Location Decisions in Service Industries
Key Concept Explanation
Having spent the past two decades in a service industry, I am naturally interested in applying operations management topics and principles to the realm of services. When I chose to investigate the relevance of location decisions to the provision of services, I must have assumed that the topic was fairly straightforward. However, the reality that I found by sampling the literature quickly corrected that unfounded assumption.
Daskin (2010) stated succinctly, “Location decisions are critical in many services” (p. 186). He went on to explain that such decisions may be strategic because of the impracticality of reversing them, as when sites for schools, hospitals, or rescue services are selected. A poor location choice can lead to high costs, poor service, and customer dissatisfaction. Location does not necessarily relate to a physical facility or a strategic decision. For example, the treatment of prostate cancer with radioactive seeds involves a tactical decision on a microscopic scale, but the service problem can be analyzed via one or more of various location models.
Meredith and Shafer (2013) discussed the concept of location in two sections of their text—first, in the context of supply chain design, and second, in relation to capacity and scheduling. They framed their overall discussion of location decisions in terms of a three-step process, narrowing from the regional/international level to a specific community, and then to a particular site. Many of the examples cited in this discussion assumed the context of producing physical goods. When they did address the location of services, they noted the following general principles:
Services are often located close to their recipients due to the relative difficulty of transporting them.
Information and communication technologies have enabled some pure services to be provided virtually.
Pure services can be delivered by attracting the recipient to a physical location (e.g., retail facilities and clinics) or by transporting the service to the recipient (e.g., police and ambulance services).
In many areas of operations management, theory and research appear to focus initially on physical production, with later adaptations to the realm of services. This appears to be the case in regards to location decisions. Nevertheless, the sources cited in the annotated bibliography demonstrate that making good location decisions is critical to the success of a service-oriented organization, and thus a worthy focus for research. According to Daskin (2010), location models can be grouped into four categories—analytic, continuous, network, and discrete—characterized by “different assumptions about the nature of demand and about where facilities can be located” (p. 189). It is significant to note that location models can be mathematically complex (e.g., Guo, Yan, & Qi, 2013).
One may tend to think of location decisions as needing to be made mostly when a firm is first launched (e.g., Murphy, Fox-Rogers, & Redmond, 2015), or when it decides to expand (Al Qur’an, 2010). Nonetheless, as two of the sources discussed in this thread make clear, changing circumstances can necessitate location decisions at other points in a firm’s life. Bowden’s (2011) research explored how a center-city disaster required professional service firms to relocate to alternate sites with no advance warning. Similarly, Mărgulescu and Mărgulescu (2014) observed that, in contrast to the generation-long trend toward globalization, some firms are choosing to reverse prior decisions to outsource or offshore certain operations.
Daskin’s (2010) Service Science seeks to “provide students with the tools and background needed to analyze and improve the provision of services in our economy” (p. xxv). The text pursues this goal by laying methodological foundations (optimization and queueing) and then applying these methods to operational issues such as inventory, scheduling, and space design. One of the application chapters, entitled “Location and Districting Problems in Services,” is the focus of this section.
The chapter begins with four examples of organizational challenges that can be addressed by the use of location models (more information is found in the annotated bibliography section). Having established the utility of location models by way of these examples, Daskin (2010) develops a taxonomy of location models “based on a classification of the ‘space’ in which the problems are structured” (p. 189). The four models are labeled as analytic, continuous, network, and discrete. Space does not permit close examination of these models, but a couple of generalizations can be made. First, each model is based on distinctive “assumptions about the nature of demand and about where facilities can be located” (p. 189). For example, continuous location models assume that demand across an area is not uniform, but rather “concentrated at specific points” (p. 194) and with varying levels of intensity. Second, each model’s assumptions lead to corresponding formulae that provide for the processing of data and the resolution of location problems.
Location decisions are clearly applicable to service industries. Locations can be physical or virtual, and they may or may not entail a physical facility or a facilitating good. Location decisions are often difficult to change, and thus can have far-reaching consequences for the success of a service organization. Nevertheless, one should not assume that location decisions are only made when a firm is formed or expands geographically. Rapidly changing conditions can force a firm to make unforeseen location decisions. Numerous factors may be relevant to a physical location decision; these include infrastructure, environmental characteristics, labor supply, customers, and more. A number of competing quantitative models can be leveraged in such cases, yet qualitative factors are not to be dismissed. It is important, therefore, that managers in all kinds of organizations understand how location relates to the process of creating value for customers (Meredith & Shafer, 2013).
Al Qur’an, M. N. (2010). Determinants of successful international expansion of professional service firms: A case study of Arabian firms. World Journal of Entrepreneurship, Management and Sustainable Development, 6(1/2), 119–132. doi:10.1108/20425961201000010
Al Qur’an (2010) reported on a case study of a single Saudi professional service firm that had successfully expanded its engineering and construction operations to various countries within the Middle East. His analysis focused both on location decisions (specifically, the evaluation of various countries as potential destinations for firm expansion) as well as the broader drivers that led the firm to expand internationally. The article is, in my judgment, not an exemplary case study, as it does not convey detailed description of the subject matter. It might have been better for the researcher to cover internationalization and location decisions in two separate articles. What the article does convey is that the firm considered both quantitative and qualitative factors in evaluating foreign location sites. Whereas quantitative factors related mostly to operating costs, qualitative factors pertained to the political, economic, legal, and other contexts in which the firm would do business.
Bowden, S. (2011). Aftershock: Business relocation decisions in the wake of the February 2011 Christchurch earthquake. Journal of Management & Organization, 17(6), 857–863. Retrieved from http://search.proquest.com.ezproxy.liberty.edu:2048/docview/1022274015
Bowden (2011) reported the results of a study of four professional service firms that suddenly faced relocation decisions after a devastating earthquake struck Christchurch, New Zealand, in 2011. With suitable locations in short supply, all four firms found alternative premises within days following the quake. By the time that Bowden wrote his research later in the year, only one of the four firms had been able to return to its Central Business District (CBD) location. The others remained displaced, yet planned to relocate to the CBD in the future. Two of these had made significant investments in outfitting their temporary sites. Two factors assisted the firms in maintaining continuity of operations during the transition. First, each firm was large enough to have offices in other cities, and thus “national assistance substituted for some degree of local managerial attention” (p. 862). Second, each firm found its alternative location “not through outside agents or other intermediaries, but through personal contacts” (p. 860).
Daskin, M. S. (2010). Service science: Service operations for managers and engineers. Oxford: Wiley. Retrieved from http://site.ebrary.com.ezproxy.liberty.edu:2048/lib/liberty/reader.action?docID=10488529
This annotation pertains to chapter 4 of Daskin’s (2010) book, entitled “Location and Districting Problems in Services.” The chapter begins by illustrating the diverse organizational needs to which location models can be applied. These include (a) the situation of emergency vehicle bases around a city, (b) the establishment of disaster recovery centers within a county, (c) minimizing the number of automated meter readers required to track customers’ consumption of utilities, and (d) determining how to distribute radioactive seeds in tissue when treating cancer. Daskin seeks to bring order the realm of location models by categorizing them into four groups that he labels as analytic, continuous, network, and discrete.
Mărgulescu, S., & Mărgulescu, E. (2014). Reshoring in manufacturing and services. Global Economic Observer, 2(1), 90–95. Retrieved from http://search.proquest.com.ezproxy.liberty.edu:2048/docview/1679936463
Mărgulescu and Mărgulescu (2014) analyzed sourcing trends in manufacturing and service industries, noting apparent changes to the overall globalization phenomenon that emerged in the 1980s. Essentially, they noted that wage stagnation in America and Europe, combined with wage growth in countries such as China and India, has made it less attractive to produce in the latter countries. As a result, signs of reshoring—“a reverse trend of returning to the country of origin” (p. 91)—have emerged of late. Reshoring has been most evident in manufacturing. However, even in the realm of services, in cases where transportation costs apply, there has been “a slowdown in … offshoring and the emergence of new strategies in the field” (p. 93).
Murphy, E., Fox-Rogers, L., & Redmond, D. (2015). Location decision making of “creative” industries: The media and computer game sectors in Dublin, Ireland. Growth and Change, 46(1), 97–113. doi:10.1111/grow.12086
Murphy et al. (2015) conducted qualitative research on the factors that had led media and computer game firms to locate in Dublin, Ireland. Their method consisted of semi-structured interviews of firm executives and other individuals with expertise in the Irish creative sector. Their goal was to assess whether classic factors purported to underlie industrial location decisions (infrastructure, skilled labor, tax policies, etc.) adequately explained the creative industries’ gravitation to Dublin. An alternative explanation for creative firms’ location choices—one that has gained influence recently—is that firms follow creative workers, who locate in areas characterized by “Technology, Talent, and Tolerance” (p. 100).
Murphy et al.’s (2015) research led them to conclude that three classic factors were uppermost in firms’ choice to locate in Dublin:
The availability of skilled labor
The presence of suitable transportation and telecommunication infrastructure
Benefits associated with the clustering of similar firms in the area
While “soft” factors such as the city’s social, cultural, and built environments were also found to play a role, these were secondary to the classic factors. Accordingly, they recommended that economic developers pay particular attention to maintaining and improving the infrastructure, which interviewees described as an area of concern.
Guo, Z., Yan, W., & Qi, M. (2013). A new optimization approach for emergency facilities locations based on fuzzy information. Information Technology Journal, 12(15), 3467–3471. doi:10.3923/itj.2013.3467.3471
Meredith, J. R., & Shafer, S. M. (2013). Operations management for MBAs (5th ed.). Hoboken, NJ: Wiley.