4. Business Informatics Principles

Enterprise resource planning (ERP) solutions usually refer to the business-management support software. Typically, this is an integrated application which an organisation can use to collect, store, manage and interpret data from their daily business activities (Bradford 2016). ERP solutions provide an integrated and continuously updated view of core business processes using a common database. ERP solutions track business resources—cash, raw materials, production capacity—and the status of business commitments: orders, purchase orders, and payroll. The applications that form the system share data across various departments (manufacturing, purchasing, sales, accounting, etc.) that provide the data (Almajali et al. 2016). ERP facilitates information flow between all business functions and manages connections to outside stakeholders (Bidgoli 2004). ERP predicts and balances demand and supply. It is an enterprise-wide set of forecasting, planning and scheduling tools, which links customers and suppliers into the supply chain, employs proven processes for decision-making and coordinates business areas such as sales, marketing, operations, logistics, purchasing, finance etc. Most ERP systems incorporate best practices which means the software reflects the vendor’s interpretation of the most effective way to perform each business process (Monk and Wagner 2009; Sneller 2014). The most widely used integrated solutions for business in companies from almost all industries worldwide are Enterprise Resource Planning (ERP) solutions. About 90% of the Fortune 500 companies use ERP solutions (HubPages 2018). A number of ERP implementations and because of that also a number of ERP users within organisations is growing very fast as well; employees are using ERP solutions daily at their work.

The organisation Gartner Group first defined ERP as a concept more than 25 years ago (Montgomery et al. 2018). ERP systems initially focused on automating back-office functions (functions which did not directly affect customers), while front office functions (functions which directly dealt with customers), e-business or supplier relationship management (SRM) became integrated later when the Internet enabled the simplified communication with external parties.

An ERP system covers the following common functional areas. In many ERP systems these are called and grouped as ERP modules (Anderegg 2000; Bradford 2016; see Fig. 4.2):

In 2013 the organisation Gartner Group (Ganly et al. 2013) introduced the term “postmodern ERP” (also called as the eXtended ERP – xERP). According to Gartner’s definition of the postmodern ERP strategy, legacy systems of monolithic and highly customised ERP suites, in which all parts are heavily inter-dependent, should be replaced by a mixture of both cloud-based and on-premises applications, which are more loosely coupled and can be easily exchanged if needed. The organisation Gartner Group has evolved its definition over time and now defines ERP as an application strategy focused on several distinct enterprise applications suites. They segment ERP into four major business process support areas: financial management systems, human capital management (HCM), enterprise assets management (EAM), and manufacturing and operations (Montgomery et al. 2018). Key characteristics of postmodern ERP are (ACC Software Solutions 2018):

Early ERP providers focused on large enterprises, but smaller enterprises are increasingly using ERP systems as well (Phillips and Ryan 2013). Main reasons for the growth of the ERP market are (Bradford 2016): it enables improved business performance (i.e. cycle time reduction, increased business agility, inventory reduction), supports business growth requirements (i.e. new product or product lines, new customers, global requirements including multiple language and currencies), provides flexible, integrated, real-time decision support (i.e. improve responsiveness across the organization), eliminates limitation in legacy systems (i.e. century dating issues, fragmentation of data and processing, inflexibility to change, insupportable technologies), takes advantage of small and medium-size organizations (i.e. increased functionality at a reasonable cost, cloud computing compatibilities, vertical solutions). These are just some of the reasons for the growth rate of the ERP market. Company SAP is a market leader, followed by Oracle, Sage, Infor and Microsoft (SMRC 2017). It is expected that ERP will remain the important basic software in the organisations (Pelphrey 2015).

Some big organisations require more advanced support for customer relationship management which is beyond basic functionalities of ERP. Such organisations are using customer relationship management (CRM) solutions to obtain advanced functionality (Fig. 4.3). CRM solutions compile data from a range of different communication channels, including a company’s website, telephone, email, live chat, marketing materials, and more recently, social media (Starzyczna et al. 2017; Yerpude and Kumar Singhal 2018). Through the CRM approach and the systems used to facilitate it, businesses learn more about their target audiences and how to best cater to their needs.

The primary goal of CRM systems is to integrate and automate sales, marketing, and customer support (Lizzote 2017). Therefore, these systems typically have a dashboard that gives an overall view of the three functions on a single customer view, a single page for each customer that a company may have. The dashboard may provide client information, past sales, previous marketing efforts, and more, summarising all the relationships between the customer and the firm. Operational CRM is made up of 3 main components: sales force automation, marketing automation, and service automation (Buttle and Maklan 2015).

The role of analytical CRM systems is to analyse customer data collected through multiple sources and present it so that business managers can make more informed decisions (Wan and Xie 2018). Analytical CRM systems use techniques such as data mining, correlation, and pattern recognition to analyse customer data (Chorianopoulus 2016). These analytics help improve customer service by finding small problems which can be solved, perhaps, by marketing to different parts of a consumer audience differently (Nussbauman 2015).

Existing software architectures often separate enterprise information systems and GIS. As a result, spatial processes and business processes are also perceived as distinct processes (Treiblmayer et al. 2011). As explained previously, ERP solutions usually refer to the business-management support software. Typically, this is an integrated application which an organisation can use to collect, store, manage and interpret data from their daily business activities. On the other hand, GIS are information systems for capturing, storing, checking, and displaying data related to positions. GIS is often isolated in information system landscapes. But decision support requires integrated workflows that cover business processes and spatial processes. Since most business data has a geographic or spatial component that can be geo-referenced on a GIS map to visualise, understanding and interpretation of data through a spreadsheet or table is not possible (Abou-Ghanem and Arfaj 2008). Although the GIS can include data about daily business activities, for example, actual and potential customers on the map for market analysis etc., they usually are not part of ERP, because of the complexity involved in handling each system. By integrating ERP systems for workflow management and GIS for location-based information management, this integration can bring many advantages in both fields. Abou-Ghanem and Arfaj (2008) pointed out that this could result in a loss of opportunities to leverage spatial analysis capabilities of GIS and business transaction management tools of ERP systems. Therefore, in recent years the level of interest in integrating GIS with ERP and legacy systems has been growing significantly. By visualising relationships, connections and patterns in business data, GIS can help to make informed decisions and increase efficacy. This makes ERP and GIS systems an integral part of a powerful IT strategy.

Abou-Ghanem and Arfaj (2008) pointed out that including GIS into business process offers features that fall into the following categories:

With integration, a user can visualise ERP system data within the GIS and can get direct access to the GIS within the ERP system. A user can accomplish more since he/she has the ability to make decisions by visualising the output of both systems on a screen in a simple visible manner, without the need to switch between systems and correlate the data between several systems. Specific ERP and GIS capabilities include functions such as (Horwitt 2009):

Organizations that integrate GIS with ERP systems include (ESRI 2007): utilities (water, electric, gas, waste, recycling), local government, oil and gas production, defence and public security, service providers (routing and logistics), real estate, forestry and forest products, waterways, airports, ports etc. With systems linked, the user can do an array of functions that could impact corporate running cost by accomplishing the following (Patel and Doctor 2013):

Another example is the interaction between CRM and GIS. GIS system Esri Maps for Dynamics CRM provides fast and easy integration of Esri maps and the Esri platform into Microsoft’s Dynamics CRM product. With Esri Maps we can see our CRM data on a map and visualise it in more meaningful way (Fig. 4.5), learn more about the areas and markets in which our CRM data reside, use the map to enhance analysis, and share all of this with others in our organisation. It includes the following features (ESRI 2018) – the user can:

Both ERP/CRM and GIS industry leaders have identified several options and methods for integration of GIS and ERP systems (Fig. 4.6). The options and methods are derived based on the system functions and ERP objects. They can build or purchase software connectors that directly connect a given ERP and GIS package. They can use passive middleware, which is fine if they can stick with generic ERP and GIS and don’t need to customise their processes. Or they can deploy frameworks for comprehensive integration with a given GIS package from an ERP vendor (Horwitt 2009).

For example, the most successful integration GIS – ERP was done by Esri and SAP, the industry leaders of GIS and ERP systems respectively, has resulted in the identification of five main technical interfaces available for integration (Abou-Ghanem and Arfaj 2008; Patel and Doctor 2013):

Another point of integration is layers of integrations which could be (Yaptenco et al. 2005): master data synchronisation, process integration, desktop integration, integrated web applications (portals) and/or integrated mobile applications. The process of selecting an integration method and options depends on several considerations such as development cost and corporate directions for integration. Industry leaders have recommended the following selection criteria:

In addition to these criteria, also development cost plays an important role in the selection process. Some solutions could provide rapid deployment, but the cost of development, consulting and maintenance could be high, and this could have a stronger impact on the technology and methods of integration.

But the most important part of ERP integration is to define the business processes and the corresponding business requirements. Yaptenco et al. (2005) define the user cases and business processes, the user interface requirements, the data model requirements, select technical connector approach based on business processes and requirements and design business logic in conjunction with the requirements and technical connector approach.

Starting from software development patterns and three-tier applications development, there are three layers of software integration: data integration, service integration and process integration (Litan et al. 2011). They also added that the level of complexity rises from data to process integration, and the level of abstraction as well. Service and process integration lead to the completeness and coherence of integrated systems. The most commonly used approaches in service integration are SOA (Service Oriented Architecture) and ESB (Enterprise Service Bus). The concept of SOA provides an approach to integrate heterogeneous software applications (Treiblmayer et al. 2011). SOA allows interacting software components to be loosely-coupled. Web service interfaces can facilitate the exchange of data and services between GIS and enterprise information systems. Thus, a workflow can be established that integrates the business process as it is covered by the enterprise information system and the spatial process as the GIS covers it. ESB generally provides an abstraction layer on top of an implementation of an enterprise messaging system, which allows developers to exploit the value of messaging without allowing writing code (Litan et al. 2011). Unlike the classical EAI approaches, ESB cuts the number of interfaces for interconnection between different systems, being capable of translating interfaces. Therefore, technological progress in Internet technology and the development of the SOA concept made it possible to embed GIS applications into common activities as well as integrating them with different systems such as ERP and CRM. Benefits of integration are efficient to use of resources, reduced costs, enhancement of customer satisfaction, increased interoperability of different departments, quick and accurate analysis and better business process management.

As we mentioned before the synergy between GIS and ERP/CRM systems offers competitive advantages to any enterprise (Fig. 4.7) in supply chain management and marketing areas. In supply chain management offer shorter order cycle, more reliable deliveries, better warehouse management and lower transportation costs (Aydin and Sarman 2006). In marketing, offer segmentation of customer by lifestyle and product category, implementation of pricing policy depending on location, site selection and delivery routing, development of target promotions and campaigns, geocoding of customers, understanding of customer spending (Hessa et al. 2004).

The term business intelligence (BI) emerged in the mid-1990s to describe the concepts of transforming business data from an information system in which operational data of business transactions are captured and stored into a database which is being used for management support (Sherman 2015). BI comprises the solutions and technologies used by organisations for the business data analysis used in management reporting. Business intelligence can be used by organisations to support a wide range of business decisions - ranging from operational to strategic. BI is a priority for organisations interested in gaining a competitive advantage. BI leverages corporate data and empowers managers with insights needed for sound business decisions. BI technologies provide historical, current and predictive views of business. BI technologies can handle large amounts of structured and sometimes unstructured data to prepare business reports for managers (Howson 2014). BI is most effective when it combines data derived from external data sources (external data) with data from company internal data sources such as financial and operations data (internal data). When combined, external and internal data can provide a holistic picture which, in effect, creates an “intelligence” that cannot be derived from any partial set of data. BI tools empower the organisation to gain insight into new markets, to assess demand and suitability of products and services.

Business intelligence (BI) and business analytics (BA) are sometimes used interchangeably, but there are distinctions between them. The term business intelligence usually refers to collecting business data for business reporting, and online analytical processing (Trieu 2017). Business analytics, on the other hand, refers to statistical and quantitative tools for explanatory and predictive modelling.

BI/BA systems use the extraction, transformation, and loading (ETL) processes which are used to retrieve data from information systems on an operational level. The BI processes collect data directly from the point where it’s generated. Data can be originated from many types of systems and applications, including business software such as enterprise resource management (ERP) or customer relationship management (CRM) applications, plain text files, or office application files such as spreadsheets. The data is moved or forwarded from its source location to a data warehouse or data mart. During this process, which is called data integration, following subtasks take place. A data quality process ensures that the information remains consistent, accurate, and “clean”— i.e., there is a process to avoid/correct/detect problems within the data that is being moved to the data warehouse. A data transformation modifies the structure of the data to satisfy the conditions imposed by the design of the data warehouse and to ensure the consistency of all information. The load process allocates the information into an information repository (such as a data warehouse or data mart) (Trieu 2017).

Users interact with an easy-to-use interface to use of tools for querying, reporting, online analytical processing tool (OLAP) etc. The same interface is also the gateway into a structured reporting environment that distributes operational reports and business decision results throughout the organisation (Fig. 4.8).

Reporting, a main task of BI, has become more graphics intensive. Business graphics, typically charts, are now a common component of reports. Access to BI data became more timely, because of that graphic dashboards were developed to monitor key business processes. Dashboards, named for their similarity to automobile dashboards, convey operational information at a glance. Dashboards and scorecards nowadays comprise only part of the available tools. Interactivity between BI tools and office applications is increasing and extending BI functionality, and mobile technologies are taking their own place in the equation, with BI providers now capable of distributing BI information to mobile devices. All these trends in the data visualisation phase are enabling more people within the organisation to become BI software consumers or users (Chung et al. 2002).

A primary goal of data visualisation used in BI systems is to communicate information clearly and efficiently via statistical graphics, plots and information graphics. Numerical data may be presented using dots, lines, or bars, to communicate a quantitative message visually. Effective visualisation helps users analyse and reason about data and evidence. It makes complex data more accessible, understandable and usable. Users may perform analytical tasks, such as comparisons or understanding causality, and the design principle of the graphic (i.e., showing comparisons or showing causality) follows the task. Tables are generally used where users will look up for a specific measurement, while charts of various types are used to show patterns or relationships in the data for one or more variables.

Dashboards are using principles described above, and they often provide at-a-glance views of Key Performance Indicators (KPIs) relevant to an objective or business process also referred to as Critical Success Factors (CSFs). The “dashboard” is often displayed on a web page which is linked to a database that allows the report to be constantly updated. For example, a manufacturing dashboard may show numbers related to productivity such as the number of parts manufactured, or a number of failed quality inspections per hour. Similarly, a human resources dashboard may show numbers related to staff recruitment, retention and composition, for example, number of open positions, or average days or cost per recruitment. The term dashboard originates from the car dashboard where drivers monitor the major functions at a glance via the instrument cluster. Digital dashboards allow managers to monitor the contribution of the various departments in their organisation. To gauge exactly how well an organization is performing overall, digital dashboards allow to capture and report specific data points from each department within the organisation, thus providing a “snapshot” of performance.

Dashboards can be divided according to the role and are either strategic, analytical, operational, or informational (Few 2006). Strategic dashboards support managers at any level in an organisation and provide a quick overview that decision makers need to monitor the “health” and opportunities of the business. Dashboards of this type focus on high-level measures of performance, and forecasts. Strategic dashboards benefit from static snapshots of data (daily, weekly, monthly, and quarterly) that are not constantly changing from one moment to the next. Dashboards for analytical purposes often include more context, comparisons, and history, along with subtler performance evaluators. Analytical dashboards typically support interactions with the data, such as drilling down into the underlying details. Dashboards for monitoring operations are often designed differently from those that support strategic decision making or data analysis and often require monitoring of activities and events that are constantly changing and might require attention and response at a moment’s notice.

Digital dashboards may be laid out to track the flow inherent in the business processes that they monitor. Graphically, users may see the high-level processes and then drill down into low-level data. This level of detail is often buried deep within the corporate enterprise and otherwise unavailable to the senior executives (Chen et al. 2012).

Balanced Scoreboards and Dashboards have been linked together as if they were interchangeable. However, although both visually display critical information, the difference is in the format: Scoreboards can open the quality of operation while dashboards provide calculated direction. A balanced scoreboard has what they called a “prescriptive” format. It should always contain these components:

Each of these sections ensures that a Balanced Scorecard is essentially connected to the business’s critical strategic needs.

With the dynamic economic landscape, businesses are increasingly looking for ways to do more with less and maximise their existing assets to extract the most value. To achieve this, BI has been a significant component in many organisations’ technology portfolios. Business intelligence can provide a pro-active approach, such as alert functionality that immediately notifies the end-user if certain conditions are met. For example, if some business metric exceeds a pre-defined threshold, the metric will be highlighted in standard reports, and the business analyst may be alerted via e-mail or another monitoring service. This end-to-end process requires data governance, which should be handled by the expert. Well-implemented BI allows organisations to focus on what’s important and make business decisions to drive performance.

Historically, business intelligence (BI) and geographic information system (GIS) technology have followed separate development and implementation paths. Customer requests for a complete operational picture and the ability to be more proactive have led to the combination of these two technologies. Regulatory requirements have also raised the visibility of both technologies within many organisations. In response to BI and GIS users, leading BI providers have been integrating the two technologies and providing innovative solutions to a growing number of end users. The users are responding with new applications that leverage the synergy of the combined technologies (Bimonte et al. 2010).

Organisations today are collecting data at every level of their business and in volumes that in the past were unimaginable. On the other hand, it is predicted that nearly 80% of all data has a kind of spatial component. Traditionally such data would be presented to users in long reports, either with graphs and pie charts or in spreadsheet format. Today the complex interrelationships of multidimensional data, integrating spatial data and visualisation are offering high impact insight to business intelligence users.

As BI has matured, the reach of GIS has expanded significantly as well (Posthumus 2008). In addition to speciality IT groups, GIS provides agility to a multitude of departments in many industries. It allows users to visualise and intelligently analyse historically underutilised data in ways not typically seen in traditional BI implementations (Gideon Adewale et al. 2016). Given the complementary natures of BI and GIS, the adoption of geographic analysis to enhance business intelligence is growing rapidly. Through the fusion of these two enterprise technologies, organisations can visualise and analyse key business data through “smart” maps to discover patterns and trends that would have been easily overlooked with traditional BI tables and charts (Fig. 4.9).

Humans think visually, therefore spatially. While traditional methods used to represent information and gain insight have been helpful, they have been limited in capabilities when it comes to performing quick visual decoding and comparison of data. Data gains immediate visual impact with the help of maps, more emphatically true for data with a spatial dimension (Rivest et al. 2005). Maps best represent spatial phenomena or relationships such as the flow of proximity, while also facilitating visualisation of statistical measures for an area or region. In addition, maps allow multi-measure displays.

Today’s GIS recognises the location component of data and associates data with geographic features maintained in a GIS. Features in a GIS are graphic representations of actual features, such as roads, rivers, and forests, and conceptual features such as political boundaries or service areas (Fig. 4.10). Associating data with features lets users organise data based on the geographic location of each record in the data. This geographic organisation, presented as a map, reveals spatial relationships and influences that cannot be identified in traditional tabular views of data.

Geographically organising data allows the utilisation of new data that may not have anything in common with existing data other than location. For instance, GIS analysts for insurance companies can map the addresses of insured structures and overlay floodplain boundaries to identify all structures within the floodplain. With this information, they can calculate the total financial impact on reserves from a potentially catastrophic flood. Other organisations, private and public, can perform this same analysis to determine the potential impact on facilities, supply chain, and employees. By carrying out spatial analysis using varied BI tools, decision-makers are able to better understand the historical, current and future aspects of business operations, derive useful insights and make the most effective decisions for their business.

GIS and BI were being implemented as the IT landscape was evolving to embrace common ways of compiling, storing, using, and distributing data. Knowing how BI and GIS were deployed in organisations presented opportunities for the proliferation of these technologies. If BI and GIS applications could work together, the benefits of these respective technologies could be realized by operational units not currently using both technologies. This would result in integrated applications expanding throughout the enterprise. Innovators in the public sector who wanted to extract more actionable information from existing data came to the same conclusion. Exposure to “new” technologies in the context of homeland security raised interesting possibilities for improving processes not directly related to homeland security. The fact that public agencies were looking at BI with the idea of integrating it with GIS was not lost on the BI providers whose success in the private sector had not been matched in the public sector. Business charting abilities of BI applications, conversely, GIS brings unique charting capabilities to BI in the form of spatial relationship and distribution charts. The portrayal of BI data as maps addresses a recognised shortcoming in BI graphics—the lack of context needed for informed decisions. For example, node-to-node supply chain performance data presented as bar charts or dashboards does not supply the location information needed for planning improvements. The same performance report presented as a map immediately shows spatial relationships between nodes that could explain variations in performance. Many organisations, both public and private, have come to understand the business cases for integrating BI and GIS and are actively exploring integration strategies (Wickramasuriya et al. 2013).

Most BI users are not accustomed to using maps as analytical tools. They typically analyse business data for patterns and trends using tables, charts, and graphs. They also benefit from OLAP data, which involves users analysing the major dimensions of business by drilling up and down through business data to uncover trends and anomalies. Although traditional BI tools are powerful and have delivered proven results, they do not incorporate a crucial component of most business information: location. Most business data contain some sort of location information: office locales, customer addresses, sales territories, marketing areas, facilities, and so on. When this data is viewed spatially on a map, patterns and trends that were once overlooked are clearly revealed.

When combining GIS with business intelligence data, organisations can answer questions like these (ESRI 2012):

Answers to these and other critical questions are delivered through the successful integration of BI and GIS that provides the following (ESRI 2012):

Immediate insight to enable rapid and informed decision making, including clear visualisation of what matters and where it matters, complemented with supporting business analytics, allowing knowledge workers to prioritise efforts and immediately become more productive.

Spatial analytics is increasingly becoming essential for obtaining accurate and actionable insight because there is a significant geographic dimension to every business transaction. There are two categories of industries. The first set is industries somehow naturally rooted in geographies like transport, telecommunication, or real estate, who depend on location-based information. The second set comprises of industries who not necessarily use geospatial data on an everyday basis, but they still depend on it for better performance. These would be retailers, insurance, banking (Devillers et al. 2007). Following industries are actively bringing spatial analytics to BI:

More and more industries are integrating spatial analytics in BI as such a system provides more comprehensive information. Spatial analytics can be used to gain operational, transactional and competitive advantage (Devillers et al. 2007).

More and more vendors of BI platforms and tools like Tableau (2018) are embedding spatial analytics functionality in their solutions. Tableau is enabling instant geocoding and automatically turns the location data into interactive maps with 16 levels of zoom or alternatively enables the use of custom geocodes to map what matters for the business. Tableau supports Choropleth maps, Proportional symbol maps, Point distribution maps, Flow maps, origin-destination spider maps, Heath maps, etc.

Open Geospatial Consortium (OCG) develops standard protocol Web Map Service (WMS) which is a standard protocol for serving georeferenced map images over the internet that are generated by a map server using data that is typically sourced from GIS database. Recently it becomes well recognised and deployed standard. The Map intelligence WMS capability provides a standard generic method of exchanging data between Business Intelligence tools and map servers capable of handling WMS. These removes two major concerns for companies which want to utilise their organisation’s BI data views. BI vendors’ platforms for which there is a Map Intelligence (MI) Client, can use map servers that can provide WMS – these are: Esri ArcGIS Server, Spectrum spatial, GE Smallworld, GeoWebPublisher, GeognoSIS, GeoMedia, Oracle MapViewer, ObjectFX Web Mapping Tools, LizardTech Express Server and SuperMap and also open source GeoServer and MapServer.

In previous sections, we focused mainly on platform/solution/tools vendors viewpoints connected with functionality embedded in their software. In this section, we will discuss the research on spatial data issues in business information systems. Using the bibliometric study, we aim to identify the stage of integration of the two fields, namely:

We analysed the development in the past period, and we want to identify the future development trends within these fields, as well. Therefore, the main objectives of the bibliometric study answered the questions, what are the dynamics of research literature production in the area of ERP and GIS integration on one side, and BI and GIS integration on the other side, and which are the most productive research topics in this field.

There are some the most widely known definitions of the bibliometric research: Hawkins (2001) defined bibliometrics as “the quantitative analysis of the bibliographic features of a body of literature”, consist of bibliographic units - books, monographs, reports, theses, and papers in serials and periodicals are analysed. For analysing research literature production (to identify patterns in the literature), the bibliometric analysis uses quantitative methods (De Bellis 2009). Moreover, Garfield (2009) is convinced that with bibliometric analysis, we can also examine “the history and structure of a field, the flow of information into a field, the growth of the literature, the patterns of collaboration amongst scientists, the impact of journals, and the long-term citation impact of a work”.

We performed the bibliometric analysis by using the Scopus database (on December 20, 2018). The Scopus database was selected, because it is easy to use, and it is also easy to transfer data into the program VOSviewer (Leiden University, the Netherlands) for further data analysis (van Eck and Waltman 2013). Namely, the VOSviewer was used in the second step of the bibliometric analysis, to obtain the bibliometric maps.

Bibliometric mapping is used with the purpose to represent scientific publications based on bibliographic data visually. With bibliometric mapping, we can produce different bibliometric maps which provide an overview of the structure of the scientific publications in a specific research field. One of the most popular ways to use bibliometric mapping is to identify specific research areas within a selected science field, with the purpose of getting a view of the size of the field and relevant subfields, and how they relate to each other (van Eck 2011). The VOS mapping technique has been implemented in a computer program, called VOSviewer (Leiden University, Netherlands) (van Eck and Waltman 2013), that is available at www.​vosviewer.​com. The VOSviewer software has visualisation capabilities. Therefore bibliometric maps can be displayed in various ways and consequently emphasise different aspects of a map. Additionally, VOSviewer allows for the use of different colours to indicate clusters of objects. Moreover, the VOSviewer software also merges terms that may be closely related to term clusters denoted by the same cluster colour (van Eck 2011). According to van Eck, the proximity of the terms can be interpreted as an indication of their relatedness.

In the first part of the bibliographic analysis, the search results from the Scopus database were obtained by using the “enterprise resource planning” key-phrase at the first level. A search revealed 12,906 bibliographic units that included this key-phrase in the title, keywords or in abstract. In the second step, the “GIS” keyword was used to identify the bibliographic units identified within the first step, that included both, “enterprise resource planning” and “GIS” key-phrases. The search revealed 40 units published between 1998 and 2018.

The search in Scopus database revealed, that the first two identified bibliographic units, that combines Enterprise Resource planning - ERP and Geographic(al) Information System – GIS, date to 1998 (although Scopus identifies three units, as presented by Fig. 4.1, two are identical). In his article, author Wilson (1998) discussed that Enterprise Resource Planning (ERP) and GIS are offering new opportunities for utility applications; article presented the IS/GIS integration, cost benefits, organisational re-engineering, as well as the geospatial implications. In the other article (Anon 1998) different software solutions that were used to predict the hydraulic behaviour of water distribution networks, to manage all data related to network assets, customer location and billing, and to demand profiles, pump curves and schedules, were discussed. On the other hand, a new software package was designed to help organisations to collate, evaluate and report corporate environmental information.

While no bibliographic units, covering the enterprise resource planning and GIS, were identified by Scopus in 1999, in 2000 and 2001 one per year were identified. A particularly interesting paper was published in 2000 (Zipf 2000), where the technology-enhanced project management in the Port Authority of New York and New Jersey was discussed. Author has stressed the importance of integrated project management systems, including GIS, electronic project management systems and enterprise-wide database systems; he argued that these technologies made it possible for timely information to be provided to project managers so that they could manage the project more effectively.

In 2004, a paper, covering the importance of integration of the Enterprise Resource Planning and GIS, in a field of tourism industry was presented to the professional public (Yan et al. 2002). Authors presented the advantages of an integrated information system of the tourism industry including its construction and functional realisation. The fusion of spatial information in the ERP system is discussed, and a spatially integrated information scheme is proposed.

After 2004 the number of publication was on average increasing – a positive trend is identified, with the peak in the volume of publications in 2006 in 2010, with 5 and 4 bibliographic units published.

That the 2004–2006 was the period when the “basic” research results on the integration of ERP and GIS were developed and publish, is also suggested by the analysis of the citations. The two most frequently cited bibliographic units, both with over 40 citations, are from 2005 (Li et al. 2005) with 61 citations, and from 2004, with 44 citations (Gayialis and Tatsiopoulos 2004). Li et al. (2005) presented a study on applying an integrated Global Position System (GPS) and Geographical Information System - GIS technology to the reduction of construction waste, where the integrated GPS and GIS technology is combined to the Enterprise Resource Planning system. Authors presented a case study with the purpose to demonstrate the deployment of the system that resulted in the minimisation of the amount of onsite material wastage.

The second highly cited publication (Gayialis and Tatsiopoulos 2004), presents the development of a decision support system used by an oil downstream company for routing and scheduling purposes. The delivery process of oil products from a number of distribution centres to all customers is very complex. The development of the operations research enabled the development of applications of the advanced planning and scheduling systems, that can be applied in practice if they are embodied in packaged information technology solutions. The second important condition is that the interface problems to mainstream ERP software applications are solved. In this study, the utilisation of advanced IT systems supports the planning and management of distribution operations effectively. This study shows that the combination of a supply chain management application with a geographical information system (GIS) integrated with an enterprise resource planning (ERP) software resulted in the innovative decision support tool; its’ use may have many benefits: optimal use of the distribution network resources, transportation cost reduction and customer service improvement.

In the second step of the bibliometric analysis, the identified set of 40 bibliographic units were used in the mapping of clusters by using the VOSviewer.

In VOSviewer, based on the title and abstract the relevant terms were identified. The minimum number of occurrence of a term was set to 3. Out of 1186 terms, identified by VOSviewer, 76 met the threshold. For each of the 76 terms, the relevance score was calculated, and the most relevant terms were selected (60% the most relevant terms). This process resulted in the identification of 46 terms. After deleting terms that are general and not associated with the topic investigated (article, research, country etc.), the process of mapping of terms was performed. Three clusters were identified, that is presented in Fig. 4.11.

Results reveal that three clusters are observed. The common characteristics of bibliographic units in a green cluster are defined by terms of resource planning, organisation, management efficiency, implementation. Thus, the bibliographic units in this cluster relate with terms associated in particular to management. The bibliographic units in the red cluster contain terms like an information system, support, function and optimisation, while in the blue cluster, in particular terms GIS, GIS technology, GIS system, ERP and integration are the most emphasised.

The three clusters are interrelated, and the dense net of connections is visible among the terms within each cluster, and among terms between clusters, as well. If the term integration is put into the central place, the most emphasised connections are presented in Fig. 4.12.

Figure 4.12 shows that the integration of GIS, ERP, information system and software solution is very topical for research as well as for the implementation in organisations.

The second part of the bibliometric analysis was also performed based on the Scopus database; we used the “business intelligence” key-phrase at the first level. A search revealed 6631 bibliographic units that included this key-phrase in the title, keywords or in abstract. In the second step, the “GIS” keyword was used to identify the bibliographic units identified within the first step, that included both, “business intelligence” and “GIS” key-phrases. The search revealed 112 units.

The first article that was identified in Scopus and is combining the Business Intelligence and the Geographical Information Systems was published in 2002. In their conference paper authors (Osianlis and Arnott 2002) presented the use of data warehousing and business intelligence technologies in an Australian water business for the management of water and waste-water services provision.

In 2004 two bibliographic units were identified; Weigang et al. (2004) presented a dynamic information system for urban bus passengers in Brasilia, using business intelligence, that was developed to optimise bus operations and increase the satisfaction of urban transportation users. To achieve these objectives, the system involves the convergence of a number of different technologies, including Global Positioning System (GPS), Geographic Information System, database, data mining, Internet and telecommunications. Thrall (2005) presented the software solution that provides the essential tools and functionality needed for deriving geospatial business intelligence, while Barnes (2005) emphasized many “layers” of GIS in today’s business and everyday life: GIS plays a key role in natural resource extraction, infrastructure management, intelligence and military defence, homeland security, business intelligence, navigation, etc.

In 2005, a groundbreaking bibliographic unit was published that links BI and GIS, which to date is the most widely cited reference (108 citations) in the Scopus database in this integrated area. The authors (Rivest et al. 2005) emphasise the importance of on-line analytical processes in which companies combine data warehouses and analytical tools to access, visualise and analyse their integrated, aggregated and summarised data. The authors emphasise that a large part of this data has a spatial component, so they emphasise the importance of spatial online analytical processing, which allows interactive spatial-time data exploration. The purpose of their paper is to show how these concepts support spatial-temporal research of data with geo-visualisation, interactivity and animation options.

The number of published bibliographic units was constantly increasing, and in 2007 the bibliographic unit with the next highest number of citations was published. Authors (Devillers et al. 2007) emphasise that geospatial data users are often facing the need to assess and understanding the data quality, that is a complex task that may involve thousands of partially related metadata. The combining concepts of GIS and Business Intelligence represent such a complex case where heterogeneous datasets have to be integrated. Authors, therefore, describe and present the approach, that provides interactive, multi-granularity and context-sensitive spatial data quality indicators that help experts to build and justify their opinions and business decisions.

The highest number of publications was in 2012 (14 bibliographic units); in the years to 2018, the number of published bibliographic units covered by Scopus ranges around 10 per year. The latest publications in 2018 show, how the integrated approach of GIS and BI may be beneficial for different aspects of quality of life of different social groups (Szewrański et al. 2018a), for detecting and predicting the flood risks for improving the water management infrastructure modelling (Szewrański et al. 2018b), for market segmentation and visualization, based on user behavior geographical distributions (Kamthania et al. 2018) etc.

In the second step of the bibliometric analysis, the identified set of 112 bibliographic units was used in the mapping of clusters by using the VOSviewer.

In VOSviewer, based on the title and abstract the relevant terms were identified. The relevance score was calculated, and the most relevant terms were selected; the process resulted in the identification of 51 terms. After deleting terms that are general and not associated with the topic investigated (article, research, country etc.), the process of mapping of terms was performed. Two clusters were identified, that is presented in Fig. 4.13.

Results reveal that two clusters are observed. The common characteristics of bibliographic units in a red cluster are defined by terms of business intelligence, decision making, decision support systems, data, analysis. Thus, the bibliographic units in this cluster relate with terms associated in particular to business intelligence and business decision-making process. The bibliographic units in the green cluster contain terms associated with the information systems and the geographic information systems, performance, knowledge and effectiveness. The two clusters are interrelated, and the dense net of connections is visible among the terms within each cluster, and among terms between clusters, as well.

The results of both bibliometric analyses therefore confirm, that integration of both fields, namely, the Enterprise Resource Planning and the Business Intelligence, with Geographic Information Systems, is very topical and important field in academic research as well as in the applied field, especially in the field of business decision making, by providing information, that could not be gathered without such an integration (or, at least, could not be captured on such a quality level). Linking ERP and BI with GIS thus represents an important quality information basis for business decision making.