Demand and capacity planning is more complex than ever
In times of economic stability, business planning is not always quite so challenging. You can expect to make reasonable forecasts as to what the demands on your business might be, and how you need to set up to meet them.
However, in these unprecedented times, businesses need to navigate their way out of the pandemic-scarred macro and micro-economic situation, and the resulting effects on domestic and international trade policies, supply chain availability, and volatility.
In addition, there are ongoing government ‘green’ agendas, rapidly changing customer demands, technology-driven business disruptors, and new, emerging competition. One thing is certain: there are so many new variables and complex correlations to consider, that the pathway to success is harder to navigate than ever.
It can feel like a minefield to evaluate these new challenges and understand how different business scenarios would affect your company’s ability to respond. Especially when you know that the penalty of failing to respond is significant.
For example, a 30-day disruption caused by supply chain vulnerabilities can lead to 3-5% EBITDA margin gaps, according to McKinsey.
So, what solutions are there to help decision makers looking to make critical decisions around future business footprints, re-shoring, recruitment and skills requirements, automation innovation and technology investments, or new supply chain configurations?
Predictive digital twins can help make sense of these complexities, providing a virtual model of your business to help you answer both tactical and strategic questions about the things that affect your ability to hit KPIs.
With a digital model of your business, you can ask “what-if” questions, such as:
- Will investment in Industry 4.0 technologies help us improve our responsiveness?
- What are the risks of consolidating production into a smaller footprint?
- Can we successfully move from having a single NDC to having regional ones?
- Should we increase stocks of raw materials or finished goods to mitigate the risk of supply chain disruption?
These complex, interconnected questions aren’t ones an individual engineer, analyst or manager can answer using a simple spreadsheet or ‘current state’ BI dashboard. They each have myriad dynamic variables, involve complex business processes, and can be subject to multiple disruptions. They can also be addressed with near-infinite potential interventions, each with their own resulting actions.
And this is why predictive digital twins add huge value.