Abstract
Agro-industrial decision-making is hampered by several, variously-natured, uncertainties. As uncertainty reduction is expensive, the decision modelling process for these industries must strive to use all available information. However, said inclusive effort should be accompanied by an effort to keep modelling assumptions transparent. This work shows the development, from a Value-Focused Thinking perspective, of a model to assess alternatives for improving the operation of a cattle fodder producer. Modelling starts by analyzing and structuring the owner’s objectives and proceeds by systematically characterizing, via value judgments or probability distributions, the connections between structured objectives. Constructing the model over a blueprint of connected objectives allows a faithful representation of the understanding of the system behavior while the methodical, one-connection-at-a-time, modelling procedure renders the assumptions used to operationalize each connection visible, facilitating their replacement if more information becomes available. The modelling approach put forward here can support industrial decision making with limited information.
Highlights
The decision making in businesses and manufactures is hindered by several uncertainties
The management of industries and manufactures is affected by uncertainty, whose reduction may be unaffordable for small or medium-sized companies
The decision modelling for such companies should strive to make the most of the information at hand
Summary
The decision making in businesses and manufactures is hindered by several uncertainties. While large companies may be able to reduce their uncertainty about some elements (for example, by running in-house laboratories) this is not the case of small and medium-sized plants. Managers of these base their decisions on rough-and-ready cost-benefit analyses that include only factors that are known either precisely or quantitatively, disregarding uncertain and qualitative ones. As commanded by the VFT, the analysis begins by identifying and structuring the owner’s objectives. The model is constructed using said structures as a blueprint, allowing a faithful representation of the owner’s knowledge of the system behavior. Subjective probability distributions are used to capture owners’ and operators’ knowledge and scales are constructed for qualitative factors, gaining insight into their importance and meaning
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
