information visualization to support management ...

International Journal of Information Technology & Decision Making © World Scientific Publishing Company

INFORMATION VISUALIZATION TO SUPPORT MANAGEMENT DECISIONS Jasser Al-Kassab ([email protected]) University of Cambridge, Institute for Manufacturing, CB3 0FT, Cambridge, UK University of St. Gallen, Institute of Technology Management, 9000 St. Gallen, Switzerland Zied M. Ouertani 1 ([email protected]) ABB Forschungszentrum. Wallstadter Str. 59, Ladenburg, 68526, Germany University of Cambridge, Institute for Manufacturing, CB3 0FT, Cambridge, UK Giovanni Schiuma ([email protected]) University of Basilicata, Viale dell'Ateneo Lucano 10, 85100 Potenza, Italy University of Cambridge, Institute for Manufacturing, CB3 0FT, Cambridge, UK Andy Neely ([email protected]) University of Cambridge, Institute for Manufacturing, CB3 0FT, Cambridge, UK

Information visualization can accelerate perception, provide insight and control, and harness this flood of valuable data to gain a competitive advantage in making business decisions. Although such a statement seems to be obvious, there is a lack in the literature of practical evidence of the benefit of information visualization. The main contribution of this paper is to illustrate how, for a major European apparel retailer, the visualization of performance information plays a critical role in improving business decisions and in extracting insights from RFID-based performance measures. In this paper, we identify – based on a literature review – three fundamental managerial functions of information visualization, namely as: a communication medium, a knowledge management means, and a decision-support instrument. Then, we provide – based on real industrial case evidence – how information visualization supports business decision-making. Several examples are provided to evidence the benefit of information visualization through its three identified managerial functions. We find that – depending on the way performance information is shaped, communicated, and made interactive – it not only helps decision-making, but also offers a means of knowledge creation, as well as an appropriate communication channel. Keywords: information visualization; management decisions; RFID; performance measurement; retail industry

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INFORMATION VISUALIZATION TO SUPPORT MANAGEMENT DECISIONS 1.

Introduction

The capacity of performance measures to inform management decisions strictly depends on how the measure is shaped, communicated, and made accessible and interactive26. Information visualization is the vehicle to supporting organizations making sense of their performance information. Used effectively, information visualization can indeed accelerate perception, provide insight and control, as well as harness this flood of valuable data to gain a competitive advantage in making business decisions21. The importance of information visualization is a topic that has been addressed in many disciplines with a common theme highlighting that people’s natural decision-making process can be enhanced by means of appropriate visualization 40. Although there appears to be a widespread acceptance of the principle that words accompanied by pictures can tell stories more effectively than words alone46, the adoption of information visualization techniques to support management decisions as reported in the literature is, however, still minimal. There is a lack of evidence supporting the benefits of information visualization on improving decision outcomes. This paper aims through a real-world case application, to contribute to the body of knowledge by evidencing the benefits of information visualization. This real-world case example illustrates how information visualization can significantly improve business performance management, particularly supporting managers in extracting insights from performance measures. Our work builds on a long stream of research that explains the main constructs of the information visualization processes. Whilst these previous research efforts focused on developing visualization techniques and information visualization frameworks, this paper aims to shed light on the practical business value of information visualization. The paper proceeds as follows. Section 2, first summarizes the state of the art on the understanding of the information visualization processes. This paper does not claim to develop an innovative process for information visualization, rather to provide evidence of the benefit of information visualization in supporting management decision. For this purpose, Section 2 introduces then three main managerial functions of information visualization in performance management, namely as: communication, knowledge management and decision support. Section 3 presents how a major European apparel retailer implemented those managerial functions. It illustrates how the apparel retailer, which implemented Radio Frequency Identification (RFID) technology in its supply chain as well as in its store, has deployed the visualization of performance information as a communication medium, a knowledge management means, and a decision-support instrument. Each identified managerial function of information visualization is illustrated through two different examples. Section 4 describes the benefits the apparel retailer gained from effective information visualization to improve management decisions. Section 5 provides a discussion of our research as well as its managerial implications, while Section 6 concludes the paper and suggests areas for future research.

Information Visualization to Support Management Decisions 3

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Managerial functions of information visualization

While written by William Playfair in 180133, the idea of charts communicated better than tables of data is still regarded as valuable in today’s world. How these charts and visual representations are created is described in the first part of this section based on a review of previous research efforts. The second part of this section aims at emphasizing three main managerial functions of visualization techniques. 2.1 The process of information visualization Information visualization can be interpreted as the visual representation of the semantics, or meanings of information 8. Information visualization is concerned with the design, development, and application of computer-generated interactive graphical representations of information9. Information visualization, however, does not only involve gathering and processing information to be displayed, but also considers defining the graphical elements and their relationship to display the set of information. Different visualization techniques are available to display information such as line graphs, bar graphs, spark lines, bullet graph, diagrams, and metaphors (e.g. Refs 15 and 43). The information visualization process as seen in the literature involves the collection, transformation, and presentation of (qualitative and quantitative) data in a visual form that facilitates exploration and understanding using interaction and distortion techniques. Card et al.7 introduced a reference model for information visualization, which provides a high-level view on the visualization process. This process is composed of three main steps, namely as data transformation, visual transformation, as well as user interaction. A similar information visualization process to the one presented by Ref 7 has been introduced by Ref 11 using the same steps. The visualization process is modeled in terms of data transformation, visualization transformation, and visual mapping transformation11. In addition to the traditional process proposed by Ref 7, Wünsche47 introduced the dimension “visual perception”. Visual perception refers to understanding how much data a person can effectively perceive before it hits the human perceptual or cognitive limits. Based on these research works (i.e., Refs 7, 11, and 47), Figure 1 summarizes the different steps the process of information visualization. First, raw data represents the set of quantitative and/or qualitative data to be visualized. The data can be collected from different sources, but are often then stored in a data warehouse. In order to visually communicate this data to the end-user, a set of transformation steps needs to be performed. The first step concerns data transformation and comprises filtering of raw data, computation of derived data, as well as data normalization. These actions result in a set of transformed data in a unified structure. The second step allows the visual transformation by mapping the transformed data, once in a ‘pristine’ form to be presented to the viewer, onto a corresponding visual structure. From this visual structure, a set of views can now be generated, which allow end-users to navigate through the display techniques, such as graphs, tables or maps using stacked or dense pixel displays, for example. Finally the viewer can interact with the visual representation using interactive

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techniques such as interactive zooming or interactive linking and brushing. The user interactions can influence the transformation process at different stages. Users can adjust their view on the data, change the visual structure, or even affect the data transformation.

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Fig. 1. Information visualization process

Information visualization helps us to make sense of the reality as well as to build the reality around us. The next section discusses the managerial functions of visualizing performance measures. Performance measures, as a set of data and information gathered and elaborated in order to qualitatively and/or qualitatively assess the efficacy and effectiveness of an investigated entity, can be considered as cues aimed to represent and communicate critical dimensions and characteristics of an organizational system and/or management action. 2.2 The three identified managerial functions of information visualization Information visualization defines a cognitive platform that promotes among managers reflections and conversations, which in turn drives a better interpretation, shared understanding, integration and coordination, problem formulation and solving, as well as decision and sense making. Figure 2 illustrates the three identified fundamental functions of information visualization, namely as: a communication medium, a knowledge management means, and a decision-support instrument. Though these functions are interrelated and affect each other, they present some distinguishing specific traits. The explanation of these functions is not meant to analyze the users’ role, rather to outline the managerial purposes for adopting information visualization to support performance management.

Fig. 2. The managerial functions of information visualization


The information visualization is one of the key dimensions at the basis of communication. Adopting different and alternative display techniques and approaches, it is possible to elaborate, package and analyse data in such a way to convey messages to be interpreted by the receiver. In addition to its role as a communication medium, the holy grail of information visualization in today’s information overload is to make the insights stand out from otherwise chaotic and noisy data9. Information visualization can enhance data exploration35 and plays a role of a decision-support instrument29. In particular, because of its ability to extend the working memory and cognition, Coury and Boulette12 found that visual representation enhanced the capability of the decision maker to process information. In addition, Foil and Huff16 point out that visual representation provides new ways of examining and improving managerial decisions. Information visualization not only supports decision making, but also plays a vital role in sense making. From this perspective, the visualization of information offers a further important function as a knowledge management means. Particularly, it can support knowledge creation and can be especially helpful to advance the individual learning and recall during group decisionmaking through interactive visualization tools5. 2.2.1 Visualization as a communication medium An important role of the visualization is the communication of information. Through visualization it is possible to build pictures that communicate information. This is fundamental within an organization in order to make sure that planned operational and strategic actions are communicated, understood, shared and executed. Through a good diagram, graph, or map it is possible to convey information that are potentially more easily translated into action. In addition, visual representation represents a fundamental way to make information easily accessible and understandable. The information visualized and communicated can be structured or unstructured. Structured information, usually in numerical format, has well-defined variables. The visualization of information is particularly useful when dealing with complex, multi-faced and layered set of information to be communicated. Simple information usually does not require sophisticated methods of visualization. But as information become more complex, it is critical to make them easily understandable and discernible. Visualization shaping and presenting information into a more effective way not only facilitate a better communication, but also support sense making. Indeed it helps users identify patterns, correlations, outliners or clusters embedded in large amounts of information, thus enhancing a decision maker’s capability of processing information3. In this perspective, the use of visualization as a communication medium has strong links with the knowledge-based processes carried out by the actors involved in the communication. The focus, contents and properties of the communication can affect how knowledge is managed, and in addition communication and particularly knowledge transfer share some fundamental working mechanisms.

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2.2.2 Visualization as a knowledge management means By adopting a knowledge-based perspective, visualization has been identified as a critical success factor for knowledge management systems6. Information visualization acts as a knowledge management means by supporting the knowledge management processes such as knowledge transfer and sharing, knowledge extraction and exploration, and knowledge creation and application (e.g. Refs 10 and 48). Visualization allows codifying information in a specific format and communicating this information. Consistently with the information processing theory, this is at the basis of knowledge transfer processes from a ‘sender’ to a ‘receiver’2. In addition, the way information is represented facilitates or hampers the human brain’s capacity to interpret information. In other words, visualization operates as catalyst for interpretations that are at the basis of knowledge extraction, exploration and creation. On the other hand, the interpretation is affected by the knowledge and cultural background characterizing the receiver of the information. This means that the visualization has to take into account the context and purpose of the knowledge management mechanisms that it aims to support. In particular, when the neutrality of the information is a fundamental condition, the visualization has to be as objective as possible. However, if the visualization is aimed at sparking creativity and generating new ideas, then it has to be more capable of stimulating intuition, imagination and fresh thinking32. In this regard visual representation may better help managers to uncover from the analysis of datasets new insights that would otherwise have gone unnoticed. Managers need to be aware that the visual representation can enhance knowledge processes, but can also bias them by constraining the attention on a limited set of alternatives, focusing the attention on the wrong variables, or encouraging not accurate comparisons. It is hence worth noticing that any visualization technique comes with pros and cons that need to be addressed on the basis of the managerial purposes of its adoption. The capacity of information visualization of affecting knowledge management processes highlights its relevance for sense making and particularly for decision making. 2.2.3 Visualization as a decision-support instrument The relevance of visual representation as a decision-support tool has been widely addressed (e.g. Refs 24, 43 and 48). Visualization can be considered as a ‘visual vehicle of thought’ to assist managers in making decisions. The fundamental idea is that visualization provides information that is easily acquirable and understandable to be translated into knowledge that in turn supports decision-making25. This is consistent with cognitive fit theory stating that a solution to a problem is ‘an outcome of the relationship between the problem representation and problem solving tasks’, which implies that better performance is achieved by users when their problem solving processes are adapted to the problem representation 44. Different studies have provided empirical evidences of how visual representation can improve decision processes, particularly in science domains such as genetics, biology or medicine27. Visual representation is considered as a way to extend human memory and to facilitate internal computation as it makes solutions more easily to be captured48. With the support of visualization, decision makers can better


solve complex problems that require the synthesis and analysis of a large quantity of information37. However, a visual representation can facilitate the process of decisionmaking, when it properly takes the features of decision tasks and the characteristics of decision makers into consideration4. The lack of an appropriate fit between the managerial task and the visual representation can be misleading rather than supporting. In the next section, a real-world case study of a large European retailer is presented. The analysis of the different stages of the information visualization process shows that managers can use the visual representation as a communication medium to affect organizational behavior, as a knowledge creation means to identify new problem solutions, and as a decision-support instrument to make informed decisions. 3.

Exploiting information visualization of large amounts of RFID data in the apparel retail industry for business performance management

This section presents the real-world case study of a business environment with large and complex datasets highlighting how information visualization has been used to support managers in carrying out real operational and managerial tasks. This section proceeds as follows. First, the case background is described. Then, the managerial functions of information visualization are illustrated through examples from the retail company. Finally, we discuss the benefit of information visualization for the retail company. 3.1.1. Case Background The investigated retail company was engaged in designing and implementing an Enterprise performance management system with the aim of producing performance information, which could drive a better extraction of insights for business performance management, both at the operational as well as at the managerial level. A series of workshops and interviews with a total of 12 top- and mid-level managers from departments such as sales, purchasing, visual merchandising (layout management) revealed that the managers were particularly interested in understanding how to collect large amount of data on try-ons and sales of the different clothing articles, as well as on in-store processes and to extract insights in order to understand patterns, to stimulate creativity and learning, as well as to support improvements of operational and managerial processes. Based on its 125-year long market existence, the retailer already disposed of extensive knowledge regarding age-, gender- and regional-specific preferences of its customers. Additionally, basic checkout data analyses were already being conducted for forecasting as well as for sales performance analyses. However, for decisions around ordering, markdown, staffing, and store process, for example, managers primarily relied on their experience. In order to challenge the traditional retail industry belief, but also to face challenges from increasing competition, consolidations, and thus increasing pressure to reduce costs23, Radio Frequency Identification (RFID) technology was introduced to systematically gather data along the entire supply chain and to provide the management with new insights. These new insights were conveyed by using classical (e.g., bar charts,

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line charts, pie charts and combinations) as well as more creative and sophisticated visualization techniques (e.g., bird-eyes view heat map). These analyses and visualizations offered the managers unprecedented insights (and hence increased transparency) on what was actually happening in their retail outlet. A more exploratory approach was therefore possible. The main challenges to be addressed evolved around the understanding and improvement of department store processes (e.g., staff allocation in the retail outlet), assortment management (e.g., complements and substitutes), as well as inventory management (e.g., front store/back store inventory). To solve these issues, managers relied on the visual representation of performance information. For the visual representation, a combination of off-the-shelf analytics and visualization and data treatment tools (MS Excel, Oracle 10g data base, Oracle PL/SQL), as well as of self-programmed software (based on Python) was used. This next section provides some details on the trial, including information on the gathered raw data (and the installed RFID infrastructure), the data warehouse, the data transformation as well as the visual transformation. Raw Data – The retailer opted for testing the implementation of an RFID infrastructure in the menswear department in one of its retail outlets with a total of 22.000 square feet in size. On average, 30.000 individual RFID-tagged items were constantly available to customers. Provided an item is not returned, the checkout reader usually represented the last part of the item’s lifecycle in the store. In total, more than 60 RFID readers recorded the movement of the items on their way from the distribution center to the point of sales (POS), including readers at escalators and elevators, on the gateways between sales floor and backroom, on shelves, and in all 20 fitting room41. Figure 3 illustrates a schematic overview of the implementation of the RFID infrastructure.

Fig. 3. RFID technology implementation in department store retail


Data Warehouse – After 18 months of data collection, the data set included a total of 13 million individual read events consisting of the article’s number, the read location, and the read event time (date and time). This data has been stored in an EPC Global Standard based database14. Data Transformation – Before the data could be analyzed, however, it had to go through several filtering, transformation and cleaning processes to ensure data quality. As a consequence, some hardware adjustments were necessary. Visual Transformation – The continuous and automatic collection of a large quantity of item-level data by the RFID infrastructure allowed the retailer to apply data mining techniques and conduct a number of analyses that went beyond what was already being done with the help of sales and inventory data. Indeed, data mining uses methods, algorithms, and techniques from a variety of disciplines to extract useful knowledge from large amounts of data in order to support decision making80, 81. Using information visualization techniques, this section describes six examples of how visualization helped to discover patterns in the data and to support the decision-making processes. The examples show how the visualization of information can be adopted as a communication medium, a knowledge management means, and a decision-support instrument. 3.1.2. Visualization as a communication medium Example 1: Staff allocation Using the RFID-gathered data in the fitting rooms as well as at the checkouts, it was possible to connect fitting room try-ons with subsequent sales events over time. Figure 4 illustrates the number of sales events of an article group, its number of try-ons during opening hours, as well as the ratio of both values using dotted lines and bar graphs for different weekdays. The visualizations show that the closing rate (ratio between sales events and try-ons) changes significantly, both during weekdays as well as opening hours. The managers derived from the Friday visualization, for example, that the retailer hasn’t benefited proportionally from visitors in the early evening hours, as in the morning, for example: the comparison of the closing rate in the morning and in the evening hours shows that an increase in try-ons could not be translated into a corresponding sales increase. This might be a consequence of a suboptimal staff allocation (e.g., insufficient staff on sales floor during these times), resulting in lost sales, especially for articles that require extensive customer counseling, such as suits. The Saturday visualization confirms an entirely different footfall of customers for that article group, as Saturday is generally the weekday for fun shopping and strolls. Still, as this analysis and visualization can be created on the level of each and every individual article group (such as trousers, shirts, or woolen articles) as well as for shop-in-shops, comparisons can be conducted and relevant patterns identified (while removing any other influencing factors). Again, the underlying assumption is that sales can be increased by an optimized staff allocation, thus increasing the closing rate as well as the customer satisfaction for selected article groups and shop-

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in-shops. Managers are hence equipped with the necessary information to allocate their staff not only during weekdays, but even during opening hours to allocate their sales staff where required at that time.

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Fig. 4. Ratio between try-ons and sales during opening hours

Example 2: Misplaced Merchandise RFID-enabled smart shelves on the sales floor constantly update their inventory level, thereby detecting correctly as well as incorrectly placed items (misplaced items). Misplacements can represent an “Out of Shelf but in Store”, or even an “Out of Shelf but on Sales Floor” situation (“phantom products”)42. Hence, the retailer was interested in the number and the duration of misplacement situations in its trial outlet. Figure 5 visualizes the occurrences of misplacements on one smart shelf. Misplacements with short durations can be explained by readings of items carried by passing-by customers or employees. Misplacements with long durations can be attributed to read events from neighboring shelves. Read events in the “middle” can be traced back as “real” misplacement events. This innovative visualization thereby communicates the information that misplacements exists and persist for several hours, thereby presenting lost sales42.

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Fig. 5. Misplacements on one smart shelf during opening hours

Various other types of visualizations have been created for this analysis, including an aggregated view on the hour-level, on the level of article groups (which article groups have most and longest misplacement situations on a given smart shelf), or on the level of weekdays. In both examples, information visualization was used as a means of communication. It communicated to the management and the employees that a suboptimal staff allocation was in place and that a misplacement problem exists, respectively. The retailer was not aware of either of these problems, which resulted in lost revenues. He now has the chance to react by an adapted staff allocation, the installation of an automatic misplacement information system, or the change of the store layout. 3.1.3. Visualization as a knowledge management means Example 1: Complements and substitutes An optimal product assortment and category management is crucial in the retail business34. For its optimization, it is important to systematically understand the customers’ perception of each category, at an early stage. Against this background and based on the RFID try-on readings in the fitting rooms, the matrix visualization in Figure 6 contrasts the jointly tried on article groups with each other. By doing so, the retailer’s decision makers get very early insights into the customers’ perception of their clothing items on an article-group (left) as well as on the brand level (right).

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Fig. 6. Complements and substitutes (left: by article group; right: by brand) – illustrative

The size of the bubbles represents the number of occurrences of the respective article group combination. The axis description lists the retailer’s different article groups. On the one hand, the “shirts-trousers” bubble, for example, shows that customers prefer to combine article groups during a try on (“complements”). On the other hand, the size of the bubbles on the diagonal, such as the “trousers-trousers”-bubble, illustrates that customers tend to take several substitute items into the fitting room (different colors, sizes or brands, hence “substitutes”). From Figure 6 we can observe that substitutes are very common for trousers but less frequent for shirts. Drilling down into the diagonal bubbles allows for the same analysis and visualization on a “by brand”-level, i.e., perceived substitutes within one article group on the brand level (see Figure 6, right hand side). Example 2: Catchment Areas (of fitting room clusters) Figure 7 provides an overview of the sales floor, subdivided into shop-in-shops for different article groups and brands. In the example, gray shadings indicate the number of products that were taken in a certain period of time to one of the fitting room clusters (marked in black). This innovative information visualization helped the retailer to optimize his sales floor layout and the positioning of fitting rooms, and gives indications about movement patterns of customers. The findings can be used to influence customer shopping paths or for product placement optimizations on the sales floor (e.g., complementary articles). The results of the analysis and its visualization showed that big differences exist in the degree to which the merchandise items are combined in the different clusters. They also showed that the more heterogeneous the clusters, the higher the cross-selling potential and the sales probability are, and that up- and cross-selling effects are stronger that downand substitutional effects. Information visualization increased the knowledge on shop and shelf space allocation as well as on shop layout design processes. Moreover, existing principles for the positioning and design could be validated.


Fig. 7. Catchment areas of fitting room clusters

In these examples, visualization was used as a knowledge management means. Visualization supported knowledge creation as it helped to extract new knowledge for employees as well as decision makers from the data to spark creativity for new ideas and to reduce reaction time. First, it helped the decision makers in an unprecedented way to use an intuitive visualization in order to include customers’ perception of the items into the assessment of their assortment, i.e., by having a more complementary than substitutional assortment. Second, it supported layout managers to learn about customer perception and behavior, respectively, and to hence position complementary items close to each other on the sales floor in order to facilitate shopping for their customers, for example. 3.1.4. Visualization as decision-support instrument Example 1: Inventory management With some transformations, the collected data can be used to calculate the back store and front store inventory levels on an article group level – in real-time as well as for any given point in time. A list can be generated for an article group, for each day of the year and be plotted (see Figure 8). This analysis and its information visualization supports the retailer in reducing the number of “out-of-shelf but in stock” or “stock-out” situations, which are responsible for lost sales and reduced customer satisfaction. It does so based on the RFID-enabled higher level of visibility regarding the distribution of the retail outlet’s inventory stock between back and front store.

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Fig. 8. Front store and back store distribution of articles over time

Example 2: Movements between front & back store Likewise, this data can be used to visualize the number of transitions between the back and front store to detecting inefficiencies and non-process compliant patterns (e.g., “loops”), such as items moving from front store to back store and back to the front store. The analysis revealed that the average transition for each item between back store and front store was 1.26 times. This analysis and its according (illustrative) visualizations in Figure 9 have helped to identify potential for improvement by visualizing the number of loops for each department, on the article group and item level. The more often has an item repeated a certain loop, the thicker the line. The increased transparency and visibility leads to an improved basis for decision-making for in-store logistics and the life-cycle management of the warehousing.

Fig. 9. Possible article flows between back store and front store – illustrative

In these examples, information visualization was used as a fundamental decisionsupport instrument. By tracking the inventory levels on back and front store (sales floor), the RFID infrastructure (and especially the readers at the transition gate between back


store and front store) allowed the management to detect (I) promotion execution errors (e.g., promotional articles, which are put on the sales floor too early), (II) stock-outs, and (III) storage inefficiencies (e.g., low inventory levels on the front store, but high inventory levels in the back store, see Figure 8). And by looking at the article flow visualization (Figure 9), inefficiencies such as loops can be easily detected and acted upon. Hence, information visualization and the RFID-generated data facilitated the decision-making and judgment process, and eventually helped to improve operational processes and store performance. 4.

Benefits of effective information visualization

This section discusses how information visualization supported the apparel retail business performance management. As outlined by the six above examples, the visualization of information has provided managerial insights to support the business performance management in the department store. In particular, the retailer used the information visualizations according to the three managerial functions: as a communication medium, as a knowledge means, and as a decision-support instrument. It made previously undetected patterns and correlations – which were embedded in these large amounts of data – visible. Information visualization enhanced the decision makers’ capability of processing information. Each step in the information visualization process describes managers in their capacity of extracting insights for decision making. Thereby, the transparency in the first three process steps is important as it provides the manager with the necessary information to trust the data sources and the data treatment. The process steps “visual transformation” and “viewer interaction” provide different ways of visualization amongst which the decision maker can chose the most appropriate for the according visualization goal. Hence, with appropriate information visualization, data generated through RFID allows for increased transparency and visibility and helps decision makers to detect hidden patterns. The combination of RFID data and information visualization allows integrating RFID into the retailer’s management cycle: on the operational level, store processes as well as customer processes are monitored by gathering RFID-generated data. The visualization of the analyses’ results allows for more insights into operational processes and thus to put in place an efficient and effective process control. On an aggregated level, the results of the data analyses improve the managerial processes in so far, as they lead to incremental process change and process innovation. Due to the strategic relevance of the project and the still ongoing implementation process, however, no detailed overview can be given on quantitative results (influence on business metrics, sales numbers, etc.) or regarding the future integration of the insights into store processes. From a qualitative perspective, however, four semi-structured interviews were conducted with top executives from the purchasing as well as from the sales departments (including visual merchandising). The visualizations were presented in the sessions, discussed and further improved. Moreover, two workshops were conducted with managers of the retailer’s holding company. The managers agreed that the new level of

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transparency, conveyed by traditional and innovative visualization techniques supported them to move from an experience-based to fact-based decision making. The managers got quickly acquainted and interacted freely with the visualization types to extract insights for the assortment, layout, as well as the in-store process management. For each of the different areas, they identified the most useful visualization types. In the example of the staffing allocation, for instance, the visualization supports the retail outlet managers to better assess the footfall of customers in the different departments and to hence adapt their staffing allocation. Comparing the inventory levels on back and front store improves the inventory management by reducing the number of out of shelf but in store situations. Table 1 provides an overview of the qualitative evaluation regarding the importance, usefulness and potential benefits for the purchasing (P) as well as sales (S; including visual merchandise) departments, using the following evaluation scheme: · Black = absolutely important and beneficial; a major driver for RFID technology and the corresponding information visualization · Gray = rather important, not a major driver · White = no important benefit/application for RFID technology and its information visualization; just a “follower”. Table 1. Information visualization evaluation by purchasing and sales (incl. visual merchandising) departments Visualization as a…

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Discussion and Summary

Different research efforts (e.g. Refs 1 and 17) addressed the question of: How does the performance of decision makers vary with the amount of information received? It has been found across different research disciplines that an increase in the amount of information provided to decision-makers can have an adverse impact on their decision-


making performance. This phenomenon is referred to as information overload in the realm of management. On the other hand, a survey conducted by Reuters in 1996 and explained in detail in Ref 28 reported that two third of interviewed managers claimed that they needed lots of information to make decisions or perform effectively. In order to manage the ever-increasing amount of information, several researchers suggested that visualization could be a solution to the information overload phenomenon (e.g. Refs 22, 24, 36 and 39). The 2010 survey jointly conducted by the MIT Sloan Management Review and IBM Institute for Business Value confirmed this suggestion and data visualization is considered to be the analytics solution that will create the greatest value20. As our case study has shown, visualizing performance information demonstrates a number of ways in which information visualization may enhance business performance management and consequently create business value and support management decisions. The ability to visually represent the large amount of collected data supported the department store managers to reduce the time required for analysis, to increase productivity, and to make effective and informed decisions. In the example related to staff allocation (cf. Example 1 from Section 3.1.2), visualizing try-ons over time data supported the retailer to optimize the number of employees on the sales floor, which lead to increased staff productivity. In the example of managing the front store and back store inventory (cf. Example 1 from Section 3.1.4), the department manager gained higher visibility of inventory levels. By providing more visibility regarding the distribution of the department’s inventory, effective decisions have been made leading for example to reducing the number of out-of-shelf but in stock or stock-out situations. In addition to improved efficiencies for routine operational processes, visualizing performance information enabled the department’s manager to uncover new insights that would otherwise have gone unnoticed. In the example related to complements and substitutes (cf. Example 1 from Section 3.1.3), by visualizing complements and substitutes on the level of categories the retailer under study was able to gain insights about what customers perceived complementarily or substitution. This new knowledge helped creating new business opportunities by improving article groups assortment. By providing empirical evidence of how information visualization can support business managers in a real-world case, this paper contributes to the research efforts addressing the challenge of evaluating information visualization in realistic setting. In addition, while current literature focuses on evaluating the usability of visualization software, our research tackles the issue of the relevance of information visualization for business performance management. This research examines the roles of information visualization in supporting management decisions and provides insights on its potential adoption. Further to the above-discussed contributions, our research has several implications for information system (IS) related research. First, information visualization plays a role in improving the information systems quality. IS system quality has been discussed in the literature, and different measures dimensions have been proposed such as well-integrated system, user-friendly-system, system flexibility or system sophistication (e.g. Refs 13 and

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18 Authors Names

19). In our research, information visualization is an important dimension toward a high quality information system aimed to support performance management. Indeed, concise and easy-to-understand outputs are unlikely without effective implementation of the proposed information visualization process. Modern information technology features such RFID technology, online analytical processing capabilities and graphical user interfaces, are crucial for a successful implementation of the information visualization process and hence high quality information system. Second, effective information visualization is associated with high organizational impact. It has been suggested by Ref 18 that improving information quality has a positive organizational impact. In our research, information visualization has been adopted as a way to improve information quality by linking IT strategy with business strategy. Indeed, the different stages of the information visualization process, i.e. data capture, data transformation, data warehouse, visual transformation and viewer interaction, have been used to improve business intelligence and to aid business decision-making. By effectively deploying the information visualization process, high quality information is designed that enhances organizational effectiveness and improve decision-making. Third, information visualization plays an important role as a support for strategic planning. In today’s competitive environment, executives want to understand optimal solutions based on complex business parameters or new information, and they want to take action quickly. In our research, information visualization is used to convert information into scenarios and simulations that make insights easier to understand and to act on. However, while visualization is seen as increasingly important in organization planning at even the highest level45, few IT applications incorporate visualization into planning. This is probably because today’s senior managers may not be fully aware of the benefits of visualization, and hence do not adopt it for strategic planning activities. 6.

Conclusions

If a picture can truly be worth a thousand words, then clever visualizations of data should hold promises in helping people making sense38. Clearly, there are many benefits from applying visualization techniques to gain insights from data. This paper has examined the managerial roles information visualization can play to extract insights to support decision-making. The identification of the managerial functions of the visualization of information helps explaining why managers should consider the visualization of performance measures as a key dimension of their Enterprise Performance Management system and a key factor affecting business performance management. The management literature has already addressed the importance of the visualization of performance information for decision-making. The retail case study showing how large-scale data sets appropriately visualized can act as a communication medium, a knowledge management means and a decision-support instrument, allows pointing out that information visualization impacts on how people make sense of the reality and shape their decisions and take actions. This suggests that visualization also plays an important instrumental role for affecting organizational behavior.


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