When searching for potential options to risky scenarios, doing a brainstorming is often the best idea. However, the brainstorming session benefits from a set of guiding option alternatives, which can be brought to the table after the initial brainstorming. The next step is to design and describe the option ranges of the core scenarios. The game board is a combination of scenarios and options with a clear link between the different scenarios. If the scenarios are not interlinked, you can advantageously create a game board for each group of scenarios.
When a list of options has been found, the next step is to identify the upside and downside of each suggested option. This assessment is conducted by creating a matrix showing the three most critical scenarios in the upper row and the three best option alternatives in the first column. A group discussion that clarifies the upside and downside of the option to each scenario will help everyone see the possible risks of and gains by selecting a specific option or doing nothing at all.
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Finance should be involved to estimate the financial implications. Below, an example of a risk and impact matrix is shown. The final step is to evaluate the speed of decision based on well-defined decision horizons for typical options. Do we need a decision now or can we consider the scenario analysis as an early warning?
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The one-pager should include:. Scenario planning enables a company to discuss and evaluate the early warnings of a wide range of risk events.
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The scenarios that have been assessed as early warnings need to be monitored to see how they have evolved and most likely will evolve. Many departments that each supply their own data and calculations are involved, and executing the conclusions reached will often be an intricate set of interlinked actions across the whole business unit.
The major advantages include:. Traditional forecasting techniques often fail to predict significant changes in the environment that companies operate in, especially in case of rapid changes or when information is limited.
Scenario planning supports different strategies, e. The key factor in implementing a successful scenario planning process is to get broad and meaningful involvement of key stakeholders, enter into an honest dialogue on strengths, weaknesses, opportunities and threats to the company and find a balance between plausibility and spread this kind of thinking throughout the entire company.
It would be very difficult for production to react to any unexpected issues or market opportunities. Hence, capacity-enhancing activities were needed.
In parallel, the forecast was evaluated as being too low compared to the strategic targets and, moreover, the utilisation assumptions behind the capacity plan were evaluated as being too optimistic. Both of these factors had a negative impact on under capacity compared to the base plan.
In order to understand the severity and additional risk of this situation, scenario planning was applied. Supply scenarios also included a best-case and a worst-case scenario. Step 3 — option scenarios : Short-term capacity extension to create more surplus.
However, this is not included in the risk assessment, but is part of the final recommendation. Step 5 — recommendation: A decision proposal was created, and key messages included:. A quick decision-making situation leading to a significant increase in short-term capacity at the factory, thereby avoiding potential delivery issues and lost sales.
All cross-functional departments understood the situation and were proactive in relation to risks and opportunities and making the extension happen. Advanced statistical analysis provides detailed insights into how your organisation should focus its facility management spending to ensure most value for money. Eight pitfalls that you should be aware of in your next Warehouse Management System implementation. Read the article in German Download the article.
Scenario planning — Why should we do it? Step 1: Scenario triggers Identify drivers for scenarios First, we need to understand which factors can or will trigger a new scenario. Step 3: Identify possible options using the game board method Describe possible options When searching for potential options to risky scenarios, doing a brainstorming is often the best idea.
As shown below an example of possible options that have been identified. Step 4: Risk and impact matrix Assess risk and impact When a list of options has been found, the next step is to identify the upside and downside of each suggested option. Step 5: Create decision proposal The final step is to evaluate the speed of decision based on well-defined decision horizons for typical options. The one-pager should include: The decision proposal supported by an appendix containing further analysis High level business case and risk impacts for each option Recommendations Plan for monitoring Proposal for risk mitigation Contingency planning Scenario planning enables a company to discuss and evaluate the early warnings of a wide range of risk events.
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Food system scenarios use a diversity of scenario types and modelling approaches determined by the purpose of the exercise and by technical, methodological and epistemological constraints. Our case studies do not suggest Malthusian futures for a projected global population of 9 billion in ; but international trade will be a crucial determinant of outcomes; and the concept of sustainability across the dimensions of the food system has been inadequately explored so far.
The impact of scenario analysis at a global scale could be strengthened with participatory processes involving key actors at other geographical scales. Food system models are valuable in managing existing knowledge on system behaviour and ensuring the credibility of qualitative stories but they are limited by current datasets for global crop production and trade, land use and hydrology. Climate change is likely to challenge the adaptive capacity of agricultural production and there are important knowledge gaps for modelling research to address.
The first documented attempt to use modelling to explore the uncertainty surrounding the world's ability to feed a growing population was possibly by Malthus in the first edition of An Essay on the Principles of Population. This essay famously put forth the hypothesis that exponential population growth and its associated demand for food would overwhelm linear growth of supply. Malthus's hypothesis has been subject to persistent challenge both empirically and theoretically Boserup ; but with population growth projected by the UN to increase to 9. In the past decade, a number of further studies concerned with the future of the global food system have been published.
In order to manage the uncertainties inherent in this system, these studies have used scenario analysis as well as model simulations. This article reviews a selection of contemporary studies which use scenario analysis and modelling to explore the future of the global food system to Case studies have been chosen to illustrate a scenario typology and to demonstrate a diversity of modelling approaches.
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We begin with a short history of scenario analysis and then provide an explanation of why scenarios have been increasingly used to manage uncertainty in systems with socio-economic and biophysical dimensions. A typology is proposed to classify the scenarios used by the case studies. An outline of the quantitative modelling approaches of the case studies is followed by brief summaries of their scenarios and a discussion of results.
The article concludes by considering some of the challenges for food system scenario analysis and modelling. The emphasis on exploring multiple futures underlines that scenario analysis does not aim to predict the future. Scenario analysis copes with uncertainty by presenting a range of plausible futures, usually without assigning probabilities to the outcomes. In particular, for complex socio-ecological systems, scenarios can be used to explore uncertainties over long-term horizons that cannot be represented by probability distributions on known parameters Swart et al.
The origins of scenario analysis lead back to the Manhattan Project in , where the limits of using probability in decision-making led to computer simulations of atomic explosions. Kahn's book On Thermonuclear War used scenario analysis to explore the uncertainties surrounding nuclear war Kahn A subsequent book The Year written in graduated his methods beyond military planning; and it was also a signal of growing curiosity in the comparative advantage scenario analysis might offer to business.
Pierre Wack pioneered the use of scenario analysis at Shell based on possibilities presented by Kahn for corporate planning. In the late s, Shell used a system of Unified Planning Machinery with a 6 year horizon to prepare its value chain for the future. Wack participated in an experiment to look ahead 15 years in an exercise called Horizon Year Planning. The striking findings of the study, which suggested that transformative change could be imminent in the oil market, provoked Shell in to migrate from predictive forecasting to a new method of scenario analysis Wack a.
The approach employed by Wack at Shell, and adapted from Kahn's early work, identifies predetermined elements in a system of interest in order that the outcomes of uncertainties, which are prioritized strategically, can be explored in multiple scenarios. The food system shares an important attribute with that of the energy system: crop-based technologies often have long lead times. Strategic planning is likely to become increasingly necessary if the world is to feed a projected 9 billion people healthily and sustainably in The food system is multi-dimensional Ericksen and includes social, economic, biophysical, political and institutional dimensions.
There are technical uncertainties concerning the quality of data available to calibrate the model and to determine input assumptions for drivers of change. There are methodological uncertainties because we may lack sufficient knowledge to create an adequate model form with suitable structure and functional forms of behavioural equations. Furthermore, a complex system may be fundamentally indeterminate. An accumulation of these uncertainties in a model simulation makes assigning probabilities to outcomes challenging.
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