As pressure mounts across all channels within the food industry, as a direct result of the corona virus, companies are increasingly turning to technology to help them meet the challenges they face to cope with increasing demand and continue to cut down on food waste.
Technology has always been an enabler to streamline and automate business process efficiency. Now, more than ever, the pressure created by this crisis on your systems will start to identify which processes within your business are ripe for technology support or even automation.
How often do you ask yourself “if we only had one clear accurate picture of the facts”? Many times, when speaking with clients, I hear this statement and why… well frankly it’s due to the number of spreadsheets different departments have grown up using which, by their nature, are off system making the ability to accurately forecast either sales or production demand is a real leap of faith.
This uncertainty often translates into over-production in order to be sure to create sufficient inventory to meet customer demands resulting in both increased cost and potentially compounding the increase in waste at the same time.
But how can you reduce your reliance on spreadsheets and cut through to the real data?
One solution is effective sales forecasting that can help create clarity when:
- A large part of production is planned at the forefront – i.e. before all orders are received.
- Many sales lines are dependent on both seasonal factors and competitors' Chain campaigns that can cause large and unpredictable sales fluctuations, even without a crisis on our doorstep!
- Consumers are increasingly demanding selection, product diversification, variation, traceability, package sizes and several other factors that make production, logistics and sales forecasting more complex… not to mention the inter-stellar spike in demand for online ordering and home delivery today.
That's why we at Columbus have begun to work far more purposefully with solutions that strengthen food companies' ability to automate the processing of historical data, predict trends, and automate impact calculations to adapt production to likely needs.
Machine Learning beats the experienced planners
Here, cloud-based Machine Learning, such as accessed through Columbus Food, plays a major role. Quite simply because the algorithms are surprisingly good at identifying patterns in demand.
In a test run with one of our industrial customers, the algorithms hit closer to actual demand than a set of experienced and highly competent forecasting specialists in 100 out of 100 cases.
Embracing technology to help you navigate your new course and succeed
Perhaps Machine Learning and Artificial Intelligence sound like science fiction? Actually, it’s not, it is here, today, alive and well and embedded across the cross-industry Microsoft Dynamics business systems ready for you to use as soon as you want to turn it on. Whether you are small or large the technology is there ready now.
What Partners can do is add “specialist” knowledge, like that needed to address the unique requirements of the FOOD Industry for example. With a well-functioning industry solution, such as Columbus Food, it will also be easier to manage logistics and adhere to principles such as First Expire First Out (FEFO) - so you can ensure the shortest shelf life inventory is sold first. This can also be combined with respect for the Chains’ policies on supply sustainability and help you comply with traceability, labelling compliance and meet any product recall assurances required.
Finally, production can be easily controlled or rescheduled when conditions suddenly change, and the flame must be turned down. For example, when a major competitor is running a campaign with a nationwide supermarket chain, which can have a major impact on one's own sales.
In short, there are quite impressive developments that have come along in recent years, which can not only help to reduce food waste and pressure on our shared resources, but also makes it possible to strengthen the bottom line at the same time.