Designing FSS for the Supply Chain:
Through Action-Oriented User Interfaces
Stavros Asimakopoulos1, Robert Fildes1, Alan Dix2,
1
Department of Management Science, Lancaster University Management School,
LA1 4YX, UK,
2
Computing Department, Lancaster University, Bailrigg, Lancaster, LA1 4YR, UK
Email: [email protected], [email protected], [email protected]
Stavros Asimakopoulos, Robert Fildes, Alan Dix
Abstract
This paper explores forecasting support systems (FSS) design issues based on
interview and observational data gathered from professional designers and users
working in supply chain forecasting. We show that combining action-based product
knowledge with historical data can be usefully incorporated in an FSS user interface.
The emergent theoretical framework depicts various motives behind the use of FSS:
the need for balanced visualization of data and product knowledge, and attention to
product behavioural characteristics. Moreover, user interfaces should support
negotiation and informal communication aspects that are generated through forecast
reports and review meetings. The framework emphasizes also the need to merge
reasonable forecasts and action-oriented user interfaces. The design implications for
existing and new FSS are discussed. Indeed, these novel human-system interactions
approach to the design of FSS as actionable and knowledge rich resources that
address temporal organizational arrangements.
Keywords: Action-oriented interfaces, FSS design, grounded theory, supply chain
forecasting.
1.
Introduction
Being able to forecast product sales demand has been widely recognized as an
important aspect of business planning and management. Prediction of future sales
demand may also help organizations to identify market opportunities, enhance
customer relationships, increase customer satisfaction, and reduce inventory
investments while improving customer service [1],[2],[3]. Forecasting support
systems (FSS) are typically employed to support managers in effective decisionmaking. Taken from [3], [dictionary] FSS can be defined as:
“A set of procedures (typically computer based) that supports forecasting. It allows
the analyst to easily access, organize, and analyze a variety of information. It might
also enable the analyst to incorporate judgment and monitor forecast accuracy.”
According to [4], FSS use organizational databases and statistical models to produce
forecasts and allow better decisions. The semantic level operations for decision
support systems are often organized into four phases: problem definition, data
selection, model selection, and execution. The syntactic level is frequently couched in
table-related language using rows, columns, and cells (as in spreadsheets). It also
includes mathematical functions and algorithms that can be applied to the data in the
tables. Sales forecasting systems are used to compute future sales demand for each
stock-keeping-unit (SKU) on a daily, weekly, or monthly basis.
The major themes considered in FSS research as used in organizations are the
following: (1) the use of statistical methods so as to ensure ‘optimal’ forecasts, and
(2) how to support users when they adjust the statistical forecasts during the
forecasting process (see for example [5],[6],[7],[8] and [9]). Current research suggests
that FSS are not used effectively so as to support users when they wish to adjust the
statistical forecasts [9],[10]. [11] In their review of forecasting software conclude with
the following three areas for promising research in FSS:
1. Data management
2. User interface design
3. Forecasting process ‘intelligent’ support.
Most studies tend to either focus on a particular task (e.g. how to support user
adjustments to statistical forecasts) or a particular system feature or function (e.g.
statistical method selection). The main obstacle of the forecasting literature is that it
has difficulty in addressing system design issues that are driven by people-system
interactions in the supply chain. Existing forecasting research does not also attempt to
equip designers to deal with the complexity of the working context, while users’ role
in designing FSS is largely left unexplored.
Moreover, there is no research to date that examines the rich supply chain context
based on designers’ and users’ accounts in such a way that is useful and informative
for FSS interface designs.
Amongst the few studies that deal with FSS design, [12] suggest that forecasting
system designers should consider the ways individuals use their systems to produce
forecasts. This is due to the fact that a mismatch between the software designer’s
model of how a system will be used and the actual use is likely to impair the system’s
functionality. [12] Suggest that adaptive forecasting systems could be designed to
recognize particular user strategies at an early stage of the forecasting process,
enabling the interface to adapt to the particular needs, strengths and weaknesses of
these users. For example, the system could highlight information that was not taken
into account sufficiently (such as the lack of fit of the chosen forecasting method or
its inability to deal with a trend in the series), and also guide the user towards the
selection of most appropriate methods.
[13] (in press) also call for research on FSS design. Specifically, they believe that
there is scope for developing and testing software facilities that allow advice and
information obtained from multiple sources to be used and combined in a structured
way. Given also the widespread use of forecasting review meetings there is also scope
for the development of group FSS, which allow managers to feed independent
estimates of, required adjustments into the system. Such improvements in system
design might also help to mitigate the pressures towards bias, both personal and
organizational, that are often evident in supply chain companies [14].
Consequently, this paper investigates FSS design adopting a human-computer
interaction (HCI) perspective. The context of research is product sales forecasting that
organizations use to inform operations that are taking place along the supply chain
(e.g. orders, production, planning, and budgeting). To do so, a theoretical framework
has been established derived from interviews with professional designers and users
and observations of FSS. Section 2 reviews the major aspects of FSS use. Section 3
describes the theoretical framework and its implications for FSS design. Section 4
summarizes the research findings for FSS design and use.
2.
Aspects of FSS use in organizations
Recent studies and reviews have identified gaps in our understanding of the
relationships between systems and techniques used for forecasting, and the
behavioural factors associated with the management of forecasting in organizations
[1],[6],[15]. Forecasting researchers argue that accurate forecasts are highly
dependent on organizational activities that enable these forecasts to be effective.
2.1
Towards effective forecasting process
In particular, [1] developed a theoretical model of the use of a forecasting system
drawing on findings from organizational behaviour and diffusion research literature
and interpreted into a multi-unit case study. The authors tested their theoretical
arguments in a study of a company with 10 separate divisions. Their final sample
consisted of interviews with 45 employees with responsibilities in forecasting (26
product managers, 15 demand planners, and 4 market researchers). [1] suggest that:
 Statistical techniques and forecasting systems should be designed to meet
user needs.
 Forecasting implementation depends on the database system and
supporting organization design.
 Effective forecasts require people’s accountability, adequate time and
resources.
[15] explored the factors associated with better sales forecasting practices in 20
organizations. The study consisted of in-depth analyses of company processes and
documents, as well as interviews with forecast users and forecast developers. The
findings revealed a set of dimensions for effective forecasting management.
Specifically, the proposed framework considers functional integration as the role of
collaboration, communication, and coordination of forecasting management with the
other functional areas (e.g. marketing, sales, finance, production, and logistics) of the
organization. The approach dimension considers the role of products and services to
be forecasted, the forecasting techniques used, and the relationship between
forecasting and planning. The systems dimension address the evaluation and selection
of hardware and forecasting systems to support sales forecasting as well as the
integration of forecasting systems with other management information systems within
the organization. Lastly, the performance dimension relates to the metrics used to
measure sales forecasting effectiveness and its impact in organizations.
These exemplar studies focus on the need for accurate forecasts and organizational
strategies that aspire to deliver an effective forecasting process. However, these
studies have neglected the role of FSS design in influencing overall forecasting
effectiveness.
2.2
The role of user adjustments in forecasting practices
The practice of sales forecasting is characterized by a persistent preference for user
judgments over statistical models [7],[8],[16],[17]. This strand of research explores
how users of forecasting systems should carry judgmental interventions combining
them with the benefits of statistical forecasting methods [6],[18],[19]. The survey
study by [5] focused on issues of familiarity and use of statistical methods, the ease of
use and the satisfaction from use of the statistical methods, and the reasons for
applying judgmental approaches to forecasts. [5] attributed user judgments to the
following: perceived accuracy, ownership and control of forecasts, incorporation of
knowledge from special events, and self-serving biases (at the individual user level),
and difficulties in to obtaining historical data, support from upper management, and a
lack of training (at the organizational level).
Recent observations of forecasting systems in operation [9] confirmed the importance
of user adjustments in organizational forecasting. The following issues were also
specifically highlighted that: (i) users adjusted either the parameters of the forecasting
method or its components (e.g. seasonal factors) in order to improve the method’s
forecasts of the underlying time series (ii) users often selected default parameter
values or sub-optimal statistical methods. [13] suggest that requiring forecasters to
record reasons for their adjustments in a standard format (e.g. by selecting a reason
from a list) might serve to reduce the number of relatively small, but damaging
adjustments that may be based on misinterpreting noise as signal or reflect gratuitous
tweaking of the forecasts [18]. A list of reasons would also allow forecasters to
understand why and how market intelligence is so often misinterpreted. In addition, a
list of reasons would assist the decomposition of market intelligence into key drivers,
thereby lessening the likelihood of double counting. Experimental evidence by [20]
also suggests that the incorporation of guidance systems such as those which allow
the formal use of analogies (e.g. past promotions and their effects) would improve the
quality of judgments based on market intelligence.
The experimental and empirical evidence point to the need for the design of FSS to
explicitly acknowledge the role of judgmental adjustments based on substantive
knowledge of market drivers.
3.
The theoretical framework and its implications for FSS design
In order to understand the design of FSS, semi-structured interviews and associated
demonstrations of systems use were gathered from 10 software designers and 10
users. Their average years of experience in supply chain forecasting was 13.3 for
users and 20.1 for designers, respectively. The grounded theory method [21], [22] has
been employed in order to coordinate the data collection, and analysis process. The
interview guide was partially adapted from [15] to address issues of organizational
forecasting but this was further enriched with questions specifically applying to
designers and users. The interviews lasted between 1 and 1.5 hours and focused on
three key areas. Specifically, the process of developing forecasts was clearly an issue
that the interviews addressed. Organizational aspects of systems use were also
considered as a focus of the interviews with emphasis on the different people involved
in the forecasting process forecast accuracy considerations, and social/organizational
factors that may influence forecasts. Lastly, system functionality and design generated
a set of thoughts and activities that people engage in when producing product
forecasts.
The resulting theoretical framework (consisting of the main properties and their
properties) accounts for FSS use on supply chain forecasting (figure 1). The
framework has then been used to structure FSS design requirements.
Figure 1: Concept map depicting theoretical framework of designers and users
Based on the framework, it is possible to outline five broad design support areas for
existing and future FSS interfaces. These are the following:
(1) Demonstrate special features of specific products
(2) Support for product knowledge generated from informal communications
during the forecasting process
(3) Provide features that enable dynamic interchange of historical data and
product knowledge
(4) Provide users with the ability to annotate and negotiate elements of
forecasting
(5) Enhance user awareness of organizational knowledge by providing
appropriate interface navigational cues.
The theoretical framework provided evidence that knowledge visibility and
availability is an essential component of user interaction with forecasting systems and
as such it should be incorporated at forecasting user interfaces. Products in the context
of supply chain act as focal points for users when forecasting. Interestingly, the
specific product characteristics and the knowledge users reported indicate socially
constructed aspects of supply chain forecasting.
3.1
Implications for FSS research and practice
This study has several implications for FSS research on the supply chain context.
First, it indicates that models and studies that are concerned with use, design and
evaluation of FSS should take into account specific aspects of the social context. Our
study suggests that the concepts of negotiation, informal communication, for example,
create the social fabric necessary to facilitate product forecasting through the creation
of deeper relationships and increased opportunities for action-oriented user interfaces.
The evidence also suggests that simply choosing and fitting statistical models and
adjusting forecasts (when appropriate) do not alone create an effective FSS for supply
chain applications. Instead, it is the connections gained through product specific
characteristics, thinking in terms of actionable forecasts, responding and negotiating
based on product specific knowledge, and informally communicates aspects of
forecasting process that improves FSS. In a supply chain context, FSS becomes even
more important because it affects user effective interaction and produced results.
This study also has multiple implications for practice. First, those designing and
implementing FSS should facilitate the creation of action-oriented interfaces as apply
to the specific context of use. An excellent way is to provide features that require
users to brainstorm when producing forecasts with regard to how they recommend
specific actions due to product forecasts. These can be very specific (e.g. advertising)
or general (e.g. better communication through regular meetings with marketing and/or
sales people). For example, in organizational forecasting some people may have a
better understanding of statistics, while others may have a better knowledge of the
market. This proposal suggested by designers and expanded in this study not only
should point to achieve effective forecasts but consider specific actions that should be
taken for richer communication and connectedness.
3.2
Limitations
Although the theoretical framework of this study provides insights into actionoriented FSS, a number of limitations must be considered when interpreting the
concepts and their properties. First, this study represents the first set of a theoretical
framework and should be subjected to further testing with different users and
designers, contexts, and specific commercial systems. It should be acknowledged that
qualitative data of this nature and grounded theories are focused, substantive and
cover the specific context but are also difficult to generalize. Rather, they are
substantive to the settings from which they are derived. On the other hand, when
reliability is considered, it is very difficult to exactly replicate a grounded theory
study because no two situations, contexts or research requirements are alike. It is thus
more appropriate to ask whether or not the emergent theoretical framework, if
consistently applied to a similar situation, will allow researchers to interpret,
understand, and adequately explain newly observed phenomena and social process.
To this extent, the grounded theory framework and that emerged from this study can
claim reliability. In addition, the method and analysis is prone to researcher bias due
to the reliance on one researcher as the primary interpreter of interviews and creator
of the essential categories of the theoretical framework. However, every effort has
been made to overcome this bias in the data analysis and interpretation process by
triangulating and cross validating by using several types of data and documents, and
by discussing interpretations with fellow researchers. Thus, the research findings have
gained validity.
3.3
Future research
Apart from applying some of the findings to similar technologies (e.g. in the design
and use of enterprise resource planning systems), the current research suggests four
strands of future work that appear particularly promising with regard to FSS design.
Firstly, the investigation of the social and organizational dimensions of demand
forecasting systems may be fruitful in order to fully appreciating the ways
technological artefacts fit within the particular setting [12] (for an early example).
Focus on the adoption and uses of forecasting systems may enable some more focused
observations on how (a) negotiation and product knowledge are developed through
time in organizational settings (b) how people develop their skills because of the
availability of the technology and (c) how the working setting encourages and/ or
possibly discourages effective user system interactions. Research of this type would
generate an integrative theory of the socio-technical process that takes place around
FSS and discuss their implications for design.
Furthermore, the concept of communication/negotiation, its elements and how these
may be interpreted through design features and characterizations has not been
discussed before in academic research on FSS. Another interesting research question
is to investigate how to design FSS to maximize user reactions to the produced
forecasts, and intentions to participate in negotiations in organizational initiatives.
Research in this area can be informed by longitudinally studying the relationships
between user interactions, social context, and how forecasters use the FSS to create a
shared understanding and knowledge resource.
This study suggests that FSS use and design is not simply a matter of providing a rich
set of features but instead technology must be seen as contributing to action-oriented
success. Future research should investigate short-term, medium-term, and long-term
actions that arise as a result of FSS use and their impact on organizational forecasting.
Another interesting avenue for research is to investigate how the content of informal
communication (e.g. user annotations and product knowledge exchange) affects
forecasting outcomes and future organizational actions. Thus, it may be possible to
determine which of the three types of action in relation time constraints should be
desirable when producing forecasts for a given context of use.
The study of forecasting systems could be further benefited if it considers design
prototypes for group and collaborative forecasting. Thus, it might be the case that a
set of design features are more suitable for single users, whereas another set of design
features is more appropriate when more than one people interact through the same
system interface. While this study has mainly looked at single-user FSS, further
research can look whether these ideas apply to other enterprise systems (e.g. ERP)
and whether design features are comparable and useful across systems. Users may
also find useful findings of this study when thinking to use or further explore the
system possibilities and its implications in their respective positions.
4.
Conclusions
It is argued that the theoretical framework proposed can play a significant role for
understanding and designing FSS for the supply chain environment. Specifically,
designers could use the theoretical framework to identify and implement design
features for interactions that take place in real forecasting practice, or create new
systems based entirely on the issues identified. The FSS design thus revolves around
the following themes:
 Richness of product knowledge
 Product knowledge combined with statistical approach on supply chain
forecasting
 Encouraging negotiation points through appropriate functionality
 Encouraging informal users’ communication
 An action-oriented FSS user interface.
Overall, it is argued that treating FSS, not as static set of technological and system
choices put together to enable more accurate forecasts but, as a dynamic, contextinfluenced artifacts, may produce more creative and rich understandings of effective
interface design. Associated research explores user tasks for supply chain forecasting
and how these may inform FSS design.
Acknowledgments
This research is funded by the Engineering and Physical Sciences Research Council
(EPSRC) as part of a three year interdisciplinary project on «The effective design and
use of forecasting support systems for supply chain management», awards number
GR/60181/01 and GR/60198/01.
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