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HUMANIT3D for Disaster Response: An Assessment of Mass Customization on Organizational Performance Under Turbulent Environments
Available online at www.sciencedirect.com
ScienceDirect Procedia Engineering 107 (2015) 223 – 236
Humanitarian Technology: Science, Systems and Global Impact 2015, HumTech2015
HUMANIT3D for disaster response: an assessment of mass customization on organizational performance under turbulent environments Abdo Shabah, MD MSc MBA* Universite de Montréal, C.P. 6128, succursale Centre-ville Montréal (Québec) CANADA H3C 3J7
Abstract Mass customization aims to produce customized goods (allowing economies of scope) at lower cost (to achieve economies of scale) using multiple strategies (modularization and postponement). Mass customization in software and hardware design is becoming more popular for users and researchers. Through a simulation experiment of emergency response organizations under turbulent environment, we aim to compare standardization and mass customization of services and assess the impact of different forms of mass customization (early and late postponement) on performance, quality and consumer satisfaction, on the use of modular dynamic ecosystem based on HUMANIT3D, an integrated collaborative ecosystem composed of UAV management system, data collection system, and 3D Geographic Information System. Our hypothesis is that mass customization performs better and achieves better quality in turbulent environment than standardization, but only when using early postponement strategies. Using mixed methods study, we try to confirm our hypothesis. © by Elsevier Ltd. This an open Ltd. access article under the CC BY-NC-ND license © 2015 2015Published The Authors. Published by isElsevier (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Organizing Committee of HumTech2015. Peer-review under responsibility of the Organizing Committee of HumTech2015
Keywords: mass customization; postponement; experiment; performance; quality; satisfaction; 3D GIS; UAV; mobile ecosystem
* Corresponding author. Tel.: +1-514-803-0920; fax: 1-866-664-6285 E-mail address: [email protected]; [email protected]
1877-7058 © 2015 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Organizing Committee of HumTech2015
doi:10.1016/j.proeng.2015.06.077
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1. Mass customization and turbulent environments The concept of mass customization, born in 1980’s, aims to produce customized goods (allowing economies of scope) at lower cost (to achieve economies of scale). This production method allows companies to gain access to new markets and approach customers whose personal needs are not met by available standardized products [1]. Mass customization relies on a “common adaptive platform with capabilities about combining and mixing-andmatching modules to achieve different product specifications” [2]. Mass customization requires three fundamental capabilities: x a solution space for development, where product attributes are aligned to specific divergent customer needs, x a process design where product components combinations correspond to these customer needs, and x a navigation aid tool to support customers in identifying their own solutions while minimizing complexity and burden of choice called “mass confusion” [2]. Mass customization is part of a continuum of process adaptation ranging from pure standardization, to standardization segmented, to customized standardization, to tailored customization, and to pure customization [3]. Pure standardization permits no differentiation, while the segmented standardization targets different customer segments products. Custom standardization includes the final assembly of modules according to specific customer needs. Mass customization includes custom specific and individualized modules that are assembled for specific customer needs. Finally, there is the full personalization of the product, which is another way to illustrate crafting [3]. There are four different approaches to mass customization: collaborative, adaptive, cosmetic and transparent. x Collaborative customization : tries to understand through discussions with customers their exact needs, while in the x Adaptive Customization : the product is designed so that the customers are able to modify according to their specific needs. x Cosmetic customization: the presentation of the product is different depending on the customer x Transparent customization: the individual customers are offered unique products or services within a specific standard format [4]. A central aspect of mass customization is modularization. Modularization is an "approach for organizing complex products and processes efficiently by decomposing complex tasks into simpler portions so they can be managed independently and yet operate together as a whole" [5]. Modularization refers both to the "tightness of coupling between components and the degree to which the ‘rules’ of the system architecture enable (or prohibit) the mixingand-matching of components" [6]. Through standardization of interfaces, modularization combines separate components inter-changeably [7] without compromising system integrity [8]. Mass customization tries to fill customer needs with unique assembly of modular components [9] [10] [11]. Configurable modular components can ensure product quality, while reducing the risk of obsolete inventory, thus reducing inventory costs [9] [12]. In addition, the capacity to co-design and co-produce with customers can enhance the potential for capturing new information from clients on actual and future market needs. A second core feature of mass customization is postponement, which is defined as “an organizational practice of delaying the timing of the ending production or service processes, considering customers specific needs or requirements, allowing end products to assume their specific functionalities, features and identities” [1]. By delaying differentiation, the organization reduces the risk and the uncertainty related to the differentiation of products or services. This requires accurate and quick information capture from consumers. 1.1. Mass Customization through an integrated ecosystem (UAV, GIS, collaborative mobile data collection) Mass customization is the new frontier in business for both manufacturing and service industries, providing an increase in variety and customization without a corresponding increase in costs. Compared to products, mass customization in the emergency response sector consists of team involvement in the process and in a new way that the emergency services and tools are used through the emergency management process [13]. Combining modularity and postponement in emergency response allows different degrees of customization. Information technology
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(mobile data collection, UAV management system, and GIS) and product integration (UAV, mobile devices) play a strategic role in supporting the response of different organizations [14]. As disasters and accidents due to various natural or artificial causes being increased, the demands for rapid responses for emergency teams have arisen. These emergency responses teams need to be both structured and flexible, and their response requires to be more customized to the individual sites and events to manage the emergency situations more efficiently. More effective response in such situations will be established only if more accurate and prompt spatial information about the changing areas due to the emergency are available. This kind of information can be rapidly generated from the airborne sensory data acquired by a UAV-based rapid mapping ecosystem. Our ecosystem, HUMANIT3D, provides such features for disaster response [15][16][17]. HUMANIT3D integrates UAV flight planner, 3D GIS, mobile data collection and visualization, and collaborative support in offline environment for essential decision making by emergency teams [18]. Customization is required in emergency response. Emergency response decisions are unique and are based on the spatial configuration in a specific context should be detected immediately or in real-time. Aerial monitoring without human operators is an important means because the emergency areas are usually inaccessible, or operator expertise is difficult to multiply in early stages of a disaster. Therefore, an integrated resilient UAV management ecosystem is highly important as the platform for the aerial monitoring. Moreover, the transmission and processing of the data could be performed in real-time, and integrated with manual data collected and integrated into the spatial changes of the target area. The main advantage of a monitoring ecosystem based on the UAV, collaborative mobile data collection, and GIS, is rapid and stable acquisition of high-resolution sensory data directly integrated with human collected data related to the emergency area where ones can hardly access for efficient disaster response. Mass customization for UAV implies having similar capacities based on different UAV configurations (such as cameras or any specific module). For software products (3D GIS, mobile data capture), mass customization aims to efficiently produce multiple similar software products adapted to unique needs of users. They accomplish so by reusing patterns and similarities, and focusing on differences in their needs and requirements [19]. The modular design and postponement strategies in software product development allow customization of software. In modular design, product platform helps to organize components that will fill the required customization [20]. Delayed differentiation is used to delay the delivery of a software product until final applications became clear and adapted, to meet the needs of customers, thus reducing development costs [21]. In this study, we use mass customization of software and products that is linked to a specific business process (here emergency response services) within a service delivery context (disaster simulation). We are considering HUMANIT3D ecosystem (collaborative environment, 3D GIS, UAV manager, and data capture) as the main tool for planning reflecting the “service delivery process” in a simulated disaster context. Customization of the ecosystem with the customer interaction is considered a representation of service delivery processes, and is offered into two distinctive modes (standardized/fixed mode or customizable mode) to fill specific consumer needs. Through the present research, within a controlled environment, we try to understand the factors influencing performance, quality delivery and user satisfaction by comparing mass customization to pure standardization in emergency response settings. To highlight our understanding of emergency organizations using mass customization as a service delivery strategy, we will use contingency theory and resource based theory to understand the factors affecting the performance, quality and satisfaction associated with mass customization. 1.2. Mass customization and contingency theory External environment is recognized as an important factor influencing organization behaviour and performance [22] [23] [24] [25]. Uncertainty of the environment is defined as the general lack of information needed for decision making [26]. When the environment is stable and secure, the organization is able to predict fluctuations and anticipate changes, including customer demand and supplier changes. For that organization, it is easier to meet the needs of customers with a range of known products, and the ability to customize becomes then unnecessary [27]. However, when the environment is uncertain and unpredictable, customization becomes an interesting strategy to distinguish the organization and gain a competitive advantage [28]. Turbulent environments are therefore of major interest for practice of mass customization [9]. To overcome environment uncertainty, mass customization provides a response for better management of resources within an
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uncertain, competitive and complex environment [9] [29]. Previous research have supported the notion of mass customization as an advantage in uncertain and competitive environment to respond to costumer changing and diversified demand [28][29]. Demand uncertainty: Demand uncertainty is an important element in the process of customization. There is little need for customization when clients request is secure and stable. The instability and unpredictability of demands leads to a greater need for variety and customization [30]. Responsiveness to demand uncertainty has a direct impact on differentiation and the variation of company's products. Customization is better in an environment where demand is diverse and uncertain [31]. Competition intensity: “In turbulent environments defined by new innovative technologies, shorter product life cycles, or increasing demand for a wider variety of products, companies are no longer able to compete on the basis of standardized products or services” [29]. The competitive environment then is considered as a major driver supporting the pursuit of a mass customization strategy [30] and becomes complementary or a replacement strategy of mass production [9]. Supply chain complexity: The complexity of the supply chain comprises the complexity of suppliers and customers [32]. The complexity of the customer arises from the variety of demands. This complexity should be reduced to improve efficiency by using product configurators to inform customers about the different solutions that meet their needs. Complexity with suppliers can also be reduced by the design of modular products, “leading to both a reduction in the complexity of primary resources or products and as well as the inventory levels” [32]. The contingency theory explains that when customer demand is stable, organizations are able to predict what customers want more accurately and take a the necessary time to make products or offer the service [30]. In this case of predictable demand, a wide variety of products (through customization) can cause confusion among customers, which may decrease their satisfaction. However, when customer demand is uncertain and unpredictable, organizations can provide added value by customizing their products with attributes that customer preferences differ greatly. In this context, there is a need of a wide variety of products and a very short time to make them. Companies that are able to produce the correct assembly in time increase of cost and or decrease in quality are more likely to satisfy their customers [34]. 1.3. Mass customization and resource-based theory The resource-based theory suggests that the tangible and intangible resources of an organization help to increase competitive advantage, especially when resources are valuable, rare, non-imitable, and have no substitutes. Companies with more resources can exploit more effectively and become more efficient than others and so gain a competitive advantage [35]. Also, at equal resources, the difference in performance between organizations is related to the way these resources are used to implement strategies and achieve goals of the organization. This is even more important in the dynamic period and competition. An organization is distinguished by strengthening its internal resources through more effective communication, planning and formulation of strategy [36] [37] [38]. Resource based theory considers integration of resources as a way to provide competitive advantage to organizations. There are three types of integration within organizations: internal integration, customer integration and supplier integration. Mass customization requires all three integration levels. Internal integration defines the platform for creation, understanding, and applying knowledge of product design and process to deal with the complexity and variety costumer needs [28]. Collaborating with external partners and taking advantage of their resources and capabilities is also very important. By this external integration, an organization is able to acquire strategic resources to improve its mass customization strategy, such as flexibility, agility, profitability, delivery time and product quality [39]. In addition, internal integration, by providing a better internal coordination, facilitates customer and supplier integration, helping at resource acquisition from external partners. Internal integration: The internal integration represents the “links and cohesion of different organizational functions working together to enable the implementation of organizational strategies” [37]. Internal integration creates an environment of collaboration by bringing closer together the interests of different actors within the organization. The integration of routines and procedures then facilitate the creation and application of resources to improve problem solving, which in turn strengthens the organizational capacity. “Internal integration allows an organization to respond quickly and effectively to the specific needs of customers despite the challenges of product complexity, product variety, flexibility and cost associated with the development” [9][29].
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Customer and supplier integration: Customization processes require external resources, which must be acquired and be distributed by integrating customers and suppliers. More specifically, the sharing of information with the client and the supplier enhances the understanding of markets, customer demand, and the availability of external resources. Customization takes place through involvement and partnership with customers and suppliers in product design. A cooperative relationship facilitate knowledge sharing through the supply chain, “reducing costs, risks, conflicts and delays, and providing feedback on product design, production processes or changes in the market” [37]. Integration with supplier creates opportunities and with customers new channels of sale. Relying customer and supplier collaboration, organizations can improve decisions on the degree of customization and price, quality and functionality of products [9][40].
Figure 1. Mass customization and hypothesis
Based on previous literature review of mass customization within contingency theory and resource-based theory, we can argue that mass customization ability is positively associated with high demand uncertainty, high competitive intensity, and high supply chain complexity. Mass customization is also dependent on the internal integration of the different functions within the organization. While this was examined in different studies, performance and quality were not assessed together, despite a known link between performance and quality within organizations. Also, consumer satisfaction related to mass customization was studied using subjective assessment, and not directly assessed from customers. Finally, studies relied mainly on self-reported measures based on historical data, and not prospective data. Using mass customization compared to standardization, and knowing the factors influencing mass customization, we are looking to verify the following hypothesis (see Figure 1): H1: under demand uncertainty, performance of organization using mass customization is better than standardization. H2: under competitive intensity, performance of organization using mass customization is better than standardization. H3: with higher supply chain complexity, performance of organization using mass customization is better than standardization. H4 : mass customization enhances internal integration more than in the context of standardization We also try to replicate the study within this different research approach about customer satisfaction related to mass customization under environment contingency: H5: under demand uncertainty, customer satisfaction of organization using mass customization is better than standardization. H6: under competitive intensity, customer satisfaction of organization using mass customization is better than standardization. H7: with higher supply chain complexity, customer satisfaction of organization using mass
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customization is better than standardization. Finally, we argue that mass customization enhances quality parameters by allowing more adapted process to customer’s needs, taking into control environment contingencies and internal integration, which we will hypothesize as follows: H8: quality is enhanced more in mass customization context than in standardization context. While we don’t hypothesis concerning customer and supplier integration in the actual experiment, we include this theme to discuss in the focus group discussion. Figure 1 highlights the relationships between different components and hypothesis to verify through the experiment. 2. Methodology 2.1. Intervention description To answer the research question, this study tries to use simulation techniques [50]. In management research, different types of simulation exist to understand management problems, such as spreadsheet simulation, system dynamics, discrete-event simulation and business games. Business games are experiments where managers themselves operate within the simulated ‘world’, which may consist of a supply chain and its environment. A variety of business game is strategic game that includes several teams of players who represent organizations that compete with each other in the simulated world. Games can be used in research to study the effects of quantitative and qualitative factors [41]. This study uses the context of service delivery by organizations (non-governmental organizations) in humanitarian context [42]. Field exercise simulations in humanitarian sector are examples of strategic games, used mainly for training purposes. They are being used widely across the humanitarian sector, in a variety of contexts and involving numerous stakeholders. Simulations are increasingly recognized by NGOs, governments and the broader humanitarian community, as highly effective and engaging ways of increasing preparedness and building capacity. Significant progress has been made in the humanitarian community in the way that simulations have been resourced, prioritized and used as a preparedness tool [43]. In the present study, the strategic game, in the format of a field simulation, includes three different contexts of service delivery represented by two different “simulation stations” using a modular ecosystem with multiple functionalities (3D Geographic Information System, mobile data capture, UAV management): cholera epidemic simulation station and early earthquake response simulation station. UVA have been used in multiple humanitarian contexts replicated in the simulated context, such as flooding [44], forest fires [45], and earthquakes [46]. All different contexts were designed in standardized scenarios with real life mannequins to simulate the service delivery under different circumstances: varied demand, varied process complexity, and a competition context between teams. Varied demand was categorized into two states: stable and unstable demand. Process complexity was used as a proxy for supply chain complexity, and was defined as simple when the case had a unique disease and needed a simple care workflow, versus complex case with multiple diseases. Competitiveness was assessed when each team were asked to compete for best performance to acquire resources at the second part of the service delivery station. Each station lasted 45 minutes, and data was observed by field experts, and recorded by video for further analysis. Three questionnaires were distributed and filled at the end of the exercise: A questionnaire filled by the simulated patients on their perception of care (customers satisfaction), a questionnaire filled by participants about their integrative work and their perception of consumer satisfaction. A feedback from experts on the performance of teams was also collected according different dimensions: the perception of the cohesion of the team (“internal integration”) and their overall performance. Automated data collection was done also on the objective performance measures of the teams (ex: number of proper triage, proper assessment, proper management, waiting times, proper transfers, etc) and their data collection quality (as a proxy for medical care quality). Individual variables were also collected within individual surveys before the simulation (such as age, level of expertise in the humanitarian world, level of technology literacy). The simulation will be held at Minnesota University in September 2015 within the global health program field simulation. Forty participants took part to the exercise and were randomly assigned to either group A or B (40 participants per group).
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Figure 2. Simulation station description
In this experience, mass customization was defined for software products as the capacity to efficiently produce multiple similar software artifacts (data collection forms, UAV missions, 2D and 3D maps) adapted to unique needs of users by reusing similarities (configurable UAV flight planner, 3D GIS modules, adaptive data capture tools) and focusing on differences in their needs and requirements [19]. The modular design and postponement strategies implemented into the ecosystem allow customization of UAV and software for the purpose of the exercise. In each scenario, the team had to use either standardized fixed ecosystem (pre-established data form collection and fixed workflow design, fixed mapping), or customizable ecosystem (modular and adaptable data collection, UAV manager, flexible 3D GIS). The customization process approach was collaborative and esthetic, being adaptable with user interaction, and being adapted to the organization (theme and logos) [4]. For both modes, either Mass customization Mode (MC-mode) or Standardization mode (S-Mode), teams were encouraged to use the ecosystem since the included software automatically logged and recorded their performance at each simulation station.
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2.2. Ethical approval All participants will be given informed consent. Approval of the study was given by the University of Montreal Research Center ethical committee. 2.3. Sample size To demonstrate a 10% difference between groups, with a 90% confidence level, we need a sample size of 67 subjects, assuming that the distribution of response is normal within a randomly selected sample. In the current our experiment, with two stations of 40 participants, we could distinguish at 90% confidence level a difference of about 9% in end results. 2.4. Measured variables Different variables will be measured during the experiment. A short definition and value range is provided, as well as a reference to the annex where the question is published and the source of the questionnaire, if it was previously used in past research. 2.5. Focus groups We will hold one focus group sessions with 12 participants coming from each group studied [47]. The session will be facilitated by a skilled moderator and lasted about one hour. The aim of the focus groups is to identify the elements related to mass customization under turbulent environment, and readiness to mass customization. The focus group will use open-ended question (see questionnaire in annex). The focus group will be audiotaped and transcribed the focus group sessions. 2.6. Construct validity In this study, the first measure adopted to ensure the construct validity will be drafting the protocol from the question and research objectives, and thus maintain the " chain of evidence" [51] . This notion is defined by Yin as the logical link that connects the research question, the methods used and the results obtained . Thus, the research problem has been studied from a conceptual framework based on literature review of contingency theory and resource based view theory. This framework was then used to collect data through questionnaires and focus group [51]. For each case, a triangulation of information was carried out with the study of several descriptions of the same event listed through questionnaire, interview and direct data from the field. This study follows then a mixed method approach as explained by Creswell [47]. Finally, the study is based on previous researches and aims to develop previously well studied concepts. 2.7. Reliability Two measures will be taken in this study to maximize the reliability of the results. The first step was to develop and follow a research protocol to document all the steps and traceability procedures [51]. The second step was to establish a database gathering all the material studied in order to make it accessible for possible future investigation of the research process . This database will be made from the qualitative analysis software Nvivo. It includes all data and references studied and memos written during the analysis process to trace the successive steps of this process. 2.8. External validity To ensure the external validity will be to specify the simulation case as precisely as possible and have the
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scenario confirmed by key experts to as close as possible to real life situations. 3. Data analysis Quantitative data will be analysed through SPSS 21 software (IBM, 2013). Baseline characteristics and data collected were compared (about performance, satisfaction and internal integration) of the two groups using 2 independent-samples t WHVWV IRU FRQWLQXRXV GDWD DQG 3HDUVRQ¶V Ȥ tests for categorical data. The primary analysis used independent-samples t tests to compare changes between groups in outcomes from standardization (baseline) to mass customization (intervention). Data will also be analysed using analysis of covariance procedures in which groups are compared according to the mass customization (intervention) value with the standardization (baseline) value as the covariate. Multiple regression analyses will be used to provide a statistical test of the possible mediating role of different factors affecting the organization performance and customer satisfaction. This analysis follows previous research on mass customization conducted by Liu [24] on mediating variables. For qualitative data, the content analysis of the focus group will be conducted with use of specialized software Nvivo . The audio material of each participant will be transcribed in a word processing file with headlines for each of the main topics of the participants comment. The different answers were synthesized into a descriptive themes. Each theme will be explored individually to highlight the ideas and mistresses force trends expressed through speech, and then extracted in descriptive and narrative paintings to highlight relationships, in this case related to the conceptual framework [48]. 4. Discussion Mass customization enables organization to design, produce, and deliver products or services that exactly meets a customer’s needs in a cost-effective way. It enables strategic flexibility to firms, and helps them gain an advantage in competitive markets [29]. Environmental turbulence has been shown in organizations as a trigger for change to adapt to the new environment. From our study, this was highlighted by the influence of environmental impact of performance in both groups. As expected, organizations with mass customization capabilities were seen as more performing, generating more satisfaction, and having improved quality of processes. We hope through this experiment to test our ecosystem in a serious game setting, and to highlight the benefit on performance and satisfaction level for mass customization group compared to standardization. 5. Conclusions Data collection systems have been used for years in the humanitarian sector, and there is enough experience and operational learning on their use in different operational contexts [50]. The fact that these systems are not very much used today relates probably to customer satisfaction with the end product, and its use under environment turbulences, which are the characteristics of human emergencies. Perhaps mass customization needs to be integrated into these tools to respond to user’s needs. A field experiment would certainly be helpful to answer this specific question.
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Appendix. Data Tables Table 1: Variables, definition, values and references: Definition Independent variables MC are defined by the capacity of service delivery to be MC capabilities adapted and recorded by the ERP through customized data entry to the clients needs; Standardization refers to the capacity of service delivery to follow specific predefined patterns as recorded on the ERP through customized data entry to the clients needs; Control variables High demand uncertainty is defined by the accelerated rate Demand uncertainty (DU) of request of services per a fixed time unit. Stability is defined by the fixed rate of the service request by time unit. Supply chain complexity is defined by the multiple and Supply chain complexity different requests of services (three different health (SCC) problems) requiring a more complex client intervention processes. Supply chain simplicity is defined by an unique request of service (a single health problem) Competitiveness is defined by the specific period where Competitive intensity (CI) each organization is explicitly requested to perform for a competition in service delivery. Confounding variables Software quality is defined by the software quality Software quality (SQ) score as defined by the ISO/IEC9126-1 (2001) adapted questionnaire Subjective questionnaire by team member Internal integration: Subjective questionnaire by expert observer perception of integration (II) Outputs and outcomes Number of information filled and workflow recorded Data collection and compared to potential information and workflow paths to workflow confirmation follow quality Ratio of properly performed processes to possible global Performance score of the score (number of proper triage, proper assessment, proper team: management) Subjective questionnaire by mannequins and by the team Costumer satisfaction on customer satisfaction towards service offered
Values Standardization (0) vs MC capabilities (1)
Stable (0) vs High uncertainty (1) Simple (0) vs Complex (1)
No competition (0) vs competition for resources (1) Score based on questionnaire Score based on questionnaire by team members Objective score calculations Objective score calculations Score based on questionnaire of mannequin satisfaction Perception of consumer satisfaction by teams
Table 2: Results Baseline characteristics Group A (mass customization) Group B (standardization) Age Years of practice Level of technology literacy (1 to 5) Difference in performance, quality and satisfaction according to production design (mass customization vs standardization) Group A (mass customization) Group B (standardization) Performance Quality Satisfaction Influence of environment on performance, quality and satisfaction Group A (mass customization) Group B (standardization) DU SCC IC DU SCC IC Performance Quality Satisfaction Influence of mass customization on internal integration Group A (mass customization) Group B (standardization) Internal integration
Abdo Shabah / Procedia Engineering 107 (2015) 223 – 236 Table 3: Questionnaire on customer satisfaction and internal integration to the simulation participants Customer satisfaction [24] Totally disagree (-2)
Disagree (-1)
Agree (+1)
Totally agree (+2)
Totally disagree (-2)
Disagree (-1)
Agree (+1)
Totally agree (+2)
͒CS1: Our customers are pleased with the services we provide for them. CS2: Our customers seem happy with our responsiveness to their problems. ͒CS3: Customer standards are always met by our organization. Internal Integration
II1: The functions in our organization are well integrated. II2: Our organization functions ͒ coordinate their activities.͒ II3: Our team leader emphasizes the importance of good interfunctional relationships.
Table 4: Questionnaire on customer satisfaction filled by the simulation mannequin and about internal integration filled by expert observers Customer satisfaction [24] Totally disagree (-2)
Disagree (-1)
Agree (+1)
Totally agree (+2)
Totally disagree (-2)
Disagree (-1)
Agree (+1)
Totally agree (+2)
͒CS1: The organization pleased me with the services they provided.͒ CS2: The organization was responsiveness to my problems. ͒CS3: The organization was up to the expected standards of quality. Internal Integration
II1: The functions in the organization are well integrated. II2: the organization functions coordinate their activities. II3: the team leader emphasizes the importance of good interfunctional relationships.
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Abdo Shabah / Procedia Engineering 107 (2015) 223 – 236 Table 5: Focus group guide (Based partially on [24]) Questions How did the different factors (demand uncertainty, competitive intensity, supply chain complexity) influence your performance the quality of your work and your customer satisfaction? How does mass customization differ from standardization? Does Mass Customization must be implemented sequentially? Following on from the Mass Production and Continuous Improvement phases. How does customer and supplier involvement impact mass customization? What are the important factors to the adoption mass customization?
What are the main constraints and difficulties in implementing mass customization?
Possible theme
Mass “confusion” Quickly changing needs, Shorter services life cycles Market is heterogeneous Rate of technological change The service level required “Quality consciousness” of customers Competitive activity in the environment Quality required by the customer Uncertain product demand High competitive intensity Need to differentiate offering Search for niches to fill Technical difficulties Organizational difficulties Cognitive difficulties
Is the transformation to mass customization practical? Is Advanced IT capability is a pre-requisite to implementing Mass Customization? Would you recommend that your organization invest the costs of the technology? Does accountability constrains the ability to apply mass customization to service settings? Does it enhance it?
Table 6: Likert scale for assessment of quality of the MC ERP design [54] Characteristics
Sub characteristics
Description
Functionality
Suitability Accuracy Interoperability Security Maturity Fault tolerance Recoverability Understandability Learn ability Operability Attractiveness Time behavior Resource utilization Analyzability Change ability Stability Testability Adaptability Install ability
The module/system is suitable to solve our problem The module/system could perform the function properly Client need to operate in different OS Store data can be based on user profile The module/system is ready to use Complete a transaction cycle without execution errors Use secondary server if main server fails Organize items logically in the interface design Provide online explanations of operation Provide online help Alert the new function is available It must be used in the future The system/module must be used Need a expert to operate the system/module It could be further developed It allows mistakes It does not need to change in the future Export to integrate with other system Need to be installed
Reliability
Usability
Efficiency Maintain ability
Portability
Scale (1 (low) to 5(high)) 1…2…3…4…5
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"Service packaging: key to successful provisioning of ICT business solutions." Journal of Services Marketing 21.5 (2007): 304-312. [15] Shabah, A. (2015a). 'Impact of Mass Customization for 3D Geographic Information Systems under Turbulent Environments'. World Academy of Science, Engineering and Technology, International Science Index, Geological and Environmental Engineering, 2(5), 281. [16] Shabah, A. (2015b). “Clinical and Research mobile platform for emergent infectious disease in low resource environement: the case of mobile electronic data capture using smartphones for ebola epidemic in Africa. Presented at World Conference in Disaster and Emergency Medicine, Cape Town. [17] Shabah, A. (2015c) “Design and assessment of mobile 3D GIS for disaster response in low resource settings. Presented at World Conference in Disaster and Emergency Medicine, Cape Town. [18] HUMANIT3D, www.humanitas.io, accessed on march 12th, 2015. [19] Krueger, CharlesW. "Easing the transition to software mass customization."Software Product-Family Engineering. Springer Berlin Heidelberg, 2002. 282-293. [20] Kuo, Tsai Chi. "Mass customization and personalization software development: a case study eco-design product service system." Journal of Intelligent Manufacturing 24.5 (2013): 1019-1031. [21] Åkerholm, Mikael, et al. "A Model for Reuse and Optimization of Embedded Software Components." Proceedings of the International Conference on Information Technology Interfaces, ITI. 2007. [22] Chen, Injazz J., and Antony Paulraj. "Towards a theory of supply chain management: the constructs and measurements." Journal of operations management 22.2 (2004): 119-150. [23] Halldorsson, Arni, et al. "Complementary theories to supply chain management." Supply chain management: An international journal 12.4 (2007): 284-296. [24] Liu, Gensheng Jason, Rachna Shah, and Emin Babakus. "When to Mass Customize: The Impact of Environmental Uncertainty*." 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ScienceDirect Procedia Engineering 107 (2015) 223 – 236
Humanitarian Technology: Science, Systems and Global Impact 2015, HumTech2015
HUMANIT3D for disaster response: an assessment of mass customization on organizational performance under turbulent environments Abdo Shabah, MD MSc MBA* Universite de Montréal, C.P. 6128, succursale Centre-ville Montréal (Québec) CANADA H3C 3J7
Abstract Mass customization aims to produce customized goods (allowing economies of scope) at lower cost (to achieve economies of scale) using multiple strategies (modularization and postponement). Mass customization in software and hardware design is becoming more popular for users and researchers. Through a simulation experiment of emergency response organizations under turbulent environment, we aim to compare standardization and mass customization of services and assess the impact of different forms of mass customization (early and late postponement) on performance, quality and consumer satisfaction, on the use of modular dynamic ecosystem based on HUMANIT3D, an integrated collaborative ecosystem composed of UAV management system, data collection system, and 3D Geographic Information System. Our hypothesis is that mass customization performs better and achieves better quality in turbulent environment than standardization, but only when using early postponement strategies. Using mixed methods study, we try to confirm our hypothesis. © by Elsevier Ltd. This an open Ltd. access article under the CC BY-NC-ND license © 2015 2015Published The Authors. Published by isElsevier (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Organizing Committee of HumTech2015. Peer-review under responsibility of the Organizing Committee of HumTech2015
Keywords: mass customization; postponement; experiment; performance; quality; satisfaction; 3D GIS; UAV; mobile ecosystem
* Corresponding author. Tel.: +1-514-803-0920; fax: 1-866-664-6285 E-mail address: [email protected]; [email protected]
1877-7058 © 2015 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Organizing Committee of HumTech2015
doi:10.1016/j.proeng.2015.06.077
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1. Mass customization and turbulent environments The concept of mass customization, born in 1980’s, aims to produce customized goods (allowing economies of scope) at lower cost (to achieve economies of scale). This production method allows companies to gain access to new markets and approach customers whose personal needs are not met by available standardized products [1]. Mass customization relies on a “common adaptive platform with capabilities about combining and mixing-andmatching modules to achieve different product specifications” [2]. Mass customization requires three fundamental capabilities: x a solution space for development, where product attributes are aligned to specific divergent customer needs, x a process design where product components combinations correspond to these customer needs, and x a navigation aid tool to support customers in identifying their own solutions while minimizing complexity and burden of choice called “mass confusion” [2]. Mass customization is part of a continuum of process adaptation ranging from pure standardization, to standardization segmented, to customized standardization, to tailored customization, and to pure customization [3]. Pure standardization permits no differentiation, while the segmented standardization targets different customer segments products. Custom standardization includes the final assembly of modules according to specific customer needs. Mass customization includes custom specific and individualized modules that are assembled for specific customer needs. Finally, there is the full personalization of the product, which is another way to illustrate crafting [3]. There are four different approaches to mass customization: collaborative, adaptive, cosmetic and transparent. x Collaborative customization : tries to understand through discussions with customers their exact needs, while in the x Adaptive Customization : the product is designed so that the customers are able to modify according to their specific needs. x Cosmetic customization: the presentation of the product is different depending on the customer x Transparent customization: the individual customers are offered unique products or services within a specific standard format [4]. A central aspect of mass customization is modularization. Modularization is an "approach for organizing complex products and processes efficiently by decomposing complex tasks into simpler portions so they can be managed independently and yet operate together as a whole" [5]. Modularization refers both to the "tightness of coupling between components and the degree to which the ‘rules’ of the system architecture enable (or prohibit) the mixingand-matching of components" [6]. Through standardization of interfaces, modularization combines separate components inter-changeably [7] without compromising system integrity [8]. Mass customization tries to fill customer needs with unique assembly of modular components [9] [10] [11]. Configurable modular components can ensure product quality, while reducing the risk of obsolete inventory, thus reducing inventory costs [9] [12]. In addition, the capacity to co-design and co-produce with customers can enhance the potential for capturing new information from clients on actual and future market needs. A second core feature of mass customization is postponement, which is defined as “an organizational practice of delaying the timing of the ending production or service processes, considering customers specific needs or requirements, allowing end products to assume their specific functionalities, features and identities” [1]. By delaying differentiation, the organization reduces the risk and the uncertainty related to the differentiation of products or services. This requires accurate and quick information capture from consumers. 1.1. Mass Customization through an integrated ecosystem (UAV, GIS, collaborative mobile data collection) Mass customization is the new frontier in business for both manufacturing and service industries, providing an increase in variety and customization without a corresponding increase in costs. Compared to products, mass customization in the emergency response sector consists of team involvement in the process and in a new way that the emergency services and tools are used through the emergency management process [13]. Combining modularity and postponement in emergency response allows different degrees of customization. Information technology
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(mobile data collection, UAV management system, and GIS) and product integration (UAV, mobile devices) play a strategic role in supporting the response of different organizations [14]. As disasters and accidents due to various natural or artificial causes being increased, the demands for rapid responses for emergency teams have arisen. These emergency responses teams need to be both structured and flexible, and their response requires to be more customized to the individual sites and events to manage the emergency situations more efficiently. More effective response in such situations will be established only if more accurate and prompt spatial information about the changing areas due to the emergency are available. This kind of information can be rapidly generated from the airborne sensory data acquired by a UAV-based rapid mapping ecosystem. Our ecosystem, HUMANIT3D, provides such features for disaster response [15][16][17]. HUMANIT3D integrates UAV flight planner, 3D GIS, mobile data collection and visualization, and collaborative support in offline environment for essential decision making by emergency teams [18]. Customization is required in emergency response. Emergency response decisions are unique and are based on the spatial configuration in a specific context should be detected immediately or in real-time. Aerial monitoring without human operators is an important means because the emergency areas are usually inaccessible, or operator expertise is difficult to multiply in early stages of a disaster. Therefore, an integrated resilient UAV management ecosystem is highly important as the platform for the aerial monitoring. Moreover, the transmission and processing of the data could be performed in real-time, and integrated with manual data collected and integrated into the spatial changes of the target area. The main advantage of a monitoring ecosystem based on the UAV, collaborative mobile data collection, and GIS, is rapid and stable acquisition of high-resolution sensory data directly integrated with human collected data related to the emergency area where ones can hardly access for efficient disaster response. Mass customization for UAV implies having similar capacities based on different UAV configurations (such as cameras or any specific module). For software products (3D GIS, mobile data capture), mass customization aims to efficiently produce multiple similar software products adapted to unique needs of users. They accomplish so by reusing patterns and similarities, and focusing on differences in their needs and requirements [19]. The modular design and postponement strategies in software product development allow customization of software. In modular design, product platform helps to organize components that will fill the required customization [20]. Delayed differentiation is used to delay the delivery of a software product until final applications became clear and adapted, to meet the needs of customers, thus reducing development costs [21]. In this study, we use mass customization of software and products that is linked to a specific business process (here emergency response services) within a service delivery context (disaster simulation). We are considering HUMANIT3D ecosystem (collaborative environment, 3D GIS, UAV manager, and data capture) as the main tool for planning reflecting the “service delivery process” in a simulated disaster context. Customization of the ecosystem with the customer interaction is considered a representation of service delivery processes, and is offered into two distinctive modes (standardized/fixed mode or customizable mode) to fill specific consumer needs. Through the present research, within a controlled environment, we try to understand the factors influencing performance, quality delivery and user satisfaction by comparing mass customization to pure standardization in emergency response settings. To highlight our understanding of emergency organizations using mass customization as a service delivery strategy, we will use contingency theory and resource based theory to understand the factors affecting the performance, quality and satisfaction associated with mass customization. 1.2. Mass customization and contingency theory External environment is recognized as an important factor influencing organization behaviour and performance [22] [23] [24] [25]. Uncertainty of the environment is defined as the general lack of information needed for decision making [26]. When the environment is stable and secure, the organization is able to predict fluctuations and anticipate changes, including customer demand and supplier changes. For that organization, it is easier to meet the needs of customers with a range of known products, and the ability to customize becomes then unnecessary [27]. However, when the environment is uncertain and unpredictable, customization becomes an interesting strategy to distinguish the organization and gain a competitive advantage [28]. Turbulent environments are therefore of major interest for practice of mass customization [9]. To overcome environment uncertainty, mass customization provides a response for better management of resources within an
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uncertain, competitive and complex environment [9] [29]. Previous research have supported the notion of mass customization as an advantage in uncertain and competitive environment to respond to costumer changing and diversified demand [28][29]. Demand uncertainty: Demand uncertainty is an important element in the process of customization. There is little need for customization when clients request is secure and stable. The instability and unpredictability of demands leads to a greater need for variety and customization [30]. Responsiveness to demand uncertainty has a direct impact on differentiation and the variation of company's products. Customization is better in an environment where demand is diverse and uncertain [31]. Competition intensity: “In turbulent environments defined by new innovative technologies, shorter product life cycles, or increasing demand for a wider variety of products, companies are no longer able to compete on the basis of standardized products or services” [29]. The competitive environment then is considered as a major driver supporting the pursuit of a mass customization strategy [30] and becomes complementary or a replacement strategy of mass production [9]. Supply chain complexity: The complexity of the supply chain comprises the complexity of suppliers and customers [32]. The complexity of the customer arises from the variety of demands. This complexity should be reduced to improve efficiency by using product configurators to inform customers about the different solutions that meet their needs. Complexity with suppliers can also be reduced by the design of modular products, “leading to both a reduction in the complexity of primary resources or products and as well as the inventory levels” [32]. The contingency theory explains that when customer demand is stable, organizations are able to predict what customers want more accurately and take a the necessary time to make products or offer the service [30]. In this case of predictable demand, a wide variety of products (through customization) can cause confusion among customers, which may decrease their satisfaction. However, when customer demand is uncertain and unpredictable, organizations can provide added value by customizing their products with attributes that customer preferences differ greatly. In this context, there is a need of a wide variety of products and a very short time to make them. Companies that are able to produce the correct assembly in time increase of cost and or decrease in quality are more likely to satisfy their customers [34]. 1.3. Mass customization and resource-based theory The resource-based theory suggests that the tangible and intangible resources of an organization help to increase competitive advantage, especially when resources are valuable, rare, non-imitable, and have no substitutes. Companies with more resources can exploit more effectively and become more efficient than others and so gain a competitive advantage [35]. Also, at equal resources, the difference in performance between organizations is related to the way these resources are used to implement strategies and achieve goals of the organization. This is even more important in the dynamic period and competition. An organization is distinguished by strengthening its internal resources through more effective communication, planning and formulation of strategy [36] [37] [38]. Resource based theory considers integration of resources as a way to provide competitive advantage to organizations. There are three types of integration within organizations: internal integration, customer integration and supplier integration. Mass customization requires all three integration levels. Internal integration defines the platform for creation, understanding, and applying knowledge of product design and process to deal with the complexity and variety costumer needs [28]. Collaborating with external partners and taking advantage of their resources and capabilities is also very important. By this external integration, an organization is able to acquire strategic resources to improve its mass customization strategy, such as flexibility, agility, profitability, delivery time and product quality [39]. In addition, internal integration, by providing a better internal coordination, facilitates customer and supplier integration, helping at resource acquisition from external partners. Internal integration: The internal integration represents the “links and cohesion of different organizational functions working together to enable the implementation of organizational strategies” [37]. Internal integration creates an environment of collaboration by bringing closer together the interests of different actors within the organization. The integration of routines and procedures then facilitate the creation and application of resources to improve problem solving, which in turn strengthens the organizational capacity. “Internal integration allows an organization to respond quickly and effectively to the specific needs of customers despite the challenges of product complexity, product variety, flexibility and cost associated with the development” [9][29].
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Customer and supplier integration: Customization processes require external resources, which must be acquired and be distributed by integrating customers and suppliers. More specifically, the sharing of information with the client and the supplier enhances the understanding of markets, customer demand, and the availability of external resources. Customization takes place through involvement and partnership with customers and suppliers in product design. A cooperative relationship facilitate knowledge sharing through the supply chain, “reducing costs, risks, conflicts and delays, and providing feedback on product design, production processes or changes in the market” [37]. Integration with supplier creates opportunities and with customers new channels of sale. Relying customer and supplier collaboration, organizations can improve decisions on the degree of customization and price, quality and functionality of products [9][40].
Figure 1. Mass customization and hypothesis
Based on previous literature review of mass customization within contingency theory and resource-based theory, we can argue that mass customization ability is positively associated with high demand uncertainty, high competitive intensity, and high supply chain complexity. Mass customization is also dependent on the internal integration of the different functions within the organization. While this was examined in different studies, performance and quality were not assessed together, despite a known link between performance and quality within organizations. Also, consumer satisfaction related to mass customization was studied using subjective assessment, and not directly assessed from customers. Finally, studies relied mainly on self-reported measures based on historical data, and not prospective data. Using mass customization compared to standardization, and knowing the factors influencing mass customization, we are looking to verify the following hypothesis (see Figure 1): H1: under demand uncertainty, performance of organization using mass customization is better than standardization. H2: under competitive intensity, performance of organization using mass customization is better than standardization. H3: with higher supply chain complexity, performance of organization using mass customization is better than standardization. H4 : mass customization enhances internal integration more than in the context of standardization We also try to replicate the study within this different research approach about customer satisfaction related to mass customization under environment contingency: H5: under demand uncertainty, customer satisfaction of organization using mass customization is better than standardization. H6: under competitive intensity, customer satisfaction of organization using mass customization is better than standardization. H7: with higher supply chain complexity, customer satisfaction of organization using mass
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customization is better than standardization. Finally, we argue that mass customization enhances quality parameters by allowing more adapted process to customer’s needs, taking into control environment contingencies and internal integration, which we will hypothesize as follows: H8: quality is enhanced more in mass customization context than in standardization context. While we don’t hypothesis concerning customer and supplier integration in the actual experiment, we include this theme to discuss in the focus group discussion. Figure 1 highlights the relationships between different components and hypothesis to verify through the experiment. 2. Methodology 2.1. Intervention description To answer the research question, this study tries to use simulation techniques [50]. In management research, different types of simulation exist to understand management problems, such as spreadsheet simulation, system dynamics, discrete-event simulation and business games. Business games are experiments where managers themselves operate within the simulated ‘world’, which may consist of a supply chain and its environment. A variety of business game is strategic game that includes several teams of players who represent organizations that compete with each other in the simulated world. Games can be used in research to study the effects of quantitative and qualitative factors [41]. This study uses the context of service delivery by organizations (non-governmental organizations) in humanitarian context [42]. Field exercise simulations in humanitarian sector are examples of strategic games, used mainly for training purposes. They are being used widely across the humanitarian sector, in a variety of contexts and involving numerous stakeholders. Simulations are increasingly recognized by NGOs, governments and the broader humanitarian community, as highly effective and engaging ways of increasing preparedness and building capacity. Significant progress has been made in the humanitarian community in the way that simulations have been resourced, prioritized and used as a preparedness tool [43]. In the present study, the strategic game, in the format of a field simulation, includes three different contexts of service delivery represented by two different “simulation stations” using a modular ecosystem with multiple functionalities (3D Geographic Information System, mobile data capture, UAV management): cholera epidemic simulation station and early earthquake response simulation station. UVA have been used in multiple humanitarian contexts replicated in the simulated context, such as flooding [44], forest fires [45], and earthquakes [46]. All different contexts were designed in standardized scenarios with real life mannequins to simulate the service delivery under different circumstances: varied demand, varied process complexity, and a competition context between teams. Varied demand was categorized into two states: stable and unstable demand. Process complexity was used as a proxy for supply chain complexity, and was defined as simple when the case had a unique disease and needed a simple care workflow, versus complex case with multiple diseases. Competitiveness was assessed when each team were asked to compete for best performance to acquire resources at the second part of the service delivery station. Each station lasted 45 minutes, and data was observed by field experts, and recorded by video for further analysis. Three questionnaires were distributed and filled at the end of the exercise: A questionnaire filled by the simulated patients on their perception of care (customers satisfaction), a questionnaire filled by participants about their integrative work and their perception of consumer satisfaction. A feedback from experts on the performance of teams was also collected according different dimensions: the perception of the cohesion of the team (“internal integration”) and their overall performance. Automated data collection was done also on the objective performance measures of the teams (ex: number of proper triage, proper assessment, proper management, waiting times, proper transfers, etc) and their data collection quality (as a proxy for medical care quality). Individual variables were also collected within individual surveys before the simulation (such as age, level of expertise in the humanitarian world, level of technology literacy). The simulation will be held at Minnesota University in September 2015 within the global health program field simulation. Forty participants took part to the exercise and were randomly assigned to either group A or B (40 participants per group).
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Figure 2. Simulation station description
In this experience, mass customization was defined for software products as the capacity to efficiently produce multiple similar software artifacts (data collection forms, UAV missions, 2D and 3D maps) adapted to unique needs of users by reusing similarities (configurable UAV flight planner, 3D GIS modules, adaptive data capture tools) and focusing on differences in their needs and requirements [19]. The modular design and postponement strategies implemented into the ecosystem allow customization of UAV and software for the purpose of the exercise. In each scenario, the team had to use either standardized fixed ecosystem (pre-established data form collection and fixed workflow design, fixed mapping), or customizable ecosystem (modular and adaptable data collection, UAV manager, flexible 3D GIS). The customization process approach was collaborative and esthetic, being adaptable with user interaction, and being adapted to the organization (theme and logos) [4]. For both modes, either Mass customization Mode (MC-mode) or Standardization mode (S-Mode), teams were encouraged to use the ecosystem since the included software automatically logged and recorded their performance at each simulation station.
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Abdo Shabah / Procedia Engineering 107 (2015) 223 – 236 Figure 3. Continuum from standardization to pure customization
2.2. Ethical approval All participants will be given informed consent. Approval of the study was given by the University of Montreal Research Center ethical committee. 2.3. Sample size To demonstrate a 10% difference between groups, with a 90% confidence level, we need a sample size of 67 subjects, assuming that the distribution of response is normal within a randomly selected sample. In the current our experiment, with two stations of 40 participants, we could distinguish at 90% confidence level a difference of about 9% in end results. 2.4. Measured variables Different variables will be measured during the experiment. A short definition and value range is provided, as well as a reference to the annex where the question is published and the source of the questionnaire, if it was previously used in past research. 2.5. Focus groups We will hold one focus group sessions with 12 participants coming from each group studied [47]. The session will be facilitated by a skilled moderator and lasted about one hour. The aim of the focus groups is to identify the elements related to mass customization under turbulent environment, and readiness to mass customization. The focus group will use open-ended question (see questionnaire in annex). The focus group will be audiotaped and transcribed the focus group sessions. 2.6. Construct validity In this study, the first measure adopted to ensure the construct validity will be drafting the protocol from the question and research objectives, and thus maintain the " chain of evidence" [51] . This notion is defined by Yin as the logical link that connects the research question, the methods used and the results obtained . Thus, the research problem has been studied from a conceptual framework based on literature review of contingency theory and resource based view theory. This framework was then used to collect data through questionnaires and focus group [51]. For each case, a triangulation of information was carried out with the study of several descriptions of the same event listed through questionnaire, interview and direct data from the field. This study follows then a mixed method approach as explained by Creswell [47]. Finally, the study is based on previous researches and aims to develop previously well studied concepts. 2.7. Reliability Two measures will be taken in this study to maximize the reliability of the results. The first step was to develop and follow a research protocol to document all the steps and traceability procedures [51]. The second step was to establish a database gathering all the material studied in order to make it accessible for possible future investigation of the research process . This database will be made from the qualitative analysis software Nvivo. It includes all data and references studied and memos written during the analysis process to trace the successive steps of this process. 2.8. External validity To ensure the external validity will be to specify the simulation case as precisely as possible and have the
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scenario confirmed by key experts to as close as possible to real life situations. 3. Data analysis Quantitative data will be analysed through SPSS 21 software (IBM, 2013). Baseline characteristics and data collected were compared (about performance, satisfaction and internal integration) of the two groups using 2 independent-samples t WHVWV IRU FRQWLQXRXV GDWD DQG 3HDUVRQ¶V Ȥ tests for categorical data. The primary analysis used independent-samples t tests to compare changes between groups in outcomes from standardization (baseline) to mass customization (intervention). Data will also be analysed using analysis of covariance procedures in which groups are compared according to the mass customization (intervention) value with the standardization (baseline) value as the covariate. Multiple regression analyses will be used to provide a statistical test of the possible mediating role of different factors affecting the organization performance and customer satisfaction. This analysis follows previous research on mass customization conducted by Liu [24] on mediating variables. For qualitative data, the content analysis of the focus group will be conducted with use of specialized software Nvivo . The audio material of each participant will be transcribed in a word processing file with headlines for each of the main topics of the participants comment. The different answers were synthesized into a descriptive themes. Each theme will be explored individually to highlight the ideas and mistresses force trends expressed through speech, and then extracted in descriptive and narrative paintings to highlight relationships, in this case related to the conceptual framework [48]. 4. Discussion Mass customization enables organization to design, produce, and deliver products or services that exactly meets a customer’s needs in a cost-effective way. It enables strategic flexibility to firms, and helps them gain an advantage in competitive markets [29]. Environmental turbulence has been shown in organizations as a trigger for change to adapt to the new environment. From our study, this was highlighted by the influence of environmental impact of performance in both groups. As expected, organizations with mass customization capabilities were seen as more performing, generating more satisfaction, and having improved quality of processes. We hope through this experiment to test our ecosystem in a serious game setting, and to highlight the benefit on performance and satisfaction level for mass customization group compared to standardization. 5. Conclusions Data collection systems have been used for years in the humanitarian sector, and there is enough experience and operational learning on their use in different operational contexts [50]. The fact that these systems are not very much used today relates probably to customer satisfaction with the end product, and its use under environment turbulences, which are the characteristics of human emergencies. Perhaps mass customization needs to be integrated into these tools to respond to user’s needs. A field experiment would certainly be helpful to answer this specific question.
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Appendix. Data Tables Table 1: Variables, definition, values and references: Definition Independent variables MC are defined by the capacity of service delivery to be MC capabilities adapted and recorded by the ERP through customized data entry to the clients needs; Standardization refers to the capacity of service delivery to follow specific predefined patterns as recorded on the ERP through customized data entry to the clients needs; Control variables High demand uncertainty is defined by the accelerated rate Demand uncertainty (DU) of request of services per a fixed time unit. Stability is defined by the fixed rate of the service request by time unit. Supply chain complexity is defined by the multiple and Supply chain complexity different requests of services (three different health (SCC) problems) requiring a more complex client intervention processes. Supply chain simplicity is defined by an unique request of service (a single health problem) Competitiveness is defined by the specific period where Competitive intensity (CI) each organization is explicitly requested to perform for a competition in service delivery. Confounding variables Software quality is defined by the software quality Software quality (SQ) score as defined by the ISO/IEC9126-1 (2001) adapted questionnaire Subjective questionnaire by team member Internal integration: Subjective questionnaire by expert observer perception of integration (II) Outputs and outcomes Number of information filled and workflow recorded Data collection and compared to potential information and workflow paths to workflow confirmation follow quality Ratio of properly performed processes to possible global Performance score of the score (number of proper triage, proper assessment, proper team: management) Subjective questionnaire by mannequins and by the team Costumer satisfaction on customer satisfaction towards service offered
Values Standardization (0) vs MC capabilities (1)
Stable (0) vs High uncertainty (1) Simple (0) vs Complex (1)
No competition (0) vs competition for resources (1) Score based on questionnaire Score based on questionnaire by team members Objective score calculations Objective score calculations Score based on questionnaire of mannequin satisfaction Perception of consumer satisfaction by teams
Table 2: Results Baseline characteristics Group A (mass customization) Group B (standardization) Age Years of practice Level of technology literacy (1 to 5) Difference in performance, quality and satisfaction according to production design (mass customization vs standardization) Group A (mass customization) Group B (standardization) Performance Quality Satisfaction Influence of environment on performance, quality and satisfaction Group A (mass customization) Group B (standardization) DU SCC IC DU SCC IC Performance Quality Satisfaction Influence of mass customization on internal integration Group A (mass customization) Group B (standardization) Internal integration
Abdo Shabah / Procedia Engineering 107 (2015) 223 – 236 Table 3: Questionnaire on customer satisfaction and internal integration to the simulation participants Customer satisfaction [24] Totally disagree (-2)
Disagree (-1)
Agree (+1)
Totally agree (+2)
Totally disagree (-2)
Disagree (-1)
Agree (+1)
Totally agree (+2)
͒CS1: Our customers are pleased with the services we provide for them. CS2: Our customers seem happy with our responsiveness to their problems. ͒CS3: Customer standards are always met by our organization. Internal Integration
II1: The functions in our organization are well integrated. II2: Our organization functions ͒ coordinate their activities.͒ II3: Our team leader emphasizes the importance of good interfunctional relationships.
Table 4: Questionnaire on customer satisfaction filled by the simulation mannequin and about internal integration filled by expert observers Customer satisfaction [24] Totally disagree (-2)
Disagree (-1)
Agree (+1)
Totally agree (+2)
Totally disagree (-2)
Disagree (-1)
Agree (+1)
Totally agree (+2)
͒CS1: The organization pleased me with the services they provided.͒ CS2: The organization was responsiveness to my problems. ͒CS3: The organization was up to the expected standards of quality. Internal Integration
II1: The functions in the organization are well integrated. II2: the organization functions coordinate their activities. II3: the team leader emphasizes the importance of good interfunctional relationships.
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Abdo Shabah / Procedia Engineering 107 (2015) 223 – 236 Table 5: Focus group guide (Based partially on [24]) Questions How did the different factors (demand uncertainty, competitive intensity, supply chain complexity) influence your performance the quality of your work and your customer satisfaction? How does mass customization differ from standardization? Does Mass Customization must be implemented sequentially? Following on from the Mass Production and Continuous Improvement phases. How does customer and supplier involvement impact mass customization? What are the important factors to the adoption mass customization?
What are the main constraints and difficulties in implementing mass customization?
Possible theme
Mass “confusion” Quickly changing needs, Shorter services life cycles Market is heterogeneous Rate of technological change The service level required “Quality consciousness” of customers Competitive activity in the environment Quality required by the customer Uncertain product demand High competitive intensity Need to differentiate offering Search for niches to fill Technical difficulties Organizational difficulties Cognitive difficulties
Is the transformation to mass customization practical? Is Advanced IT capability is a pre-requisite to implementing Mass Customization? Would you recommend that your organization invest the costs of the technology? Does accountability constrains the ability to apply mass customization to service settings? Does it enhance it?
Table 6: Likert scale for assessment of quality of the MC ERP design [54] Characteristics
Sub characteristics
Description
Functionality
Suitability Accuracy Interoperability Security Maturity Fault tolerance Recoverability Understandability Learn ability Operability Attractiveness Time behavior Resource utilization Analyzability Change ability Stability Testability Adaptability Install ability
The module/system is suitable to solve our problem The module/system could perform the function properly Client need to operate in different OS Store data can be based on user profile The module/system is ready to use Complete a transaction cycle without execution errors Use secondary server if main server fails Organize items logically in the interface design Provide online explanations of operation Provide online help Alert the new function is available It must be used in the future The system/module must be used Need a expert to operate the system/module It could be further developed It allows mistakes It does not need to change in the future Export to integrate with other system Need to be installed
Reliability
Usability
Efficiency Maintain ability
Portability
Scale (1 (low) to 5(high)) 1…2…3…4…5
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