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The study’s findings provide several important observations on the pharmaceutical industry’s analytical prowess, and offer several suggestions for improvement.
The global pharmaceutical industry is weathering a storm of unprecedented market conditions. Over the past five years, patent protection has expired on products accounting for more than $80 billion in annual sales, and in spite of steadily rising R&D costs, pipelines have failed to deliver replacements.
Against this background of looming competition from generics, the industry is holding as much as $46 billion in excess inventory. In this environment, it is no surprise that companies throughout the industry are hungry for opportunities to improve the efficiency of their operations, better understand their customers’ demands and devise more creative responses to the marketplace’s challenges. Supply chain analytics provide a key means to make progress in each of these areas.
Accenture’s Global Pharma Industry Supply Chain & Tech Ops study provides a unique perspective on the industry’s progress and challenges with respect to supply chain analytics. Involving 25 pharmaceutical and biotech companies from around the globe and across industry segments, the study covers a wide range of topics from supply chain strategy and organizational design to planning, fulfilment and compliance. It forms an excellent vantage point from which to observe the impact of analytical capabilities on participants’ supply chain performance.
The study’s findings provide several important observations on the pharmaceutical industry’s analytical prowess and offer several suggestions for improvement.
"Analytics" is ultimately about making better decisions, faster. Past performance is certainly an important input, but analytics seeks to not only understand what happened, but also ask: What does it mean? The emphasis moves from measurement to understanding, incorporating statistical techniques, along with modeling and forecasting tools to develop insight into trends and then translate that insight into action.
Analytics are of critical importance for making and sustaining both operational and strategic improvements across the functional areas of the supply chain.
Accenture’s study clearly identifies analytics as an area on which companies throughout the industry should be focusing. But for guidance on how analytics can be best deployed, it’s best to look outside the pharmaceutical world at examples of how analytics have driven improved performance in other industries:
A leading big-box retailer in the United States has been able to leverage two decades of experience in collecting and reporting on product data to radically democratize decision making, pushing decisions on reorder points, product mix and discounting to a local level and allowing store employees to custom-fit sale items to conditions in the community.
Forward-thinking Internet retailers in several categories have invested heavily in developing predictive models of user behavior that allow them to direct advertising and product recom-mendations based on users’ likely preferences and their own inventory and margin requirements.
One of the world’s largest manufacturers of building materials uses a predictive model of traffic and weather conditions, which allows them to guarantee a 20-minute arrival window for perishable mixed cement, a capability that has enabled them to charge premium prices for the most basic of commodities.
A leading global beverage manufacturer also relies on statistical modeling with weather inputs to determine the appropriate product mix and stock points in advance of the critical Fourth of July holiday in the United States.
Several common themes emerge from these examples. One is a cultural focus on analytics; 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 drive differentiation—analytics are used 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 capabilities come together to make analytically advanced companies more customer-centric than their competitors.
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 are typically scattered throughout a fragmented landscape of manufacturing execution system.
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 companies find themselves challenged by the fact that their customers’ data are often of less-than-sterling quality.
But having the data, while necessary, is far from sufficient to develop strong analytics. Learning where the organization can produce reliable data (or perhaps more importantly, where it cannot) is a problem that can only be solved through experience and experimentation. Companies that have advanced analytical capabilities typically developed them by focusing first on using the best data they had, and working to increase the quantity and quality of data only after building an ability to make meaningful data-based decisions.
In fact, organizational factors that break the link between data and decisions are often the biggest obstacles to overcome. Too often, supply chain organizations in the pharmaceutical industry operate in disconnected functional silos that encourage decision making based on tradition, rather than data. Perhaps the most critical first step toward better analytics is to develop a focus on facts and a willingness to challenge assumptions.
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 that allow product manufacturing and replenishment strategies to be tailored to the stages of the product lifecycle in real time.
This analytically driven nimbleness has allowed leading consumer goods manufacturers to increase their speed-to-market while improving their management of working capital—critical capabilities in a world where product lifespans are shrinking year after year. The utility of such an approach for pharma companies facing tougher generic competition and lengthening R&D 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.
If the challenges facing the pharmaceutical industry are large, so are the opportunities. The recent wave of merger and acquisition activity offers especially tantalizing opportunities for the consolidated companies. Improved analytics in business simulation, network optimization and risk modelling offer the potential for greatly enhanced synergies, and a quantum jump in supply chain capability. The path blazed by pioneers in other industries offers pharma companies the prospect of comparatively rapid advance toward strong analytical capabilities and the benefits that go with them.
For companies wondering how to begin building analytics capabilities, taking a closer look at working capital can be an excellent place to start. A short, two- to four-week investigation of working capital using a strong analytics approach can provide both short-term opportunities for financial benefit and insights into which areas should be prioritized to develop analytics capabilities in the long run.
For companies looking to accelerate the development of their analytics capabilities, Accenture Analytics can be a crucial partner in developing the insight to make better decisions, faster.
July 21, 2010
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