The proliferation of eCommerce has led consumers to expect shorter lead times. To cope with this expectation, Manufacturers are increasingly switching to a make-to-stock strategy.
Supply chain optimisation and especially demand forecasting becomes critical in ensuring service levels and fill rates are met. Demand forecasting has been practiced for over half a century and has taken on a special significance in the last year. Depending on the stage of the product life cycle, the industry average forecast error is estimated to be between 20% to 50%. High forecasts lead to excessive inventory that drives up cash-to-cash cycle times and storage cost. Low forecasts lead to slippage of due dates and missed revenue.
Ecosystm Research finds that in Southeast Asia
- 63% of Manufacturers are looking to leverage AI for supply chain optimisation
- 48% of Manufacturers are specifically focused on demand forecasting
- 77% of Manufacturers find integration with internal systems and other AI solutions the primary challenge in AI deployments
Factors influencing demand is multifaceted. Many businesses rely on time series based historical sales figures as it is the data that they have access to. The evolution of the internet has facilitated access to a range of near real-time exogeneous data such as advertisement campaigns and weather. These were not possible in the past.
Data science and AI are key in propelling businesses into this frontier. But at the same time, business leaders are sceptical as more than 80% of AI projects reportedly do not end up in production. Leveraging the new data available – including those in unstructured format – can be a challenge. But business leaders also grapple with enabling AI models for ease of integration with other IT systems. To ensure that these models can be put into operationalised state, and ready to be used by end-users, it is imperative that organisations get this right.
Join us on the 9th of September for this virtual event dedicated to organisations in the manufacturing sector. We will address demand forecasting challenges through a business and technology lens.
For Business Leaders who are looking to adopt a data science scoping methodology to ensure a data science project is well-setup for success:
- Secrets to success in a Data Science MVP
- Data Science MVP methodology
- Methodology application workshop
For Technical Leaders who are looking beyond open-source technologies into end-to-end data science platform to help accelerate the delivery of data science projects such as demand forecasting:
- See a live end-to-end demonstration on assembling a demand forecasting solution
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