Measuring Productivity of Institutional Food Service Providers

Transcription

1 Measuring Productivity of Institutional Food Service Providers Mangesh Gharote 1, 2, Debabrata Das 1 1 SJM School of Management, IIT Bombay, Mumbai, India 2 Tata Research Development and Design Center, TCS Ltd. Pune, India mangesh.g@iitb.ac.in, debabrata.das@iitb.ac.in Abstract The objective of this study is to explore the relationship between quality and productivity of different food service providers. Quality is measured in terms of food and service provided by the service provider. We measure the service productivity from customer perspective and compare the different providers on multiple criteria. A case study on different service providers at a large institution was undertaken to illustrate this relationship. We measure and compare service productivity using DEA-super efficiency model and multi-criterion decision making method, TOPSIS; and the results showed a relationship between quality and productivity. Keywords: DEA, Food Quality, Service quality, Service Productivity, TOPSIS 1. Introduction Johnston et al. (2004) states that productivity management is very important for service organizations, but there is relatively little empirical research on this topic. Service productivity measurement is more difficult to measure than productivity, in manufacturing (Becker et al. 2012, Rutkauskas et al. 2005). The reason is that input and output of service sector productivity consist not only of quantitative elements, but also qualitative (Reid, 2005), and these factors are correlative (Gummesson, 1992). Service productivity is defined as the ratio of quantity of output and quality of output to the quantity of input and quality of input (Rutkauskas et al. 2005). Service productivity is further distinguished between operational and customer productivity. In this paper, we are addressing service productivity from customer perspective. There are several factors which affect service productivity in institutional services, where students are the customers. The qualitative output measures considered are food and service quality, as perceived by the customers. The service quality is determined by the facility, cleanliness and hygiene, serving quality, amount of waiting time in the queue, provision, and availability. The quantitative measures considered are the students served, mess (cafeteria) staff, and meal cost.

2 Barros (2011) proposed a framework for the evaluation of hotel efficiency using DEA. Antony et al. (2004) explores the link between service quality and business performance in the hotel industry. Nailul et al (2009), in their paper, studies the attributes that influence customer satisfaction and determines their relationships with customer satisfaction pertaining to a hotel. In the limited literature survey, we did not come across any papers measuring service productivity and showing its relationship with food and service quality. 2. Case Study - Background The institution under study is IIT Bombay. There are thirteen hostel messes that serve food to the students. Each hostel mess provides meals (that is, Breakfast, Lunch, Tiffin and Dinner) through the day to the students. The hostel mess varies in different aspects: Scale: Number of students served varies from 185 to 1900 Type of Governance: Government (6 hostels) and Private (7 hostels) Meal Cost: Varies from Rs 45 to Rs 90 per day Perceived Quality: Food and service quality perceived by the students of each hostel is different The institute is planning to move towards privatization of all the hostel messes (cafeteria) for better control and management of food services to students. A major food poisoning incident took place recently when several students were admitted to the hospital. Hence, this study to evaluate service providers on service quality and productivity becomes relevant Data Collection Data was collected through questionnaires from students and through structured interviews with mess managers. Student s strength across various hostels is almost 7020, out of which, 10.7% responded to our survey. 89% of data was collected through online survey. Since response from few hostels was not statistically significant, we did an offline survey and 11% data was collected through this survey. The feedback from the students was collected through questionnaires based on the following four parameters: Quality of food (Good, So-So, Bad) Service quality a. Serving and hygiene (Good, So-So, Bad) b. Food availability: In a month, on an average, the number of times students have to wait, due to unavailability of food on time. Scale used was monthly count: 0 times, less than 5 times, and more than 5 times. c. Peak hour service: Average waiting time during peak period in minutes. Scale used: 0 minutes, less than 5 minutes, and more than 5 minutes.

3 The data pertaining to meal cost per day, number of students served, mess staff and number of cooks were collected from mess managers of each hostel (refer Table 2), through a personal faceto-face structured interview Data Analysis Tables 1 summarize the preliminary results. 24% of the total students said that the food quality is good. More than 50 % of the students of the hostels 3, 5, 7, and 11 responded that their mess food is good. Overall, 43% of the students are partially satisfied with the quality of the messfood as they responded on So-So. Hence, we can infer from this knowledge that overall food quality of IIT hostel mess, as judged by the students, is good. Further, it can be inferred from data (Ref Fig. 1) that overall food quality of private service providers is better than government service providers. Hostel Table 1 Preliminary results on food and service quality Survey Food Quality Service Quality Response Rate Good So-So Bad Good So-So Bad H1 17% 20% 37% 43% 51% 37% 12% H2 6% 4% 61% 36% 18% 43% 39% H3 4% 59% 32% 9% 64% 32% 5% H4 31% 4% 21% 74% 19% 47% 34% H5 15% 67% 22% 11% 70% 24% 6% H6 7% 3% 56% 41% 22% 56% 22% H7 5% 57% 43% 0% 52% 48% 0% H8 7% 33% 43% 24% 33% 48% 19% H9 22% 12% 43% 44% 29% 43% 28% H10 4% 15% 69% 15% 50% 42% 8% H11 6% 61% 32% 7% 46% 54% 0% H12 9% 29% 45% 26% 48% 41% 10% Tansa 18% 9% 61% 30% 21% 39% 39% 10.7% 24.0% 43.0% 33.0% 39.0% 43.0% 18.0%

4 Hostel Fig. 1 "Good" food quality comparison of private Vs government mess Meal Cost Per Day Table 2 Data collected from Mess Manager No of Students Served Mess Staff (Including Cook) Cooks Private Or Government Organized H Pri H Gov H Gov H Gov H Pri H Gov H Pri H Pri H Gov H Pri H Gov H Pri Tansa Pri 3. Methodology To measure service productivity, we have used Data Envelopment Analysis (DEA). DEA is a multi-factor productivity analysis model for measuring the relative efficiencies of a homogenous set of decision making units, introduced by Charnes et al. (1978). Using DEA, we could evaluate the hostel mess only on a few parameters and also could not incorporate preferential information into DEA. Hence, we used Multi-Criterion Decision Making (MCDM) model-topsis (Olson, 2004) method to compare different hostel messes. The MCDM method and research was entirely separate from DEA research until 1988, when Golany presented combined interactive, multipleobjective linear programming and DEA (Adler et al., 2002). We have used these methods separately and compare different service providers. TOPSIS stands for Technique of Order

5 Preference by Similarity to Ideal Solution. This method considers three types of attributes: Qualitative, Quantitative and Cost attributes. Keeping all the points in mind, we chose TOPSIS as the best possible technique to rank the hostel messes among various MCDM techniques Measuring Services Productivity Using DEA We used DEA-super efficiency model to evaluate the performance of different hostel messes. The following factors are considered in the DEA model: Input: Number of mess staff and cost of meal per day. Output: Number of students served, food quality and service quality. We have solved the above DEA problem for each DMU using MS Excel solver. The results are shown in the following table. Hostel H 12 H 11 H 7 H 10 Tansa H 5 H 1 H 3 H 4 H 2 H 6 H 8 H 9 Efficiency Rank Findings: Although food quality of H11 is better (61% good) and cost per meal is very low (Rs. 40 only) as compared to H12, yet, H12 surpasses H11 in efficiency score as H12 serves a large number of students (1900). H7 and H10 topped because of better food quality or service quality. On the other hand, H9, H6, H2, H4 are at the bottom due to poor food quality, and moderate service quality Comparing Hostel Mess Using TOPSIS We have considered the following criteria for comparing different hostel messes: Food quality and service quality, as the set of benefit attributes Meal cost per day, non-availability of food in peak hours, students served per staff, and students served per cook, as the set of negative attributes Weights for each attribute were computed using rank sum weights method. TOPSIS selects the alternative that is closest to the ideal solution and farthest from the negative ideal alternative. The result of TOPSIS analysis has been summarized in the following table: Hostel H 11 H 3 H 7 H 5 H 10 H 8 H 12 H 9 H 1 H 2 H 6 Tansa H Rank C i * Findings: Based on the relative closeness to the ideal solution (C*), it ranked H11, H3, H7 on top and Tansa, H4 at the bottom; reason being high weight to food quality, service quality and meal cost.

6 4. Discussions DEA has ranked H12 as high productivity even though it is not at the top in food and service quality. None of the methods (DEA and TOPSIS) have ranked H5 on top even though it has highest rating in both food and service quality (see Table. 1). Hence, we can highlight that, if food and service quality is high, it does not necessarily imply that service productivity is also high. A strategic matrix is presented as a result of DEA and TOPSIS analysis in Fig. 2 for the decision makers. It places H3, H5, H7 and H11 in the best service provider quadrant, while H4, H6, and H9 hostel messes are in under-performing quadrant and needs immediate management attention. H2 and Tansa-Hostel mess are lagging in service quality, while H1, H10 and H9 need to improve their food quality. Acknowledgements Fig. 2 Strategic Matrix We extend our thanks to the following people, for critically reviewing the paper and giving suggestions for improving the paper: Dr Maitreya Natu, Dr. Sachin Lodha and Girish Palshikar from Tata Consultancy Services (TCS). We would like to acknowledge the guidance given by our faculty Prof T.T. Niranjan and Prof Subhas Babu from IIT Bombay. References Adler N., Friedman L. and Sinuany-Stern Z., (2002). Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research, 140:

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