In the beverage industry, Artificial Intelligence (AI) has been used for a long time and with great success for sales forecasting. Such forecasting systems of the next generation bring advantages to production planning.
Table of Content
Using AI for Sales forecasting
Areas that AI can help a grocery delivery business
How to optimize the Supply Chain thanks to Data Science
The results of the digital transformation of companies on their Supply Chain
Sales volumes in the food industry often fluctuate unexpectedly. This creates challenges in the company, as food has to be produced, transported and provided in line with demand. If too much is produced, a storage and logistics problem arises, possibly rejects, which generate costs. If, on the other hand, too little is produced, sales are lost and customer satisfaction could be damaged. A food manufacturer must therefore be able to estimate the expected sales volumes well. This is the only way to optimally align production, purchasing, personnel planning as well as material, warehouse and logistics planning
for an uninterrupted flow of goods.
Using AI for Sales Forecasting
Predictive analytics is the technical basis of modern AI-based forecast systems. This technology can create sales forecasts much more precisely, considering internal and external data sources, than conventional approaches based on Business Intelligence (BI)
or statistical processes.
Learn predictive analytics systems
from data from the past. They recognize complicated patterns and combine them with current data from business operations, possibly also with external data sources that have an influence on sales, such as the weather or the day of the week. From this, powerful prediction models are derived that reflect complex data patterns from reality in the food industry.
Artificial neural networks form the software basis here, which work according to the principles of the human brain. This means that they learn from data and derive data processing algorithms from it. Conventional methods, on the other hand, provide algorithms that are based more on a purely mathematical foundation and thus less present the reality of life.
Areas That AI Can Help a Grocery Delivery Business
Such an AI has been tried and tested for years, is in use thousands of times and is stable. In individual areas of the food industry,
such as the beverage industry, AI has been used for a long time and with great success for sales volume forecasts. Forecast systems also work equally well for the industries of meat, fish & poultry, fruit, milk, baked goods and all other products for which the best-before date (BBD) plays an important role.
The implementation of AI should be easy with the right AI development agency.
The central success factor and guarantee for rapid implementation would be the quality of internal data. With the aid of the AI ??system, a company can reduce the error rate in sales forecasts and achieve savings in production and logistics especially in terms of variable cost items. The changes to the production plan could be improved, the number of shifts required and the ability to deliver would also improve. The positive effects of the improved sales forecasts would be noticeable throughout the supply chain. These positive results mean that AI can also be used for other tasks in this company in the future.
Introducing AI forecasting systems is easier than you might think and costs less than you might think. There is no need to invest in hardware or software, as rental software can be used. The user only has to provide 'data', such as the sales volumes in the past, so that the AI ??system can learn on the basis of this data or experience.
There is also hardly any need to build up new know-how, because that is what the AI ??partner company brings with it. This should take over many of the work steps and ideally offer an all-round carefree package for a small start-up project (pilot), consisting of a small concept, AI expert, blueprint, trial or test system, data sources and possibly also funding.
How to Optimize the Supply Chain Thanks to Data Science
While some choose relocation, for economic and environmental reasons, but also to simplify their value chains, others rely on the precision of Data Science to optimize their Supply Chains.
Indeed, data has invited itself into the very complex world of Supply Chains. The ordinary supply chain has gradually become “Supply Chain 4.0” or “Connected Supply Chain”. Several methods exist to put Data at the service of supply chain efficiency:
Use a logistics solutions editor who provides companies with Data Labs open to their customers. In these Labs are Data engineers specializing in Supply Chain that companies can occasionally assign to solve logistical problems.
Employ Supply Chain Data Scientists / Analysts. More and more companies are employing Data Scientists and Data Analysts specializing in supply chains. They come to ensure the coordination between the data of the different services.
The Results of The Digital Transformation of Companies on Their Supply Chain
The first result is of course technological. Many companies have developed new technological platforms, tailor-made for their own Supply Chains, using Data Science and Artificial Intelligence tools.
For example, IKEA has equipped its warehouses with shelves for customers, which they use to fill virtual baskets in real time. The tablets are directly linked to IKEA's storage software, and suggest other products if the selected products are no longer available, or notify the sales department that there is a lot of demand for a particular product, thanks to machine learning algorithms. It is a way to optimize production volumes, storage space and the sales process.
Data Science also makes it possible to improve customer relations. In terms of Supply Chain, it is essential to segment the customer base: this is what Machine Learning
allows to do, in a precise and automated way . Indeed, this artificial intelligence technology aims to adapt supply to demand, and therefore serves above all to optimize the last link in the Supply Chain: sales. The customer will be offered promotional offers and services, which are based on their previous purchases and tastes, and which aim to make the customer experience better.
The impact of data science can be felt in different aspects of different industries. Data science
can be used to optimized business processes and make decisions based on data. Also, it helps with predictions which would help prevent wastage of resources. If you want to use data science in your delivery business, you should meet with a leading data analytics company.
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