Tender Process Optimization in Pharma


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Business Case

This post is about the importance of having a structured method to make unbiased decisions. This case of a pharmaceutical industry involves misleading tuition from sales reps that could have an outcome of a big loss in revenue and profit. 

What was done here was to analyze the buyers’ decisions using Lean Six Sigma. At first glance, we see that the company had around 30% of its revenue coming from selling pharmaceutical products. The challenge proposed for the team was to find the best option to increase the sales representation success rate by 5%. 

Starting the Project

First we have to talk with the sales team to find a way to increase the success rate to achieve the goal of 5%. Usually when a sales manager sits with the sales representatives to talk about things that you can do to increase sales. They talk a lot about product problems or processes in other areas. But the most common bias of sales reps is that to increase the sales, the company leads, or finance leads, you have to reduce the price. 

However if the marketing or sales director is a sharp guy. He would answer back to its reps, telling them: “Guys, if I need to reduce price to increase sales then I don’t need sales rep.; I just need a web portal.”For any company, the sales and marketing goal is to sell the product with the biggest profit based on the value, not on price.  

Returning to the project there is a clear 5% increase goal, also the normal sales team argument that to achieve it they need to reduce the price. Therefore Lean Six Sigma enters to break this common-sense argument.

The project was set up and we started mapping the process, with more specific project goals. It was clear that the main goal was to improve tender management because Hospitals usually use tender bids to buy supplies. 

In this case, was a need in more concrete and specific terms ways to improve communication. So that the message from upper management arrived at the vendors on time. With this, we could bid more tenders and received in no time the feedback from the vendors so that the company needed to win.

Applying Lean Six Sigma

Then we started collecting factors, variables, that might have an impact on the goal of the project. Here there was that common situation where sales reps pushed very hard on price reduction. But before there is a conclusion we need to have all inputs that might affect the process. After that, they analyzed all those factors using statistics. Then comes prioritization, we selected a few more meaningful factors to start collecting data and do the statistical analysis. 

Furthermore, we told the sales reps: “Okay. The price might be incorrect, but let’s check the previous standards, to see what was the result.” Then including the sales team, there was a prioritization.

The data of the factors were collected and statistically analyzed, to see what we did was price plotted by the time to see the variation of price. Plus, discarded two outliers without representativeness. 

When we analyzed the areas with more concentration of wins, we’ve had three areas. They accumulated 73% of the winning rates. At this point, we started to focus more on the data concentration, and the statistical analysis in these three areas because it was a clear Pareto effect. It will be more accurate to see what were the sources of losses. 

Unveiling Myths

After we gather all the data from the tenders in those three areas. We found out that the company won 50% tenders. An unusually key factor here was that 13% of negotiations the company was suspended. Before that, the general assumption was that we were losing 50% of the deals. And now we have a scenario wherein reality we lost only 36% of the time, and this was a big thing. 

Considering the suspension in 13% of the deals, we could workaround to fix to leverage that 13% so that we could win more than 50% of time. Through a shift on that 13% lost deals due to an inside problem, the team could more than double the 5% goal. 

Consequently, a task force was initiated to change that and continuing the statistical analysis. In this part, the focus was on price concentration and price modes. After going through the data we had the biggest insight of the whole project when we did an analysis of the average price in each scenario, of the winning, losing, and suspended deals.

This came as a surprise to the sales team because the average price was the same in all cases. Now we could assume that there was no need to reduce the price to win more tenders. The statistical analysis discredited and proven wrong the initial bias of the first hypothesis. 

If there was a situation where the management had agreed to reduce the prices there would have no positive outcome, and the profit would have been reduced exponentially. So now we started analyzing further factors to identify the reason behind why we were losing so many tenders. 

Finding Real Sources & Problem Solving

Amidst that, the task force focused on why our company was suspended in 13% of the deals. We started discussing and analyzing the team. Here two variables we observed to occur on many occasions where the documents did not arrive on time or they arrived with the wrong documentation to the meetings. In this kind of situation either wrong or late documentation is an automatic suspension and lost deal. 

As the next step, the solution for this project is to implement a software that could expedite the tender process. Basically, we implemented the software to transform the bidding process. Also responsible for the collection of the proper documentation in the signatures of directors. 

All of this was initially on paper, very slow and problematic if a director was on a business trip. Back then a bid could not be completed until the director came back to the headquarters and signed the documents.

 By a digital transformation the company could create a digitized dossier.  The documents could be collected much faster, and the director could sign the tender dossier. Even if he or she was on vacation or traveling for business, or in any other place. 

Project Results

Immediately after the software implementation, the company increased 2.5% in closed deals. It had a positive financial impact of $2.2 million dollars in the revenue. All of this without 1% price reduction. 

This is a real example of the pharmaceutical industry regarding price. And it’s just one of the many instances that Optness has tackled using a structured method to take unbiased decisions.