Wine Production Case Study

Description

The following case study illustrates a scenario, proposed by Donnafugata, related to the wine production. It involves a Wine Producer who wants to maximize his production in order to adapt it according to the monitored market needs. Other actors of the scenario are the Quality Manager, the Agronomist (i.e., an expert of a branch of agriculture which deals with field-crop production and soil management) and the Oenologist (i.e., an expert of wine and wine production). They have to observe the vineyard parameters and to react to critical conditions that may happen during the cultivation phase. Critical conditions may be represented by overcoming the threshold for some particular environmental parameter. The case study also shows the processes involving the harvesting of the grapes and the logistics to deliver the product to retailers.

Business Goals and Domain Assumptions

In this sections the Business Goals and the Domain Assumptions for the current case study will be reported.

Business Goals

Table BG1. Business Goal WINERY-S-BG1

Field	Description
UniqueID	WINERY-S-BG1
Short Name	Observe Market Needs
Type	Business Goals.
Description	Starting from the domain information, the system shall provide a way to infer critical conditions from the analysis of market needs. It shall react in an automatic way to those critical conditions, both selecting predefined reactions and inferring reactions from a knowledge base. Standard reactions are provided in the scenarios and in the domain sections.
Rationale	Maximize sales volume.
Involved Stakeholders	Quality Manager
Conflicts	None
Supporting Material
Priority of accomplishment	Must have.

Table BG2. Business Goal WINERY-S-BG2

Field	Description
UniqueID	WINERY-S-BG2
Short Name	Observe vineyard cultivation
Type	Business Goals.
Description	The system shall provide a way to infer critical conditions from observing vineyard parameters. It shall provide a way to react in an automatic way to those critical conditions, both from selecting predefined reactions and inferring reactions from a knowledge base. Notifications to the Quality Manager, Oenologist and Agronomist shall be included in such predefined reactions. Other standard reactions are provided in the scenarios and in the domain sections.
Rationale	Maximize sales volume and wine quality.
Involved Stakeholders	Quality Manager, Oenologist, Agronomist
Conflicts	None
Supporting Material
Priority of accomplishment	Must have.

Table BG3. Business Goal WINERY-S-BG3

Field	Description
UniqueID	WINERY-S-BG3
Short Name	Observe maturation, fermentation and harvesting
Type	Business Goals.
Description	The infrastructure shall provide: a way to infer critical conditions from observing vineyard parameters; a way to react in an automatic way to those critical conditions, both from selecting predefined reactions and inferring reactions from a knowledge base. Notifications to the Quality Manager are included in such predefined reactions, especially if the critical conditions require manual interventions. Other standard reactions are provided in the description of the case study. Moreover, the Quality Manager shall be able to control quality parameters explicitly.
Rationale	Maximize sales volume and wine quality. In particular, this business goal handles the management of the critical conditions during phases following cultivation.
Involved Stakeholders	Quality Manager, Oenologist
Conflicts	None
Supporting Material
Priority of accomplishment	Must have.

Domain Assumptions

Table DA1. Assumption WINERY-S-DA1

Field	Description
UniqueID	WINERY-S-DA1
Short Name	The system to be should be driven by a self-managing business process
Type	Domain assumption
Description	The overall business process must be designed such that it shall perform self-management, that is, it shall implement the so-called MAPE cycle, that adheres to the scenario related to this assumption. In the MAPE cycle, the execution of the business process is based on a paradigm that involves resource Monitoring, collected data Analysis, intervention Plan, and action Execution. In the case of the proposed scenario, monitoring comes from the physical infrastructure (see next assumption), and the remaining parts of the paradigm must be implemented by the self-managing business process, which permits to define intervention plans and action executions after a critical condition detection as required by the related scenario. In this approach, detection of market changes and reaction to these changes shall be implemented as a particular instance of the MAPE cycle within the autonomic infrastructure.
Rationale	See Description.
Involved Stakeholders	Quality Manager
Conflicts	None
Supporting Material
Priority of accomplishment	Should have.

Table DA2. Assumption WINERY-S-DA2

Field	Description
UniqueID	WINERY-S-DA2
Short Name	Vineyard is equipped with a wireless sensor and actuator network.
Type	Domain assumption
Description	This assumption arises from the necessity of examining parameters of vineyards, which are spatially distributed among cultivation fields. This infrastructure can be seen at the business level from two possible points of view: by a process manager component of a workflow engine or from a query layer infrastructure that offers the ability of programming events generation to deploy directly into the sensor networks. Sensors are needed to observe quality attributes of grapes during the phases of the production process.
Rationale	A distribute Wireless Sensor Network (WSN) infrastructure shall be able to sense the environment of vineyards and collect data.
Involved Stakeholders	Quality Manager, Agronomist
Conflicts	None
Supporting Material
Priority of accomplishment	Could have

Table DA3. Assumption WINERY-S-DA3

Field	Description
UniqueID	WINERY-S-DA3
Short Name	Time between harvesting and processing should be limited
Type	Domain assumption
Description	This assumption is a very simple constraint on the business process, that requires that the time between harvesting and processing of the grapes must be limited, typically within one hour, or it must be related to specific requirements of a particular production.
Rationale	Maximize the quality of the final product.
Involved Stakeholders	Quality Manager
Conflicts	None
Supporting Material
Priority of accomplishment	Must have

Table DA4. Assumption WINERY-S-DA4

Field	Description
UniqueID	WINERY-S-DA4
Short Name	Logistic is supported through a RFID system.
Type	Domain assumption
Description	This assumption constrains the design of the physical and logical infrastructure for the observing and querying of temperature during the distribution phase. Moreover each bottle has associated a RFID, each pallet has associated a RFID data logger. In this case, since it is necessary to track and record the temperature information of moving packages, RFIDs and data loggers are suggested to be used. Moreover, to provide an uniform method for querying in such a physical architecture, a query layer for pervasive infrastructure should be used, such as PERLA. Finally, this infrastructure should be interfaced with the self-managing business process, in order to use it as a source for reacting to possible critical events regarding distribution.
Rationale	See Description.
Involved Stakeholders	Delivery Company, Retailer
Conflicts	None
Supporting Material
Priority of accomplishment	Could have

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Scenarios

The figure shows the general use-case diagram for the Vineyard case study.

Table S1: Scenario WINERY-S-CH-1

Field	Description
UniqueID	WINERY-S-CH-1
Short Name	Cultivation Handling
Related To	WINERY-S-BG1, WINERY-S-BG2, WINERY-S-DA1, WINERY-SDA2
Involved Actors	The actors involved in the cultivation handling scenario are the agronomist, the oenologist, the quality manager and the Wine Grower. Moreover, the market has some role on determining which vineyard should be cultivated.
Detailed Operational Description	Cultivation handling is mainly performed by the agronomist, the oenologist and by the Wine Grower; it is supposed that they handle N vineyards of a wine producer (winery). For each vineyard, the handling process implies the analysis of functional parameters such as temperature, humidity, light, wind speed, etc. in specific months of the year. The kind of vineyards to be cultivated are determined by information coming from the market, in the sense that using statistical data about sales of previous years, the enterprise infers which vineyards are more likely to be cultivated in order to produce the kind of wines that will maximize sales. The agronomist and oenologist determine the vineyard quality by analyzing gathered information. It may happen that those actors could detect critical conditions on which some recovery actions should be performed in order to react and prevent damages for the wine production. Critical conditions can involve some events on the environment (such as frost destroying the vineyard), or some other events involving the measurement of the quality versus its estimate coming from market information. The identification of the recovery actions is performed by the quality manager together with the oenologist and the agronomist. Actions include notifications and complex processes to be performed by different actors.
Problems and Challenges	The main problems arising with the described complex scenario involve: handling the complex process of vineyard cultivation management; identification of recovering actions; automatization of observing vineyard parameters, detection of critical conditions and performing of recovery actions. provide an automated way to infer an estimate of market needs;
Additional Material	The following Activity Diagram shows the sequence of the activities to be done in the current scenario.

Table S2: Scenario WINERY-S-CH-2

Field	Description
UniqueID	WINERY-S-CH-2
Short Name	Managing the Market Needs
Related To	WINERY-S-BG1, WINERY-S-BG2
Involved Actors	Market and Wine Producer
Detailed Operational Description	Inputs derived from market needs must be properly managed. In this scenario, it must be possible for the information system to get forecasts for the current year, in terms of specific sales volume, together with wine kind and its quality. Thus, the information system of the enterprise should be able to infer the kind of grapes and consequently the vineyard to be cultivated. Moreover, from the observing vineyard parameters activity, it should be possible to estimate the quality of wine based on the health status of the vineyard (based also on the information gathered during all the production phases). During the management process, it must be possible to detect some critical conditions regarding the estimated wine quality. One of those condition is the following: ”the estimated Qs from the observing activity seems to be too much different from the quality Q desired from customers”; other conditions are more low level and they are related to specific vineyard conditions. A possible response action to these conditions is buying from other producers an amount of grapes automatically suggested by the observing system.
Problems and Challenges	The main problems arising with the described complex scenario involve: proper detection (inference) of market needs; proper identification of recovering and response actions.
Additional Material	The following Activity Diagram shows the sequence of the activities to be done in the current scenario.

Table S3: Scenario WINERY-S-HFM

Field	Description
UniqueID	WINERY-S-HFM
Short Name	Harvesting and Fermentation
Related To	WINERY-S-DA1, WINERY-S-DA2, WINERY-S-BG3
Involved Actors	Quality Manager and Oenologist
Detailed Operational Description	In those three phases, the quality manager should be helped to control quality attributes to keep the wine production quality at the required level. The controlled phases are the following: Harvesting; is a critical part of the wine production process. Usually, it is necessary to: Minimize the interval between harvesting and grapes processing; Evaluate climatic conditions for harvesting (depending on the particular kind of grapes or production, they may require specific climatic conditions); Fermentation: Chemical analysis (both “in loco” and in the lab) to monitor quality and avoid critical events, such as high concentration of acetic acid or presence of dangerous bacteria; those events must be properly communicated so that they can be properly managed by manual intervention; acidity, humidity and temperature must be recorded in each cellar to monitor the quality of the produced wine. In any transportation sub-phase, humidity and temperature must be observed.
Problems and Challenges	The main problems arising with the described complex scenario involve: provide a distributed and secure infrastructure for observing critical parameters, both during fermentation and harvesting; monitor critical parameters during any transportation phase; minimize the time between harvesting and the grapes processing.
Additional Material	The following Activity Diagram shows the sequence of the activities to be done in the current scenario.

Table S4: Scenario WINERY-S-DS

Field	Description
UniqueID	WINERY-S-DS
Short Name	Distribution and Sale
Related To	WINERY-S-BG3, WINERY-S-DA1, WINERY-S-DA4
Involved Actors	Quality Manager, Wine Producer, Delivery Company and Retailer
Detailed Operational Description	During the sales phase the Wine Producer interact with the Retailer to stipulate contracts. The orders will be delivered by the Delivery Company. During the sale phase, the quality manager is interested in the returns in order to compare them with the conditions of the product during all the life-cycle models. During the distribution phase, the quality manager must be able to assure that, starting from monitored values, the temperature variation meets some strict requirements (i.e., no wide fluctuations and it must be kept within a specific range).
Problems and Challenges	The main problems arising with the described complex scenario involve: provide a distributed and secure infrastructure for observing critical parameters during distribution; provide an infrastructure to track market information and predict changes in the market in a narrow time scale.
Additional Material