Empowering business growth with disruptive technologies like the Internet of Things (IoT), predictive analytics, and artificial intelligence has become a norm in IT, and machine learning is leading the way, as software applications are becoming smarter to improve our business and personal lives. With massive improvements in hardware and Big Data, machines can sense, understand, interact, predict, and respond to solve industry business problems.
Bio-pharmaceutical brands are critical intellectual property for life sciences companies, and marketing intelligence and insights are powerful ways to improve brand recognition and marketing ROI. Similarly, service ticket intelligence can automate error and issue classification and customer support ticket responses, improving service levels for medical devices.
A few key questions can help determine whether a use case is fit for machine learning. For example, can you automate the high-volume task? Is there a pattern involved in the business process’ unstructured data sets? Enterprise data is transformed into business value, with the help of a model, by using input and output parameters. Predictive models may have some bias with respect to the degree to which a model fits the data, and the variance amount can change with a model’s parameters.
There are a number of potential use cases for machine learning in life sciences. Here are some that you may wish to incorporate into your business model.
- Quality must be enforced in supply chain and manufacturing business process for regulatory compliance. Root-cause analysis is a key aspect of corrective and preventive action (CAPA), which aligns with industry initiatives like QbD (quality by design), PAT (process analytical technique), and CPV (continued process verification). There is a clear need to identify main causes for reported defects in material assets and understand the impact of identified causes to manage the overall defect count. Based on gathered data, machines can predict what production can be produced vs. planned for a specific duration (based on historical production), thereby preventing deviations and nonconformances. Analyzing the cause of deviation from standard cycle time for manufacturing equipment, and prescribing measures to achieve standard cycle time, affect yield and scrap.
- Life science companies spend huge amounts on direct and indirect materials and services with contract organizations. Machine learning services help commodity managers optimize global spend. Common machine learning uses in strategic sourcing and procurement include: assessment of contract-negotiation behavior, optimization of contract awards to suitable candidates, detection of single-sourcing risks, and determination of components to outsource to contract manufacturers. Intelligent enterprise strategies can recommend replacements for poorly performing suppliers; replace a supplier that poses a compliance risk; select additional suppliers to comply with purchasing policies, expansion to a new territory, or adding a category of spend; or find cheaper options for materials or services.
- Learning management is critical in regulated industries, and training is a big part of human resources’ duties in life sciences. In hiring, HR business partners can identify the best candidates by parsing resumes into structured information, then visualize candidate profiles by skills, education, and experience, to compare and generate best-fit scores of profiles to jobs and vice versa. Talent management can take a more personalized approach towards career mapping based on employees’ unique situations, skill trajectory, and training, thereby opening opportunities to employees for fast-track growth.
- Consider use cases where matching algorithms are used extensively for shared services like cash. Matching incoming payments with invoices is now a simplified process for intelligent enterprises to clear volumes of backlog data. Machines can match accounts receivable invoices based on learned criteria and provide a confidence score to help finance to clear payments faster (e.g., if the matching rate is within a given threshold). For payments that cannot be cleared automatically due to lower confidence levels, a list of the best-fitting invoices can be generated in order to save time identifying relevant receivables.
- Similarly, accounts payables must release payment blocks to pay supplier invoices and receive cash discounts for early payment. Based on historical data, current user interaction, and machine learning algorithms, the system can react automatically or suggest resolution proposals. Decisions may be based on supplier rating, deviation vs. cash discount available, or purchasing category. Matching invoice line items with purchase order line items, and providing remittance advice to reduce manual errors, are ways automation helps life science accounting.
- Sales and marketing can leverage machine learning during sales negotiations with wholesalers, hospitals, clinics, and retail pharmacies by capturing keywords, sentiments, competitors, and new contacts to feed into deal scoring, ultimately improving the win rate. Bio-pharma sales reps can share marketing collateral of interest to physicians and key opinion leaders. Third-party prescription data can create target groups for behavior-based marketing campaigns to boost sales. Thus, machine learning can help build customer loyalty with proactive retention strategies in the life sciences industry.
Smart business process enabled by machine learning, automation, and artificial intelligence can help achieve intelligent enterprise goals for the life science industry, particularly as the IoT technology adoption rate improves.
SAP machine learning services in its SAP Leonardo IoT platform help life science companies automate and prioritize routine decision making processes in order to adapt to rapidly changing business environments.Comments