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Volume 24, Issue 4
Supply-chain analytics can lead to increased profitability.
As the pharmaceutical industry weathers some of its biggest challenges in decades, supply-chain analytics may offer the solution to some of the industry's biggest problems. Accenture's Global Pharma Industry Supply Chain & Tech Ops study—completed in May 2010—offers some key insights into how analytical capabilities could help transform supply chain operations.
While most study participants—25 pharma and biotech companies from around the globe and across industry segments—would agree that "supply chain analytics are a crucial part of our strategic priorities," their efforts are largely focused on developing greater visibility into supply chain performance. There is certainly room to improve in this area, but the true value of analytics goes far beyond simple performance management.
Indeed, companies that indicated stronger analytical capabilities (e.g., closer integration with customers on demand forecasting) also consistently demonstrated higher margins. Data availability is the most fundamental requirement for strong analytic capabilities. This is an area in which the pharma industry continues to struggle. Supply-chain data is typically scattered throughout a fragmented landscape of manufacturing, enterprise resource planning (ERP), and laboratory information management systems (LIMS), which do not exchange information. Pharma companies' ability to pull information from outside the organization is not much better. Few have been able to develop tight links with customers, and even where these links are in place, the customers' data are often of less-than-sterling quality.
But having the data, while necessary, is far from sufficient to develop strong analytics. In fact, organizational factors which break the link between data and decisions are often the biggest obstacles to overcome. Too often, supply-chain organizations in the pharma industry operate in disconnected functional silos which encourage decision making based on tradition, rather than data.
Traditional thinking, for example, might dictate a decision to build inventory to ensure that all orders are filled. Analytical thinking might instead suggest tracking customer inventory levels and patient demand to optimize order performance.
As organizational capabilities mature and data quality improves, focus will shift from using analytics to enhance the effectiveness of traditional processes to building new ways of operating. In the consumer goods industry, for instance, manufacturers are increasingly turning to point-of-sale data from their retail customers to design algorithms which allow product manufacturing and replenishment strategies to be tailored to the stages of the product life cycle in real time.
The utility of such an approach for pharma companies facing tougher generic competition and lengthening research and development timeframes is obvious. What's more, the industry's current focus on improving product traceability and supply-chain security will tend to build exactly the kind of links with customers and distribution partners that can provide the data to drive more analytically oriented forecasting and replenishment.
For guidance on how analytics can be best deployed in the pharma industry, it's helpful to look at how analytics have driven improved performance in other industries:
Such high performers have a quantitative mindset, constantly using data to challenge assumptions and separate "what we know" from "what we think we know." Equally important is a focus on using analytics to seek out prospective sources of competitive advantage, rather than just measuring past performance. Finally, these companies have moved beyond internal data to draw information from the outside world where necessary. All these factors come together to make analytically advanced companies more customer-centric than their competitors.
If the challenges facing the pharma industry are large, so are the opportunities. The recent wave of merger and acquisition activity offers especially tantalizing opportunities for the consolidated companies.
For example, tax-optimized supply-chain analytics can be used to help rationalize manufacturing and distribution networks in the most efficient means possible while increasing margins. Improved analytics in the areas of business simulation, network optimization, and risk modeling offer the potential for greatly enhanced synergies and a quantum jump in supply- chain capability.
Send your Final Word story ideas to Editorial Director Michelle Hoffman at firstname.lastname@example.org.
Eugene Jones, is a senior executive at Accenture, a global management consulting, technology services, and outsourcing company. He leads the supply-chain practice for Accenture's Life Sciences industry group.