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Citation Informatyion: Sun Z (2016) A Framework for Developing Management Intelligent Systems. International Journal of Systems and Service Oriented Engineering (IJSSOE). 6(1) 37-53. DOI: 10.4018/IJSSOE.2016010103.

A Framework for Developing Management Intelligent Systems Zhaohao Sun Department of Business Studies PNG University of Technology, Lae 411, Morobe, PNG [email protected], [email protected]

Abstract: This paper proposes a framework for developing management intelligent systems (MiS). The proposed framework identifies the main management functions, intelligent systems and decision support systems (DSS) for planning, organizing, leading and controlling, and their corresponding applications as the core components of MiS. It integrates the main management functions with intelligent systems and DSS in a context of decision making by managers in organizations. This paper also examines intelligent systems for management and management decision making. The approach proposed in this paper might facilitate research and development of MiS, management, intelligent systems, and information systems.

Keywords: Management intelligent systems (MiS), information systems, artificial intelligence (AI), decision making, intelligent systems (IS), decision support systems (DSS).

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INTRODUCTION

Management intelligent systems (MiS) is an emerging paradigm that integrates management with intelligent systems (IS) (Sun & Firmin, 2012). The core components of MiS are still unclear. Integrating IS with management is still a big challenge. Many textbooks on information systems cover management activities based on information technology (IT) and information systems (Laudon & Laudon, 2011; Bocij, Greasley, & Hickie, 2008). A large number of papers have contributed to a detailed account of the management activity of managers based on IS. Examples includes intelligent supply chain management (SCM) (Khan, Al-Mushayt, Alam, & Ahmad, 2010), marketing IS (Martínez-López & Casillas, 2009), and intelligent customer relationship management (Baxter, Collings, & Adjali, 2003). There is little literature on the main management functions and decision making (DM) of managers in organizations based on IS. IS should be applied to each of the main management functions in order to aid managers to realize their organizational intelligence, that is, it is important for MiS to look at IS for planning, organizing, leading, and controlling (Sun & Firmin, 2012). This consideration leads to the following issues for developing MiS: How are intelligent systems managed? How can management and intelligent systems be integrated? Based on our early work (Sun & Firmin, 2012), this paper will address these issues by presenting a framework for developing MiS through integrating the main management functions with IS taking into account DM of managers. To this end, the rest of this paper is organized as follows: Section 2 provides some background on this research. Section 3 and 4 examine intelligent systems for management and intelligent systems for management decision making respectively. Sections 5 and 6 propose a framework for developing MiS and a strategic model for MiS. Section 7 looks at theoretical, managerial and practical implications of this research. Section 8 discusses the related

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work and the limitation of this research. The final section ends this paper with some concluding remarks and directions for our future work. BACKGROUND

This section provides a background on information systems, management information systems, and intelligent systems for research of MiS. Management is what managers do (Robbins, Bergman, Stagg, & Coulter, 2012, p. 12). More specifically, management is the process of managers‘ coordinating and overseeing the work activities to ensure their completion. The main management functions or activities consist of planning, organizing, leading and controlling (Terry, 1968; Robbins, Bergman, Stagg, & Coulter, 2012). Management functions in organizations have remarkably improved with the development of advanced ICT and information systems over the past decades (Turban & Volonino, 2011) (Laudon & Laudon, 2015). Information systems as a discipline encompasses the concepts, principles, methodologies and processes for two broad areas of activity within organizations: 1. acquisition, deployment, management, and strategy for ICT resources and services; and 2. packaged system acquisition or system development, operation, and evolution of infrastructure and systems for organizational processes (ACM, 2010). Information systems are vital to problem identification, analysis and DM of managers (ACM, 2010). Management information systems are information systems for management through providing reports on organizational performance to help management monitor and control business (Sun & Firmin, 2012). Management information systems consist of information systems for customer relationships management (CRM) (Chaffey & White, 2011), business process management, human resource management, financial management, procurement management, SCM (Laudon & Laudon, 3

2015), marketing management (Casillas & Martínez-López, 2009) and marketing analytics (Turban & Volonino, 2011; Bocij, Greasley, & Hickie, 2008). Intelligent systems (IS) as an applied field of AI (Russell & Norvig, 2010) encompasses the principles, methodologies, techniques, processes and applications in real world problem solving contexts. Currently, IS is a field that studies intelligent behaviors and their implementations as well as their impacts on human society (Sun & Firmin, 2012). An IS is a system that can imitate, and/or automate intelligent behaviors of human beings, and solve problems that were previously solved by humans through generating representations, and adopting inference procedures and learning strategies (Schalkoff, 2011). An IS embodies a form of computing that is based on an inexact, fuzzy and ambiguous model (similar to human reasoning) (Schalkoff, 2011). The difference between IS and non-intelligent systems is that the former emphasize the representation, emulation and simulation of intelligent behaviors towards their implementation using computing devices or software systems; the latter stress to realize one or a series of human actions or behaviors (Sun & Firmin, 2012). In theory, non-intelligent systems can provide the same ―correct‖ solutions to real world problems. They can tackle problems if, and only if, the data that they manipulate are complete or exact. Otherwise, a non-intelligent system will provide either no solution or an incorrect one. In contrast, IS recognizes that the available data might be incomplete, uncertain or fuzzy, and they can work in such situations and still arrive at a reasonable solution (Negnevitsky, 2005). IS is important for business management in general and information systems in particular. Business intelligence can be considered an application of IS to business (Sun & Firmin, 2012). For example, a review of three well-known textbooks illustrates common themes: IS and decision support systems (DSS) although they have different classifications for IS. The first text explores business information systems (Bocij, Greasley, & Hickie, 2008) and the second, management information 4

systems (Laudon & Laudon, 2015), and the third, information technology for management (Turban & Volonino, 2011). More specifically, Bocij et al (2008) introduce expert systems, business intelligence, data mining, AI, neural networks and knowledge management as a part of DSS. Laudon and Laudon (2015) briefly introduce IS for decision support in their text. Their introduction covers expert systems, case-based reasoning (CBR), fuzzy logic systems, neural networks, genetic algorithms and intelligent agents. Turban and Volonino (2011) introduce IS as a part of business intelligence and DSS. Based on the above discussion, the interrelationship among information systems, IS, MiS and DSS is illustrated in Fig. 1. From Fig. 1 we can see that IS and information systems share some commonality. Furthermore, there are many methods, principles and techniques of management that are useful for managing and developing IS, for example, data management, information management (Laudon & Laudon, 2011), knowledge management (Chaffey & White, 2011), and experience management (Sun & Finnie, 2005). All of these have been playing a significant role in IS, because data including big data (Sun, Strang, & Firmin, 2016), information, knowledge, and experience are the strategic resources for IS and their applications (Laudon & Laudon, 2015).

IS

MiS

Information Systems

DSS

Fig. 1. The interrelationship among information systems, IS, MiS and DSS

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INTELLIGENT SYSTEMS FOR MANAGEMENT

This section looks at IS for planning, organizing, leading, and controlling in some detail by extending our early work. Intelligent Systems for Planning IS for planning aim to imitate and extend some or all planning behaviors of managers of organizations, for example, corporate planning (Thierauf, 1982) and supply chain planning (Laudon & Laudon, 2015). More specifically, they should imitate and automate definition of goals, establishment of strategies for achieving these goals, and development of plans to integrate and coordinate activities (Sun & Firmin, 2012). To this end, data management including big data management (Mcafee & Brynjolfsson, 2012), information management, and knowledge management (Sun & Finnie, 2005) should be the basis of any IS for planning. Data mining, business analytics and big data analytics (Sun, Zou, & Strang, 2015), knowledge based systems (KBS), expert systems and intelligent agents (Laudon & Laudon, 2015) have been developed to aid planning processes. All these intelligent techniques are decision support tools for intelligent planning system (IPS) (Smith, 1992). Case based systems can also facilitate intelligent planning, because similar goals usually have similar strategies for achieving these goals (Finnie & Sun, 2003). IS for planning can also be called IPS. IPS has been studied since the 1980s (Smith, 1992). A system architecture for an IPS is shown in Fig. 2. In this architecture, knowledge base (KB) consists of general organization knowledge, decisionsupport knowledge and specific plan knowledge for actions, resources, products, sample plans, actual plans, goals and tradeoffs (Smith, 1992). The multi-inference engine consists of action inference engine, resource inference engine, and product management inference engine. The multiagent planning system includes multiple agents (Russell & Norvig, 2010) such as a plan 6

builder, plan monitor, plan recovery agent, knowledge manager and risk analyzer. The plan builder, plan monitor, and plan recovery agent require corresponding plan building knowledge, plan monitoring knowledge, plan recovery knowledge for supporting their own DM. The knowledge manager is responsible for building and managing KB. The multi-user interface includes end user interface, developer interface and external interface. Multi-user interface

Multiagent planning system Knowledge base Multi-inference engine

Fig. 2. A system architecture of an IPS

Intelligent Systems for Organizing IS for organizing aim to coordinate, digitize, and optimize organizing behaviors of managers. More specifically, they should improve, replicate and computerize decomposition of tasks, grouping of persons who complete the decomposed task, and allocation and deployment of organizational resources (Robbins, Bergman, Stagg, & Coulter, 2012, p. 360). Case based systems can facilitate intelligent organizing, because similar decompositions of tasks have similar allocations and deployments of organizational resources (Sun & Finnie, 2004). Enterprise resource planning (ERP) systems have automated allocation and deployment of organizational resources to some extent (Turban & Volonino, 2011). IS for organizing also include CRM software, and SCM software. CRM software as an intelligent system use intelligent technologies to organize business processes and marketing activities including customer service and technical support. The main CRM vendors are SAP 7

(www.sap.com), Oracle (www.oracle.com) and salesforce.com (CRM, 2015). SCM software as an intelligent system involves using intelligent techniques to organize supply activities, associated material flows, and information flows to organizations (Chaffey, 2009). SAP and Oracle are among the leading providers of SCM applications (Turban & Volonino, 2011). Intelligent tools or apps for organizing include spreadsheets and project management specific applications. Spreadsheets are regularly used to assist managers to organize data and information. Project management applications such as Microsoft Project are used to assist managers to organize activities, in particular the planning and scheduling of activities. Tools like Microsoft Project have intelligent capabilities evidenced through the automation and generation of management reports such as Gantt charts (Trautmann & Baumann, 2010). Their functionality aids the organizational process facilitating improved efficiencies, and information quality (Caniels & Bakens, 2011). Microsoft Project is one of the most widely used tools for project management (Trautmann & Baumann, 2010). The intelligence of these systems lies in their ability to simplify organizing tasks compared to traditional organizing approaches. Intelligent Systems for Leading IS for leading aim to imitate and automate leading behaviors of managers. More specifically, they should replicate motivation techniques for subordinates, help to resolve work group conflicts, influence individuals or work teams, select appropriate communication channels, or deal with individual or group behavior issues, and understanding attitudes, behaviors, personalities and motivations of the individuals and teams (Sun & Firmin, 2012). Group DSS and executive DSS as IS have aided managers for managing a team to some extent (Turban & Volonino, 2011). Intelligent agents and multiagent systems provide a better understanding of collaboration, coordination, cooperation, conflict resolution of people within a team (Sun, Meredith, & Stranieri, 2012). 8

Understanding attitudes, behaviors, personalities and motivations of individuals and teams is still a big challenge for the development of IS for leading. Although there are not special IS for leading, there are many IS or intelligent apps that are used to assist leading behaviors of managers. Enterprise social networks (ESN) are private social networks that are restricted to employees and members with whom they are affiliated or have a business relationship (e.g. suppliers and customers) (Turban, Bolloju, & Liang, 2011). ESN offer tools similar to those provided by public social networking sites such as Facebook and LinkedIn. ESN as an IS have been available in many large organizations for facilitating leading of managers. ESN can help managers to understand the behaviors of their employees and groups through enterprise social networking applications including communication, collaboration and innovation, training and learning, knowledge management, management activities and problem solving (Turban, Bolloju, & Liang, 2011). All these applications can help managers to improve their leading skills and activities. It should be noted that leading process has been decomposed into more detailed activities (e.g. understanding, motivating and managing employees and teams, human resource management, conflict resolution, negotiation, collaboration (Robbins, Bergman, Stagg, & Coulter, 2012)). In this sense, a new concept for this terminology might be introduced with the development of MiS to update the appropriateness of intelligent leading systems. Intelligent Systems for Controlling IS for controlling emulate control behaviors of managers. More specifically, they should mimic monitoring and evaluation of activities, measurement of actual performance, comparison of actual performance against the established standards and recommendations of managerial decisions (Sun & Firmin, 2012). IS for controlling can also be called intelligent control systems. Intelligent control systems have used intelligent techniques including knowledge based systems, expert systems, 9

neural networks, machine learning, multiagent systems, and fuzzy logic to process control and process automation (Astrom & McAvoy, 1992; Schalkoff, 2011). There are a vast number of studies on intelligent control systems for process control over the past few decades. On one hand, the process control is different from control activities of a manager, and the technology for intelligent process control might not be appropriate for the control process of mangers. For example, fuzzy controllers for washing machines might be not useful for control process of managers (Zimmermann, 2001). On the other hand, process control has commonality with the control process of managers. For example, they include activities such as monitoring, comparing, and correcting work performance based on feedback and established standards. Any intelligent techniques used for process control might also be useful for the control process of mangers. For example, fuzzy logic is useful for recommending alternative strategies for control to the manager (Zimmermann, 2001), because work progress performances are in the form of a fuzzy set, such as ‗relatively satisfactory‘. Digital surveillance, CCTV (closed circuit TV) camera, and intelligent agents have been used to monitor and evaluate activities and make recommendations for managerial decisions. An intelligent system for control (ISC) is shown in Fig. 3. This is based on IS for process control (Astrom & McAvoy, 1992). For example, one of the important functions of a professional development review report system is to control the performance of academic staff members. The ISC is a knowledgebased multiagent system for control. The knowledge base includes the performance knowledge of an entity that requires control, called a controllee, and knowledge for supervising, monitoring, evaluating and recommending. The multiagent system includes multiple intelligent agents (Sun & Finnie, 2004) such as supervisor, monitor, evaluator and recommender. The recommender will propose alternative strategies for adjusting work practices of the controllee to the manager. The manager will finally select one of the alternative strategies to ask the controllee to carry out. 10

Controllee interface

Controller interface Recommender

Evaluator Knowledge base Monitor

Supervisor

Fig. 3. A system architecture of an ISC We have examined IS for planning, organizing, leading and controlling respectively. In fact, many IS include not one but multiple functions of management. The most comprehensive IS for enterprise management might be ERP systems which integrate many management facets of an enterprise, including accounting management, manufacturing management, marketing management, human resources management, SCM, CRM, and finance management (Turban & Volonino, 2011; Schneider, 2011). It should be noted that much research is required to investigate effective IS for main management functions in a real world context. There are still few significant attempts towards unifying them into a comprehensive intelligent system to automate planning, organizing, leading and controlling at an organizational level. Any attempt in this direction would be important for research and development of MiS. INTELLIGENT SYSTEMS FOR MANAGEMENT DECISION MAKING

This section examines intelligent systems for decision making (DM) and management DM (MDM).

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Intelligent Systems for Decision Making A decision refers to a choice that one makes between two or more alternatives after careful thinking (Turban & Volonino, 2011). DM is a multi-step process of selecting from choices or alternatives for solving a problem or reaching a goal through problem recognition, information search, problem analysis, alternative evaluation and choice (Zhuang, Wilkin, & Ceglowski, 2013; Sun, Zhang, & Dong, 2012). A DM process also involves events or activities leading up to the moment of choice and beyond, whereas the decision is the final outcome of the process of DM (Sun, Zhang, & Dong, 2012). IS have been applied to assist decision structuring, evaluation, understanding, and DM, and assume formal DM responsibilities (Tonn & Stiefel, 2012). IS for assisting decision structuring, evaluation, and understanding focus on helping users deal with DM situations, understand the problem that needs attention, and assist users to begin structuring the decision problem and obtain the problem statement (Tonn & Stiefel, 2012). The IS will then assist the user in choosing an appropriate structure to model the decision problem using multi-attribute utility analyses, decision tree, influence diagrams and rule-based cognitive architectures. IS will thus assist the users to evaluate a set of decision alternatives. IS for assisting DM actively help managers search for and evaluate potential solutions to decision problems when there is a complex case with a big number of potential decisions (maybe tens of thousands of potential solutions) to identify and evaluate (Tonn & Stiefel, 2012). IS are designed to search the solution space or solution base for a satisfactory solution based on the manager‘s values that can be incorporated into an evaluation function. A neural network-based IS uses feedback to train the neural net that will capture management decision solutions evaluation criteria and weights (Zimmermann, 2001). ―Similar decision problems have similar solutions‖ of CBR can be used to obtain a satisfactory solution (Sun, Wang, & Dong, 2010). Advanced IS are designed to lead groups 12

of managers through a DM process (Tonn & Stiefel, 2012) where every manager has his/her own preferred solutions. IS have assumed responsibility for many decisions whenever decisions have to be made, and human arbitration is unavailable or not trusted (Tonn & Stiefel, 2012). For example, during the Olympic Games, many IS were deployed to arbitrate game decisions and outcomes. An intelligent system for driving from one place to another, the GPS navigation system, delivers the car driver the best navigation features including landmark guidance, spoken street names, premium safety alerts and speed limit alerts. IS for home loan determine home loan approval based on the information provided by an applicant to a banker who inputs the applicant information to the system. An intelligent system approves or disapproves credit cards to purchase immediately when online purchases are made. Any decision including a number of processes and big data has been delegated to IS to be responsible for DM, either partially or completely, to overcome potential weaknesses of human beings such as forgetfulness and difficulty when dealing with complex processes. Decision Support Systems IS for DM can be called intelligent decision support systems (DSS). The intelligence of DSS is provided by knowledge base and inference engine (Turban E. , 1995). This is the reason why DSS are considered a part of IS, as shown in Fig. 1 of Section 2. DSS have been developed since the 1960s (Turban E. , 1995; Power, 2007). DSS combine models and data to solve semistructured and unstructured decision problems with intensive user involvement (Turban & Volonino, 2011). AI has been applied to DSS to automate decision support for solving repetitive managerial problems (Turban & Volonino, 2011). The basic components of a DSS include a database (DB), a model base (MB), a knowledge base (KB) and an inference engine

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(IE), and a graphic user interface (GUI) (Laudon & Laudon, 2015) (Turban & Volonino, 2011). For short, a decision support system can be represented as follows: DSS = DB + MB + KB + IE + GUI More generally, DSS are composed of the following subsystems: data management, model base management, knowledge management and knowledge base subsystems, and communication management. Like software as a service (SaaS) in cloud computing (Sun & Yearwood, 2014), DB, MB and KB can also be delivered as a service, and are represented as DBaaS, MBaaS and KBaaS respectively. In this way, a service-oriented DSS can be represented as: Service-oriented DSS = DBaaS + MBaaS + KBaaS + IE + GUI Delen and Demirkan (2013) developed a conceptual framework for service-oriented DSS. Various web-based dashboards have become important visualization tools for supporting decisions of decision makers (Kirtland, 2015; Turban & Volonino, 2011). DSS for Management Decision Making MDM involves all DM of managers when they complete their management functions in an optimal way to achieve their business objectives. Managers as decision makers are at the center of MDM (Zhuang, Wilkin, & Ceglowski, 2013). Every management function involves MDM as a matter of survival (Delen & Demirkan, 2013; Terry, 1968). Based on the discussion of the previous section, IS for MDM, called DSS for MDM, consists of DSS for planning, DSS for organizing, DSS for leading, DSS for controlling, that is, DSS for MDM can be briefly represented as follows: 14

DSS for MDM = DSS for planning + DSS for organizing + DSS for leading + DSS for controlling DSS for planning are one of the earliest applications of DSS, which was introduced in 1971 (Power, 2007). Since then, many DSS for planning have been developed: DSS for corporate planning (Thierauf, 1982), DSS for strategic planning (Thierauf, 1982), DSS for financial planning (Turban E. , 1995), and DSS for infrastructure planning (Kulshrestha, 2015). A decision support system for production planning is used to aid the production planner when choosing the values of the principal parameters governing just-in-time manufacturing (Gravel, Kiss, Martel, & Price, 1994). A decision support system for planning of soil-sensitive field operations is used to optimize route planning in terms of minimized risk for soil compaction (Bochtis, Sørensen, & Green, 2012). There is not a special decision support system available for organizing although organizing has been realized in every decision support system, to some extent, because it is a fundamental function for any information processing. For example, organizing business activities is a basic function of both Groupwise (Groupwise, 2015) and Outlook. There are not many DSS for leading either. The reason is that leading has been realized in every DSS through decision support to some extent. There are many DSS for controlling, because they are a lasting topic in development of DSS since 1971 (Power, 2007). In reality, many DSS for MDM involve not one but a few management functions. DSS for planning and control were developed in the 1970s (Power, 2007; Thierauf, 1982). VisualDSS (http://www.esi-group.com) is a decision support system for organizing, controlling, and managing interactively produced intellectual property to aid enterprise managers to deliver the best product at the least cost with a shorter time-to-market.

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A FRAMEWORK FOR DEVELOPING MIS According to the Macmillan Dictionary (2007) and Oxford Advanced Learner’s Dictionary (2005), the term framework has three meanings: 1. Parts of system that support the system development; 2. A set of beliefs, ideas and rules that is used as basis for making judgments and decisions, etc.; and 3. The structure of a particular system. Thus, a framework for MiS is a triad which can be represented as

, where

1. C is the parts or components that support the development of MiS; 2. I is a set of beliefs, ideas, concepts, and rules that is used as the basis for developing MiS 3. S is the structure of MiS as a system. In what follows, we look at each element of the proposed framework. C in the Framework The four management functions in Section 2 are also what a manager of an organization does to realize organizational goals and improve organizational performance. Hence the core components of MiS, C, includes planning, organizing, leading and controlling (Robbins, Bergman, Stagg, & Coulter, 2012). MiS should seek IS and techniques to improve, optimize, automate, and integrate planning, organizing, leading, and controlling (Sun & Firmin, 2012). This leads to IS for planning, organizing, leading, and controlling, as discussed in Section 3. All of these IS support intelligent management in terms of intelligent planning, intelligent organizing, intelligent leading and intelligent controlling for managers of organizations, discussed in Section 3. Thus, all these four IS are also the core components of MiS, and belong to C. Every management function requires managers to make decisions (Taylor, 2012). Then DSS for planning, DSS for organizing, DSS for leading and DSS for controlling, discussed in Section 4, are 16

necessary for managers to make effective decisions on planning, organizing, leading and controlling. Therefore, DSS for planning, organizing, leading and controlling are also the core components of MiS, and belong to C. In summary, the core components of MiS, C, in the proposed framework,

, consist

of: 

Planning, IS for planning, and DSS for planning;



Organizing, IS for organizing, and DSS for organizing;



Leading, IS for leading, and DSS for leading;



Controlling, IS for controlling and DSS for controlling.

I in the Framework We have proposed a set of ideas, concepts, and rules that are used as the basis for developing MiS in the previous sections, all of them belong to I. Furthermore, human resources, logistics and SCM (Turban & Volonino, 2011), production and operations, sales management, strategic marketing and marketing-mix (Schneider, 2011), to name a few, are interesting areas of management or business. They can be considered as the applications of MiS, and belong to I. For example, SCM can be considered as an application area of management and business (Chaffey, 2009). Similarly, machine learning, artificial neural networks, data mining, bio-inspired optimization algorithms, big data analytics (Sun, Strang, & Firmin, 2016), fuzzy logic, genetic algorithms and evolutionary computation, probabilistic graphical models, probabilistic logic, and support vector machines belong to IS or AI (Russell & Norvig, 2010; Schalkoff, 2011). These then belong to I, and can be used to develop IS or DSS for planning, organizing, leading, and controlling (Sun & Firmin, 2012).

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S in the Framework We have looked at the basic structure of MiS, which belongs to S, to some extent, in Sections 3 and 4. The strategic model for MiS proposed in next Section will reveal the structure of MiS as a system in depth. So far, we have proposed a framework for developing MiS,

. It should be noted

that this framework is temporal and dynamic, because it will evolve with the development of MiS. A STRATEGIC MODEL FOR MANAGEMENT INTELLIGENT SYSTEMS

This section further elaborates the framework for MiS proposed in previous Section through presenting a strategic model for MiS, which integrates main management functions with IS taking into account MDM of managers in organizations. A model is an abstract representation of an object or a system (Pearson, Baughan, & Fitzgerald, 2005). The context for the proposed model is MiS. The strategic aspect of the model provides a high level description for MiS, which is free from concrete implementation of any MiS. The model depicted in Fig. 4 provides an illustrative representation of the proposed framework for MiS in Section 5. This section focuses on interpreting the model, the processes, and the relationships depicted in the model. Rectangular shapes represent processes or systems, while the flow lines represent the sequence of available information, data, and techniques, and the arrows indicate directional flow and interrelationship between the two processes or systems. The strategic model for MiS is presented in three levels, as shown in Fig. 4. The top level represents the DM and DSS of managers and organizations (Monahan, 2000). The middle level represents the four management functions that a manager undertakes (Robbins, Bergman, Stagg, & Coulter, 2012). The bottom level represents IS (Schalkoff, 2011). All these belong to C of the proposed framework,

, discussed in Section 5. 18

DM and DSS

Planning

Organizing

Leading

Controlling

IS Fig. 4. A strategic model for MiS (Sun & Firmin, 2012)

The top level supports MDM. It is a central function of the model and of management (Monahan, 2000). The information, techniques of DM and DSS can be used to support main management functions represented in the middle level (Sun & Firmin, 2012). The middle level consists of the four main management functions: planning, leading, organizing and controlling (Robbins, Bergman, Stagg, & Coulter, 2012). The interconnected nature of these processes is illustrated through their relationships with both the DSS at the top level, and the IS at the bottom level. The one-way relationship from DM and DSS to the four functions indicates that each of the mentioned management functions requires DM techniques or DSS (Turban E. , 1995), and can support the DM process involved in each of the management functions. The one-way arrow from the middle level to the bottom level indicates that the management functions require IS to automate or improve planning, leading, organizing and controlling. The bottom level consists of IS. The one-way arrow from the bottom level to middle level indicates that IS provide information and intelligent techniques to structure, evaluate and understand decision choices and decision management (Taylor, 2012) and applies intelligent techniques (Schalkoff, 2011) to develop intelligent planning, organizing, leading and controlling systems as discussed in Section 3. The IS are connected to the DSS via a one-way arrow, indicating that the IS provide information and techniques to develop intelligent DSS to automate and improve DM.

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The proposed strategic model can be further extended, as shown in Fig. 5, which can be considered the S in the Framework FMiS=(C,I,S), mentioned in previous section. This extended strategic model extends the three levels of the model to five levels. The extended model shows an aggregated illustration of the proposed framework for developing MiS in Section 5 with the key elements DSS, main management functions and IS from Fig. 4, along with the management function oriented DSS or IS in Fig. 5.

DM or DSS

Level 1

DSS for planning

DSS for organizing

DSS for leading

Level 3

Planning

Organizing

Leading

Controlling

Level 4

IS for planning

IS for organizing

IS for leading

IS for controlling

Level 2

Level 5

DSS for controlling

IS

Fig. 5. An extended strategic model for MiS

In Fig. 5, Level 1 is the top level in Fig 4. Level 2 consists of management function oriented DSS, DSS for planning, organizing, leading and controlling, as discussed in Section 4. Level 3 is the original middle level in Fig. 4. Level 4 is composed of management function oriented IS: IS for planning, organizing, leading, and controlling, as discussed in Section 3. Level 5 is the same as the bottom level in Fig 4. In what follows, we mainly look at the newly added two levels: Level 2 and Level 4 which reflect the application of DSS and IS in each of management functions. The incorporation of these two additional levels provides the model and the proposed framework with a

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systematic emphasis, simplifying interpretation and application for the stakeholders of MiS (e.g. organizations, managers, researchers and practitioners). Level 2 has a one-way arrow from Level 1 to itself, which indicates that the DSS for planning, organizing, leading and controlling are the result of applying DM techniques and DSS to each of the management functions. The two-way arrows between Levels 2 and 3 indicate that the DSS for planning, organizing, leading and controlling, discussed in Section 4, can improve or automate the process of each management function. Conversely, every management function at Level 3 faces new challenges in a competitive business management to Level 2 and requires to improve the existing DSS or to develop new DSS for each of the management functions. Therefore, Level 2 offers management function-focused DSS to support DM in each management function to meet specific management needs. Level 4 has a one-way arrow from Level 5 to itself, which indicates that IS for planning, organizing, leading and controlling are the result of applying intelligent techniques or IS (Schalkoff, 2011) to each of the management functions. The two-way arrows between Levels 4 and 3 indicate that the IS for planning, organizing, leading and controlling, discussed in Section 3, can improve or automate the process of each management function. Every management function at Level 3, in turn, also faces new challenges in a competitive business management to Level 4, and requires to improve the existing IS or develop new IS for each of the management functions. Therefore, Level 4 represents the intelligent tools, techniques and systems to support each main management function. Different from Fig 4, Fig 5 has two dashed rounded rectangles. The upper rectangle surrounds Levels 1, 2 and 3 while the lower one surrounds Levels 3, 4, and 5. The upper rounded rectangle represents the interrelations between the DM and DSS at Level 1 and the management functions at Level 3 through the DSS for planning, organizing, leading and controlling at Level 2. The lower rounded rectangle represents the interrelations between the IS at Level 5 and the management 21

functions at Level 3 through the IS for planning, organizing, leading and controlling at Level 4. The intersection between the upper dashed rounded rectangle and the lower one is Level 3 with planning, leading, organizing and controlling, at the center reflecting its central importance in MiS. Moreover, the lower dashed rounded rectangle reflects what we examined in Section 3, while the upper dashed rounded rectangle shows the focus of Section 4. THEORETICAL, MANAGERIAL AND PRACTICAL IMPLICATIONS

This section looks at theoretical, managerial, and practical implications of this research for researchers, developers, organizations, managers and practitioners. There are two key theoretical implications of this research for the researchers and developers of MiS or related fields: 1. The proposed framework provides a road map for further research and development of MiS. This might help researchers in the fields of business, management, marketing, information systems, IS, to name a few, to deepen their understanding of MiS. It is also possible for the researchers and developers in these fields to conduct research and development of MiS. For example, they might develop IS for strategic marketing planning or DM (Martínez-López & Casillas, 2013) or intelligent decision management systems (Taylor, 2012) taking into account each of the core components of MiS and their interrelationships and integrations discussed in Sections 3, 4, 5 and 6. 2. IS and DSS should be developed at each management function level rather than at three management levels (strategic, managerial and operational (Laudon & Laudon, 2015) to automate or optimize management activities. This is the basis for integrating IS and DSS with various managements including CRM and SCM. This research has two key managerial implications for organizations and managers:

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1.

The proposed framework might be of interest to organizations and managers to access new business models including the proposed strategic model for MiS explored in Section 6, and to uncover the unexplored relationships between MDM (Monahan, 2000), management (Robbins, Bergman, Stagg, & Coulter, 2012) and IS (Schalkoff, 2011). These uncovered relationships will, in turn, promote the research and development of MiS.

2.

Managers can deepen their understanding of the importance of applying IS and DSS in their managerial activities using the approach of this research. Managers can also use the proposed approach to acquire or develop IS or intelligent DSS for improving or automating, some if not all, their managerial activities and DM.

This research has two practical implications for management practitioners and computing practitioners: 1. The computing practitioners might use the proposed framework to develop a new intelligent system for planning, organizing, leading and controlling or integrate the existing IS for management and MDM to develop enterprise DSS or intelligent enterprise information systems (Turban & Volonino, 2011). 2. Intelligent modeling techniques have dramatically enhanced understanding of management, business and marketing environment (Atkinson & Castro, 2008; Martínez-López & Casillas, 2013). These IS provide information to organizations and practitioners, to assist them with planning, organizing, leading and controlling. The practitioners of business, management and marketing would use the proposed approach to seek new IS or DSS for improving their activities of planning, organizing, leading and controlling.

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RELATED WORK AND LIMITATIONS

A basic search in Scopus and Google Scholar (i.e. article title and key words) reveals that the number of papers or books published on “management intelligent systems” (in the title of papers) is two (retrieved on 05 November 2015). One is our early work published in 2012; another is the conference proceedings (Casillas, Martínez-López, & Corchado Rodríguez, 2012). There are no papers with a title including “framework” for MiS so far. This means that our research is the first attempt to propose a framework for developing MiS. Furthermore, we have mentioned a number of scholarly researches on management, information systems, IS, and DSS. All these motivated us to develop this research. In what follows, we will discuss two researches related to our work. Sprague (1980) proposes a framework for the development of DSS. His framework consists of three parts: Part one consists of technology, developmental approach and tools for DSS. Part two develops a descriptive model for assessing the performance objectives and capabilities of DSS. Part three outlines several issues for developing DSS. This research has direct methodological implication for our research and motivated us to do this research. Walsh et al (2010) propose a framework of IT user culture that has implications for organizational IT strategy towards a strategic model of user IT culture. Their work has an indirect methodological implication for our future research, that is, in future work we will conduct a survey of international scholars and use data-driven methods to verify and improve the proposed framework for MiS. There are, at least, the following two limitations of the proposed approach in this paper. First, management mainly includes planning, organizing, leading and controlling. In fact, there are more management functions in management. For example, staffing and coordination as management functions have been studied widely (Turban & Volonino, 2011; Bocij, Greasley, & Hickie, 2008; Terry, 1968). Further, there are three levels of management in an organization: 24

operational management, tactical management, and strategic management (Grant, Butler, Hung, & Orr, 2011), which correspond to activities of operational managers, middle managers and top managers of organizations respectively (Robbins, Bergman, Stagg, & Coulter, 2012). A number of information systems have been developed to enhance each level‘s management and DM (Laudon & Laudon, 2015). The proposed approach has not included a discussion of the IS and DSS for strategic, tactic and operational management and decision which should be examined in the future work. Second, the proposed approach has not examined which intelligent methods or techniques are most appropriate to MiS (Russell & Norvig, 2010). A vast number of intelligent methods and techniques have been developed since 1956 (Russell & Norvig, 2010; Schalkoff, 2011). These methods and techniques have been applied to various applications successfully. Therefore, in the future, we should look at detailed intelligent techniques such as machine learning, CBR and fuzzy logic in intelligent planning systems and intelligent organizing systems, similar to the work of MartínezLópez and Casillas (2009). CONCLUSION

This paper examined management intelligent systems (MiS) by providing a framework for developing MiS and a strategic model for MiS. This framework identifies management functions, IS and DSS for planning, organizing, leading and control as the core components of MiS and their interrelationships. The strategic model for MiS, as the structure part, S, of the proposed framework, integrate the main management functions including planning, organizing, leading and controlling with DSS and IS taking into account DM of managers in organizations. The approach will facilitate research and development of MiS, management, IS, DSS, and information systems.

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There is a huge number of management areas discussed in business and IT fields such as emergency management, big data management (Mcafee & Brynjolfsson, 2012), risk management, and knowledge management (Sun & Finnie, 2005). The following six separate branches are well-formed in management as a discipline: human resource management, operations management or production management, strategic management, marketing management, finance management, and IT management (Turban & Volonino, 2011; Terry, 1968). Therefore, it is significant for MiS to develop IS for each of these six branches, for example, IS for marketing management (MartínezLópez & Casillas, 2009), based on IS and DSS for planning, organizing, leading and controlling. We will further examine MiS by incorporating some of these IS in the future work. Intelligent management systems in operations have been studied for over a decade. In the future work, we will integrate intelligent operations management systems with MiS. Finally, future research is needed to verify the proposed framework and strategic model for MiS, methodologically and temporally. ACKNOWLEDGEMENTS Our thanks to Professor Francisco Martínez López of University of Granada, Spain and my colleague Ms Sally Firmin of Federation University Australia for their discussion and constructive comments for completing this article. REFERENCES ACM. (2010). Retrieved 3 16, 2016, from IS 2010 Curriculum Guidelines: http://www.acm.org/education/curricula/IS%202010%20ACM%20final.pdf Astrom, K. J., & McAvoy, T. J. (1992). Intelligent control. J. Proc. Cont., 2(3), 115-126. Atkinson, R., & Castro, D. (2008). Digital quality of life. Washington: Information Technology and Innovation Foundation. Baxter, N., Collings, D., & Adjali, I. (2003). Agent-Based Modelling — Intelligent Customer Relationship Management. BT Technology Journal, 21(2), 126-132.

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