Sales through tenders in healthcare are becoming a vital element in the strategy of manufacturers. Healthcare tenders affect purchases of medical supplies and equipment, purchases of medicines (especially for non-patent pharmaceuticals), and services.
Healthcare tendering can be defined as: “The bulk purchase of goods and services through a competitive bidding process.”
Often, tender-based purchases are implemented to minimize the price for the duration of a contract. This practice is expected to reduce the cost as a result of price competition. It regularly achieves economies of scale and can also reduce some inefficiencies caused by segmented distribution systems.
By some estimates (3), around 25% of the turnover in Pharma is currently coming from tenders. Pfizer, for example, has a network of more than 80 countries planning, managing, and tracking tenders (3).
Bidding for healthcare products is a common practice in both developed and emerging countries (1-2). Healthcare companies operating in global markets know that tenders are an inescapable reality wherever price sensitivity is an essential factor in the purchasing criteria.
The drive to use bidding is growing in Western Europe (1), even with its widespread tradition of publicly funded health systems.
Key markets, such as China, Brazil, and Russia, are making progress in expanding health coverage, which will likely generate a greater demand for public contracting and cost-saving strategies such as tendering. According to the same research by McKinsey, in the Asia-Pacific region, almost 51% of the population will have some form of universal health coverage in the next ten years.
Successful Tender management is challenging
The management of tenders for healthcare goods and services, however, involves significant challenges.
The types of tenders can be quite varied. Seemingly, the main types of bidding processes are:
- Open tender, which is preferred in a public tender.
- Tender, which is restricted to those who have previously proven to be qualified suppliers.
- Negotiated tender
Assuming the suitability of the bidders, open and restricted tender have the following procedure:
- Public notice of the specification by the procuring entity
- Presentation of offers by bidders
- Evaluation of offers
- The award
Negotiated tender follows the same procedure for the most part; but, instead of carrying out a mere evaluation of the bids, the procuring entity enters into negotiations with bidders on their initial bids.
On the other hand, contracting authorities often establish strict criteria in terms of competence, professionalism, and qualifications that must be met for a provider to be successful.
Even though the lowest price is the key criterion for selecting the winning supplier, some countries may take other elements into account, such as the ability to supply in quantity, supplier quality, or local manufacturing. From market to market, levels of competition and pricing tendencies may vary
High-quality data sources and analytics
To obtain profitability a company must have access to timely and reliable sources of high-quality data, that allow them to make better decisions.
First of all, providers need to gather and structure vast amounts of content published and managed on public websites.
Due to the volume of information about new tender opportunities and awards worldwide, it is not efficient to employ people to conduct the repetitive task of browsing through web pages.
This is where automated data crawling services become invaluable. It plays an important role in giving organizations a significant advantage over their competitors.
Experts who are responsible for participating in tenders require timely business intelligence with data that allows them to make better decisions.
Once the data has been extracted from the information sources, it is necessary to structure it using natural language processing tools.
Next, it is necessary to combine public sources with private company data.
For example, an automated tool is able to recognize the name of a molecule or a medical device, which often comes in many different shapes and forms (even with typographical errors), and link this to a product that the supplier manufactures and can sell.
Predictive analytics looks at patterns in data to make predictions about future events based on current and historical data.
Automated algorithms can help us to carry out value-adding tasks with a significant edge over human experts. Such tasks include:
Price forecasting: Outputs a prediction of the winning price at tendering opportunities.
Demand forecasting: Returns the total demand for a given product in each market. How many tendering opportunities will happen in the next year (or quarter) for a given product? How many will we win? How much will we sell? At which price?
Revenue and margin forecasting: Used to predict and optimize revenue and margins, as well as expand the discovery of new business opportunities.
To summarize, although sales through tenders are becoming an increasingly vital element in the strategy of healthcare manufacturers, successful tender management remains challenging. To obtain profitability a company must have access to timely and reliable sources of high-quality data that allow them to make better decisions
1. Leopold C, Habl C, Vogler S. Tendering of pharmaceuticals in EU Member States and EEA countries: results from the country survey. 2008. Available from: https://ppri.goeg.at/sites/ppri.goeg.at/files/inline-files/Final_Report_Tendering_June_08_7.pdf
2. Drug tendering: drug supply and shortage implications for the uptake of biosimilars
3. Making tenders work globally: The Pfizer approach
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HEALTHCARE TENDERING AT KONPLIK
Konplik has over six years of experience in market intelligence for tendering in healthcare. Within the area of tender management, we have been actively delivering hundreds of projects to leading healthcare companies in more than 40 countries.
These are our areas of expertise:
1) Scraping: We gather unstructured information, which, when working on the WWW, is dealt with using scraping.
2) NLP structuring: We convert the raw source into a structured or semi-structured format.
3) Transformations: We combine the data we gather with the company’s private data.
We, then, perform complex and custom transformations – including custom filtering, fuzzy product matching, and fuzzy de-duplication on large sets of data.
4) Business ready insights: Subsequently, we apply any standard predictive analytics or data mining techniques to extract insights.
5) Prediction: We also use Artificial Intelligence-based algorithms to predict and optimize revenue and margins, as well as to expand the discovery of new business opportunities.