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Report to the Vermont Cheese Council

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Report to the Vermont Cheese Council MAKERS & MONGERS Exploring Social Networks in the Regional Supply Chain for Vermont Artisan Cheese Prepared for: The Vermont Cheese Council By: Rachel A. DiStefano, M.S., Food Systems, University of Vermont Dr. Amy B. Trubek, University of Vermont June, 2014 I. Executive Summary Three major themes emerged from the social network analysis of supply networks for Vermont artisan cheese: types of relationships, varieties of retail operation, and importance of distributors. The results indicate that the regional supply chain is a multiplex system stemming from a complex balance between a cheesemaker’s goals and the needs of various retailers. Relationships: The relationships between cheesemakers and retailers ranged from highly social and personal to distanced and business minded. The social network is clustered around several well-connected cheesemakers and retailers. The scale of production and number of years in business influences a cheesemaker’s centrality in the social network. Retail: The majority of Vermont cheesemakers sell to retailers both inside and outside of Vermont. Vermont cheesemakers rely on a diverse variety of retail venues – from ultra-local general stores to large national supermarket chains – across the region. Food cooperatives, primarily in Vermont, emerged as highly central in the social network. The consistent champions of Vermont cheese outside of Vermont appear to be small specialty retailers – either dedicated cheese shops or gourmet food stores. Distributors: Distributors play crucial roles as intermediaries between cheesemakers and retailers. When distributors become involved in selling Vermont artisan cheese, cheesemaker-retailer relationships often resemble a sales network rather than a social network. Page 2 II. Background Why do people like Vermont artisan cheese? What makes it unique, desirable, and good to eat? Previous research with consumers has demonstrated that social information related to where and how Vermont artisan cheese is made is important—not just for preference but for physiological sensory experience. This social information about the farm, cheesemaker, animals, and cheesemaking process is known as the farm story or cheese story. Who tells the story and how is it told? For Vermont consumers, the story is relatively easily to come by, for example, by purchasing cheese directly from the producer at farmers’ markets, eating a local cheese board at a farm-to-table restaurant, or having firsthand knowledge of what a farm looks like. Due to in-state market saturation, however, Vermont cheesemakers are increasingly encouraged to expand their supply chains to reach more urban, regional consumers. In these spatially extended marketplaces, retailers occupy a key intermediary position between producers and consumers. Indeed, focus groups with consumers of Vermont artisan cheese pointed to the importance of cheesemongers—defined in this research as highly specialized cheese retailers who do not just sell cheese but also source and promote it—in shaping their preference for Vermont artisan cheese. Yet, despite their importance from both a producer and consumer perspective, little is known about retailers, their specialized knowledge, and their relationships to others in the Vermont artisan cheese supply chain. In the fall of 2013, the Taste of Place Working Group at the University of Vermont conducted a survey of Vermont artisan cheesemakers to better understand and explore their relationships with retailers in the region (defined as New England and New York). Social network analysis is a quantitative technique that allows us to visually map the network of relations between a group of actors—in this case, Vermont cheesemakers and regional retailers. Qualitative comments from cheesemakers, along with information on retailer and distributor websites, provided rich detail, depth, and nuance to our analysis. This report summarizes the main findings and discusses potential implications. A set of recommendations is also offered. Methods An online questionnaire was distributed to every licensed cheesemaker in Vermont (see Appendix A for a copy of the survey instrument). Thirty-five (35) Vermont cheesemakers participated in the survey (see Appendix B for a table summarizing demographic variables of cheesemakers). Participants were shown a list of retailers in the New England/New York region (compiled from an online database maintained by Culture Magazine, and supplemented by lists found on the Vermont Cheese Council’s website) and told to identify those that had sold their cheese in the past year. They were then asked a set of questions about each retailer they identified. In addition, several demographic questions collected information about their cheesemaking business (for example, how long they have been licensed to make cheese in Vermont, what styles of cheese they make, how many employees they have, etc.). After the survey was complete, follow-up phone calls and e-mails were used to collect additional data on the intermediary role of distributors and their impact on the quality of relationships in the network. Page 3 III. Social Network Analysis Figure 1. Network graph showing ties between Vermont cheesemakers (red; n = 35) and regional retailers (green; n = 300). The 35 cheesemakers identified relationships with 300 retailers across the region. Figure 1 is a pictogram of the social network—known as a network graph or map—with red circles representing cheesemakers and green circles representing retailers. A line connecting a red and green circle (i.e., a tie) indicates that a relationship was identified by the cheesemaker. Those cheesemakers and retailers with many ties (i.e., more connected) are located in the center, while those with only one tie (i.e., less connected) are located on the periphery. All 35 cheesemakers reported a connection to at least one retailer. The two “dyads” on the outer periphery represent cheesemakers who are connected to a lone, singly-connected retailer (i.e., not identified by any other cheesemaker). The network is clustered around several well-connected cheesemakers and retailers located in the center of the graph. What do we know about these central actors? A basic descriptive measure commonly used in social network analysis is centrality. Centrality is simply a measure of the number of connections a given actor has. The following sub-sections highlight several key demographic trends. Page 4 This Cheesemaker Went to (Retail) Market Table 1 displays a list of all cheesemakers in the network—each assigned a two-digit code for confidentiality—in descending order of their number of retailer ties. Exact rankings are not as important as general positioning (categories: high, medium-high, medium-low, and low centrality). Table 1. Cheesemakers (N = 35) Ranked by Centrality Score Rank Cheesemaker Code # of Ties Category 1 35 125 2 69 113 3 46 85 HIGH 4 29 78 5 23 70 6 82 56 7 37 38 8 83 32 9 16 31 10 51 25 MEDIUM-HIGH 10 76 25 11 81 23 12 22 22 12 63 22 13 27 18 13 59 18 14 38 12 14 41 12 15 21 11 MEDIUM-LOW 16 13 10 17 53 9 17 61 9 18 20 8 19 48 6 19 68 6 20 62 5 21 19 4 21 39 4 LOW 22 64 3 23 44 2 23 71 2 24 18 1 24 32 1 24 56 1 24 78 1 Page 5 On average, Vermont cheesemakers were connected to 25 retailers across the region. The range was large: 10 of the 35 cheesemakers identified 5 or fewer retailers, while the top-most central cheesemaker identified 125. Years in business and scale appear to be important factors related to cheesemakers’ centrality. Statistical analysis confirmed a relationship between centrality score and number of years being licensed to make cheese (r = .59, p < .001), such that cheesemakers who have been in the business longer tend to have more connections to retailers in the region. This may be because it takes several years to become established and build up these relationships. Alternatively, it could be an indication of the time needed to acquire capital to increase one’s scale of production. Those just starting out may begin at a smaller production scale due to constraints of economic capital or infrastructure, or as a strategy to minimize economic loss due to failure. Low production volumes reduce the likelihood of selling cheese to many retailers. Indeed, a strong positive correlation exists between a producers’ reported pounds of cheese sold and centrality scores, r = .771, p < .001. Table 2 reports the results of these and other correlation analyses based on cheesemaker attributes. Table 2. Correlations Between Cheesemaker Attributes and Centrality Scores Years Animals Styles Pounds Sales Employees Years licensed to make cheese --- Number of different types of animals .087 --- Number of styles of cheese .059 .366* --- Pounds of cheese .820** .055 .367* --- Gross sales .550** .003 .521** .896** --- Full-time employees .554** .026 .227 .663** .667** --- Centrality .590** .228 .535** .771** .773** .807** ** p < 0.01 level * p < 0.05 level Note that, due to the self-report nature of the survey, centrality scores and rankings represent cheesemakers’ perceptions of where their cheese is being sold in the region, and are not necessarily an accurate reflection of sales, geographic reach, or relative importance. In many cases, they are probably underestimates. For instance, Cheesemaker 78 reported selling only at their on-site retail store but later told me that they also use a distributor, who “picks up anything that is left.” While they had a list of stores that the distributor sells to—and, therefore, stores their cheese might be sold in—they did not actually know which retailers order their cheese from the distributor. Thus, it is important to recognize that cheesemakers may be connected to additional retailers in the region, yet, because they are not aware of which ones, they cannot perceive or report a relationship. When this is the case, it is impossible to tell how many stores a cheesemaker is actually present in.
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