
consequently substitute some products for certain customer
segments. For example, consider the situation where components X, Y
and Z are initially prioritized as first, second and third,
respectively, out of eight components. The manufacturer is running
low on component X, and component Z has not been selling well. In
order to force the system to substitute component Z when component
X reaches a very low level, the priority of component Y can be
changed to a much lower priority such as "fifth" and the priority
of component Z could be changed to second. In order for such a
technique to be successful, current sales information is
required.
[0044] There are several case specific issues that need to be
considered in order to achieve the goals of dynamic prioritization
of products. The considerations include where the needed
consumption information is stored; how the selection priorities are
determined; which products are involved in the dynamic (or
adaptive) prioritization of components; and which authorization
concepts are necessary and/or suitable for changing assignments of
customer segments to allocations (such as alerting a user) and
suggesting new priorities with approval and possible alteration. A
suitable interface, such as an intuitive computer display, can be
presented so that a user can interact with the system to simulate
how different priority choices would affect the outcome. Such
simulations may be run only for "high value" or expensive products,
such that this dynamic strategy may be applied only where a
manufacturer would expect a good return.
[0045] Another profit optimization implementation keeps requested
demand in line with available supply at the best prices achievable
(to ensure the highest profit). In particular, FIG. 6 illustrates a
method 200 for optimizing profit based on a dynamic price-driven
model of demand.
[0046] The demand and allocation reservations for products and
components are monitored 202 continuously and, in regular intervals
such as days or hours, compared 204 with the demand forecast.
Rather than adjusting the allocation of customer segments to
material allocations, the price of components or characteristics is
used as an instrument to steer demand away from strongly requested
materials towards better available materials. Thus, customers are
offered 206 a less expensive substitute component or components of
the product in place of a requested component. If the customer
accepts 208 the offer, then the product is manufactured 210 for a
first price. If the customer does not accept the offered substitute
component, then the product is manufactured 212 with the desired
component for a second, higher price. Thus, if inventory of a
particular component A is low but inventory of component B is high,
the manufacturer may raise the cost of A and/or lower the cost of B
to shift demand. Such a technique reduces the cost of
substitutions, or if the customer insists on the desired component
choice, increases the revenue for highly demanded products and/or
components. In order for such a technique to work, the system must
dynamically monitor the progress of product demand and the
inventory of component parts so that prices for components can be
adjusted in a timely manner.
[0047] Consequently, forecasts and actual demands for product
components are compared and lead to price changes of components.
These price changes may be imposed in fixed increments.
Alternately, the optimal price changes (and thus the optimal new
prices) could be determined according to price sensitivity
functions using an adaptive pricing engine. This profit
optimization application therefore limits or controls costs for
specific products or configurations, and adapts the revenue model
to the current situation.
[0048] In order for this dynamic price-driven technique to be
successful, a manufacturer must be willing to support an adaptive
pricing scheme. A pricing engine visible in the customer or call
center interface (for example, SAP Internet Pricing &
Configuration Engine offered by SAP AG and SAP America, Inc.) must
be updated with a new pricing scheme consistently and in regular
intervals. In addition, a relationship must be defined between
expected over/under-consumption fluctuations that may occur during
a period and the price change. In general, a price elasticity value
must be known to calculate the relationship. An optimal pricing
scheme can only be determined with an adaptive pricing engine that
takes into account price elasticities and cannibalization effects
learned from historical data. The historical data may be obtained
from a data warehouse or other database, which may be developed by
a manufacturer and may be industry specific. Based on pricing
engine results, an initial increase in price could be made in
specific incremental price steps.
[0049] There are several additional case specific issues that
should be considered in order for the goals of the dynamic
price-driven model to be achieved. The conditions include a
determination of which components are considered for such a costing
approach; how price changes are determined, and whether these price
changes are based on information stored in or determined from the
data warehouse, such as past consumption patterns related to the
respective product or to option prices; the desired price stability
and how often prices can be changed; and options that can be
presented as prices in a configuration engine. A heavily consumed
component might contribute to several options and will thus affect
more than one price.
[0050] Yet a further implementation of profit optimization ensures
that unprofitable products are not offered to customers. In
particular, FIG. 7 illustrates a profit optimization method 250
wherein component costs are considered during a
profitable-to-promise check.
[0051] This technique considers real cost (at an sufficiently
aggregated level) during the profitable-to-promise check to ensure
that customer orders are priced profitably in any supply chain
state, and possibly also cover the cost for product and location
substitutions. Several different types of product cost should be
considered in profit determination when a product is ordered. These
costs may include assembly costs, available components with known
purchase cost, location substitutions (with transportation cost
added), substitute components having a higher purchase cost, and
any urgent missing supplies having an associated real cost of
purchase. Thus, referring to FIG. 7, a product cost value is
determined 252 based on one or more of the costs described
above.
[0052] Once cost and price are determined, a contribution margin is
determined 254 and compared 256 to a desired target range for a
particular customer segment. If the contribution margin is within
the target range 258 then the product price is maintained 260. If
the contribution margin is below the target range 262, a
profitable-to-promise check is performed 264 using a different
profile to increase the contribution margin to the desired level,
which may include generating a lower product price by allowing only
a certain amount or a certain cost for substitutions or no
substitutions at all. If a less costly product configuration is
possible 268, then the product can be offered 270 at a lower price.
If not, then the product price is increased 272 in order to
maintain the required contribution margin. If the product price is
found to be above the target range 262, the product is being
offered 266 to the customer at a premium price and no adjustments
are necessary.
[0053] The above cost profiles must be updated regularly (in an
ultimately ideal case after each order). Activity-based costing
information can help determining process dependent cost such as
transportation or assembly cost.
[0054] The current SAP APO ATP solution offered by SAP AG and SAP
America, Inc. allows limits to be placed on the number of product
component substitutions during an ATP check. If there is more than
one planned substitution, a user can select the relevant one. If
more than one relevant substitution are returned, then the user
selects between options. Moreover, an ATP check could run with
different profiles (no substitution limitation and a cap on the
allowed substitution cost) to allow a choice between results.
[0055] In order for the goals of the component costs during
profitable-to-promise check to be met, the current cost for
components, assembly and transportation (transfer shipments as well
as customer delivery) must be maintained and available. In
addition, restrictions for alternative profitable-to-promise checks
must be defined and customer segments specific. Yet further, a
suitable aggregation level of production, sales, transportation and
promotional cost must be defined.
[0056] There are several additional case-specific issues that
should be considered. The conditions include the frequency of cost
determinations; the performance and application load of the
profitable-to-promise implementation as a complete process; and
whether an update of rules during an ATP check seems inadequate,
difficult and/or too resource-consuming to perform.
[0057] FIG. 8 illustrates a profit optimization method 300 wherein
a product price is determined during the profitable-to-promise
check. The objective is to improve the cost foundation of decision
making by utilizing information about the current state of the
supply chain when determining cost for fulfilling a specific
request.
[0058] Referring to FIG. 8, a contribution margin for each order is
determined 302 based on product price and cost for components,
production, sales, transportation, promotion, marketing and other
factors. This cost determination improves the objectivity for price
negotiations by sales personnel and helps to determine potential
alternatives for fulfilling the customer need that are less costly.
Prices are calculated 304 for different product configurations to
ensure that product demand will be met for preferred customer
segments. Next, a selection of different product configurations
with different prices are presented 306 to each customer based on
that customer's product request. The customer then chooses between
different fulfillment options that each differ in their conditions,
such as the original product demand (no component substitutions),
lead time, price, or quality. The product is then manufactured 308
according to the customer's selection. Thus, a customer may choose
to receive a product at a later time for a cheaper price wherein no
substitute components are used, or to receive the requested product
soon but at a higher price and with one or more substitute
components. This method therefore utilizes price and substitution
capabilities to drive demand such that the preferred or most
valuable customers can be served at any time.
[0059] In order to be able to present concurrent responses on a
given customer request, a strategy must be in place that defines
how the different responses are determined, and how many responses
are generated for a customer in any one of a plurality of
situations. It is also important to manage the volume of data
presented in response to a customer (especially in an online
environment) to guarantee that the system performs acceptably
fast.
[0060] FIG. 9 illustrates a profit optimization method 350 wherein
spot bundle pricing is offered to customers. The objective is to
use bundling strategies and suggestive pricing to achieve defined
sales goals.
[0061] Instead of just fulfilling the requested demand, the spot
bundle pricing strategy attempts to offer to the customer products
or services that relate to the original demand and that can be
delivered at a positive profit margin together with the original
demand. Referring to FIG. 9, the profit margin amount is determined
352 for the original customer product order. Potential bundled
product packages are identified 354 that would contribute to
overall profit. Next, a probability value is calculated 356 that is
equal to the likelihood that the customer would accept a bundled
product package at a special price (such as a discount price). If
the probability value is greater than a predetermined value 358,
then at least one bundled product package is offered 360 to the
customer. If the probability is not greater than the predetermined
value, then a bundled product package is not offered 362 and the
product is manufactured according to the original customer order.
The probability value may be an arbitrary number, such as a 75%
(seventy-five percent) chance based on historical customer data
that a bundled product package would be accepted, or may be based
on a feasibility calculation, or may be based on any other basis
identified by the manufacturer.
[0062] The spot bundle package and pricing technique opens up the
opportunity for a manufacturer to offer attractive products to
customers in a manner that does not jeopardize the ability to
fulfill other customer demands. Knowledge of the profit margin of
the original order fulfillment, and knowledge about potential
synergies of delivering more than the originally requested demand,
may permit special discounts to be offered depending on the
price-elasticity of the additional offering. Information about the
customer is also required to determine the willingness to accept
the additional offer at a price that optimally contributes to the
overall profit (in terms of expected acceptance and profit margin).
In addition, order-specific pricing of bundles may be implemented
according to a pricing and discount strategy (which may be derived
from customer price elasticity functions).
[0063] The invention as described above can be implemented in
digital electronic circuitry, or in computer hardware, firmware,
software, or in combinations of them. Apparatus of the invention
can be implemented in a computer program product tangibly embodied
in a machine-readable storage device for execution by a
programmable processor executing a program of instructions to
perform functions of the invention by operating on input data and
generating output. The invention can be implemented advantageously
in one or more computer programs that are executable on a
programmable system including at least one programmable processor
coupled to receive data and instructions from, and to transmit data
and instructions to, a data storage system, at least one input
device, and at least one output device. Each computer program can
be implemented in a high-level procedural or object-oriented
programming language, or in assembly or machine language if
desired; and in any case, the language can be a compiled or
interpreted language. Suitable processors include by way of
example, both general and special purpose microprocessors.
Generally, a processor will receive instructions and data from a
read-only memory and/or a random access memory. Generally, a
computer will include one or more mass storage devices for storing
data files; such devices may include magnetic disks, such as
internal hard disks and removable disks; magneto-optical disks; and
optical disks. Storage devices suitable for tangibly embodying
computer program instructions and data include all forms of
non-volatile memory, including by way of example semiconductor
memory devices, such as EPROM, EEPROM, and flash memory devices;
magnetic disks such as internal hard disks and removable disks;
magneto-optical disks; and CD-ROM disks. Any of the foregoing can
be supplemented by, or incorporated in, ASICs (application-specific
integrated circuits).
[0064] To provide for interaction with a user, the invention can be
implemented on a computer system having a display device such as a
monitor or LCD screen for displaying information to the user and a
keyboard and a pointing device such as a mouse or a trackball by
which the user can provide input to the computer system. The
computer system can be programmed to provide a graphical user
interface through which computer programs interact with users.
[0065] A number of embodiments of the invention have been
described. Nevertheless, it will be understood that various
modifications may be made without departing from the spirit and
scope of the invention. For example, several of the profit
optimization implementations may be combined to provide further
data for determining how best to allocate resources and price
products to reach manufacturing goals. Accordingly, other
embodiments are within the scope of the following claims.
* * * * *
References