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Only these enterprises which are able not only to identify customerseeds and requirements but also flexibly respond on them can succeed in present very quickly varying business environment. From this resulting customer's pressures on time compression needed for orders fulfilment and simultane-ously requests of management on reduction of locked-up capital in inventory create environment in which systems controlled by demand begin to assert. Knowledge of demand and sales derived on the base of forecast and their sharing play significant role. Therefore this article deals with the question of changing relevance of demand forecasting and particularly with the forecast utilization in the demand planning from the enterprise point of view. The methodology of the demand planning and its integration with other busi-ness processes is also described here.
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6
th
International Scientific Conference
May 13–14, 2010, Vilnius, Lithuania
BUSINESS AND MANAGEMENT 2010
Selected papers. Vilnius, 2010
ISSN 2029-4441 print / ISSN 2029-428X CD
doi:10.3846/bm.2010.151
http://www.vgtu.lt/en/editions/proceedings
© Vilnius Gediminas Technical University, 2010
1119
ROLE OF DEMAND PLANNING IN BUSINESS PROCESS MANAGEMENT
Vladimira Vlckova
1
, Michal Patak
2
University of Pardubice, Studentská 95, CR-53210 Pardubice, Czech Republic
E-mail:
1
vladimira.vlckova@upce.cz,
2
michal.patak@upce.cz
Abstract. Only these enterprises which are able not only to identify customerneeds and requirements
but also flexibly respond on them can succeed in present very quickly varying business environment.
From this resulting customer’s pressures on time compression needed for orders fulfilment and simultane-
ously requests of management on reduction of locked-up capital in inventory create environment in which
systems controlled by demand begin to assert. Knowledge of demand and sales derived on the base of
forecast and their sharing play significant role. Therefore this article deals with the question of changing
relevance of demand forecasting and particularly with the forecast utilization in the demand planning from
the enterprise point of view. The methodology of the demand planning and its integration with other busi-
ness processes is also described here.
Keywords: demand forecasting, demand planning, business process management.
1. Introduction
At present, the substantial survival ability of the
company rests in adaptability to constant changes of
the environment. The success is seen by the compa-
nies not only able to reveal customers´ wishes but
rather capable of flexible reactions to their require-
ments. If the company wants to stand up to compe-
tition, it must accelerate and make more efficient
not only the partial internal company processes but
also the management of tangible and intangible in-
formation flows within the whole supply chain.
The growing pressure of the supply chain customers
on accelerated reaction of suppliers enforces short-
ening time periods for processing orders. It is, how-
ever, often possible to carry it out only at the ex-
pense of enormous effort of the production
company and leads to non-economical rise of costs.
That is why management seeks the ways of elimi-
nating or reducing the costs. It creates the environ-
ment in which the systems controlled by the de-
mand are applied. The systems controlled in such a
way are an essential prerequisite for the creation
and setting of the production and all related proc-
esses that are to maximum degree balanced and si-
multaneously adaptable.
There are a number of principles and method-
ologies for management of production, stock, hu-
man resources etc. However, the rapid development
of information technologies during the last decade
moved these theoretical procedures to an entirely
different level. The information systems play a key
role mainly in operative planning and management.
Many companies cannot nowadays imagine running
the business without accessing relevant information
from ERP (Enterprise Resource Planning) systems.
The concept of ERP does not relate merely the
planning methods but is rather a synonym for a
group of complex information systems, designed
for the management of internal company processes.
In consequence of more efficient information
use through ERP, the partial planning methods as
MRP II (Manufaturing Resource Planning), Sales
and Operations Planning (S&OP) or APS (Ad-
vanced Planning and Scheduling) are developed.
All these methodologies of planning and man-
agement and their software support have some-
thing in common. They enable optimization of
company processes in a short time providing the
volume of real sales of particular customers´ par-
ticular products is known, well in advance.
However, where to get the information if the cus-
tomer service lead time is often a far smaller in-
terval than the lead time required by the organiza-
tion to produce or distribute the product? In these
cases the expertise in demand and sales prognosis
play a significant role.
The research carried out in numerous produc-
tion companies, primarily of chemical and food
processing industry in the Czech Repub-
lic (Hambálková 2009; Paták 2009; Roháčová
2009), however, showed the changing and
strengthening role of the demand and sales fore-
cast is often underestimated by management and
thus it is not paid an adequate attention.
The aim of this paper is to demonstrate the
significance of the demand and sales forecasting
for the production company and support the use
of these forecasts via the demand planning. It de-
scribes the demand planning methodology and its
integration within other company processes.
The research methods were a method of struc-
tured literature research and a method of in-depth
V. Vlckova, M. Patak
1120
interview with managers of selected chemical and
food processing companies.
2. Significance of demand forecasting
The forecasting is a process in course of which
possible future variants of a phenomenon or ob-
ject, maybe even variant solutions of ways leading
to future situations are formulated. The forecast-
ing creates a basis for planning company processes
(Johnson 2009). It enables managers to plan future
needs and consequently make rational decisions.
Forecasting is a continuous process that requires
product managers to think about markets and un-
derstand those (Haines 2008).
Forecasting methods were developed since the
1950s for business forecasting and at the same
time for econometric purposes. The application in
software modules makes it possible to forecast for
a lot of items in a few seconds (Stadtler 2008).
Accurate demand forecasts are an important input
to decision models used in APS. Forecast errors
are directly related to required safety stocks, while
frequent adjustments of demand forecasts can lead
to dramatic changes in plans (Stadtler 2005).
If the company wants to maximize the effect
of accessible methods for internal company proc-
esses, it must build on objective and evaluated
demand forecasts. The choice of optimum fore-
casting procedures and following use of obtained
forecasts may become a competitive advantage.
Together with other modern methods it accelerates
other company processes, reduces the costs and
increases the value for the customer.
The demand forecast determines the volume
of products, place and time horizon in which they
will be needed. In relation with the demand fore-
cast it is necessary to deal not only with the quan-
titative aspect of the needs (the volume demanded
by customers) but also their qualitative aspect (the
type of customers´ needs). The accurate demand
forecast is thus important for the production and
distribution management but also for e. g. areas of
marketing (distribution of sales forces, communi-
cation, promotion and planning of new products),
finance (current need of money, budgets and cal-
culations), investment designs (production facili-
ties, workshops and warehouses), research and
development (innovations) and human resources
(structure and labour force volume planning, train-
ing).
It is important to accept the process of fore-
casting as a part of company planning. A lot of
small and middle-size businesses neglect this ac-
tivity or avoids it on purpose as it evokes feelings
of vanity with most practitioners (Šindelář 2009).
The future is always stochastic. In case of market
turbulences it is even more valid. The forecast
thus, based on its character, cannot ever be consid-
ered entirely reliable. The opportunity for the fore-
cast use, nevertheless, does not depend only on the
confidence level. Every evaluated forecast repre-
sents an efficient instrument for decision making
as every decision issues from a particular future
forecast. It is not then surprising that in the recent
years we have been meeting the concept of de-
mand planning more and more often.
3. Demand planning
Demand planning represents a set of methodolo-
gies and information technologies for the use of
demand forecasts in the process of planning. The
aim is to accelerate the flow of raw materials, ma-
terials and services beginning with the suppliers
through transforming to products in the company
and to their distribution to their final consumers.
The demand planning process is done to help
the business understand profit potential. Indirectly
it sets the stage for capacity, financing, and stake-
holder confidence (Sheldon 2006). The implemen-
tation of the demand planning enables to deter-
mine the closest possible forecast to the planning
horizon and decide the volume of production,
stock and sources capacity distribution among par-
ticular products to maximize the profits of the
whole company.
The key requirement for efficient company
management is sharing the mutual forecast. How-
ever, the research carried in production companies
showed individual departments of the company in
some cases draw up forecasts on their own and
thus they base their planning on different figures.
This provokes conflicts among the resulting activi-
ties of in-company plans (Gros, Grosová 2004).
The same situation happens also in case when the
company prefers approved financial plan which
does not correspond with the updated forecast re-
sults.
The forecasting should always be the process
which is essential and determining for other com-
pany processes, including financial planning. The
financial plan, however, often represents the main
motivation source for company managers as it re-
flects requirements of the company top manage-
ment and main strategic company goals.
While managing processes via the demand
planning the managers should not be assessed ac-
cording to their meeting the financial plan but
rather according to their ability to predict the fu-
ture development of both the demand and demand
control so that the main strategic goals are
ROLE OF DEMAND PLANNING IN BUSINESS PROCESS MANAGEMENT
1121
achieved by economically the most advantageous
way.
It is evident the demand planning does not
represent only one of many tools of managing the
company processes. It is a whole philosophy of
company planning and decision making on strate-
gic, tactic and operative levels. With regard to the
current turbulent environment escalating require-
ments on a prompt company response to custom-
ers´ orders, especially the pressure put on ready
operative decision making.
3.1. Methodics of demand planning
Methodics of the company demand planning
(Formánek 2004) can be divided into six steps:
understand essential forecast principles;
integrate systems for forecasting and planning;
identify key factors influencing the demand
level;
identify and understand customer segments;
select appropriate forecasting techniques;
build a system for measuring performance and
error rate of forecasts.
Every forecasting process should start with
making aims and purposes of the resulting forecast
clear. The company should precisely define the
area of the future demand, the volume of which it
tries to estimate. In this phase it simultaneously
determines the time horizon of forecast defined as
a time gap between the point, for which the fore-
cast is carried out, and the point, when it is carried
out. These decisions should correspond with the
needs of other company processes for the resulting
forecasts to be used efficiently both at the strate-
gic, tactic and operative levels. Though, for their
decision making the partial company departments
require forecasts of different aggregations and
forecast horizons, the forecasts should not be per-
formed at the level of individual company depart-
ments. The only one central unit should be in
charge of final demand forecasts. This is the only
way for the company to ensure the creation of an
integrated demand forecast issuing from the same
information and sheltering all the company proc-
esses.
If the company wants to acquire the most pre-
cise and reliable demand forecasts, it should utilize
all information about future jobs it may get. It
should be aware of what customers it will produce
for and what distribution ways will be used to
serve them. It should also identify their needs,
wishes, requirements and determine factors that
could significantly influence the demand volume.
At the same time it should know the reliability of
this information and systematically collect the in-
formation necessary for the chosen forecasting
methods. While collecting and classifying this in-
formation it is adequate to use all available seg-
mentation techniques and methods of cluster
analysis (Bottomley, Nairn 2004). When applying
the procedures in the right way, there is an oppor-
tunity to reduce considerably the number of fore-
casts of the whole production portfolio to forecasts
of the product categories (e. g. product lines) in
particular customer segments.
In connection with in previous paragraph
mentioned the term Hierarchical Demand Planning
(HDP) can be found in the literature. HDP is based
on the assumption of independence among vari-
ables, and this allows for simple and easy aggrega-
tion and separation of plans and data (Nielsen,
Steger-Jensen 2008).
A variety of modeling techniques are avail-
able for producing forecasts. Based on data pat-
terns, forecasting horizon, data availability and
business requirements the choice of technique dif-
fers (Voudouris, Owusu, Dorne, Lesaint 2008).Via
appropriate combining of the forecasting tech-
niques it is possible to estimate quantitative influ-
ences of the identified factors and set the demand
forecast (Lehmann, Winer 2005).
The most frequently used statistical forecast-
ing method is the time series technique. It uses
historical data sequenced by time and projects fu-
ture demand by the same time sequence (Crum,
Palmatier 2003). POS data is rich in information
for building forecast models. Building a good
forecasting model with POS data is demonstrated
in many case studies (Andres 2008; Gallucci,
McCarthy 2008).
While a quality forecast is a good basis for a
demand plan, the forecast needs to be modified for
external activities that will have an impact on the
demand for the product being forecasted. The im-
pact of promotional events needs to be integrated
into the forecast and demand plan so that the accu-
racy of both is improved (Gattorna et al. 2003).
As soon as the forecast is elaborated in detail
into individual product forecasts geographically
allotted along a time period, it is labelled “sales
forecast”, which is a more unambiguous term es-
pecially for the sales management. It is an objec-
tive and evaluated forecast of sales that the com-
pany is capable of carrying out in the future.
To make the forecasts objective, the practice
integrates numerous unbiased experts for obtaining
required forecasts. Another way of making the
forecasts objective rests in using several methods
for forecasting the same phenomenon and the ob-
tained results are finally mutually compared. As
every forecast is preconditioned by a complex of
V. Vlckova, M. Patak
1122
external and internal factors, this fact should be
reflected in the alternatives of the potential future
development. That is why the forecasts should be
always drawn up in variants.
The evaluation of the variant forecast is car-
ried out in terms its credibility and confidence.
The credibility of the forecast can be understood
as a degree of its true value, i. e. as the approach-
ing of the model future image to the reality. The
confidence of the forecast is determined by the
probability with which it is likely to expect the
individual forecasts variants to come true.
Though the forecast cannot ever be considered
entirely reliable, the company should arrive at an
agreement over the final forecast and its reflection
in all the company plans. Only this way leads to
fulfilling the basic concept of the demand plan-
ning, i. e. the company should not for example
accept decisions on production based on its wishes
but only on the set forecasts.
The forecast, or as the case may be, the sales
plan set on its basis is necessary to be compared
with the real sales. The discrepancy between the
forecast and the real sales value in the forecast pe-
riod is the forecast error. Its value should issue in
correction activities of the company.
Watching the validity and accuracy of the par-
tial forecasting methods may then help with the
selection of appropriate methods, specific in rela-
tion to a particular situation.
It is important to realize the demand planning
use in practice is not a mere creation of a perfect
system for carrying out the demand and sales fore-
cast (Blanchard 2008). The objective of forecast-
ing is to predict demand whilst the aim of demand
planning is to shape the demand and produce a
resource requirements plan (Voudouris et al.
2008). Without the right forecast - planning sys-
tem integration it is not possible to use efficiently
the information provided in the forecasts.
3.2. Forecasting and planning system integration
First and foremost the company should perceive
the demand planning as an instrument of the mar-
keting management of the company. Every mar-
keting oriented company needs to integrate the
company plans with forecasts as these forecasts as
such more or less represent a real view of the fu-
ture requirements and wishes of the customers to
which the company should efficiently adapt.
However, there is a feedback between the
marketing and demand planning. The information
system aimed to support the demand planning
should save, classify and process also information
about the influence of the marketing management
on the future sales volumes. Via assessing these
influences it is possible to control the demand effi-
ciently to achieve optimum management of the
other company processes.
Fig. 1. Scheme flow of information in processes planning
ROLE OF DEMAND PLANNING IN BUSINESS PROCESS MANAGEMENT
1123
This feedback can be demonstrated by the fol-
lowing example. The forecasting result of the pro-
motion influence on the sales might be a future de-
mand for products the company is not able to
produce in time e. g. due to the capacity limits in
production. If, however, they consider more mar-
keting strategies, they will certainly find the one
that supports the strategic company goals and si-
multaneously reflects the production capacity po-
tential.
The right applying of the demand planning in
practice should involve also the demand control
which will lead to such sales forecasts in which all
the company sources are utilized to the maximum
degree.
If the forecast is to be used in all the company
processes, the relation of these processes with fore-
casting must be assured in such a way for the fore-
cast to be the essential initial information for other
company planning. Also the company processes
feedback to the forecasting itself must be assured in
the same way. This cannot be achieved without the
support of integrated in-company information sys-
tems. That is why the demand planning is often per-
ceived as a superstructure of the ERP systems. The
scheme flow of individual processes is depicted in
the Fig. 1.
There are numerous applicable modern analyti-
cal instruments which due to new technologies en-
able real time planning and large information vol-
umes processing as detailed as possible, e. g. sales
of individual customers or sales categorized accord-
ing to delivery locations (Formánek 2007). The
software products for the demand planning, traded
in the current market, use advance statistical func-
tions combined with expert estimations of the given
market situation and development, gained from
internal and external collaborators (Knolmayer et
al. 2009). As a rule they provide a unified platform
for creating a quality demand forecast that can be
shared in real time by all company departments.
In an operational setting, software now permits
automatic forecasting and the integration of fore-
casts into planning. But large numbers of series are
still being forecast by the crude methods contained
in planning systems while opportunities to apply
more sophisticated and precise techniques are not
offered. So there is still much room to apply ad-
vances in statistical forecasting to current business
processes (Kusters et al. 2006).
3.3. Demand planning as instrument of business
processes management
The result of the demand planning process is the
establishment of independent requirements which
will trigger the planning activities as distribution,
production and procurement planning (Dickers-
bach 2009).
Frequent situation which is solved in moni-
tored companies is that the period which is re-
quired for realization of all activities from pur-
chase, through production up to distribution is
longer than is acceptable for customer. If all the
forecasts represent credible quantitative estimates
of the future sales, the company could efficiently
control all the company processes even in these
situations. It could be labelled as managing the
processes by the real demand known to the com-
pany well in advance, which means before the
moment when the real product demand comes
into existence. It is obviously a purely hypotheti-
cal situation which does not happen in practice.
Every forecast should be, however, variant
and evaluated by the confidence. Every sales fore-
cast can be thus generally determined in terms of
the forecast confidence interval when the forecast
confidence is understood as the probability, under
which the company carries out the future sales in
the volume complying with the given value inter-
val. In forecasting by using statistical methods the
limits of the production confidence interval can be
exactly determinated. While exploiting the qualita-
tive forecasting methods (expertise, intuition), the
pessimistic variant of the forecast can be the lower
limit and the optimistic variant can be the upper
limit of the interval (Fig. 2).
The knowledge of the forecast confidence in-
terval can be used e. g. while planning the pro-
duction of cycle and safety stock of the final
products in the production company.
In common practice of the demand planning
the company would generate such stock reserves
that would cover the sales as far as reaching the
lower confidence interval limit of forecast while
covering uncertain sales belonging into the fore-
cast confidence interval would cover the safety
stock. This, however, would be still relatively
high in relation to the whole volume of sales.
The sales forecast for fast-moving products
is usually well predictable by means of extrapola-
tion of the last sales time lines. The highest prob-
ability density in these cases will appear in the
middle of the confidence interval. It is possible
to prove the high speed of product moving would
cause the safety stock to get constantly renewed
on average in the volume of half the interval.
That is why it is useless to consider this produc-
tion to be the safety stock production but it can be
a part of the common cycle stock production.
Thus there comes to the decrease of the safety
stock (Fig 3a).
V. Vlckova, M. Patak
1124
When the product moving decreases (Fig
3b), it is more efficient to reduce the safety stock
by means of adapting the production processes so
that they are controlled by the real demand, i. e.
by orders. One of the possibilities rests in a
prompter response to customers´ requirements
not only in the company but along the entire sup-
ply chain (Christopher 2005).
Fig. 2. Limits of the forecast confidence interval
This way of planning the production obvi-
ously means the width of the forecast confidence
interval will remarkably influence the volume of
the safety stock and the requirements on the
change of the company processes. Though both
the facts will assure a high reliability of meeting
all the future customers´ requirements, they bring
the company costs related to the capital locked-up
in stock, to maintaining stock and changing proc-
esses. The demand planning partial target thus
should also be the effort seeking the ways to con-
stant decreasing of the width of the production
confidence interval.
The suggested way of utilizing the knowledge
of the forecast confidence interval allows not only
more efficient mass flow management in the com-
pany, but they can be generalized for the manage-
ment of all activities of all but to using it generally
for managing all the company processes.
Demand planning processes provide the tools
for understanding, projecting, and managing de-
mand in the supply chain network (Sehgal 2009).
Since a supply chain involves the synchronisation
of a series of inter-related but different stages of
business processes influencing multiple trading
partners, its demand planning and forecasting can-
not rely on a single, stand-alone forecasting tool
(Min, Yu 2008).
Fig. 3. Cycle and safety stock planning focused on
utilization of the forecast confidence interval
4. Conclusions
All the supply chain links nowadays face a heavy
pressure from the part of the customers, which is
to make them shorten the process time of their or-
ders. A significant role is played by the develop-
ment of the information technologies. Especially
within the operative planning there comes to the
development of partial planning methods such as
MRP II, S&OP, APS. However, a frequent prob-
lem of the production companies rests in the issue
of how to get, i. e. well in advance to predict cor-
rectly and precisely the initial information on the
sales volumes of individual products with individ-
ual customers.
Our research in numerous companies showed
if the companies do not use systems controlled by
the forecast, whether for they do not trust the final
forecasts or for they are not able/willing to work
with them, they must seek other ways of compen-
sating this insufficiency in the reliable forecast
creation competence. The production companies
must then often face very unbalanced utilizations
of all the capacities and solve these situations only
at with great effort leading often to inadequate rise
of costs, e. g. by raising the stock of finished prod-
ucts, extraordinary shifts, hiring large numbers of
ROLE OF DEMAND PLANNING IN BUSINESS PROCESS MANAGEMENT
1125
contemporary staff at the expense of the regular
staff.
In such cases demand planning can be starting
point for companies which exploits forecasting
methods and effectively interconnect them with
other business processes. Investigations realized in
recent years indicate that key factor for successful
implementation of demand planning in companies
is very forecasting and planning integration. We
also pointed out on the basic principals of this in-
tegration.
Software products for the demand planning
enable flow of planning the company processes on
individual forecasts. Using them, management
however does not have exploited always possibili-
ties to manage company processes on the base of
all information, which each forecast provides. As
we have pointed out the knowledge of the forecast,
its accuracy and confidence enables more efficient
deciding on whether in the given case it is mean-
ingful to manage the company processes according
to the forecast or adapt them to the control of the
real demand.
The demand planning is a significant instru-
ment for creating the forecast, its integration with
the in-company plans and business processes man-
agement. It is a whole philosophy of company
planning and decision making on strategic, tactic
and operative level.
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... High-quality prediction always requires combination of quantitative method with qualita-tive assessment of the situation on the basis of the managers' experience and intuition in the given area. (Vlčková, Paták 2010;Branská 2010). ...
... The research in numerous companies showed if the companies do not use systems controlled by the forecast, whether for they do not trust the final forecasts or for they are not able/willing to work with them, they must seek other ways of compensating this insufficiency in the reliable forecast creation competence. The production companies must then often face very unbalanced utilizations of all the capacities and solve these situations only at with great effort leading often to inadequate rise of costs, e.g. by rising the stock of finished products, extraordinary shifts, hiring large numbers of contemporary staff at the expense of the regular staff (Vlčková, Paták 2010). ...
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Resumo: O PRESENTE TRABALHO SUGERE UM PROCESSO SUPORTADO POR UM SOFTWARE PARA O PLANEJAMENTO DA DEMANDA POR VENDAS EM EMPRESAS QUE ATUAM EM CENÁRIOS DINÂMICOS. O MÉTODO UTILIZADO COMBINA DADOS QUANTITATIVOS, ORIUNDOS DE MÉTODOS ESTATÍSTICOS, E INFORMAÇÕES QUALITATIVAS, ORIUNDAS DAS ÁREAS FUNCIONAIS DA ORGANIZAÇÃO E DO RESPECTIVO MERCADO NO QUAL A ORGANIZAÇÃO ATUA. O RESULTADO OBTIDO APONTA QUE O MÉTODO APLICADO MELHORA A ASSERTIVIDADE DO PLANO DE DEMANDA NOS ITENS DE ALTO GIRO, PRINCIPALMENTE POR SEREM UM PEQUENO GRUPO DE PRODUTOS E ESTAREM SEMPRE EM EVIDÊNCIA NAS AÇÕES COMERCIAIS EM RAZÃO DO IMPACTO RELEVANTE EM TERMOS DE VOLUME DE VENDAS.
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... A variety of modeling techniques are available for produc- ing forecasts. Based on data patterns, forecasting horizon, data availability and business requirements the choice of technique differs [20]. Through appropriate combining of the forecasting techniques it is possible to estimate quantitative influences of the identified factors and set the demand forecast [14]. ...
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Hierarchical Demand Planning (HDP) is an intricate part of most companies today. HDP is based on the assumption of independence among variables, and this allows for simple and easy aggregation and separation of plans and data. However, the most commonly used arguments for grouping and subsequent aggregating is shared traits contrary to the assumption of independency. One of the predominant issues is the conflicting objectives on different decision levels. An example of this is found in hierarchical forecasting of demand. When forecasting on e.g. a product family level to establish capacity requirements, the objective is usually to achieve a Mean Error (ME) of zero. This conflicts with forecasting for Demand Planning (DP) purposes on SKU level, where minimization of the Standard Deviation of Error (SDE) might be more important. In this paper these issues are addressed through a simple example of hierarchical forecasting and use of a Goal Programming (GP) approach to satisfy both objectives. It is found that some general guidelines for handling multiple objectives within HDP can be inferred from this, leading the way for a holistic demand planning framework.
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Service chain management enables service organisations to improve customer satisfaction and reduce operational costs through intelligent and optimised forecasting, planning and scheduling of the service chain, and its associated resources such as people, networks and other assets. The area is quite broad, covering field force and workforce automation, network and asset planning and also aspects of customer relationship management, human resources systems and enterprise resource planning. Furthermore, it addresses the key challenge of how all these technologies and systems are integrated into a cohesive blueprint. In this book, Christos Voudouris and his group together with experts from industry and academia present the latest innovations and technologies used to manage the operations of a service company. The viewpoints presented are, based on the BT experience and on associated research and development in collaborating universities and partner companies. The focus is on real-world challenges and how technologies can be used to overcome practical problems in a "don't just survive, thrive!" approach. The unique combination of technologies, experiences and systems, looked at from the different perspectives of service providers and users and combined with advice on successful benefit realisation and agile delivery of solutions, makes this an indispensable read for managers and system architects in the service industry.
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We present an overview of the history of forecasting software over the past 25 years, concentrating especially on the interaction between computing and software. Initially we create a framework by describing important developments of computing technology in terms of hardware and software environments. We then concentrate on two different application areas of forecasting software: (1) research oriented forecasting software often used to analyze a small number of series (for example, in market research); and (2) forecasting modules in planning environments which are often partially automated due to the large number of time series involved. Finally we make some suggestions as to where forecasting software has room for improvement.
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Supply Chain Management (SCM) is not a buzzword like many others in management and IT - it is here to stay. "Megatrends" such as globalization and increasing specialization result in a complex division of labor at national and international levels. Lean and agile supply chains have become a major management target, and the interorganizational coordination of business processes has become highly relevant. The complexity of today's supply chains cannot be mastered without the support of powerful information systems. SAP has established itself as the market leader in this type of IT systems. The book describes SCM using a pyramidal framework and relates it to the SAP SCM Solution Map. Desired features of IT support for SCM are formulated, and the extent to which SAP systems provide these features is shown. We describe the functionalities of SAP APO™ in detail, present case studies on implementing and running SAP SCM™ systems, and discuss SCM aspects of the new SAP Business ByDesign™ approach.
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Recognising the value of information sharing among supply chain partners, a growing number of firms have expressed keen interest in jointly forecasting customer demand and co-managing business functions. In particular, such interest sparked the rapid development and implementation of Collaborative Planning, Forecasting and Replenishment (CPFR) that was proven to be successful in minimising safety stocks, improving order fill rates, increasing sales, and reducing customer response time. Despite increasing popularity, key drivers for the successful development and implementation of CPFR have not been fully understood by practitioners and academicians alike. This paper unveils the invisible challenges and opportunities for adopting and implementing CPFR. Also, it provides an overview of CPFR in comparison to other alternative forecasting techniques such as agent-based forecasting and focus forecasting, while synthesising the past evolutions and future trends of CPFR in a supply chain setting.