The global pharmaceutical market has long passed the $1 trillion mark and is projected to grow at 6.3 percent CAGR through 2022. Pharma supply chains are complex, highly regulated, and reach a customer base rivaled by few. The increased access to copious amounts of data and the rise of data-driven technologies­ — including artificial intelligence, IoT, and cognitive computing — are transforming capabilities to manage this massive network of manufacturers, distributors, pharmacies and patients.

The businesses that will ultimately thrive are those at the forefront of digital transformation, capitalizing on these new technologies — while those who do not will lag. For 2018 and beyond, the pharma supply chain will need to venture into the world of digital transformation.

1. Most large pharmaceutical companies struggle with end-to-end and “outside-in” visibility — and it’s time for that to change.

Many pharmaceutical companies — often large companies with annual revenues in the tens of billions of dollars — do not actually have full insight into their supply chains from end-to-end.

Spreadsheets are still prevalent and data is still siloed in ERP, MES, LIMS, and other external “outside-in” sources. Error-prone manual processes, guesswork due to incomplete data, and data latency have a decidedly negative impact on the supply chain. Decisions are made with the best data and tools available, but they would be much more powerful — and many could even be automated — if digital transformation were able to be leveraged.

Dozens of data sources could help companies better manage supply, quality, and cost: real-time data availability from the shop floor to the patient; data collected from internal sources; third-parties; social media; trends in patient health; and even weather patterns. But digital transformation necessitates first bringing it all together in one place. It is critical that silos are broken down and data from across the organization is harmonized and then enriched with external data like weather, regulatory and customers data. What you end up with is a unified dataset that connects your business, the outside world, and the inner workings of your enterprise strategy.

One concrete example comes from German pharmaceutical company Merck KGaA. Merck utilizes this harmonized, real-time information — everything from supply chain performance to stock-keeping units to data collected from the company’s ERP — to optimize its operations. By placing “sensors” throughout its supply chain that gather data about inventory distribution practices and availability for every SKU, Merck has end-to-end visibility. The dataset helps them to process orders in the shortest time possible by enabling them to shift production or materials to different locations as needed. In the case of forecasting, analytics and algorithms applied to the data enables Merck to forecast more accurately than traditional tools and humans in 80 percent of their predictions.

This example reflects the industry shift to technological advancements that can offer not only a full picture of a company’s complex supply chain, but also suggestions for how that company can save money, move inventory smartly and make more intelligent supply chain decisions.

2. “Artificial intelligence” and “big data” may sound like buzzwords, but their potential to transform the pharma industry is powerful.

Just because artificial intelligence and machine learning have become buzzwords recently does not mean they are all talk. A few months ago, Crunchbase reported that in the third quarter of 2017, a whopping $1.165 billion was invested in American AI startups.

AI is poised to transform pharmaceutical supply chains with breakthrough capabilities to process huge amounts of real-time data and make intelligent recommendations to usher supply chains into a truly data-driven future.

Rather than relying on the limiting rules of traditional software, AI taps into machine learning algorithms to learn and refine in real time as it crawls internal and external data sets. That could include inventory data, supplier performance, demand fluctuations and even weather or road conditions.

AI combines this seemingly disparate knowledge to make recommendations or decisions on optimal actions. Think of it almost as a self-driving business based on cognitive automation — the same way that a self-driving car learns about road conditions and takes stock of potential dangers, the pharma supply chain will have the ability to learn, think and take action based on traditional data points as well as new ones.

Take the available-to-promise (ATP) function, for instance, which responds to customer order inquiries based on resource availability. In traditional software, ATP is fundamentally a rules-based calculation based on theoretical lead times and allocation rules that are variable and volatile. Using those data points in ATP calculations can result in wrong ATP dates.

In contrast, AI can automatically generate a “supply chain map,” showing details about an order that include metrics like allocated quantity and expected delivery date. The AI system then delivers highly accurate recommendations and predictions based on machine learning and data science, not simple rules-based ATP calculations.

ATP is just one example. AI’s powerful cognitive automation capabilities can be applied to all supply chain processes, from demand and supply forecasting to inventory optimization, manufacturing performance, procurement automation and supplier reliability assessments.

Predictive capabilities are getting more powerful, and applications of AI in pharma in particular are building momentum. Business leaders who adopt these technologies in the early stages will be the ones blazing the trail in the pharmaceutical space in the years to come — and those who eschew the progress that is made risk falling behind.

3. M&A and Pharma are almost synonymous, but take note: there may be a shift from horizontal to vertical.

Most pharma M&A activity over the last several decades has been horizontal and dominated by manufacturers. We are now starting to see what was once unthinkable — M&A activity across the network of manufacturers, distributors, pharmacies and insurers.

In the last few years, we’ve seen Roche acquire Genentech and Gilead Sciences acquire Kite Pharma; we’ve even seen Pfizer look to strike deals with companies like Allergan and AstraZeneca. But in December we saw the largest U.S. pharmacy chain CVS rock the healthcare industry with its decision to buy Aetna for $69 billion. This was bold news. Combined, the company will generate a whopping $240 billion in annual revenue. In merging health insurance services with retail offerings, we may be seeing the beginning of a wave of vertical integrations.

M&A creates prolific opportunity for pharma companies to expand their portfolios and reduce cost, but combining supply chains? No easy feat. The end-to-end supply chain visibility we discussed earlier is especially challenging after a merger. For example, Pfizer has gone through four major and multiple minor acquisitions over the last 18 years. You have two companies with completely different IT and ERP systems that now need to do business as one. Now what?

Embracing digital transformation is the key to making large scale mergers as seamless as possible. Supply chains are the core artery of big business, so quickly harmonizing data across multiple supply chains is a good place to start with the digital transformation journey. Especially in pharma, a steady, functioning supply chain can make or break a business’ success as it undergoes major changes. The ultimately goal is customer satisfaction, and AI goes a long way to meet those expectations, even as they evolve.

A transparent and dynamic supply chain is fast becoming crucial for pharma companies to thrive in a complex global industry. Firms that utilize modern and advanced technologies are already asserting leadership positions. Digital transformation is seen more and more as a harbinger of active and integral business transformation and will be key in bringing our pharma businesses to the forefront of digital transformation and growth.

REFERENCES
Nash, Kim. Merck Deploys AI for Self-Driving Supply Chain. The Wall Street Journal. Dec 2016.
Page, Holden. Falling Q3 Seed Funding Could Stunt AI’s Hyped Innovation Cycle. Crunchbase (Oct 9, 2017).