Novis Advanced Analytics solution for mass consumption and distribution

Did you like our article?

Based on the requirements we have been working on for several clients, at Novis we have developed a platform that uses accelerators to build custom Advanced Analytics solutions, integrating SAP data with exogenous information sources, while using big data and Artificial Intelligence (AI). We can deliver solutions both for demand forecasting as well as logistics and sales optimization.

What are our clients’ motivations?

In a data-driven world, organizations strive to make strategic decisions based on data analysis and not just on intuition, in order to understand and better serve their customers.

We are seeing that, in particular, mass consumption companies are requiring solutions to resolve issues related to demand forecasting, using models that capture seasonal and exogenous variables, geographical location, and history for each point of sale, in order to be able to make a more accurate prediction in the following processes:

• Sales planning: results must be predicted, and sales estimates gaps anticipated. This requires tools that help design and quantify the impact of marketing actions (promotions, campaigns, discounts, etc.).
• Design of marketing actionsbased on evidence, to understand the correlation between customer demand and possible marketing actions. For example, what are the most appropriate channels and offers for each customer segment?
• Another area is production planning, where if more accurate forecasts are possible, production can be more efficient and effective, avoiding thus stock breaks for some products and overproducing others.
• Associated to the previous is the planning of raw materials and supplies purchases, which in certain industries must be done months in advance, as supplies come from distant markets and economies of scale exist for larger purchase volumes.
• Logistics planning to better handle where to have stock for each product and determine how to optimally supply a distribution center network.

Another need is to equip the sales force with better tools to serve their customers (points of sale) and thus generate more sales to the end customer. Some examples:

• Next purchase order forecasting, to help a sales rep who travels a route in having more time to get to know and guide his customer. When arriving at the point of sale, he/she may have a suggested order based on history that considers variables such as seasonality, holidays, upcoming sports events, etc.This same information could be available in a self-service site to facilitate the point of sale or end customer order, making the purchase experience easier.
• Product recommendation engine, which correlates the behavior information in similar points of sale and identifies products that are not yet being offered at a particular point of sale which could work well, adding them to the customer’s offer.
• Model oriented to the introduction of new products. Use the customer characterization database to segment and define the best candidates for new products.

These solutions, which facilitate the sales team tasks, also enable the marketing team to better design and expedite the delivery of promotions to their target segments, ensuring delivery, to later have information about results and evaluate the creation of new promotions.

Why now?

Commercial processes quantitative analysis may be taken to much more sophisticated levels with the adoption of big data and artificial intelligence (AI), where the relationship between consumer behavior and a series of exogenous variables, such as seasonality, holidays, festivities, sports events, location, point of sale characteristics, etc., may be better understood and modeled.

AI does not currently require highly specialized and experienced human resources as before to design models, for today there are optimized algorithms to solve different problems and which provide that specialized knowledge. AI has simplified the models behind this, and the effort required by experts to build them.

Finally, thanks to the cloud you may have access to the huge computing power needed, paying only for the time spans used, thus resulting in more reasonable costs.

The sum of these technologies enables massive analyses of relationships, faster processing, and reduced complexity, making it possible to evaluate diverse scenarios and compare forecasts before deciding.

Why Novis?

Based on our experience with different clients, we have developed a platform to automate the creation and operation of the processes that control this type of solutions. Usually these require custom software development, and we can do it, for we have the architecture and dataflow models that speed up the complete development. We know where the data is in the SAP solutions and how to move it to the cloud analytics environment. Our methodology simplifies the creation of data pipelines, adapting them to the particularities of each client. We have accelerators for this process.

We have observed that these initiatives contribute considerably to our clients’ knowledge, and the first results of value may be perceived in about three months. However, this is a continuous flow of value, where more is gained as they understand in depth the data and the algorithms and how to use them in forecasting.

For more information about our services, we invite the SAP community to contact us.

Author: Patricio Renner.