A conceptual model for selecting mobile & wireless solutions in field-services

In this decade, Mobile & Wireless (M&W) technologies have strongly emerged. Whenever possible, many companies have re-shaped their businesses to be mobile businesses (m-business), trying to find how this kind of technologies would support their processes and enhance their performance. Pushed by the lowering costs of both broadband radio networks (such as GSM/UMTS, Wi-Max, etc.) and hardware devices (such as smart phones, PDAs, pagers, GPS, mobile printers, etc.), M&W applications have become remarkably affordable even in small businesses. A lot of leading organizations, with large field staffs (e.g. for sales or customer services purposes) have yet deployed M&W solutions, allowing workers to productively and profitably interact each other to share critical data (i.e. on service contracts, on warranty status, on ordered spare-parts). As highlighted by Kakihara and Sorensen (2001), mobility in computing and in interpersonal communications can be provided through continuous accessibility to company backend systems and back-office people via mobile handheld devices. Relevant benefits can be achieved, with a strong impact in the short and long run overall performance: for example, automating field-data processing (e.g. printing an invoice directly at the customer site, after performing an out-of-warranty repair) usually leads to an increase in workforce productivity, a decrease in error-induced costs (reworks, etc.), an acceleration in payment time; furthermore, some kind of M&W applications can induce higher effectiveness in managerial practices and better process optimization: for example, using GPS/SMS for a real-time tracing of the workforce state (idleness, time-to-finish, etc.) and position in the served territory, can enable a more effective dispatching of technicians to customer requests. Unfortunately, many organizational and technical issues are still present, and for a company selecting and introducing effectively M&W technologies could be a hard challenge to face with. Even if a number of recent publications (mainly case-based research) show that efficiency and effectiveness of business processes can be improved through the use of M&W, to the best of our knowledge, managers have still little guidance for an effective selection of M&W technologies for their specific purpose. To partially fill this gap, this paper presents a conceptual model to support managers in M&W technologies selection, specifically devoted to the context of field-service. In a field-service delivery process, the server’s personnel, a vehicle and some equipments travel to a customer location in order to deliver services on a customer’s site. In this context, an appropriate mobility management can be the major competitive driver. Even if we mostly refers to technical assistance services (e.g. preventive or corrective maintenance), to be provided after the product sales (after-sales services) on an installed-base that cannot easily be moved (field-services), this definition is broad enough (Agnihothri et al. 2002) to include other kind of services, such as pick-up and delivery services (e.g. garbage collection, laundry) or emergency services (e.g. ambulance, fire, police). After reviewing a lot of managerial literature on this subject, a classification of the different types of M&W technologies in terms of devices, applications and network connectivity has been provided. The benefits of the reported implementations, if declared, have been deeply discussed. The well-known Perrow model (1967) and other important scientific model (Whitey et al., 1983, Pugh et al., 1969, Van de Ven and Delbecq, 1974, Mohr,1971, Galbraith, 1973, March and Simon, 1958, Duncan, 1972, Thompson, 1967, Cyert and March, 1963), all pointing out the role of technology in supporting business processes and organization, have been used as a starting point for developing our model. Consequently, tasks complexity has been assumed as the main driver influencing the appropriateness of M&W applications in supporting the field-service activities. In facts, complexity causes uncertainty, and uncertainty leads to both inefficiency and variances in performance (ineffective performance). Oppositely, M&W technologies, if appropriate, cushion uncertainty and raise up the overall performance. According to Grandori (1995), the complexity to be faced is composed of two independent components: the epistemic and the computational complexity. Epistemic complexity is due to the missing knowledge about the possible state-of-the-world a field-service should be performed within, about the possible actions to put in practice, about the cause-effect relations strength, about the probabilities associated to each expected or unexpected occurrences. On the other side, computational complexity is due to both the number and the pace-of-change of the elements characterizing the context where the field-service should be provided within (i.e. customers to be satisfied, technologies to be trained on, products to be assisted, etc.). In order to operationalize the measure of computational and epistemic complexity, a set of questions have been defined. For validation purposes, some case-studies (partly self-performed, partly retrieved in literature) have been measured accordingly, in terms of complexity, in terms of adopted M&W technologies, in terms of declared benefits. As a result, the case-studies have been definitively positioned in the model framework, and finally the compliances with the model concepts have been discussed. Preliminary findings are very interesting: the model seems to be sufficiently simple in its application, but valuable in providing effective indications. In our model, the M&W solutions portfolio is described, and the different M&W applications are put in relation with the complexity of the tasks they are supposed to support.