Sales and Operations Planning is the central point of most enterprise decisions as big bets are placed on anticipated demand which result in portfolio planning and procurement outcomes. This age-old process is critical to unlocking spend for product and services but sadly is somewhat of a dying art as enterprises' commitment to it wanes in favour of individuals’ intuition, compounded by consumer and economic uncertainty. Now, as so many companies must cut costs in response to long-term structural changes in demand, there has never been a more important time to stay disciplined and enhance planning through recent innovations in real-time data and the resulting insights.
Our demand planning solution QU has been in-market for 12 months, helping planners and marketers understand changes in future buying behaviour, rather than rely on historical data. What’s exciting is seeing the early results. QU is improving the Weighted Absolute Forecast Accuracy by more than the industry benchmarks of 50-60%, (and even closer to 80% for a client in South East Asia).
QU is bringing the analysis of real time demand from over 300M consumer signals to our clients’ desktop. Planners arrive at work to be greeted by the future behaviours which are material to demand, meaning decisions on marketing, sourcing or procurement can be made outside of lead time windows, thereby optimising cost-to-serve.
There are infinite potential ‘demand drivers’ for every product (SKU) - far too many for any planner to effectively consider in a forecast. This is where QU’s artificial intelligence plays its part to identify and illustrate the drivers of demand important for each SKU, and alert you to any significant change. Demand drivers can include anything from specifications of a product or service, to the sentiment expressed on social media. Demand drivers are dynamic, and their influence shifts over the course of a product’s life cycle. For example, the further out the forecast horizon, the less important historical sales data becomes and the more important it is to understand the human behaviour associated with the demand.
By deploying QU, clients can understand the unexpected changes in consumer behaviour that could throw plans off course and remediate them before they cause inventory issues. QU is especially valuable as demand volatility increases, because consumers spend more time creating data through their research. This in turn produces additional (aggregated) data points about future intentions. Combine this with advances in data science, and we are now able to see the impact of drivers on future buying decisions quickly enough to mitigate inventory issues or drive acquisition.
Those of us in planning, operations and marketing should use data - not intuition - to bring the voice of the customer into an integrated planning process. In a downturn, brands look to sales to make their numbers but optimising supply chain and demand planning offers significant leverage too, especially on key metrics like working capital and cost-to-service.
Interested in using AI to optimise your supply chain planning? Contact us for a demo.
Health, animal welfare, the environment - no matter the reason, the plant based trend is here to stay. You may have noticed plant based eateries popping up left, right and centre as more restaurants and retail outlets adapt their menus and shelves to cater for their vegan or plant based clientele. Environmental concerns, animal welfare concerns […]
Kevin Cooney, Acting CEO, Farmlands, discusses how AI could be the answer to forecasting demand in an increasingly volatile ag industry. Political and social pressure on farmers to make changes to farming practices to address growing climate and environment concerns gained new impetus this month with Environment Minister David Parker, saying that the farming sector […]
In 2019 African swine flu devastated China’s pork production and it turned to South America and the USA, once tariff wars settled down, for pork and beef to avoid shortages. Now, supply chains in these countries are again in trouble due to COVID-19 shutting down or severely restricting production capacity, putting at risk supply to […]
The new iPhone range is finally here - What was the initial consumer response and what does this mean for planners looking to forecast in one of the most unpredictable launch years to date? To answer this, we analysed consumer buying signals coming out of the iPhone 12 launch announcement and from the equivalent iPhone […]
Consumer buying behaviour is continuously evolving and your forecasting should be too. Using state-of-the-art AI and real-time demand sensing, QU brings more accuracy to your new product introduction and predicts Demand Drivers™ ahead of time.
We are excited to welcome Rebecca Kemp to our team as Chief Product Officer, responsible for understanding the customers’ perspective and ensuring global market fit for QU, our cloud-based demand driven prediction platform. Rebecca brings a self-described obsession with customer focus, believing that only by living in their world can digital products truly hit the […]
An old adage in business is that the closer you get to your customer, the better you will understand what they really want – but what happens if those customers are constantly changing their position? In this new world of Covid19, the ‘new normal’ is characterised by a high level of uncertainty and volatility in […]
Our co-founder, Jamie Cormack, presented Quantiful at the Icehouse Flux Demo Day in front of over 250 investors. Watch his video below!
This week we bid farewell to our wonderful team of interns who have been with us for the last few months. Before they leave, we asked them a bit about their time here, what they learnt and their best memories. We will miss you team, good luck for the future!
Last week I was lucky enough for Quantiful to send me to attend the 2018 Apache Spark AI Summit in San Francisco. Apache Spark is an open-source large scale computing framework. Essentially, it provides an easy way for data scientists to crunch and process large amounts of data. Apache Spark allows programmers to scale the […]
