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S.M.A.R.T gains

Our projects translate into business impacts – be they savingsmargin improvements or revenue increases – that are specific, measurable, achievable, relevant and time-framed.
This is due to the very nature of the approach used. Indeed mathematical modeling imposes that one clearly identify and quantify the objectives, levers of improvement and their logical interrelations from the outset. Any solution is then expressed as a desired value for each variable of interest (e.g. production level, stock, selling price) and its expected impact on the bottom line. Therefore, our solutions not only provide guidance on the “What” but also on the “How”, making them highly valuable and effective when implemented in real-life situations.


High Return On Investment

On average, our projects have demonstrated performance improvements of 5 to 10%. In many complex industries, these last percentiles of improvement still have a significant value, and optimization tools are often the only means of reaching them. Such gains, associated with a well-focused project of reasonable size (typically between a few weeks and a few months), enable a really fast payback. Furthermore, as they mostly come from operational improvements, these are recurring savings, as opposed to one-time savings on, for example, structural costs.


Objective decisions and management alignment

Using mathematical models to support decision-making enables us to better justify and communicate decisions based on facts. Indeed, since these are clearly identified as model inputs, decision criteria become transparent and objective. And the solution proposed by the model can be proven as scientifically optimal with respect to the defined goal.

Therefore, once they have agreed on the inputs and the design of the model, managers from different departments will more easily accept and implement the proposed optimal solution for the company, even if it sometimes goes against their own objectives. It really helps to render the discussion between departments that might have diverging interests more objective (e.g. growing sales versus maintaining production costs, or lowering purchasing costs versus keeping high quality standards) for the good of the company as a whole. The role and support of the management is therefore essential in successfully implementing N-SIDE’s solutions.


Increased pro activity and risk control

Mathematical models enable the swift simulation of many alternative decisions. They can drastically simplify and accelerate processes that are usually cumbersome and time-consuming, such as fine-tuning planning, reviewing supplier’s offers, sequencing production orders… Time previously spent finding a single feasible solution can now be used by the team for higher added value tasks, such as trying to anticipate changes in the business environment and simulating diverse “out of the box” scenarios. By describing the underlying processes rather than the usual practice, a model can help identify innovative alternatives, as well as unsuspected risks.
To conclude, models help managers take a step back from their daily routine and therefore shift to a more proactive management mode.


People development

Leading such a project is a highly valuable experience for those selected. It represents a unique opportunity for qualified managers to build expertise around their company’s value creation levers. Upon completion, they will have developed new business-specific abilities, new capacities and gathered a wealth of knowledge. This will enhance their awareness of the global impact of decisions as well as the scope of project management and related responsibilities. Thanks to their exposure to quantitative management methods and advanced models, they will have a heightened understanding of the economic impact of technical choices as well as the significance of analytic and modeling skills.


Knowledge aggregation

Models describe an organization and its processes as faithfully as possible. For that purpose, they are fed not only with operations data, but also, and most importantly, with the company’s quantifiable knowledge base of processes, whether internally developed spreadsheets, models, scientific literature or the accumulated experience of operators. This task of collecting the existing knowledge within the company is essential to any N-SIDE project.

The resulting model plays the role of the organization’s memory. The know-how is centralized, formalized and maintained in a well-documented application that is widely accessible. When experienced people switch departments or retire, their knowledge is retained within the organization. For these reasons, models are also efficient tools for training newcomers, as they can familiarize them with a variety of process situations without affecting the smooth running of actual operations.


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