Business Process Modelling in Demand‐Driven Agri‐Food Supply Chains

Agri ‐food companies increasingly participate in demand‐driven supply chains that are able to adapt flexibly to changes in the marketplace. The objective of this presentation is to discuss a process modelling framework, which enhances the interoperability and agility of information systems as required in such dynamic supply chains. The designed framework consists of two parts: an object system definition and a modelling toolbox. The object system definition provides a conceptual definition of business process in demand‐driven supply chains from a systems perspective. It includes an application of the Viable Systems Model of Stafford Beer to supply chains, and classifications of business processes, control systems and coordination mechanisms. The modelling toolbox builds on the terminology and process definitions of SCOR and identifies three types of process models: i) Product Flow Models: visualize the allocation of basic transformations to supply chain actors and the related product flows from input material into end products (including different traceability units based on the GS1 Global Traceability Standard); ii) Thread Diagrams: visualize how order driven and forecast driven processes are decoupled in specific supply chain configurations (positions Customer Order Decoupling Points), and how interdependences between processes are coordinated; iii) Business Process Diagrams: depict the sequence and interaction of control and coordination activities (as identified in Thread Diagrams) in BPMN notation. The framework is applied to several agrifood sectors, in particular potted plants and fruit supply chains. The main benefits are: i) It helps to map supply chain processes, including its control and coordination, in a timely, punctual and coherent way; ii) It supports a seamless translation of high level supply chain designs to detailed information engineering models; iii) It enables rapid instantiation of various supply chain configurations (instead of dictating a single blueprint); iv) It combines sector specific knowledge with reuse of knowledge provided by generic cross industry standards (SCOR, GS1)

Virtualization of food supply chains with the internet of things

-Internet technologies allow supply chains using virtualizations dynamically in operational management processes. This will improve support for food companies in dealing with perishable products, unpredictable supply variations and stringent food safety and sustainability requirements. Virtualization enables supply chain actors to monitor, control, plan and optimize business processes remotely and in real-time through the Internet, based on virtual objects instead of observation on-site. This paper analyses the concept of virtual food supply chains from an Internet of Things perspective and proposes an architecture to implement enabling information systems. As a proof of concept, the architecture is applied to a case study of a fish supply chain. These developments are expected to establish a basis for virtual supply chain optimization, simulation and decision support based on on-line operational data. In the Internet of Things food supply chains can become self-adaptive systems in which smart objects operate, decide and learn autonomously

The social influence of investment decisions : A game about the Dutch pork sector

Policy makers and researchers foresee four investment strategies for conventional pig farmers in contested pork production regions: (1) continue with a cost-price reduction strategy through modernisation and scale enlargement; (2) convert to an intermediate market segment with higher requirements as to animal welfare and environment than conventional; (3) convert to a niche market segment with higher requirements as to animal welfare and environment than intermediate; or (4) quit farming. For policy makers, it is interesting to gain insight in intensive livestock farmer's perceptions regarding these investments and in processes of social interaction that influence farmer decision-making and the potential diffusion of investment strategies over time (Edwards-Jones, 2006). The aim of this explorative study is to analyse the effect of social interaction on diffusion of investment strategies in capital-intensive livestock production systems with groups of Dutch pig farmers, using a simulation game. The game is designed in such a way that contextual factors do not provide a limiting factor. Furthermore, the game is constructed to stimulate interaction and to trigger imagination of participants. Our main research questions for the analysis of the results of the game sessions were: (1) ‘what are differences in diffusion of investment strategies between sessions?’ and (2) ‘to what extent does social interaction affect diffusion of investment strategies?’ A total of seven sessions were played, with 4–8 pig farmers and/or participants who were affiliated to the sector as advisor or successor. All game sessions were video- and voice- recorded, and interaction between participants was transcribed per game session. First, differences in diffusion of investment strategies between sessions were explored. Second, the causes for differences in diffusion between sessions were explored, by looking at the type of investment strategy, communication between participants, and processes of influence. Special attention was given to the influence of opinion leadership. The results of this research show that (1) only investment strategies with a financial benefit did, under influence of social interaction, result in high adoption; (2) for high adoption to occur, communication between participants was necessary; (3) opinion leaders played an essential role in high adoption of investment strategies; and (4) there was a common understanding among participants that favoured scale enlargement. The gaming methodology triggered participants to communicate their tacit knowledge, i.e. assessment criteria that are important in real-life investment decisions, and to experiment with investment strategies.</p

A Farm Information Model for Development and Configuration of Interoperable ICT Components to support Collaborative Business Processes – a case of late blight protection

Farm enterprises2 The objective of this paper is to describe a farm information model and a proof of concept that demonstrates how a collaborative Business Process for farming can be configured using this farm information model. Knowledge to develop this model and a proof of concept is obtained by case study research focusing on the collaborative Business Processes of spraying and crop protection of potatoes against late blight disease. need to collaborate with numerous actors that are part of Agri-Food Supply Chain Networks (AFSCNs) such as governments, advisory services, contractors, processors, input providers and certification bodies. This collaboration is required to produce food in a more sustainable, safe and transparent manner. To collaborate efficiently and effectively, information needs to be shared within collaborative Business Processes. The information sharing within such collaborative Business Processes should be supported by an ICT infrastructure consisting of interoperable ICT Components. Currently, most of the available ICT Components are not interoperable, hindering data exchange between ICT Components of various vendors. Consequently, this situation is hindering optimization of farm production processes and collaboration in AFSCNs. Therefore, a platform, called FIspace, is being established for multiple domains that support the development and configuration of interoperable ICT Components into a system that is able to support collaborative farm Business Processes. To develop interoperable ICT Components and configure these in an easy and flexible manner to support farm enterprises, a farm information model is, amongst other models, required.The presented farm information reference model is able to describe the relations between a farm enterprise and its collaborators, the Business Processes related to the supporting ICT Components and the data messages for data exchange between ICT Components

Scenarios for the Development and Use of Data Products Within the Value Chain of the Industrial Food Production

Part 4: Data-Driven Production ManagementInternational audienceThe industrial food production is currently caught between the increasing demands of numerous stakeholders, economic profitability and the challenges of digitization. A solution to face these various challenges can be seen in the aggregation of data into higher-value, independent data products that can be offered and sold on a buyer’s market. Large amounts of heterogeneous data are already available in the value chain of the industrial food production, e.g. throughout the data-driven harvesting of primary products, further processing by interconnected production facilities and the information-intensive product distribution to end consumers. However, the data is usually only evaluated and used locally for the optimization of internal processes or, at the most, within comprehensive partnerships. The purpose of this paper is to identify new revenue opportunities for current and future players in the industrial food production by using data as an independent economic good (data products). For this purpose, scenarios for the development and use of data products via Industrial Internet of Things platforms are developed for a food technical reference process, the industrial chocolate production and its value chain. On this basis, examples for different types of data products and their value propositions are derived. The results can not only serve food producers and relevant stakeholders but all industrial producers as an input for the future, yield-increasing orientation of their business models