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1. Introduction In both developing and developed countries small-scale firms dominate in the agricultural sector (Nichter and Goldmark, 2009). They generally have difficulties to satisfy high-value agro food markets requirements (Kirsten and Sartorius, 2002), while using their technological assets in a cost effective way (Cuevas, 2004). Lack of managerial ability and knowledge on coordination management and processing techniques impact on stakeholders’ performances and usually lead small-scale firms to inefficient production and inflated costs (Li, 2012). Dairy sector confront the same sort of issues aggravated by features such as limited control of milk quality along the chain, risk of unpunctual processor’s payment to farmers, and few processors’ incentives for encouraging farmers to produce good milk quality. This overall situation excludes small-sized dairies from urban retailers such as supermarkets and makes both farmers and processors more sensitive to economic shocks (Mather, 2005).

Providing financial and non-financial support can help small-scale dairies in performing well and being more competitive (Beyene, 2002). This support can take various components such as processing techniques, accountability and firm management, which can be met by training sessions and usual management tools such as spreadsheet budget applications. Nevertheless, there is also a need to develop support regarding strategic issues such as market orientation and design of payment system (Bennett et al., 2006), a domain much less investigated for small-scale firms (de Carvalho and Costa, 2007). In that respect, modeling combined with scenario analysis may be instrumental for helping managers in evaluating the best strategic decisions to make (Le Gal et al., 2011), both in terms of products to be processed and incentives such as quality-based payment systems of milk to be implemented towards dairy farmers.

This study presents a decision support tool called DairyPlant developed for supporting small-scale dairies in (i) improving their economic profitability by selecting relevant market orientations and (ii) reinforcing their coordination processes with dairy farmers by designing alternative milk quality payments systems. The study was conducted in a Peruvian Andes area, where an increased annual milk production has been observed but where farmers and processors still show little concern for quality norms. After describing the rationale of the approach, the software objectives and its conceptual basis are presented. Then, its structure and data processing are described. Finally, the use of the software is illustrated with some cases.

2. Rationale Small-scale dairy processors in developing countries interact in formal and informal chains according to their type of targeted markets and control processes (Farina et al., 2005). Depending of the milk availability in the area and the demand of dairy products throughout the year these chains may compete for ensuring permanent milk supply. This situation plus the fact that there is not any formal contract between stakeholders, lead small-scale dairy processors to apply strategies to secure milk suppliers (Siqueira et al., 2008) e.g. offering attractive prices to farmers or paying bonus for good milk quality. On the market side, dairies may target a diversity of retailers from local shops to supermarkets (Reardon and Hopkins, 2006). They have to decide accordingly the type of products to be processed, from raw milk to much sophisticated dairy products. Processing costs, input and output costs and milk quality may become critical aspects for benefiting from the market orientations they plan.

In developing countries milk payment is based on quantity rather than quality (Espinoza-Ortega et al., 2007; Gorton et al., 2006). Nevertheless, quality requirements of formal markets start pushing stakeholders to demand higher levels of raw milk quality. The establishment of successful milk quality premium programs can attract new dairy suppliers, motivate the rest of milk producers to focus their efforts on farm management practices (Botaro et al., 2013) and improve the general milk quality status at plant gate (Nightingale et al., 2008). Certainly, most of small-scale dairies are currently facing many operational issues common in small-scale firms, such as the availability of adequate technical and economic knowledge and data (Le Gal et al., 2003). But the use of a supporting tool adapted to the dairy production in these areas and dedicated to strategic issues such as the design of alternative payment system, could contribute to increase their mid-term profits on one hand, and encourage dairy farmers to improve their milk quality to get a better income on the other hand (Garrick and LopezVillalobos, 2000).

In the last decades, the use of different simulation models has allowed dairy industry worldwide to evaluate “ex-ante” potential solutions to given issues such as selecting a milk price or dairy product portfolio (Table 1), and the potential impacts of manufacturing processes on their performances (Geary et al., 2010; Roupas, 2008). Simulation models have been used for better understanding dynamics between stakeholders and designing efficient dairy supply organizations, which would increase market share, reduce cost, increase profitability and enhance milk quality (Tripathi, 2011). In other industrial sectors simulation tools have supported the design of new payment schemes (Lejars et al., 2010), a better cooperation in negotiation agreements (Foroughi, 2008) and have facilitated strategic discussions between stakeholders (Hall et al., 2007; Le Gal et al., 2008). Despite all these benefits, few reports exist in the literature regarding simulation tools adapted to and used with smallscale dairy processors or considering milk quality-based payment systems in developing countries.





The approach described in this paper and based on the design of a simulation tool called DairyPlant attempts to contribute to this issue.

Table 1. Simulation models reported around the world for the dairy processing sector (source: Geary et al.

, 2010)

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3. Material and Method DairyPlant was designed based on a participatory research conducted with five small-scale dairy processors in the Mantaro Valley (75º18´ longitude West; 11º55´ latitude South; 3,200 meters above sea level) in Peru’s central Andean region. They were monitored weekly from May to July 2013 in order to estimate production functions from raw milk to dairy product. Then, two of them were selected, based on their willingness to adopt innovative incentives, to carry out the support process and to discuss the feasibility to implement payment systems including milk quality components.

This process included the following steps. Firstly, the support process, its objectives and the general idea behind the simulation tools were clearly explained to the processor in order to avoid misunderstandings. Then, an interview with the processor was conducted to better understand its dairy circumstances and management processes. Quantitative data were collected such as volume of milk collected per day, dairy product produced, price of dairy products, cost of processing dairy products, as well as qualitative ones, such as ways of selecting processed products and paying farmers. These data were used both to design a software structure able to cope with a variety of dairy cases, and to construct a base scenario as close as possible to each given case.

The base scenario was simulated in order to compare its outputs to the figures known by the processor. Calibrations were made if the processor estimated that certain results were not representative and/or if a lack of consistency was detected. Once a satisfactory representation of the manufactured process was achieved, the construction of alternatives scenarios jointly with the processor began. Building alternatives scenario included modifications in (i) processor’ current portfolio towards higher value products; (ii) the volume of milk collected per day and (iii) the payments to his milk suppliers. Outputs from these alternative scenarios were discussed and the support process was evaluated with the processor in a final meeting.

4. DairyPlant description

4.1. General overview DairyPlant aims to support individually small-scale dairy processors in comparing various marketed product portfolios according to their own objectives such as diversifying their range of products, modifying their processing unit or maximizing their returns. It also allows the design of milk payment systems based on volume and milk quality composition, allowing processors to evaluating the impacts of a given system on their profits. DairyPlant also includes an evaluation of each milk supplier’s gross product in order to look for solutions which could improve both stakeholders’ economic results. Each simulated scenario provides hypothetical values linked to the dairy circumstances (total milk quantities and quality collected daily, variable and fixed costs for each processed product) and each milk supplier’s daily supply in terms of quantity and quality. These output values provide the base for discussions with dairy processors and potentially farmers if they are included in the support process.

DairyPlant represents the milk supply from farm to plant gate and the manufacturing process, by considering the volume of milk collected daily by a given processor (Figure 1). Each milk supplier, should-it be an individual farmer, a group of farmers or a private collector, is characterized by (i) his daily milk quantity supplied to the dairy processor; (ii) his values of up to three quality components (milk composition or milk hygienic values); and (iii) his capacity to increase, decrease or keep his current quality levels if the payment system is changed. This capacity is subjectively assessed by the processor based on the knowledge he has of his suppliers, since there is no direct mathematical relation included in the software between the variation of the payment system and the modification of milk quality supplied by each supplier.

DairyPlant calculates the outputs of the dairy manufacturing process based on (i) the total raw milk collected into dairy products, (ii) its average quality components and (iii) processing equations specific to each processed product. The proportion of milk used in the production of each dairy product is entered by the software user according to the product portfolio selected for a given scenario. The list of product manufactured allows the introduction of intermediate products in the analysis e.g. cream for butter or whey for ricotta cheese. The yield of each dairy product, i.e. the quantity of milk or intermediate product required to produce 1 kg of dairy product, is also defined by the software user based on existent formulas or in-situ controlled experiments. Processing costs are split into milk collection, product-related processing, packaging and marketing costs. Each fixed cost is also defined and split between processed products according to each processor’s choice. At the end of the simulation, processors obtain the total profits related to a given dairy portfolio. Up to 10 marketed dairy products and 5 intermediate processed products can be included in scenarios.

DairyPlant also allows the design of a milk payment system for a plant. It includes a milk base price plus a combination of up to three quality variables, either chemical or hygienic, assuming that these variables are actually measured at the plant gate and so defined for each supplier. For each variable the user gives the base value and a penalty and/or bonus for each point respectively above or below the base value. So, simulations may include payment systems with (i) only bonuses and no penalties;

(ii) both; or (iii) a fixed base price only. The calculation of milk cost for the processor and of gross product for the suppliers can then be carried out according to the quality supplied by each one to the plant. Suppliers’ profits are not calculated since it is considered that processors usually do not have access to their private cost information.

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manipulation and understanding by the stakeholders involved in the support process. It is split into three modules (Table 2): “Parameters” (variables which take the same value for a group of plants), “Input” variables (specific to one given plant: range of dairy products, milk quantity and quality per famer, payment system, costs and gross product per dairy product) and “Results” (calculated variables for the given case: plant profit and farmers’ gross products). Each module is split into several sheets according to the kind of information required or provided. Each scenario is described and run for one day considered as representative of the plant business throughout the year, and saved in one Excel file. The software is available in three languages: English, French and Spanish.

A table of contents is presented after the welcome page, describing the information about each sheet of the application. A range of colors are used to characterize different groups of cells according to their content names, parameters and input variables, output variables. Parameters and input cells can be filled either manually or from a list defined by the user himself. The user can move from one sheet to another by going back to the content sheet and by clicking on the required sheet in the list provided, or by using downward and forward icons included at the top of each sheet. A “reset” icon is also provided to delete input values in one sheet or in a whole scenario.

Table 2: Commented list of the variables included in DairyPlant

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4.2.2. The “Parameters” module The “Parameters” module is split into two sheets. The first one includes variables required in the “Input” module for characterizing a given scenario. Each variable may take a range of values that are defined by the user: the list of possible processed dairy products, milk components involved in the quality evaluation (e.g. protein, fat, total solids), the perception of quality improvement per farmer (e.g.

=, + or -), and the fixed and variable cost items related to the processing or the marketing of dairy products. These values constitute lists that are active in some input cells, where they can be selected by the user according to a given scenario.



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