BOLBORETA INNOVA GROUP / BOLBORETA DESARROLLA / PORTFOLIO BOLBORETA DESARROLLA / Procurement Optimization Platform
Grouped Purchasing Optimization Platform
Grouped Purchasing Optimization Platform
A data‑driven solution with predictive models that aggregates orders, improves pricing, and recommends the optimal time to purchase
SME Distributing Dental Products
Distribution/Dental Healthcare
PHP + LAMP stack
Grouped purchasing platform with predictive analytics
CONTEXT
The client, an SME specializing in dental product distribution, was undergoing a broader digital transformation and operational optimization process. The company needed to better leverage the available data on customers, purchases, suppliers, pricing, and demand—turning fragmented information into a practical tool to improve decision‑making, personalize service, and strengthen competitiveness within the dental sector. The objective was clear: increase purchasing efficiency and improve supplier negotiation by making smarter, data‑driven use of commercial information.
THE TECHNOLOGICAL CHALLENGE
The challenge was to build a custom tool capable of analyzing multiple business variables—customers, products, suppliers, pricing, and purchase history—to identify behavioral patterns, detect grouped purchasing opportunities, and recommend the optimal moment to place orders.
Key challenges:
1
Turn dispersed and h Transforming heterogeneous, dispersed data into actionable intelligence
2
2 Design logic to detect common buying interests among customers
3
Calculating the optimal order timing to secure better purchasing conditions
4
Automating analysis to reduce manual workload and improve accuracy
5
Building a scalable system that grows with data volume and transaction activity
6
To provide a platform that is useful for both day-to-day operations and business strategy
TECHNICAL AND BUSINESS OBJECTIVES
Data‑driven grouped purchasing platform to convert commercial data into more precise purchasing decisions
Identification of aggregation opportunities among customers with similar purchasing needs
Recommendation of optimal purchasing moments to improve supplier pricing and final cost
SOLUTION DELIVERED
An internal, web‑based intelligent purchasing hub was developed to collect, analyze, and exploit business data—recommending grouped purchases and optimizing order timing. The platform was conceived as a decision‑support tool with the capacity to evolve as data volumes grow and business rules are refined.
Key Components:
- Data import and centralization system for commercial information (customers, products, suppliers, pricing, history)
- Management dashboard with key metrics for monitoring and decision‑making
- Analytical algorithms and predictive logic to detect purchasing patterns and aggregation opportunities
- Visual layer for consulting products, sales, stock, orders, and analytical results
- Iterative, evolution‑ready design, adaptable to new scenarios and usage patterns
TECNOLOGIES USED
WORKING METHODOLOGY
Phase‑based structured approach: analysis, functional definition, development, and validation
Direct collaboration with the client to understand the business model, data, and value creation mechanisms
Initial coordination and scope definition
Functional definition and design of the user‑experience
Progressive development of the platform
Developing analysis and recommendation logic
Final audit, load testing, and deployment
Product‑oriented mindset, prioritizing real‑world usefulness and decision‑making impact
RESULTS ACHIEVED
Tangible, measurable impact
- Intelligent centralization and exploitationof sales, product, stock, and supplier data
- Product identification: best‑selling, lowest‑performing, best‑priced, and worst‑priced
- Real‑time visibility into stock levels and detection of critical shortages
- Monitoring of recent orders and their evolution
- Performance indicators linked to system recommendations and their impact on purchasing decisions
- Technology foundation prepared to support data growth and future functional expansion
ADDED VALUE:
The project’s differentiating value lay in combining business insight, automation, and data analytics into a single operational tool—creating a technology foundation that transforms commercial data into smarter purchasing decisions.
What made the difference:
- Custom‑designed algorithmstailored to the company’s real operating context and the dental sector
- Ability to incorporate historical purchasing data and customer behavior to continuously refine recommendations
- Preventive approachto pricing improvement and purchase‑timing anticipation
- Architecture designed to evolvewith new data and business rules
- Reduced dependenceon manual analysis and non‑scalable processes
- End‑to‑end technology partnership, from analysis through deployment
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