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Our team consists of data scientists, linguists, data engineers, data analysts, and data visualizers with deep experience in various industries. Our vision is to bring together simple, yet powerful data analytics solutions to help small and medium size businesses better understand customer needs and grow faster.


We primarily design, develop, and deploy customized technology and business management platforms for small and medium size firms as well as research organizations, allowing them to strategize their marketing and new product development process and target new areas of growth. Our highly interactive and intelligent business management dashboard allows our business users to visualize and analyze various business activities, by mapping sales & marketing data, business to business (B2B) social media content, and competitive landscape, which form the basis of our diverse business intelligence services.


Ridwan Arizar

Ridwan started his career in the Petrochemistry (BP) but soon found out his interest lies in quantitative finance. He has since then held senior roles with ING, IKB Deutsche Industriebank and Sean Options, a market maker. After the 2008 credit crisis he decided commodities were a safe haven and started to work for several Energy companies including oil major Shell amongst them. After that he started his own consultancy CForsa and has since then advised banks (ING), asset managers (Aegon) and large commodity producers (ArcelorMittal, Bayerngas GmBH). His interest lies in improving supply chains using ML and AI plus the latest technologies (IoT & Blockchain).

Ridwan holds a PhD in Physics from Technical University Delft (TUDelft) and a MSc in Aerospace Engineering as well from TUDelft.

Stanley Wijaya

Stanley is a true hands-on software developer and data engineer that knows the tricks in and out of dealing with BIG Data in the broadest sense. He’s the person that you turn to when things go wrong with systems and databases. Stanley has worked for Major B2C and B2B Marketers but as well with institutions that monitors companies creditworthiness along the business supply chain (Dun & Bradstreet). He has experience on setting up, maintaining and improving IT-infrastructures of large organisations. His interest lies in developing smart products and apps in the Transportation and the Business management sector

Stanley hold a BSc (major) in Informatics and (minor) in Business Economics both from Rotterdam University of Applied Sciences.

Arie Twigt

Big Data Developer: who builds all kinds of stuff from Cognitive Search Engines in Elasticsearch to Fraud Detection Models in R. The person to go that brings the idea from your head to minimal viable product. Arie is in heart and soul a Data Science & Analytics developer. While being part of Singulair Solutions he also holds a role as Big Data Architect with Sogeti Nederland. He has advised banks (Rabobank, BNP Paribas), asset managers (Aegon) and government institutions on the areas of Big Data, underlying technology and necessary infrastructure. His interest lies on the Analytics-part in Business Intelligence & Analytics by giving and developing courses and tutorials. Next to that he is continuously experimenting with data- related topics like: Predictive modelling, Developing dashboards, Developing visualisations and always on the search to apply new technologies like Blockchain and IoT.

Arie holds an MBA in information and knowledge management from the Vrije Universiteit Amsterdam and a BSc in marketing and Sales from The Hague University of Applied Sciences.


Predictive Analytics and Opinion Mining

Optimize web content and analyze customer demographics, increase conversion rates and effectiveness of online sales, and enhance brand awareness.

Data Mining and Visualization

New product development, forming new marketing campaign, and customer segmentation.

Business Management Tools

Providing Customized Technology and Business Management Tools, functioning on high performance, interactive Singulair Solutions platform.

Social Media Analytics

Monitor competing brands on social media and review web content that compare businesses.





A Data-Driven and Machine Learning based approach to enhance Menu-Engineering


Every restaurant/cafeteria owner has to deal with so-called Menu Engineering, which in layman terms is the study of the profitability and popularity of menu items and how these two elements influence the placement of these items on a menu. The concept of menu is rooted in work performed in 1970 by the Boston Consulting Group to help businesses segment their products in a way that supports analysis and decision making. The goal is straightforwardly to increase the profit margin per customer. A restaurant that takes a proper initial approach with menu-engineering would on a rough basis see a 10-15 percent increase in profit on a continuous basis. Menu-Engineering consist of 4 core steps shown below:

In this used case we at Singulair Solutions try to enhance the Menu-Engineering with the 21st century tools, i.e. putting it into a Data-Driven framework combined with tools from Machine Learning (Deep neural networks and Agent-based Learning). In this way we’ll make the approach self-learning and is always looking for optimal answer for the menu items under constantly varying (business) conditions, which takes the burden away from the restaurant manager, who on her/his turn could focus more on other areas of the business.

Our Approach

As a Restaurant or cafeteria keeper, you need to solve each business day the following core problems:

  • What and menus do I need to serve and how many of each
  • What ingredients do I need to buy and how many
  • How much do I need to price my menu items: too low pricing will decrease profit margin, while too high will make the customers not choose for the menu item
  • How much staff do I need for the kitchen and serving

And the above core issues need to be solved under certain constraints, which are more or less entwined

  • Maximize the profit margins, which in gross terms equals sold menus – bought products – staffing cost – other cost
  • Reduce food waste, i.e. don’t produce more dishes than sold on a daily basis
  • Do my dishes fulfil certain maximum calorie and minimal nutritious constraints

The two latter bullet points contribute to a green and healthy image towards the customers. Summarized the steps that we take in the enhancement of Menu-Engineering can be done in the following six steps and has a lot to do with so-called optimal resources allocation.

  • Step 1: Start with a menu with 2 or more dishes
  • Step 2: Set up the problem mathematically where supply and demand are equated and constraints for calories, nutritional health and waste production are incorporated
  • Step 3: express the former in terms of cash expenses and set up a numerical model where one can search for optimal solutions of menu items creation, while fulfilling the constraints
  • Step 4: Connect the formulated model with a datasets (preferable stored in a centralized database), such that the model can be trained and predict future optimal menu composition
  • Step 5: Solve the problem within a certain environment where a combination of Machine Learning Algorithms combined with Deep Neural networks and Agent-based Learning to minimize the so-called value function (this are basically directly related to the margins, food waste and constraints)
  • Step 6:The predicted outcome should be used strategically by the restaurant management to set up tasteful, low-calorie and nutritious menu planning keeping the restaurant financially sustainable


1 1 May 2019.

Singulair Solutions is working on a digital solutions that enables a more and efficient and transparent flower supply chain, combining Supply Chain analytics, IoT and Big Data Analytics

1. March 2016.

Singulair Solutions is a proud partner within a newly funded European project, "POROUS4APP" which brings in high added value materials at lower cost for various applications.

Working with Singulair Solutions

We provide data analytics and web marketing solutions to small and medium size enterprises.

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