Let’s stop making assumptions for sustainability goals… Sustainability, emissions, carbon reduction and net-zero are all hot topics for manufacturers right now…. And rightly so. According to the U.S. Energy Information Administration (EIA), manufacturing accounts for 81% of industrial energy consumption and the UK’s Government Office for Science recent report, Manufacturing was directly responsible for 11.2% of the UK’s Gross Domestic Product in 2010, and 16.5% of its final energy demand. Yet when it comes to understanding what in the manufacturing process in consuming all this energy, we are making assumptions.
Why we need to stop making assumptions for sustainability goals
At Nick Leeder & Co we are working we manufacturers on their overall transformations in manufacturing, and without fail, sustainability (as well as profitability) are are the heart of their goals. We are also working with with solution providers in defining and developing capabilities to help manufacturers track and report on sustainability goals. What we are seeing in the market is a fundamental flaw in understanding how energy reduction and sustainability goals are understood and measured. Almost without file, modelling companies are using are “assumptive” rather than “consumptive“. Let us explain further…
What is the difference between assumption and consumptive modelling
Assumptive modelling is a method of projecting future results based on certain assumptions. It makes predictions based on the assumption that certain conditions will remain the same in the future.
Consumptive modelling, on the other hand, refers to the modelling of the consumption of resources, such as water or energy. It predicts the amount of resources that will be consumed, taking into account factors such as population growth and changes in consumption patterns.
So how most companies understand their impact on the environment is assumptive modelling, making predictions based on assumptions. We argue that companies need to switch to consumptive modelling, predicting resource consumption based on consumption patterns and other relevant factors.
| What are the risks with assumption modelling? | What are the benefits of consumptive modelling? |
|---|---|
| Over-reliance on assumptions: Assumptions may not always be accurate, leading to flawed predictions | Resource planning: Consumptive modeling helps in planning and allocating resources efficiently. |
| Lack of adaptability: If the assumptions turn out to be incorrect, the model may not be able to adapt to changing conditions. | Better decision making: By predicting resource consumption patterns, consumptive modeling can inform decision making in various industries such as agriculture, energy, and water management. |
| Limited perspective: Assumptions may only be based on limited information and data, leading to a narrow perspective on the situation. | Early warning system: Consumptive modeling can serve as an early warning system to identify potential resource shortages and inform appropriate interventions. |
| Unforeseen events: The model may not take into account unexpected events or developments that can significantly impact the results. | Sustainable resource management: By predicting resource consumption patterns, consumptive modeling can inform sustainable resource management practices and ensure that resources are used efficiently and effectively. |
| Bias: The model may be influenced by the biases or opinions of the person making the assumptions. | Improved forecasting: Consumptive modeling provides more accurate forecasts of resource consumption compared to other methods. |
Overall, the accuracy of assumption-based models depends heavily on the quality and validity of the assumptions used. Inaccurate assumptions can lead to incorrect predictions and flawed decision making. And frankly, from what we have seen, this is too often the case.
Whilst consumptive-based modelling provides valuable information for decision making and helps in promoting sustainable resource management practices.
Examples of consumptive modelling in manufacturing?
An example of consumptive modelling in manufacturing is predicting the consumption of raw materials, energy, and water in a production process. This type of modeling helps the manufacturing company:
| Plan and allocate resources efficiently | By predicting the consumption of raw materials, energy, and water, the company can make informed decisions on resource allocation and avoid overproduction or stock shortages. |
| Reduce waste | By predicting resource consumption patterns, the company can identify areas where waste is occurring and implement measures to reduce it. For example leakages in compressed air systems. |
| Improve sustainability | By forecasting the consumption of resources, the company can implement practices to reduce its environmental impact and promote sustainable resource use. This is key to support standards such as ISO 50001. |
| Increase efficiency | By using consumptive modeling to predict resource consumption, the company can identify inefficiencies in its production process and implement measures to improve efficiency. For example, moving from scheduled maintenance to predictive maintenance practices, reducing both waste and downtime. |
For example, a company producing steel may use consumptive modelling to predict the consumption of iron ore, coke, and coal in its production process. The model will take into account factors such as production volume, process efficiency, and changes in demand for the steel product. This information can be used to optimize resource allocation and reduce waste in the production process.
How does a company move from assumptive modelling to consumptive modelling?
This section over-simplifies the complexity and effort needed to move from assumptive modelling to consumptive modelling, but the steps are fairly well documented from experiences in other industries:
| Data collection | Collect data on resource consumption patterns, such as the amount of raw materials, energy, and water used in the production process. |
| Analysis | Analyze the collected data to identify patterns and trends in resource consumption |
| Model development | Develop a consumptive model using the collected data and information on production processes, demand for products, and other relevant factors. |
| Model testing | Test the model against actual resource consumption data to validate its accuracy. |
| Implementation | Implement the consumptive model in the company’s decision-making process, using it to inform resource allocation, reduce waste, and promote sustainable resource management practices. |
| Monitoring and refinement | Continuously monitor and refine the model, updating it with new data and information as needed to ensure its accuracy and relevance. |
The move from assumptive modeling to consumptive modeling requires a significant investment in data collection, analysis, and model development. However, it can lead to more accurate predictions and better-informed decision making, ultimately improving the company’s bottom line and achieving its sustainability goals.