Contract data analysis can help predict pharma demand: Agiloft

The ability to gaze into a crystal ball and predict the demand for products would give any pharmaceutical firm an edge in the market. According to a representative from business process management specialist Agiloft, companies already have a resource at their disposal that could be helpful in making such predictions: contract data.

Andy Wishart, Agiloft’s CPO, spoke to Outsourcing-Pharma about contract lifecycle management (CLM), in-depth contract analysis, and how to use the information to elevate outcomes.

OSP: You said, “New research has found that nearly half of European and US pharmaceutical companies have admitted their competitiveness is suffering because they cannot use data insights​.” Could you please explain how that came about, and why it’s a problem?​

AW: According to an Aspen Tech survey, almost half of senior pharma company decision-makers said their companies are not proficient in using data to predict product demand and adjusting output accordingly. This highlights a long-known truism within the industry: the comparatively slow pace of digital transformation has led to a widespread lack of predictive capabilities for the sector.

These shortcomings are hurting pharma organizations’ ability to leverage real-time data, resulting in serious repercussions for individual and public health, safety, and wellbeing. It also, of course, significantly impacts the companies’ bottom line.

Pharma companies urgently need to accelerate the transition to Pharma 4.0 to stay competitive and meet today’s customer demands. Pharma 4.0 is a specific framework for adapting digital strategies in pharmaceutical manufacturing.

OSP: Is this a case of too much data, or collection of data that isn’t truly useful, or the collection technology outpacing analytical capabilities—or something else?​

AW: Yes, it's a combination of all those factors and more. There’s too much data from thousands of documents in different file formats, stored across various platforms. Pulling insights from this data is a difficult and time-consuming challenge, especially without the right technology.

Pharma companies need to be able to sift through thousands of documents to mine the data they need to make smarter business decisions. In these cases, it’s valuable to use artificial intelligence (AI) and machine learning to pull specific searches and sort through relevant results to find the exact information needed.

OSP: Why is predicting demand for product, or class of products, valuable for pharma companies and their research partners?​

AW: Leading pharma companies stand out against competitors due to their ability to use data effectively across all aspects of the drug manufacturing process. Data-driven organizations are faster to innovate and respond to opportunities and threats more swiftly. Therefore, when there is a sudden spike in demand for a certain drug, they can promptly adjust the amount manufactured to meet demand, so patients aren’t left with a shortage of the medicine or vaccine they need.

Being able to forecast these changes in demand using data from vendors, partners, or trends gives companies a huge advantage. In addition, when there is a drop in demand for certain drugs, pharma companies can adapt to manufacture less product, matching the lower demand. That way, they aren’t stuck with surplus that may expire before reaching patients.

OSP: How can pharma firms and researchers make better use of all the data they collect?​

AW: Pharma companies can mine contract data to drive better business outcomes in many areas. Contract data insights can be used to negotiate better deals with partners, determine how much of each product to manufacture to avoid waste, monitor for upcoming contract expirations, and anticipate any regulatory issues to prevent fines.

By leveraging technology to extract this critical information, pharma leaders can make well-informed, strategic decisions that will drive the business forward. CLM is a beneficial tool for mining-specific data and clauses to draw relevant insights. Automated CLM helps teams find specific information from contracts immediately, rather than spending hours or days manually sifting through hundreds of thousands of documents.

OSP: Please explain CLM, and how it can be useful to companies to get more value out of their activity.​

AW: Contracts are the “relationship DNA” of businesses that define the terms of every transaction. CLM software automates the workflows associated with contractual agreements. Implementing a modern CLM system can lead to significant improvements in cost savings and efficiency.

By harnessing CLM’s in-depth contract analysis, pharma companies can leverage contract data to get more value out of business transactions, forecast demand changes, and drive revenue with faster deals and supplier onboarding. In addition, this CLM data can be used to provide customers with a better, more cohesive experience with all their information easily accessible and stored on one secure platform.

OSP: What technological tools are out there that could enable pharma firms to mine contract data?​

AW: A CLM system that harnesses the power of no-code AI is simple to use and highly effective for mining contract data. No-code AI uses a no-code platform to deploy AI and machine learning models, giving enterprises the ability to quickly classify, extract, and analyze data.

Once trained on the organization’s clause and contract types and risk preferences, the prebuilt AI will accurately perform the AI function in the CLM system. For example, the AI function can extract all relevant contract metadata from documents or rate the level of contract risk based on your organization’s specific clause language standards and more.

OSP: How might companies like Agiloft help?​

AW: CLM platforms like Agiloft automate the workflows associated with initiating, executing, and monitoring contractual agreements. This helps businesses increase sales, drive efficiency, and reduce security and compliance risk.

Enterprises can customize their AI-based CLM system, so it provides seamless integration with pre-existing applications and the rest of the IT ecosystem. With a no-code AI platform, this level of customization is possible without needing to write a single line of code, which significantly cuts down deployment times and costs for organizations.

OSP: Do you have anything to add?​

AW: Since AI is the most rapidly changing area of technology today, models built on proprietary frameworks a year ago are already obsolete. Therefore, it’s important to implement a CLM platform that is configurable as your business evolves and grows. Before pharma leaders decide to invest in CLM, it’s crucial that they make sure that the system will remain operational and adaptable for years to come.

For organizations that want to take the first step in implementing AI-powered CLM, it's best to begin the search with a third-party software research firm, preferably based on real customer experiences like Gartner’s Peer Insights. These sources provide valuable information that pharma leaders can use to zero in on the right CLM solution that will fit the needs of their organization.