Collaborative vintage planning
“a network of decision makers, each with different goals, who collaborate and share rich information in order to arrive at decisions that mutually affect them. ‘Adaptive’ relates to a capacity to react creatively to situations in a fast and flexible way”
Those research results describe the complexity inherent in organising a grape harvest where most, if not all, the participants are independent of each other, but interdependent on the timely exchange of information. The key piece of information required for effective planning of grape intake is the aggregated understanding of when grapes will reach the desired ripeness (maturity) – the harvest plan. The other key part of an adaptive supply network is the sharing of information – the collaboration element. Part of our vision is to facilitate the sharing of the harvest plan with other interested parties such as harvesters, transport operators and contract processors, and this is a key feature on the enterprise subscription roadmap.
Of course, the flow of information is not unidirectional – information about harvester availability, chemical spray plans (from growers) and winery capacities (from contract processors) can all be incorporated into the harvest plan – as long as everyone involved is part of the conversation based on the plan. When we get there, that will be collaborative harvest planning.
Share your intake planning with the supply network; harvesters, transporters and crushing wineries
Latest updates from the blog …
We've been spending some time thinking about how we price the Harvest Plan service, and the discussion usually comes down to a relationship to what we call, here in Australia at least, a “block”. What does that mean? What is a block? How do we define it so we can use...read more
In closing the last post, we introduced our prediction widget, which is a playground to get a feel for how predictions work. It is a graphing tool that shows how different prediction models behave on a set of real samples. It shows two linear models: One Baume per...read more
In the last post, we considered the situations where we had little or no historical data for the vineyard of interest. In this post, we consider our preferred situation: we have data. When we have historical data In this case, we can model ripening rates with a view...read more