=Paper=
{{Paper
|id=Vol-2030/HAICTA_2017_paper89
|storemode=property
|title=Strategic Decision Support Systems for Logistics in the Agrifood Industry
|pdfUrl=https://ceur-ws.org/Vol-2030/HAICTA_2017_paper89.pdf
|volume=Vol-2030
|authors=Maria Kamariotou,Fotis Kitsios,Michael Madas,Vicky Manthou
|dblpUrl=https://dblp.org/rec/conf/haicta/KamariotouKMM17
}}
==Strategic Decision Support Systems for Logistics in the Agrifood Industry==
https://ceur-ws.org/Vol-2030/HAICTA_2017_paper89.pdf
Strategic Decision Support Systems for Logistics in the
Agrifood Industry
Maria Kamariotou1, Fotis Kitsios1, Michael Madas1, Vicky Manthou1, Maro
Vlachopoulou1
1
Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece,
e-mail: tm1367@uom.edu.gr
e-mail: kitsios@uom.gr
e-mail: mmadas@uom.gr
e-mail: manthou@uom.gr
e-mail: mavla@uom.gr
Abstract. In recent years, the agrifood sector has experienced many societal,
economic and technological changes. Such transformations significantly
influence the entire food processing chain from agricultural production,
through food processing to the distribution of food to customers. As Supply
Chain Management (SCM) emphasizes on seeing the whole supply chain as
one system, Decision Support Systems (DSSs) are used to define the influence
of strategic issues on logistics and to identify the most effective processes to be
performed because of the strategic issues with the highest either positive or
negative impact on the logistics. Managers try to deal with the current complex
environment using the Strategic Information Systems Planning (SISP) process.
The purpose of this paper is to propose a strategic DSS framework, which
combines both the strategic management process and the SISP process to
provide a holistic approach to effective decision-making in logistics in the
agrifood industry.
Keywords: Decision Support Systems; Strategic Management; Business
Strategy; Strategic Information Systems Planning; Logistics; Agrifood
1 Introduction
During the last decade or so, there has been an increasing attention of researchers
in the strategic role of logistics. The strategic significance of logistics is conceived in
businesses that place special emphasis on customer service as the output of their
business (Korpela and Tuominen, 1996).
As the business environment is getting more and more complex and competitive,
an effective and timely decision-making is necessary. The implementation of
decision support technology is becoming significant and calls for reduced complexity
along with improved efficiency. Many researchers have studied the efficiency of
Decision Support Systems (DSSs) (Alalwan, 2013). DSSs are used to define the
influence of strategic issues on the logistics and to identify the most effective
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processes to be performed because of the strategic issues with the highest either
positive or negative impact on the logistics (Korpela and Tuominen, 1996).
Previous studies in this area were focused on the importance of Information
Technology (IT) to boost decision makers to make more efficient decisions.
Especially, previous researchers highlight the benefits of using computer-based
systems to support logistics management, especially in transportation and
warehousing (Accorsi et al., 2014; Moynihan et al., 1995).
In recent years, the agrifood sector has experienced many societal, economic and
technological changes. Such transformations significantly influence the entire food
processing chain from agricultural production, through food processing to the
distribution of food to customers. The innovation is a crucial factor for firms in this
sector and plays an important role in sustaining and enhancing their competitiveness
(Baregheh et al., 2012).
The development of technology and the use of computer networks have changed
production processes, access to, transfer and use of information in the agrifood
sector. The easiest access to knowledge and the easiest sharing of information can be
achieved through the spreading of communication technologies (Sturiale and
Scuderi, 2011).
Despite the fact that Small and Medium Enterprises (SMEs) are considered the
main pillar of contemporary economies and have a key role to play particularly in
rural economies, few studies have focused on examining the innovation practices of
the agrifood sector SMEs (Baregheh et al., 2012). This sector is the largest one
within the EU and is one of the key drivers of the EU economy, contributing to both
economic output and employment (Baregheh et al., 2012; Gold et al., 2017). As
Supply Chain Management (SCM) emphasizes on seeing the whole supply chain as
one system, current research examines mostly how to improve the position of
agrifood actors in developing countries (Gold et al., 2017). Furthermore, the
literature focuses on the specific challenges of smallholder farmers in accessing
global chains due to market constraints, deficient infrastructures and lack of
resources (Gold et al., 2017).
The purpose of this paper is to propose a strategic DSS framework, which
combines both the strategic management process and the Strategic Information
Systems Planning (SISP) process to provide a holistic approach to effective decision-
making in logistics in the agrifood industry.
The structure of this paper is the following: A theoretical framework based on the
literature review about strategic planning and DSS, as well as, strategy and DSS
models in logistics are initially analyzed in Section 2. In Section 3, the steps for a
framework linking the DSS and SISP process in logistics in the agrifood sector are
discussed, whereas the final section summarizes the concluding remarks of the paper.
2 Logistics Strategy
Researchers have recently focused on the strategic role of logistics. The strategic
significance of logistics is conceived in businesses that are interested in customer
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service as the output of their business. Logistics is a holistic approach which includes
the management of material and information flows and it is important so as to satisfy
customers' demands. Logistics strategy must be aligned with business strategy
because it identifies the selection of products, services and markets and determines
the goals of the logistics system of the company (Korpela and Tuominen, 1996). The
Sustainable Supply Chain Management is significant because it allows businesses to
permits supply chains with sustainable goals (Shi et al., 2015).
As managers in strategic planning define the key goals for the company in order to
compete businesses in a turbulent environment, the logistics strategic planning
process starts with situational analysis which contains the formulation of the vision,
strategic goals, objectives, strategies and action plans. The aim of this analysis is to
identify the strengths, weaknesses, threats and opportunities by analyzing both the
logistic system and the business environment. Moreover, an analysis of strengths and
weaknesses provides results in logistical structure and logistical costs, inventory
management, transportation, Information Systems (IS), organizational structure, co-
operation with other corporate functions and materials handling and transportation.
The scanning of both the environment and the resources are significant to identify the
long-term direction for the logistics function.
Specifically, the vision, describes a desired future situation identified for the
logistics organization. The vision and the strategic objectives present the direction of
the implementation of the logistics activities and the logistic vision that must both be
aligned with business vision. The goals and strategies support the translation of
logistics vision in specific performance measures and operating models such as
customer service, transportation, order processing, inventory management,
warehousing, IS and organization. The definition of goals and the formulation of
strategies are aligned with other business functions such as marketing and production
to increase business advantages. Moreover, action plans constitute a detailed
description of the operational and short-term activities that are required for the
implementation of the strategy.
Organizations use the strategic management process scan the environment, which
significantly influences the logistics function. The logistics strategic management
process contains three phases. The first phase is the definition of the trends and the
evaluation of the impact and urgency of the identified trends, the next phase is the
evaluation of priorities and the last one the development of a plan with responses to
the issues.
The logistics strategic management can form strategic decision outcomes or
environmental forces. Responses to the issues may require changes in the vision,
objectives, strategies or action plans, as a result strategic issues management process
supports the periodic planning process. The process of strategic management forms
aligns the advantages of strategic planning with the flexibility of continuous strategic
management. Furthermore, it combines the business's logistics processes with the
capability to be strategically oriented and to face the external and internal
developments (Korpela and Tuominen, 1996).
The impact of innovation on business success in the agrifood sector seems to be
very much comparable to that in other industries. In the past, agrifood businesses
tended to pay attention on reducing production costs rather than delivering benefits to
the final customers. Recently, pressures arising from globalization, the need to ensure
783
food safety, nutritional quality and customers’ demand for convenience, variety and
quality, combined with new opportunities offered by the biotechnology revolution
have all led to a changing attitude. Hence, the agrifood sector is increasingly oriented
toward developing products that take into consideration customers’ demands (Fortuin
and Omta, 2009).
3 Strategic DSS Models in Logistics
Strategic decisions imply the design of a distribution/logistics network is complex
because it involves significant commitments in resources over several years.
Strategic logistics planning, including required customer service levels, aims to
minimize the inventory-related costs which are combined with producing and storing
products from manufacturers to customers (Moynihan et al., 1995). As a result, the
logistics strategy is significant for long-term competitive advantages in business,
especially in a logistics distribution network which is important in transportation and
inventory cost. Furthermore, it is crucial to customer satisfaction regarding logistics
response (Kengpol, 2008).
Previous researchers in this field focused on the importance of IT to support
decision makers to achieve more efficient decisions and to enhance their
effectiveness. Specifically, previous surveys focused on the benefits of using
computer-based systems to support logistics management, especially in
transportation and warehousing (Kengpol, 2008; Salam and Khan, 2016; Songbai et
al., 2010). Limited surveys have been conducted in the areas of inventory and
product forecasting (Accorsi et al., 2014; Moynihan et al., 1995).
A DSS is defined as “an interactive, flexible and adaptable Computer Based
Information System which uses decision rules, models and model base as well as a
database and the decision makers apply decisions in solving problems which would
not be willing to manage visualization models per se” (Waxlax, 1993).
Another definition is based on the view that a DSS is “an interactive and
adaptable Computer Based Information System which helps non-organized
management problems” (Alyoubi, 2015; Moormann and Lochte-Holtgreven, 1993).
Table 1 summarizes different DSS and their functionalities in logistics. Then, the
similarities among them are discussed in the next paragraphs. These findings are a
basis for the suggested DSS model.
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Table 1. DSS in logistics.
DSS DSS Functionalities Reference
DSS for operational Data used is related with Fanti et al. (2015)
and tactical
products and services prices,
decisions in logisticsresource and budget allocation,
payroll cost, cost per product
Simulation events such as
demands, departures and
arrivals of means of
transportation at terminals and
acquisitions and releases of
resources by vehicles
Identification the performances
of the systems
Evaluation the selected
parameters which can improve
the performance indices
DSS model for Demand analysis Songbai et al. (2010)
vehicle routing Analysis of data (number of
drivers, strength of vehicle,
mileage per vehicle)
Decision analysis for the
transportation personnel
requirements, vehicle demands,
path choosing optimization and
resource transportation
information
Logistics Preliminary analysis Kengpol (2008)
distribution network (information such as GMS
locations, transportation costs
of listed distribution centers
and customers)
Evaluation of the alternatives
for the logistics distribution
network
Estimation of the delivery time,
quality, unexpected demand
Calculation of the
transportation cost
The implementation and
feedback
The first step in the strategic management process comprises the scanning of the
external and internal environment. The second step involves the estimation of the
effect and urgency of the issues, the evaluation of priorities for the previous problems
and the identification of the type of response for these issues. Finally, the last step
contains the planning of the required responses for the strategic issues. DSSs are used
to define the impact of strategic issues on the logistics and to identify the most
785
effective processes to be performed in coordination with the strategic issues with the
highest either positive or negative effect on logistics (Korpela and Tuominen, 1996).
Some basic features have to be considered for the development of the systems.
DSS involve many basic components as follows. Firstly, the data component usually
contains a Database Management System (DBMS). The DBMS involves modeling
tools and general programming languages. Data used can either be internal or
external, either cross-sectional data or time series. Internal data come from
organization’s internal functions and concern products and services prices, financial
data, resource allocation data, data related to costs such as payroll cost or cost per
product. The external concern is about competition market share, government
arrangements and anything that comes from external sources such as market
research, government agencies, the web. The data stored in the DSS database are
used as input to the optimization processes associated with models. The DSS
information is provided by other data files, which could be business’ internal or
external files. The next module is the model component, which includes a simulation
model, a mathematical model as well as optimization algorithms to support the
analysis of the impact of the selections on the system performances (Fanti et al.,
2015; Yoo and Digman, 1987). Precisely, several methods, models, theories, and
algorithms are implemented to develop and analyze the alternative decisions in DSS.
Examples of these techniques are the intelligent analysis of data, the simulated and
fuzzy modeling, the use of genetic algorithms and neural networks, the decision-
making theory and fuzzy theory (Kondratenko et al., 2014).
A significant area of DSS in Logistics has been applied to perform an evaluation
of supply chain. Current changes in global production had intensified supply chain
complication and increased the argument that logistics strategies are significant
aspects of business strategy. Recent business environment highlights the need for
supplier relationship development to improve businesses sustainable management.
The purpose of the supplier selection and assessment process is to limit risk and
increase overall value to the customer. As supplier selection is considered to be a
multiple criteria decision-making process, this process signifies an even more
complicated problem. The decision-maker needs to analyze a large amount of data
considering multiple factors to apply a more effective evaluation. Businesses have to
pay attention to each factor to reduce the costs and to increase their profit, because of
the increased globalization of trade and the expansion of competition. Businesses
formulate strategies concerning the supplier selection process paying attention to the
sustainability and environmental responsibility requirements, to deal with the higher
level of competition. Several researchers argue that sustainability is a significant
aspect which has to be considered by managers in supplier selection and performance
evaluation.
Thus, the criteria taken into consideration for the supplier selection and
assessment can be categorized as follows. The first category concerns quality, the
second one is the price, the next category is related to the capability of
supply/delivery, and the fourth type involves factors about the service. Another
category sought by decision makers take into consideration for the supplier selection
and assessment is the Environment Protection. In conclusion, management system,
corporate social responsibility, and performance are the last categories (Karthik et al.,
2015).
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4 Conceptual Model
As the agrifood sector can rely on the principles of innovation management,
developed in high-tech sectors (Fortuin and Omta, 2009), the proposed model (Figure
1) is based on previous SDSS and combines the phases of SISP process in order to
suggest a completed model for strategic decisions in logistics in the agrifood sector.
More specifically, Yoo and Digman (1987) proposed a DSS model for strategic
management. This model involves four subsystems. The first one, named
“Environmental Analysis Subsystem”, is used for gathering information related with
inventory, production, R & D, marketing, industry, raw materials, human resources,
financial resources, market, technology, economic conditions, government and
culture necessary for forecasting and projecting both the external and internal
business environment. This information is gathered by the staff, customers,
managers, consultants as well as literature. The second subsystem is the “Goal-
setting Subsystem” which involves a model base which generates alternatives
models. One or more of them are selected according to identified goals and
objectives as well as business’s mission and purpose. In the Goal-setting subsystem
the results of the control phase should be used as an input as well as in the strategy
operating subsystem. Furthermore, the results of each phase of the strategic
management process can be used as an input in the strategy operating subsystem for
reparative actions and future effectiveness. Moreover, the Decision Support
Subsystem includes a DSS database, a DSS model base and application programs
which maintain the flow of information within the system. The DSS database
contains files of historical, managerial and environmental data as well as files on
various transactions. The DSS model base includes models which are useful for the
solution of strategic problems. The last subsystem is the “Strategy Operating
Subsystem”, in which the decision maker identifies, evaluates and selects alternative
strategies. Then, he implements the selected strategy, and evaluates based on
information provided by the decision support subsystem. This subsystem maintains
each phase of strategic management process as it has been previously presented.
The model includes four categories of subsystems, named; Environmental
Analysis Subsystem, Goal Determining Subsystem, Decision Support Subsystem and
SISP Subsystem (Figure 1).
Fig. 1. Proposed SDSS for Logistics in the agrifood sector
The first subsystem includes the identification of the problem, for the business to
make the appropriate decisions. The Situation Analysis is the first phase of SISP
process and it is contained in this subsystem. Generally, in the current phase, existed
787
business, organizational and IS are analyzed. Moreover, businesses analyze the
current external IT and business environment to determine new trends in IT.
Managers analyze strengths and weaknesses concerning economic conditions,
logistical structure and logistical costs, inventory management, transportation,
warehousing, IS and materials handling.
In times of globalization and increasing competitiveness, the determination of
threats and opportunities in the business environment is crucial for the sustainable
economic success of every company. It becomes even more important in the agrifood
industry because companies are highly interdependent. The awareness of
developments in markets, products, business partners and competitors considers as a
significant factor in economic success for businesses. The analysis of the information
needs a systematic scanning and a linkage with the needs of network companies
(Fritz, 2009). Innovation contributes to organizational success, performance and
survival. It is often driven by pressure from the external environment, and especially
from competition, deregulation, isomorphism, resource scarcity and customer
demand. In the agrifood sector it is of particular interest as it aims to support or
improve the performance (Baregheh et al., 2012).
Information about distribution channels, economic situation of suppliers, relation
demands to product characteristics, market segments where competitors are active
and buying power, quality of suppliers are required (Fritz, 2009; Manthou et al.,
2004). Other information required includes data such as rural economy, the
environment, food production, healthy eating and consumer values (Volpentesta et
al., 2013). However, the efficient use of the information sources for competition
monitoring in the agrifood businesses requires a focused, systematic and automated
analysis of their content. Also, each company aims to integrate this information with
the results of the business and the analysis of its environment (Fritz, 2009) (Figure
2).
Fig. 2. Environment Support Subsystem
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In the second subsystem, the Strategic Awareness phase of SISP is involved.
Strategic Awareness includes key planning issues concerning the identification of
goals and the development of the team which will participate in the implementation
phase of the process. The main objectives which have to be identified are related to
customer service, transportation, order processing, inventory management and
warehousing (Figure 3).
Fig. 3. Goal Determining Subsystem
Next, the Decision Support Subsystem includes the Database, Data Model and
Application programs. The previous two subsystems provide information to the
Decision Support Subsystem. Next, this subsystem generates an output, which is
used as input for the interacting subsystems. Thus, managers can gather, store and
reclaim the necessary information about external and internal environment and
historical data (e.g. about transportation or supplier selection and evaluation), which
will help them to create alternative scenarios. Then, managers will evaluate this
information and they will select the best choice, which will be developed in the next
subsystem. The output of this subsystem includes alternative decisions about drivers’
and vehicle transportation, KPIs, cost rate, cost per unit of material flow (Zviran,
1990). Other indicators which are used are responsiveness and agility, cost and
efficiency, food quality and sustainability (Gold et al., 2017). Data can be stored for
further working out and sensitivity analysis. They can also be categorized in external
files if further processing is required. The user interface helps this process by
providing a set of menus and question/ answer dialogues (Zviran, 1990). Once the
problem is determined, mathematical models based on the problem are implemented
that support the development of alternate solutions. Furthermore, the models are
created to analyse the alternatives. Next, the selection of the most suitable alternative
follows.
Overall, several methods, models, theories and algorithms are implemented to
develop and analyze the alternative decisions in DSS. Examples of these techniques
are the intelligent analysis of data and the fuzzy theory (Figure 4).
789
Fig.4. Goal Determining Subsystem
The SISP Subsystem includes the last three phases of SISP process; Strategy
Conception, Strategy Formulation and Strategy Implementation Planning. In Strategy
Conception the identification of important IT goals and objectives for
implementation are applied. The organizing team evaluates them and formulates the
technological strategy, which will be applied in the next phase. Then, there is
Strategy Formulation through which the definition of new IT architectures,
processes, projects and the priorities over them are implemented. Finally, Strategy
Implementation Planning involves activities concerning changes in management
process, such as the implementation of the opportunities, the goals, the plans and the
new processes, the action plan, its evaluation and control (Brown, 2010; 2004;
Dooley and O'Sullivan, 1999; Kamariotou and Kitsios, 2017a;b; 2016; Kitsios and
Kamariotou, 2016a;b; Maharaj and Brown, 2015; Mentzas, 1997; Mirchandani and
Lederer, 2014; Newkirk and Lederer, 2006; Newkirk et al., 2008). Results from the
Strategy Implementation Planning phase should feedback into the Goal determining
subsystem as well as each phase in the Strategy Information Planning subsystem for
corrective action and future effectiveness (Yoo and Digman, 1987) (Figure 5).
Fig.5. Strategic Information Planning Subsystem
The proposed model has few advantages in comparison with the previous ones,
which have been presented in Table 1. Those models have been implemented for
specific logistics functions such as distribution, vehicle routing and tactical decisions.
790
The proposed framework (Figure 1) is based on the strategic process of DSSs and it
involves the phases which are based on the formulation of business and IT strategy,
which are excluded from the previous models. The identification of objectives, the
analysis of business and IT environment, the organization of planning team, the
evaluation of opportunities, the improvement of business processes and the
assessment of the process, are significant phases when managers formulate IT
strategy. So, the proposed framework can be implemented by decision makers in
each function of logistics in agrifood sector.
The proposed model gives some benefits to decision makers. First, various
strategic decision variables and steps can be considered comprehensively. Second, it
can be considered as an effective strategic management system which makes easier
the decision making process. Next, the system provides updated information to
managers as they can scan the business and IT environment. So, the environmental
uncertainty is minimized and company risk under dynamic change. Another benefit
is that the evaluation process is implemented in order to examine whether the
strategy is being implemented and whether the goals are being achieved. If not,
corrective action may be necessary to change the implementation activities or even to
change the strategy itself. Finally, system includes various levels of managers, so
their participation enhances the increased use of the system and the effectiveness in
decision-making.
5 Conclusions
The combination of strategic planning with DSS is a new research area. It can
significantly improve the strategic decision making effectiveness. Careful design is
critical to obtain the advantages of SDSS. Further expansions in DSS research area
and IS will provide new motivations for successful SDSS developments (Moormann
and Lochte-Holtgreven, 1993).
DSSs are based on the needs for information of the existing organizational
functions. In the future, DSS will try to involve tools based on environmental
changes and information needs, which will facilitate decision makers so as to adapt
their working practice for future demands (Salmela and Ruohonen, 1992).
A framework which combines SISP process and DSS in logistics is suggested.
The suggested framework contributes to the agrifood sector and enhances the
communication among producers and consumers, enhancing a redistribution of value
for primary producers. Furthermore, if agrifood producers use the DSS, the latter can
give customers insight into sourcing and production methods, enable producers to
monitor their customer base closely and make supply chain visibility and
transparency a sustainable competitive advantage (Volpentesta et al., 2013).
This paper research contribution is two fold. Firstly, it aims to bridge the gap in
the literature regarding the connection between SISP processes and DSS.
Furthermore, it suggests a new framework for decision making with general
applicability to various industries, including the agrifood sector.
By defining the phases that support managers’ decision making, implications for
future research are presented. Academics and managers should expand, visualize and
791
test this model, to evaluate the effectiveness of SISP phases in the process of decision
making. As the framework has not been tested yet, the results of an exploratory study
will be summed up in an expanded conceptual model for future research.
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