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The process yield values DPYx are entered in the second sheet for each possible dairy product defined in the first sheet (Figure 2). The user defines the raw material used and the quantity of raw material required to produce one kg of a given processed product. This quantity is based on existent formulas or controlled experiments. These formulas can include milk quality components when they impact the processed yield (see equation (1) for an example with Andean cheese). These processing yields are essential to determine the amount of dairy products produced by a processor, based on both the collected milk quantity dedicated to each dairy product and its average quality of all the daily deliveries.

–  –  –

Where:

DPYAC = Andean cheese yield (kg milk / kg of processed product) T S = Average value of total solids for all the daily deliveries Figure 2: “Process Yield” sheet 4.2.3. The “Input” module The “Input” module is divided into four sub-modules: Plant scenario (1 sheet), Supply (1 sheet), Fixed costs (1 sheet) and Marketed products (10 identical sheets, one per product). In the “Plant scenario” are entered the plant and scenario names, that are then reminded at the top of the other “Input” and “Output” sheets. It also includes the type of payment system applied (Figure 3). The software user enters at least a base price, and optionally a base value (%) for up to three milk components and the economic value of each point respectively higher (bonus) or lower (penalty) than the base value. A table allows to defining the list of dairy products that processors are or would like to produce, the raw material for each dairy product and the proportion of raw product (milk or intermediate) used in the process. This list is linked to the processing yields sheet to calculate the final quantity of each dairy product (Eq.3).

–  –  –

Where:

Quantity_ DP = Total quantity of dairy product k k MV = Total volume of milk (or intermediate product) %MVk = Proportion of MV dedicated to produce dairy product k DPYk = Processed yield for dairy product k (ii) The “Supply” sheet regroups information regarding suppliers’ daily milk delivery. Up to 100 suppliers can be defined. For each of them, the user enters his daily volume and the milk quality values for the 3 components selected in the “Plant Scenario” sheet. The total volume of milk collected in a day and the weighted average values of milk quality composition are then calculated for the plant.

The user can also introduce the plant values directly should farmers’ individual figures be unknown by the processor. In that case, DairyPlant is mainly used to evaluate and compare various scenarios of market orientations. Quality improvement is qualitatively defined by the processor based on his knowledge about his suppliers’ behavior regarding milk quality management. It is there as a reminder for the user to change subjectively and hypothetically the quality values of each farmer if a given payment system is applied. This flexible procedure is based on the principle that a quality-based payment system will affect farmers’ behavior and performances. But without mechanistic relation regarding these changes the range of variation for each quality component is left to the user’s appreciation.

(iii) In the “Fixed costs” sheet are entered all the costs which are independent from the quantity of milk collected per day. The user chooses the kind of costs in the list entered in the ”Parameter” sheet, the total amount of each cost and its distribution between the 10 possible marketed products based on the processor’s evaluation of this distribution. (iv) Each “Marketed product” sheet represents the daily processing of one dairy product. The market value of a dairy product can be modified to simulate different scenarios of price. Fixed costs are calculated based on the figures entered in the “Fixed costs” sheet, while variable costs are entered based on two categories: the costs related to ingredients used for processing raw material, such as salt or bleach, and the costs related to the packaging and marketing of the final dairy product. When milk is the raw material used, its purchase cost is calculated according to the quantity used multiplied by the average price paid for the total milk quantity collected daily.

Figure 3: “Plan scenario” sheet 4.2.2. The results module Once the scenario is characterized, DairyPlant simulates the corresponding milk processing of the dairy plant. Results are presented in two separated sheets called “Plant results” and “Farmer results” respectively (Figure 4). based on (i) ; the total production costs (fixed and variable) linked to the dairy plant operation; the adjusted milk price corresponding to each farmer after bonuses and penalties and his gross product according to his quantity of delivered milk. This gross product corresponds to the milk cost for the processor.

The “Plant Results” module calculates the profits obtained by the dairy processor according to his product portfolio, his marketing strategy which drives sale prices, and his cost structure. The profit per marketed product is calculated from the following variables: (i) gross product according to the total amount of marketed product and its average sale price, (ii) milk cost for product processed from raw milk, (iii) processing and marketing costs, and (iv) fixed costs when they have been distributed between products. All these variables are sum up to obtain total figures at plant scale. Total fixed costs are directly included when they are not distributed between products. Intermediate materials are not included in the cost structure since they are considered as a free processing by-product. Some indicators are also calculated for each marketed product and at plant scale in order to facilitate the analysis of results by processors: respective share of total costs between milk, variable and fixed costs, profit per unit, respective share of total plant profit between marketed products, and ratio between costs and profits in percentage.





The “Farmer Results” module calculate the gross product of every supplier of the plant based on his amount of milk supplied, multiplied by the purchase price by the plant. This price is calculated according to the payment system entered in the “Scenario” sheet and the individual milk quality figures entered for every supplier in the “Supply” sheet. Each supplier can then assess quickly the consequences of a given payment system on his gross product. Finally, all the results can be copied in a new Excel file for further analysis and graph design in order to facilitate the discussion with processors and eventually farmers.

Figure 4: “Plant results” sheet

5. Illustration of the model: Improving dairies profitability and implementing quality based delivery rules for small-scale producers DairyPlant was tested with two small-scale dairy plants (DP1 and DP2) in the Mantaro Valley to show them potential benefits they could expect from modifying their current portfolio or from adopting a payment system based on milk quality. Moreover, this approach attempted to develop a prospective thinking with them about milk quality, since they currently neglect the importance of rewarding their milk suppliers and managing milk quality on their manufacturing process. A reference scenario was simulated in each dairy in order for the processor to understand the tool structure and to validate the description of his plant structure and his current operation. Two alternative scenarios were then configured and simulated in order to address two issues for their potential impact on the plant total profit: diversifying marketed product portfolio towards higher added-value products, and introducing quality-based payment system. Moreover a simulation process was conducted on a virtual dairy plant to assess the impact of introducing a season-based payment system.

Product portfolio Fresh cheese is the main manufactured product of Peruvian dairies. While it represents between 70 to 80% of the milk processed, it does not necessarily provide the highest profits. Various scenarios based on the different distribution of raw milk between alternative products were evaluated. The scenarios varied according to the amount of milk processors were able to divert from fresh cheese towards other dairy products and the total profit expected at the end. Results show that both DP1 and DP2 can have better profits if they diversify their product portfolio (Table 3). DP1 profits may increase by 65% after reducing 45% the milk used to produce fresh cheese by producing more aged cheeses, yogurt and majarblanco (a product based on the reduction of milk and sugar). DP2 obtained 60% more profits by replacing 20% of the milk from fresh cheese to produce aged cheese.

The two dairy processors also suggested the simulation of processing more milk volume in order to keep their level of fresh cheese manufactured. DP1 increased its profits by 45% and DP2 by almost 100% if they collect one third more of milk. Nevertheless, competition for milk supply in the area will make this second alternative impossible. Indeed, a deeper on field evaluation about the feasibility of these two alternatives i.e. processes more milk or expanding other markets could be instrumental as a complement of our findings.

Table 3: Simulation of the variation of product portfolio and milk volume collected from two small-scale dairy processors at Mantaro Valley

–  –  –

Bold values represent the distribution of raw milk from fresh cheese to the rest of dairy products Quality-based payment systems Milk chemical composition was measured per farmer supplying DP1 and DP2. Based on these figures, an average value of 11.6% total solids () was chosen in both cases as the base price of a qualitativebased payment system of milk. In the reference scenario, up to 70 percent of dairy farmers receive lower milk prices than they should receive and around 25-30% of them are overpaid. Although the lack of milk chemical quality control seems to be advantageous for dairy processors, this situation is quite risky since the farmers underpaid could prefer to join a processor who offer higher milk prices in a context of high competition for milk.

Simulation of a quality-based payment system considering an increase of 0.3% of total solids after the implementation of a bonus of 0.03 per unit above 11.6% and a penalty of 0.10 per unit below showed differences on dairy processor total profits of less than 5%, mainly because the two small-scale dairy processors analyzed receive already milk with adequate level of total solids. Nevertheless, the simulation gave to small-scale dairy processors the possibility to better estimate the maximum amount of money they can pay per liter of milk to each of their dairy farmers. Moreover, dairy processors realized that if they apply a quality-based payment system they will be reducing the overpaid farmers, rewarding correctly farmers who provide good quality and ensuring suppliers’ loyalty without increasing considerably their milk cost.

Simulation of economic processors’ impact according to the seasonal variation:

Milk production in the Peruvian Andes is affected by seasonal variations. In the rainy season, milk availability is high due to good forage production. In the dry season milk production is lower and processors compete to collect enough volume. How these variations impact on dairy profits was simulated considering a virtual small-scale dairy which process 950 liters of milk per day and collect milk without significant variations in chemical quality (Table 4). Three scenarios were designed for each season: (i) keeping the same milk price but collecting 15% less of milk, (ii) increasing the milk price by 15% with the same amount of milk collected; (iii) decreasing milk collection by 15% while increasing milk price by 15%. These three scenarios may potentially occur in the two seasons because of competition between processors to buy milk in dry season, and between farmers to sell milk in rainy season.

Results highlight the sensibility of small-scale dairy processors to the seasonal variation and the importance to manage different strategies throughout the year. During dry season, offering dairy farmers an extra payment of 15% per liter of milk to keep the same amount of milk collected can provide more profits comparing than keeping the same milk price but losing 15% of milk collected.

Similarly, processors’ profits can be affected by more than 40% if higher competition for milk force processors to increase by 15% the price per liter of milk while they reduce by 15% the milk collected.

An opposite situation occurs during rainy season, where profits can be increase until 50% if processors collect 15% plus and pay 15% less per liter of milk. Nevertheless, the feasibility of this second scenario will depend from the availability of suppliers to accept a reduction on milk price and the processors’ capacity to process and sell the surplus of dairy products produced.

Table 4: Performances variations of a virtual small-scale dairy processor at Mantaro Valley* according to the season of the year

–  –  –

5. Discussion DairyPlant can be seen as a simple dairy processing model compared to the sophisticated predictive models used by the dairy industry in developed countries. However, the transparency of this simulation tool and its use as part of participatory support approach favored the active involvement of small-scale dairy processors in the construction of the spreadsheet application and the alternative scenarios, and in the discussion of possible implementation of the alternative scenarios simulated quality payment systems. Indeed, such tools allow processors to quickly assess how their profits would be impacted by different scenarios while understanding clearly how calculations have been carried out to obtain a given result. As such DairyPlant is close to the companion modelling approaches which try to avoid the “black box” effect of complex models (Voinov and Bousquet, 2010).



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