Predictwiser.Cloud
AI-driven Supply Chain Planning for the Food Industry Beverage Industry
Our clients
Easy integration in
less than 4 days
Predictwiser.Cloud’s Potential
95%
Monthly
Forecast Accuracy
80%
Less
Stock-Outs
36%
Reduction
Food Waste
30%
Less
Inventory Costs
20%
More
Revenue
We have dedicated ourselves to the following question:
“How can we develop an AI-driven supply chain planning tool that is easy to integrate, requires no prior knowledge of supply chain management or AI, and can be offered at a price that allows SMEs to have access to the latest technology?”
Sustainable AI
For us, protecting the environment and resources has been an integral part of the development of our AI since the beginning of our journey and is therefore key to achieving our vision of leading the food and beverage industry into a more sustainable future.
48000
Tons of CO2 saved
197000000
Liters of Water saved
Predictwiser.Cloud’s Benefits
Extremely userfriendly
and easy navigation
No additional consulting
or training needed
ROI from day one
with Predictwiser.Cloud
Fair pricing for small and
medium-sized enterprises
No prior knowledge
of AI or SCM needed
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Plug & Play Integration
Predictwiser.Cloud
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Frequently Asked Questions
Why is a sophisticated demand planning software so important?
A big problem for SMEs are issues related to poor demand forecasting software. Since demand planning is the first step in supply chain planning, any impact on the demand accuracy reverberates with increased impact through the supply chain planning. SME`s in general suffer more from this problem due to the lack of scale and lack of technical resources to utilize advanced demand planning software. Stockouts and failure to deliver is a major issue for any company due to loss of customer trust and loss of revenues. However, food and beverage manufacturers experience the impacts of poor demand planning software also on the overstocking case, since their products have expiration dates. Next to food waste, overestimating the demand will also lead to increased inventory of items and overproduction on the production line. Although this is a major problem for Small and Medium Enterprises in the Food and Drink sector nearly all enterprises still use Excel as their main demand forecasting software. Without the help of sophisticated & advanced planning tools, 70% of those food and drink manufacturers experience stock-out situations or waste due to poor demand planning.This over reliance on Excel is due to the fact that smaller companies cannot afford the technical and financial resources to acquire and operate sophisticated demand planning software with complex machine learning demand prediction models.
What is our competitive advantage?
Currently on the market there are several players working in demand prediction, but most of the existing companies rely on standard statistical forecasting algorithms. This kind of software performs a statistical fit of existing statistical models on past data and can automatically select the statistical formula with for the prediction. It is important to notice that this decision is made based on the past. Most of these models do not leverage existing Machine Learning techniques in order to do demand forecasting. While you can make predictions based on statistical models (called statistical inference), the statistical models are usually built for inference about the relationships and variables and not for having the most accurate predictions. One clear example is that all statistical models are interpretable – a human can look at the model and infer some information about the process and about the data. Machine Learning, on the other hand, has the goal of having the most accurate predictions. Many Machine Learning models are not interpretable – it is not evident what the weights on the neurons on an Artificial Neural Network mean. By using Machine Learning there is much to be improved on prediction accuracy for demand planning. Although using Machine Learning would make the most sense, Machine Learning is a complex topic, requiring a deep knowledge and understanding of the field to select the best models to be trained and deployed. After using the models for prediction, it is important to monitor performance since a model performance tend to decay over time, via a process called concept drift.
With our technology StreamWiser, we streamline the process of model monitoring and performance tuning. Our technology is able to automatically decide which models are best for a given prediction – and adjust in real time to changes of scenario. You can consider StreamWiser to be your Data Science department focused solely on getting the best predictions possible.
Let our tech do the heavy lifting of demand prediction – training models, selecting models, monitoring performance – so that you can focus on what really adds value: your business.
How much data do we need?
Our AI demand planning software is more accurate the more data you feed into the model to be trained and tested. Thus, for our demand forecasting software to reach its full potential we need at least 6 months of data. But we are aware of the fact, that there are many start-ups out there who are new to this business and are keen on using sophisticated demand planning software from day one as well. For those start-ups, we have customized models. Feel free to contact us we can get a better insight into your exact business situation and understand your needs.
How long is the implementation process for our demand forecasting software?
Depending on the settings of your ERP system the implementation can be done within one hour or one day at the latest.
How is the software coping with dynamic environments?
We live in a time of great change. Due to price explosions and material shortages, companies will have to focus on extreme cost control. The ability to quickly adapt to a changing market environment and take advantage of opportunities arising from this change will be the most important key for companies to gain a competitive advantage. A digital supply chain is a key element for future profitability and sustainability in the food and beverage industry. To accelerate the transformation, we have dedicated ourselves to the following question since our company’s inception: “How can we develop an AI-driven supply chain planning tool that is easy to integrate, requires no prior knowledge of supply chain management or AI, and can be offered at a price point that gives SMEs access to the latest technology?” Our goal is to empower our customers to make data-driven decisions. We are convinced that only by looking at data integration, data analytics, and the subsequent operationalization of data-driven decisions together can we lay a strong, resilient and agile foundation for innovation and growth in the 21st century.
What are the biggest benefits of Predictwiser.Cloud?
Predictwiser.Cloud was and is still continuously developed in close cooperation with our clients to solve their biggest pain points when it comes to Demand-, Inventory- and Production Planning. Based on our first module which is Demand Planning, the user benefits from fully automated demand forecasts. This means the user doesn’t need to think about which algorithms to select for forecasting the products, everything is automated in the backend, where our in house technology StreamWiser™ is orchestrating the automatic model selection. Within this module the user has the opportunity to feed in her personal experience when it comes to demand sensing and is therefore able to adjust the demand. After the predictions are generated they are used as input for the second step which is Inventory Planning or Optimization. The higher the accuracy of the predictions the higher the savings potentials are in inventory optimization. Our state of the art algorithm used for inventory optimization was developed with the Chair of Logistic and Supply Chain Management of the Technical University Munich. It is considering food and beverage specific parameters that you cannot find at any other supply chain software vendor. One special input parameter for example is that our algorithm accounts for different shelf life requirements within the multiple sales channels. In the third module which is our Production Planning, the user benefits from a fully automized and cost optimal production plan, telling the user exactly when to produce, what to produce and how much to produce. In a nutshell we are automating all processes along the supply chain in an easy and customer friendly way where no prior knowledge in AI, supply chain or analytics is needed. So our clients can focus on their core competencies which are generating innovative products, cutting costs and increasing their profit margin.
What is Automatic Material Requirement Planning (AI-MRP)?
Predictwiser.Cloud’s Production Planning module is responsible for the automatic disposition. This means your supply chain planner is receiving cost optimal production recommendations which help to maximize the company’s profit and increase the planner’s efficiency. The planner now has more time to focus on more strategic sand decision relevant tasks.
Value added for companies which rely on automatic Material Requirement Planning (MRP):
Automatic disposition is relieving the supply chain planner by automatically providing information about the cost optimal production or purchase quantities.
Thanks to an end-to-end integration of inventories, material requirements and demand forecasts, the automatic scheduling is controlled specifically according to the respective requirements. Perfectly aligned inventory levels ensure lower costs and a higher profit margin. This helps the planner to focus on more strategic and decision relevant tasks instead of calculating these quantities in a time-consuming manual process, for example by using Excel.
In this context, Predictwiser.Cloud uses the sales plan and demand forecast to determine the dynamic safety stock level.
The optimum order frequency and the optimum order point are also calculated automatically, considering not only ordering or storage costs but also supplier vacations, Chinese New Year, fixed order dates, delivery reliability and numerous other parameters. The calculations take place fully automatically and self-learning in the background! Following parameters are being use in our Optimization Algorithm:
- Demand forecast,
- Shelf life,
- Pre booked orders,
- Lead time,
- Dynamic safety stock levels,
- Chances of stock out vs. food waste,
- Purchase-, Transportation- and Inventory Holding Costs