NATIONAL INSTITUTUE OF
INDUSTRIAL ENGINEERING (NITIE)
A Paper on
“Advanced Planning and Scheduling
from SCM perspective”
Prof A. D. Raoot
ANKUSH SETHI (01)
AVANISH KACHHAWAHA (03)
NIREN ATHAWALE (02)
ERP Assignment Group 1
Advanced Planning and Schedulling
1. What is APS
2. Scope of APS
3. Differences in Planning horizons
4. Planning and scheduling
5. Comparison of APS and ERP capabilities
6. Features of APS
7. Theory and practice of APO in supply chain domain
8. Implimentation Statergy
Robert Bosch Case Overview
For many manufacturers the demands of meeting rising customer expectations and
lowering production costs in an environment of more products, more complexity and more
choice is placing great stress on the effectiveness of their planning and scheduling processes.
Organizations have already adopted ERP solutions with varying degrees of planning and
scheduling capabilities. Yet, operations executives acknowledge that these same systems are
becoming outdated, lacking the speed, flexibility and responsiveness to manage their
increasingly complex production environments.
They know too that as the business grows its operations, expands its product range and adopts a
global manufacturing supply chain – they must seek alternatives to the current ways of doing
things if they are to remain competitive and responsive to customer needs.
APS is a new revolutionary step in enterprise and inter-enterprise planning. It is revolutionary,
due to the technology and because APS utilizes planning and scheduling techniques that consider
a wide range of constraints to produce an optimized plan:
Machine and labour capacity
Customer service level requirements (due dates)
Inventory safety stock levels
Sequencing for set-up efficiency
This paper also discusses the basic functionality of planning and scheduling in Advanced
Planning and Scheduling systems (APS). Three basic planning options - concurrent planning (or
unconstrained planning), constrained planning and optimization - are analyzed. The planning
functionality is radically improved compared to MRP and MRP II. APS is relevant for
production-organizations. Also distribution-organizations can benefit from implementing APS
for supply chain management.
The objective of this paper is to map the characteristics of advanced planning and scheduling
systems and to find out the (use) fullness of these systems.
The origins of production planning and scheduling
Traditional planning and scheduling systems originated in the 1960s with the advent of Material
Requirements Planning (MRP) which evolved into Manufacturing Resource Planning (MRP II)
and finally into Enterprise Resource Planning (ERP), where a financial component was
In those days the demands placed on manufacturers were very different from today. The
principle objective of planning was to synchronize all levels of production backwards from the
customer due date, aligning all work orders according to date order and providing a target date
for bought-in purchased parts. The primary inputs to the planning process were bills of material,
bills of routing orders, inventory and work-in-process.
With these planning and scheduling capabilities built into ERP systems, manufacturers were able
to meet business demands as they were at the time. Since then, ERP systems have been refined,
but they are still based on the same concepts and iterative processes as they once were. The
planning engine in an ERP system is essentially the same today as it was in the 1970s.
In an ERP system, a Master Production Schedule (MPS) is used to establish a plan for the
factory, balancing off sales demand (orders, forecasts or some combination of the two) with
inventory and planned supply. The resulting MPS plan becomes the input for the MRP
calculation which explodes through all bills of material to synchronize manufacturing and
purchased orders to the master plan. It is a proven and efficient process and has led to many
documented benefits for manufacturers worldwide.
Since the MRP calculation assumes infinite production capacity, additional capabilities have
been added into the planning process including Rough Cut Capacity Planning (RCCP) and
Capacity Requirements Planning (CRP). While it added something to the planning process,
traditional CRP is a bit of a blunt instrument. The results tell the production manager whether the
factory is over-capacity, even to the point of which work centers and work orders are causing the
problem. But it is left to the production planners to juggle orders around to solve the problem and
there are few, if any, ‘what-if’ scenario capabilities.
Comparative costs of alternative plans are never considered and the calculations ignore other
things that limit capacity such as labor skills, preferred production sequences, start-up and shut-
down losses and industry-specific scheduling problems such as shelf-life. However, among the
primary deficiencies of traditional ERP planning is the iterative nature demanded by the process
and the time taken to make adjustments in the face of change.
The planning processes of MPS, RCCP, MRP and CRP are all separate, sequential and iterative.
It is a time-consuming task to investigate capacity shortfalls, make changes – often manually –
and then repeat all the processes to re-check feasibility. Fundamentally, the systems identify
problem areas rather than solving the problems. Overlay the frequency with which new orders
are introduced into the plan and it is easy to see why yesterday’s reactive planning methods are
no longer sufficient for today’s dynamic supply chain challenges.
Planning and scheduling in the 21st century
The planning and scheduling functionality of years past no longer provides the responsiveness
and agility businesses need in order to prevail in today’s competitive, demand-driven
Today manufacturers must contend with globalization and meet steadily increasing customer
demands for more choice, lower prices, faster delivery and higher quality. A typical production
environment must now contend with increasing levels of product variation, faster new product
introductions, shorter reduction runs, longer supply lines, and it must be prepared to work
collaboratively with customers, suppliers and co-manufacturers.
Manufacturers are responding by moving increasingly to demand-driven production strategies,
employing a number of techniques to more closely align supply with customer demand, with
techniques that vary according to the industry. These include make-to-order, configure-to-order,
collaborative demand planning and other demand sensing methods.
As the world of manufacturing has moved on, new manufacturing concepts such as lean
manufacturing, six sigma, just-in-time, theory of constraints, agile manufacturing and demand-
driven supply networks have arisen.
A “lean” manufacturing strategy is favored by many executives who see simplifying their
manufacturing as the best response to changing customer requirements. The simplification
brought about by “going lean” usually changes the requirements for planning and scheduling, but
even the leanest operations still need to plan ahead and schedule production resources and
So what new planning processes, techniques and technologies are employed to support modern
Advanced Planning and Scheduling Systems (APS)
1.What is APS?
From its humble beginnings in fast materials requirements planning (MRP) and constrained
production scheduling programs, APS technology has blossomed into one the most important
advances in business applications. Its impact on the manufacturing planning and scheduling
process is more revolutionary than evolutionary. For the first time, manufacturers have
planning tools that can absorb vast complexities to produce optimal plans. More importantly,
APS leverages the planner’s knowledge with responsive decision support tools rather than
enslaving him or her with an endless barrage of exception messages.
An APS system can function in a number of environments and types of complexity. When
companies start to integrate within their organization an APS tool can be helpful, because the
MPS-MRP-CRP planning process can take place simultaneously. An APS tool really benefits
companies integrating with outside organizations. The APS tool can be helpful in dynamic
environments, because it has the advantage of being really fast in recalculating the plans
whenever necessary. Another benefit of this system is that it facilitates the combination of
information of multiple sites and that it calculates an optimal plan for a complete supply chain.
The key to understanding APS is that it is a new technology, not a rehash of 30-year-old MRP
programs. In much the same way that the microwave oven revolutionized cooking and CDs
changed the way we listen to music, APS technology is changing the way manufacturers plan.
APS leverages the incredible advances in computer technology over the last 10 years. Generally,
an APS application utilizes this memory capacity to store models or representations of the
business environment against which it runs specially designed algorithms to solve for the best
Amongst the criteria that an APS will address in determining the optimal production schedule is:
• Capacity of machines and labor
• Labor skills– if only a few resources can operate a particular machine the systems will
recognize this as a capacity limitation
• Special tools
• Material availability – if delivered materials are known to be due on a certain date, there
is no point in planning production as if the materials would be available – which is what
most systems do.
• Production sequence – APS can recognize the optimal sequence of production (eg: “light-
to-dark” or “can size”) which will minimize lost time due to change-over’s and clean-
downs. This capability alone can boost productivity by as much as 25% without any
investment in new equipment.
The concept of optimization means that APS weighs the constraints and other business rules to
find the optimal use of available material and plant capacity. This enables the business to meet
such objectives as minimizing total cost (often from inventory and setup reductions) and
maximizing overall plant operations to fill the most customer orders on time.
2.THE SCOPE OF APS
APS as an umbrella technology embracing the following concepts:
• Simultaneous consideration material and plant resources
• Optimization algorithms that incorporate constraints and business goals
• Leverage for memory-resident programs and databases to provide real-time plan and
schedule creation with net change regeneration
• Real-time decision support
• Real-time available-to-promise
The scope of APS is not limited to factory planning and scheduling, but has grown rapidly to
include the full spectrum of enterprise and inter-enterprise planning and scheduling functions
(as shown in figure: 1)
Figure 1: APS solutions related to the time horizon
• Strategic and long-term planning addressing such issues as the following:
Which products should be made?
What markets should the company pursue?
How should conflicting goals be resolved?
How should assets be deployed for the best rate of ROI?
Currently, none of the major APS vendors offers strategic planning as part of its product
suite. Time horizon: 2+ years
Planning Manufacturing Planning
Detail Distribution Planning
Supply Chain Planning
Sales and Operation Planning
Supply Chain Network Design
Seconds/ Hours/ Weeks/ Quarters Years
Minutes Days Months
• Supply chain network design: Optimizes the use of resources across the current network
of suppliers, customers, manufacturing locations, and distribution centers. It is helpful
for locating new facilities within an existing supply chain network and determining the
optimal way to fulfill customer demand. What-if analysis can be performed to test the
impact of closing or moving facilities on profits and customer service levels. Supply
chain network design tools are often applied to optimize the balance between stocking
locations and transportation costs. Time horizon: 1+ years.
• Demand planning and forecasting: Demand planning addresses creation of demand
through promotions and external events. Demand forecasting uses statistical and time-
series mathematics to forecast future demand from sales history. Demand forecasts are
often considered unconstrained as they reflect what customers want, not necessarily
what can be produced. Time horizon: 6 to 18 months.
• Sales and operations planning (SOP): Loosely defined by most vendors, SOP is the
process of converting the demand forecast into a set of operation plans for sales and
manufacturing. This process may include the use of manufacturing planning or supply
chain network optimizers to determine if forecast demand can be met. Time horizon: 6 to
• Inventory planning: Determines optimal levels and location of finished goods
inventory to achieve the desired customer service levels. Essentially, it calculates the
optimal level of safety stock. Time horizon: 6 to 12 months
• Supply chain planning (SCP): Optimizes the use of manufacturing, distribution, and
transportation resources (including materials) to meet forecast and actual demand.
Generally, SCP works with aggregate-level resources and critical materials to develop
a constrained production plan. SCP generally spans multiple manufacturing and
distribution sites and may provide some level of supply chain synchronization. Time
horizon: 3 to 6 months.
• Available-to-promise (ATP): Determines whether a customers request date can be met
and/or the next best date from existing inventory and production orders. A subset of
ATP functionality, often called capable-to-promise, looks at available plant capacity
and determines whether an order can be inserted into the schedule to meet the
customer’s request date. In current APS products, ATP may be an explicit function or
a capability supported by what-if analysis. ATP may take place at the SCP,
manufacturing planning, or production scheduling levels. Time horizon: 2 days to 6
• Manufacturing planning: Develops a master schedule constrained by material
availability, plant capacity, and other business objectives. This is generally done for a
single plant. Manufacturing planning may include a complete MRP explosion or work
with only critical materials. The depth of material planning often depends on the
complexity of the bill of materials and the desired replanning time. Time horizon: 2
weeks to 3 months.
• Distribution planning: Determines the best deployment of finished goods inventory to
meet forecast and actual demand. May consider actual transportation costs and material
allocation requirements and support vendor managed inventory (VMI). Time horizon: 2
weeks to 3 months.
• Transportation planning: Optimizes outbound and inbound material flow to minimize
transportation costs and/or maximize the utilization of private truck fleets by
consolidating shipments into full truckloads, when possible, planning routes and
sequencing delivery/pickup locations. It often uses current carrier freight rates in order
to support lowest cost shipping calculations. Time horizon: 1 week to 3 months
• Production scheduling: Determines the optimal sequencing and routing of orders on the
plant floor based on detailed product attributes, work center capabilities, and material
flow. Time horizon: 1 shift to 1 month
• Shipment scheduling: Determines the optimal time and method to ship an order to meet
a customer due date. Time horizon: 1 shift to 1 week
• Intercompany collaboration: Provides the ability for planners to collaborate with
customers and suppliers via the Internet in the development of the demand plan for the
purchase of materials or synchronization of feeder plants. Time horizon: 1 day to 6
Generally, the planning process is divided into these levels because they are performed by
different parts of the organization at different times. From a practical standpoint, even today’s
most powerful computers cannot simultaneously optimize all of these planning levels. Several
years ago, as APS technology was evolving, some vendors gave prospective customers the
impression that single-solver engines would be able to optimize entire supply chains. This has
not happened, nor is it even possible or required at this time. In fact, none of the surveyed
manufacturers had implemented all of these levels. Most manufacturers had implemented only
one or two of these planning functions in APS technology, yet dramatic results were achieved in
3. Differences in planning horizons
The enumerated solutions can roughly be divided into three levels of planning and scheduling:
Supply Chain Planning
Figure 2: Relationships of major planning functions with typical data flows
3.1. Supply Chain Planning
This SCP group takes a forecast and looks at actual demand, after which a constrained operation plan for
both manufacturing and distribution is generated. A multi-plant constrained master schedule is the output
of the SCP process for manufacturing. To create this output the material availability’s and plant capacities
are accumulated. For some industries, transportation requirements and set-up sequencing are considered
“SCP determines what should be made given the available resources to achieve business goals.”
3.2. Manufacturing Planning
The output from manufacturing planning generally is a constrained master schedule for a single
plant or a group of similar plants. This master schedule considers the constraints in a more
detailed perspective than in SCP. In manufacturing planning a full MRP I/II explosion can be
included in the process.
Frequent interface points Other interface points
“Manufacturing Planning determines how and when it should be made based on material and
resource constraints to meet customer demand.”
3.3. Production Scheduling
The goal of this group is to translate the output of the supply chain planning to an operational
plan and work orders. Here is where the ultimate specification takes place on the basis of which
the suppliers will deliver. The production departments produce and distribution receives and
ships the products. APS supports the planner by continuously adapt or suggest adaptation of the
planning and scheduling based on the recent information. Product scheduling is designed to
produce the most efficient production schedule (where the throughput times are minimal, the
output maximal and the costs are low).
4. Planning and scheduling
An APS system uses the following planning and scheduling approach:
4.1 Advanced Planning
This planning process is order-centric and the role of planning in APS is to determine what
demands on the production system will be met over a given planning horizon. The input to the
planning process includes information on manufacturing capacity and demand data. Demands
may be of several types: customer orders, forecast, transfer orders (i.e., orders from other plants),
released jobs, or replenishments of safety stock. Manufacturing system data includes bills of
material, work center availability, part routings through work centers, and inventory (both on-
hand and scheduled for delivery). The output from the planning process is a feasible plan, which
provides release and completion times for every demand. Like MRP, APS takes into account the
availability of materials. Unlike MRP, it also takes into account the capacity of work centers to
process the material and satisfy demands.
It is in fact often desirable for a plan to be somewhat tentative, since it covers a planning horizon
subject to disruptions. Forecast may not be accurate. Deliveries may be delayed. Equipment may
fail. Unexpected rush orders may be received. Therefore planning is not expected to be highly
detailed. Individual machines may be aggregated into a work center with no determination of
which will be used by a specific order. Setup times may be averaged since sequencing at this
time is premature. Buffer times may be defined, especially prior to processing on bottleneck
machines, to allow for possible disruptions. The end result is a “scheduleable”plan.
4.2 Advanced Scheduling
The role of the scheduler module in APS is to produce a detailed list of operations specifying
which orders are to be worked on at which work centers and at what times. The input to this
module includes all demands to be satisfied, including the internal orders added by the planner
module when an end item required a component to be manufactured. It includes the current
material inventory levels as well as planned deliveries or purchased materials. It also includes the
same manufacturing system data as that provided to the planner module but uses a more detailed
representation of that data. Detailed information used by the scheduler module that is not
pertinent to the planner module includes:
Variable run times based on the machine and operator actually assigned.
Rules for selecting machines and operators based on skill sets and quality requirements.
Variable setup times based on the previous and next part characteristics such as part type,
family, colour, width, etc.
Rules for sequencing jobs at work centers, based on minimizing setup and other factors.
Allowable shift overruns.
Rules for selecting from a list of prioritized jobs based on due date, slack, cost and other
5. Comparison of ERP and APS capabilities
ERP Planning Advanced Planning & Scheduling (APS)
Separate, iterative planning
processes for MPS, RCCP, MRP, CRP
Single, holistic planning run in a single pass -
Planning assumes infinite production
APS uses capacity as a known limitation and plans
Planning assumes materials will be
delivered on time
APS can operate to constrain production plan
based on material availability
Plan assumes production on “primary”
work center or machine only
APS will use all permitted alternatives of how a
product can be made and select the ones which
optimize overall use of
ERP can “cost” a plan – but can’t suggest
the lowest cost plan
APS can use the costs of alternative production
methods to recommend a cost-optimal plan
Single site planning Multi-site, enterprise-wide planning
Identifies problem areas and highlights
them for users to solve
Solves planning issues by recognizing capacity and
Supports “available-to-promise” from
known stock and planned production
Supports “capable-to-promise” to support accurate
Limited or no scenario planning Full support of what-if scenario modeling and
rapid solve times makes this an accepted practice
Limited to planning only the production
areas where the ERP system is installed
APS solutions can connect to multiple systems
inside and outside the organization for global
No industry specific differences in
Infor APS solutions dedicated to the industries for
which they were designed
6 . Features Of APS
An APS system has a number of features that enable it to be clearly differentiated from
traditional planning systems such as MRP I/II and DRP.
6.1 Concurrent planning
In the traditional planning process, as in the case of MRP I/II and DRP, three main variables can
materials (raw material and semi-manufactured articles)
The traditional planning process is the so-called ‘waterfall approach’, in which the planning
process is undertaken sequentially. It starts with an MPS, after which MRP I/II and CRP are
performed. In case of ‘concurrent planning’, however, the three main variables are considered
simultaneously. This results in synchronized, optimal planning for the chain as a whole, based on
the most up-to-date data.
6.2 Constraint-based planning
A second important characteristic of APS systems is that account is taken of the constraints
present in an enterprise, such as capacity and materials. APS systems use these constraints to
model the production and distribution environment. The performance that an enterprise can
achieve is determined by the constraints.
Various constraints can be identified as:
Sequencing for set-up efficiency
The speed of planning is an important characteristic. Improvements in computer processing
power and software design have lead to good response times. As a result, a customer can be
informed about the delivery possibilities within a few seconds. The person in contact with a
customer who wishes to place an order has a strong negotiation position since he has a picture of
the possibilities that the company can offer the customer. If the company is not able to satisfy the
customer’s wishes, he is immediately able to offer alternatives to the customer. Speed is also
important during the planning cycle. Since all the links in the chain are now closely co-ordinated,
delays in one link can have an amplified effect in the subsequent links.
It is possible to indicate preferences in APS for purposes of strategic decision making. It is
possible to regard certain customers as strategically important. In APS this is interpreted as a
customer with a higher priority. These strategic customers must be considered as such
throughout the whole organization. This avoids a situation in which one sales organization
regards a particular customer as strategic, while for another sales organization the same customer
One of the first, and still most common applications for advanced planning and scheduling
products, is decision support using the facility for what-if simulation. It is possible for various
alternatives to be entered into the system and for the system to maximize company profit and/or
minimize costs, subject to the condition that the order can be delivered on the date required by
the customer. The planner can examine various scenarios under which the order is delivered and
the system subsequently indicates the consequences of the various scenarios for existing orders.
A graphical interface makes it easy for the planner to compare the various alternatives computed
by the system, so that the most acceptable solution can then be chosen. The planner can ‘play
around’ with the data, with the most acceptable alternative being chosen and used as new input.
While all APS products can be used for simulation and what-if analysis, some vendors provide
more complete facilities to compare plans and schedules. This ranges from the ability to have
multiple copies of different plans visible for die-by-side comparison (such as ERP systems) to
the ability to produce cost analyses of various planning options.
6.6Available to Promise (ATP)
APS can be used to obtain a better insight into ATP. ATP represents a rolling balance of
“unconsumed supply” (uncommitted portion of the inventory) over time. “Unconsumed supply”
is inventory on hand, plus planned supply, minus existing commitments to customers. The ATP
allows a company to see what inventory has not yet been allocated and what can be done with
that inventory for potential customers in a specific period. When an ATP function receives an
order, it slots the order for the day (or days) on which there is sufficient supply available to cover
the order quantity. Based on the slotting dates, the function proposes a delivery date (or dates) to
6.7 Capable to Promise (CTP)
CTP derives from the real-time APS engine a delivery date by adding a customer order in the
system, where after this engine determines when the order is scheduled to be produced, by
looking at available material and capacity.
6.8 Profitable to Promise (PTP)
ATP and CTP only look at the possibility to deliver the order on time to the customer. It would
be better to be able to accept the order based on the financial implications for the company. This
is called profitable to promise. With PTP you can assure that the right customer gets the right
order at the right time, which is most profitable to the organization.
This is the possibility of making promises concerning delivery times and delivery dates and also
fulfilling such promises. It is possible to inform the customer of the ultimate delivery date. When
the customer places his order, the company gives the delivery date and has the possibilities to
adhere to that promised date.
Optimization means generating the best solution to a specific problem. APS can be used to
optimize both tactical and strategic business issues. At the tactical level the system can help to
optimize sourcing, production and distribution plans. At strategic level APS supports in
optimizing the network configuration. Different techniques can be used to solve the optimization
Theory of constraints
7. THEORY AND PRACTICE OF ADVANCED PLANNER AND
OPTIMIZER IN SUPPLY CHAIN DOMAIN
Supply Chain Management technology of SAP is a comprehensive tool set which provides solution not
only for Supply Chain Planning but also Supply Chain Execution, Supply Chain Coordination and Supply
Advanced planner and Optimiser (APO) and SCM
SAP APO is a robust tool designed to enable your organization to achieve the self-contradictory goal of
improving customer service while reducing costs.
Supply chain execution: From the moment a shipment to a customer leaves your organization’s shipping
docks, transactional data is being gathered through the use of enterprise requirements planning solutions,
such as mySAP ERP. This information, whether it is about inventories, open customer orders, new
product specifications, existing production schedules, or numerous other details, is fed into SAP APO to
support ongoing planning. The combination of SAP’s real-time Core Interface Functionality (CIF) and
liveCache planning model allows for continuous planning updates of this transactional and master data
throughout the day.
Supply chain Coordination: Using tools like SAP Event Management, updates to plans can be triggered
by key events, such as a supplier’s production lot passing final quality inspection. While event
management tools monitor critical pieces of the supply chain puzzle, other tools, such as SAP Inventory
Collaboration Hub, gather inventory data across the entire supplier base. Looking forward in the supply
chain, demand and inventory from a customer, or even a customer’s customer, is brought into the mix.
Supply Chain Collaboration : Using out-of-the-box Web collaboration capabilities, SAP APO enables
organizations to share demand and promotional plans in real time with customers, any of whom may
view, comment on, or even edit the plan. SAP APO supports intercompany collaborative processes, such
as vendor-managed inventory (VMI) and the Voluntary Inter-industry Commerce Standards Collaborative
Planning, Forecasting, and Replenishment process (VICS CPFR), and it has been certified by the Uniform
Code Council as being interoperable with other vendors.
Continuous Cycle : As SAP APO assists your organization’s strategy to be continuously refined in the
process of coordination, planning, and collaboration, it also enables near-term plans to be committed to
execution, all in real time. Products scheduled for production are produced, packaged, and placed on a
truck to turn a customer’s order into a shipment. That real-time information is then, of course, fed back
into the overall plan.
Following figure explains relationship between all the modules of supply chain and APO.
A supply chain optimisation problem
Generally, optimization problems seek a solution where decisions need to be made in a
constrained or limited resource environment. Optimized plans are generated based on plan
objectives and constraints. The constraint-based rules are extended with some extra rules (titled
decision variables and penalty factors). As the optimization is based on cost and profit the
constraints might be overruled if this reduces the total costs.
Decision Variables are within the planner’s span of control.
When and how much of a raw material to order from a supplier
When to manufacture an order
When and how much of the product to ship to a customer or distribution centre
Constraints are limitations placed upon the supply chain
A supplier’s capacity to produce raw materials or components
A production line that can only run for a specified number of hours per day and a worker that
must only work so much overtime
A customer’s or distribution centre’s capacity to handle and process receipts
Objectives and implicit objectives
Objectives maximize, minimize, or satisfy something, such as the following:
Maximizing on/time delivery
Maximizing profits or margins
Minimizing supply chain costs or cycle times
Maximizing customer service
Maximizing production throughput
Satisfying all customer demand
The implicit objective is maximized by minimizing the penalty costs for:
Supplier capacity violation
Transport capacity violation
Any unused supply
Using alternate resources
Resource capacity violation
Safety stock violation
Using alternate routings
The following penalty cost factors are used explicit in relation to decision variables:
Exceeding resource capacity
Exceeding material capacity
Exceeding transportation resource capacity
The optimization process drives penalties out of the solution, tending to drive the most costly
penalty factors out first. A high degree of accuracy in setting penalty factors is not as important
as the relationship between penalty factors.
Example of optimisation
Figure 3 An example of an optimised plan in relation to on-time delivery.
In the first three periods of this example, there is no difference between the optimized plan and
the constrained-based plan. A backlog occurs in the third period because the hard machine
constraint makes it impossible to meet the peak demand. However, production in the fourth time
period has been increased compared to the CBP example. Recall that in the CBP example, some
of the period 3 demand was backordered and not met until period 5. In the optimization the cost
of labour overtime in the fourth period is balanced against the cost of carrying the backorder into
period 5. If the backorder quantity is large, and if the customer is likely to accept a two period
delay, and if the cost of overtime is relatively low, then optimization would suggest the solution
in figure 3.
7.1 Optimisation framework
As part of the planning process, the structure of the supply chain need to be represented. This is
typically done using a network model which graphically visualizes a supply chain and is used to
depict the parts of a supply chain being considered in the planning process.
1 2 3 4 5
Optimisation - Max On-time Delivery
Figure 4: Network representation of a Supply Chain
Figure 4 represents a manufacturer’s supply chain Usually referred to as a network
representation, the nodes represent facilities that add value to the supply chain. Nodes occur from
the sources of raw materials and intermediate products to the consumers of the finished products.
The arcs or links connecting the nodes represent transportation lanes for materials, semi-finished,
and finished products.
7.2 Optimisation solvers
To APS developers, optimization is a systematic approach to improving the plan or schedule
based on the constraints of the business. Some vendors attempt to achieve optimization by
applying a single algorithm to a wide range of problems, while others maintain a library of
algorithms or “solvers” which can be used in a trial fit approach. There are different techniques
that can be used for optimization:
1. Linear programming –
2. Genetic Algorithms –
3. Theory of Constraints-
7.3 A standard LP-model for optimisation
Currently commercial SCP software assumes a rolling schedule concept, where each planning
cycle a mathematical program is solved, either to optimality or some heuristics are applied. For
uncapacitated SCP problems without lot sizing restrictions it is rather straightforward to
formulate an LP model that fits in this rolling schedule context.
8. Implementation Statergy
To come to a successful implementation, it is preferable to choose a stepwise approach. The
people inside the organization can see the results en get enthusiastic about the system. This will
prevent that the project will take years before the results are visible. An example of a stepwise
approach is to begin with the introduction of an APS-system over a couple of production-
locations and to extend this in a next step.
Table 1. Choices with the implementation of an APS-system
Strategic choices Supply chain concept
Organisation- managementconcept for supply
Commercial strategic policy
Involvement suppliers and customers (chain
Tactical choices Priorityrules; which customer gets
Aggregationlevel in managing
Integration APS with ERP
Information architecture and datamanagement
Customerorders dispatching/Customer service
Operational choices Procedures and day-to-day decision making
Office hours/attainability planning-department
KPI’s in scorecards and reports
Linkin-pinfunction between central planning
and local execution
8.1 Points of attention
There are different aspects to be taken into account when implementing an APS:
Supply chain management concept
The first pitfall is the lack of a strategic concept for supply chain management and the
commercial strategic policy (for example the role of national sales organizations). It’s
evident that the concepts also enclose the role of suppliers and customers (chain integration).
APS is a rather new development where little experience has been gained. The development
has not been completely evaluated, so one can encounter unforeseen problems.
Continuous changes in the system should be avoided. These changes will result in
nervousness in the organization, what of course is not good. When a customer is told that he
will receive his order at date X, it is not right when the next day it is changed in delivery date
At high level in the organization one knows how to work with APS en how the system will
look. Instead of the lower organizational level where they don’t now this. These people need
to get enthusiastic and motivated as well. Working with APS means managing from another
central concept. Another point is the constant changes together with APS. A lot of processes
and activities, like planning and the transfer of information go much faster now. One should
take care that people don’t loose the overview in the organization and ‘drown in' the new
Because APS is not in the last stage of development, it is still the question which cases APS
can handle and which not. During the implementation there are new software-releases and
also the hardware is improved already.
The financial resources of an organization should be sufficient to complete an
implementation. An implementation of an APS system throughout the whole chain of a big
organization can cost around 50 million euro’s. A small implementation is possible from one
The actuality, availability and purity of the data is often a big problem. A characteristic of an
APS is that planning problems are solved with a mathematical model. APS-suppliers suggest
that they offer an optimal solution. Those optimal solutions are based on submitted variables;
not the whole chain with all its innumerable variables are optimized. When those predictions
are not so hard, than a rather simple calculations gives much better results than a complicated
8.2 Integration with existing systems
An enterprise usually has a number of existing transaction-oriented systems, in which much data
is stored in databases. The APS system extracts from these transactional systems all the
necessary data, such as order status, new orders and other current production and distribution
information. This information is obtained from the Bill of Materials, Bill of Resources and
routings, present in the existing
Figure 5. APS in relation to existing systems
Using this information, APS systems perform calculations to optimize the entire chain, after
which the adjustments (after possible personal amendment by the planner) are returned to the
existing systems. This is where the hidden strength of APS lies. Without first having to
standardize all the transactional systems throughout the organization (with all the efforts that this
involves) the first logistical improvements can be achieved by adding APS.
APO’s primary elements are:
Supply chain cockpit
An intuitive and configurable graphical user interface to manage and optimise the supply chain. It
consists of a highly intuitive, graphical interface that acts as the top enterprise planning layer
covering all planning areas such as manufacturing, demand, distribution, and transportation. All
employees in the Plan -> Source -> Make ->Deliver cycle of supply chain management can use it
to their advantage.
It Provides advanced forecasting and demand planning tools that enable companies to capture
changes in demand planning signals and patterns as early as possible.
There are 3 types of forecasting methods.
1) Staistical forecasting predicts future demand based on historical data.
2) Casual Forecasting: Multiple Linear Regression (MLR) enables you to include causal
variables (like climatic conditions, price, advertising) in the forecasting process. MLR
investigates the historical influence of these variables on demand to produce a forecast.
3) Composite Forecasting: This combines forecasts from different individual forecasts
(statistical or causal forecasts) for a particular brand, product family, or product. Each
individual forecast is based on the same historical data but uses a different technique. The
underlying objective is to take advantage of the strengths of each method to create a
single "one number" forecast.
There are 5 types of demand planning tools in APO
1. Promotion planning : In APO Demand Planning, one can plan promotions or other special
events separately from the rest of your forecast. For e.g. millennium, or repeated events such
as quarterly dvertising campaigns, trade fairs, trade discounts, dealer allowances, product
displays, coupons, contests, free-standing inserts, as well as non-sales-related events such as
competitors' activities, market intelligence, upward/downward economic trends, hurricanes,
2. Life Cycle Planning : A product's life cycle consists of different phases: launch, growth,
maturity, and discontinuation. In this process, one can model the launch, growth and
3. Collaborative Demand Planning: Collaborative Demand Planning between
manufacturers and their distributors allows both partners to streamline their work processes
and ultimately benefit from a more accurate forecast, better market transparency, greater
stability, reduced inventory, and better communication.
4. Charactristics Based Forecasting: In SAP APO Demand Planning one can
create a forecast based on the characteristics of configurable end products. Characteristics-
Based Forecasting allows you to forecast many different variants of the same product and
react swiftly to changes in market demand. Orders can also be placed with your suppliers for
assemblies and components in a timely fashion.
5. Kit planning: : As well as planning demand for a product, one can also forecast
dependent demand at different planning levels by exploding bills of material. This can be
used for a kit that consists of several finished products (that can also be sold separately).
Supply network planning and deployment
SAP APO Supply Network Planning integrates purchasing, manufacturing, distribution, and
transportation so that comprehensive tactical planning and sourcing decisions can be simulated and
implemented on the basis of a single, global consistent model. Supply network Planning uses
advanced optimization echniques, based on constraints and penalties, to plan product flow along the
supply chain. The results are optimal purchasing, production, and distribution decisions; reduced
order fulfillment times and inventory levels; and improved customer service.
The Deployment function determines how and when inventory should be deployed to distribution
centers, customers, and vendor-managed inventory accounts. It produces optimized distribution plans
based on constraints (such as transportation capacities) and business rules (such as minimum cost
approach, or replenishment strategies).
Production planning and detailed scheduling
Production planning enables the planner to create feasible production plans across the different
production locations (also with subcontractors) to fulfill the (customer) demand in time and to the
standard expected by the customer.
Detailed scheduling delivers optimized order sequences that can be released for production.
Solvers simultaneously take into account constraints and costs to schedule the optimized order
Thus both the functions ensure the smooth and optimal flow of materials and resources on a plant-by-
plant basis. Production planners have advanced tools to create optimised, feasible production
1) Collaborative Shipment Forecasting:
Based on Internet enabled planning books, one can exchange and adjust forecast information
between your customers and your carriers.
2) Load consolidation:
SCM provides different possibilities to consolidate deliveries and orders to shipments. It is
possible to combine orders based on rules and strategies, based on optimization logic in
APO. During Load Consolidation, SCM will consider multi- dimensional capacity constraints
of the resources. This function is provided by APO.
3) Mode and route optimization:
The goal is to create least cost transportation plan while guaranteeing the customer service.
4) Carrier Selection:
APO offers four options to do a carrier selection. Priority, freight cost, Business share, Freight
exchange are the four options ensures the correct carrier selection.
5) Collaborative Shipment Tendering
The assignment of a carrier to a shipment needs to be confirmed by the carrier (tendering).
Rewards of planning:
1. Reduced costs
2. Improved customer service
3. Increased responsiveness
4. Improved productivity
5. Improved efficiency
6. Increased return on assets (ROA)
7. Increased revenues,
8. Faster return on investment (ROI),
Again, specific customer financial benefits will vary, but the
following ranges can be used as a guideline:
• Increased ROA (3%-5%)
• Reduced inventory carrying costs (5%-10%)
• Reduced lost sales-revenue uplift (3%-6%)
• Reduced administrative expenses (5%-10%)
• Reduced transportation costs (10%-15%)
In essence, SAP APO 4.0 is the latest step in the continuing evolution of supply network
management. The demand planning and supply network planning capabilities it provides result in
an enhanced visibility across the network. All stakeholders can now work from a single, accurate
demand plan. Efficient and optimal planning for production, warehousing, and transportation is the
rule of the day. You can meet the dual challenge of improving service and controlling costs, and
ultimately reach the pinnacle goal: increasing profits.
Global Available to Promise
Utilises a global, multi-level, rule-based strategy to match supply with customer demand. It also
performs multi-level bill-of-materials and capacity checks in both real-time and simulation mode to
enable delivery commitments for customer orders.
Within the last three decades there have been rapid changes in the way products and services are
developed, manufactured and distributed. This is caused primarily by altering market conditions,
including quickly growing product diversity, the need for quick and accurate response times,
high quality and flexibility in delivering new products, and speed of innovation. Advanced
information technology concepts are indispensable in providing answers to these challenges.
Quite simply, APS is a planning revolution. APS will do to MRP what the PC did to
typewriters. It’s a technology that leverages the planners knowledge with the tremendous
capabilities of today’s distributed computing. For manufacturers, the question is not whether
APS is necessary, but how soon it should be implemented. Thus far, the results have been
great, even at companies that by their own admission had lousy planning processes. Early
adopters of APS have dramatically improved the competitiveness of their companies with only
limited implementations. With well financed vendors quickly expanding product suites to reach
new industries and further stretches of the supply chain, no manufacturing or distribution
concern is beyond the onslaught of a competitor armed with APS.
APS sounds like the key for supply chain management: transparency, it fits over the existing
systems, it optimizes and it offers control and acceleration. But one has to question what it is all
about: the co-operation between humans in the organization, co-operation with customers,
founded choices about the logistical concept, controlled processes and procedures and the
information systems as support. APS will support the people with taking the complex decisions.
With planning, scheduling, and collaboration tailored to specific industries and industry
standards, a unified architecture, and an open technology framework that ensures flexibility and
interoperability. As an integrated solution, mySAP SCM also eliminates many of the costs
associated with piecing together and supporting a collection of best-of-breed or disparate
systems. Measurable benefits typically include reduced overall project costs, as well as reduced
IT maintenance and support costs.
Advanced software-packages will never succeed in eliminating humans. There will probably
never be a magic computer that solves everything on its own. On the contrary, because computer
systems take over the repetitive work of humans, those people can devote themselves to more
Robert Bosch Case Overview
This case study presents the strategic issues faced by the CIO’s of the
information technology division of the corporation, Robert Bosch GmbH, and
its US subsidiary, Robert Bosch USA. The corporation has traditionally
followed an international/multidomestic strategy and used multiple
information systems at its plants/divisions/business sectors. Due to higher
pressures to cut costs, increase interchangeability of products among the
many plants worldwide, and fulfill customer requirements, the corporation
was moving to a global strategy.
Dr. Eggensperger, CIO of the corporate IT division of Robert Bosch (QI)
acknowledged that there were some challenges in implementing SAP R/3
worldwide. QI had standardized on use of SAP R/3 during 1995 and it had
ended up with many locations in Europe implementing SAP R/3 systems
non-uniformly. In order to rectify this, QI focused on standardizing the IT-
systems for use in every plant location using a top-down approach.
During 1999, the Board of Management of Robert Bosch GmbH stated that
the current implementation of information systems was not acceptable and
did not meet corporate requirements. In order to fulfill the requests of the
Board, the CIO of QI considered the following strategies for implementation
options: standardize the ERP approach within the entire Bosch group,
implement a domestic ERP approach for each country, or implement
product-division based ERP approaches within the global operation.
In addition to the requirement for a corporate strategy, there were requests
to modify the IT infrastructure in several domestic markets. Don Chauncey,
the CIO of the US Operations (RBUS) had to decide whether to continue with
the current variety of information systems or to shift to a uniform SAP R/3
system for all the divisions of RBUS. In order to meet the current business
needs, many different information systems developed by multiple vendors
were implemented throughout RBUS. Some of these systems were not
functioning well and there was pressure on Don to make changes. For
example, the software supplier of the financial system announced that they
will support the current AS/400 system only until the end of 2001. The
Human Resources (HR) department recommended that PeopleSoft will be
their choice to address the payroll/human resource needs. Changes were
also required in the logistics area at the plant level. During August 1999,
Don proposed implementing SAP R/3 in the Financials (FI), Human
Relations/Payroll (HR), and Logistics at the plants in North America. This
proposal was put on hold by the executives who headed the North American
Operations Committee (NAOC). They asked for additional information on the
project cost drivers. Don, as the CIO of the domestic level organization, was
faced with the decision to either stay with the multiple information systems
or champion implementing SAP R/3 throughout RBUS. He was unsure which
of these information technology (IT) solutions would be most effective in
coping with the growing and changing business of RBUS.
Salient Features of the Case:
This case lays emphasis on the following points:
(a) Importance of the alignment of business strategy and the IT strategy in
a company at both corporate level and country level.
(b)To identify the issues faced by RBUS in moving to a standardized
corporate-wide information system such as SAP R/3.
(c)How a ERP system can be implemented in many ways with resulting
benefits and problems.
(d)If an ERP package is not implemented correctly, it might lead to many
different ERP systems existing within an organization that do not
communicate with each other.