Supply chain analytics: pharma has to do better

By Eugene Jones

The global pharma industry is currently weathering a storm of unprecedented market conditions. Accenture research has found that over the past five years, patent protection has expired on products accounting for more than US$80 billion (€60 billion) in annual sales, and in spite of steadily rising R&D costs, pipelines have failed to deliver replacements.

“If the challenges facing the pharma industry are large, so are the opportunities. For companies wondering how to begin building analytics capabilities, taking a closer look at working capital can be an excellent place to start.”
-Eugene Jones

Against this background of looming competition from generics, the industry is also holding as much as US$46 billion (€35 billion) in excess inventory, according to a recent Accenture study. In this environment, it is no surprise that companies 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.

Analytics, in this sense, is best understood to mean fact-based decision-making that incorporates statistical and predictive techniques to explain and forecast trends. The facts and trends in question may certainly be related to supply chain metrics, but analytics go beyond simple measurement of past performance to provide insight into what may lie in wait.

Analytics are of critical importance for making and sustaining both operational and strategic improvements across the functional areas of the supply chain. Accenture's Global Pharma Industry Supply Chain & Tech Ops study - completed in May 2010 - provides a compelling perspective on the industry's progress and challenges with respect to supply chain analytics. Involving 25 pharma and biotech companies from around the globe and across industry segments, the study covers a wide range of topics, from supply chain strategy and organisational design to planning, fulfillment and compliance. It offers an excellent vantage point from which to observe the impact of analytical capabilities on supply chain performance.

While most study participants would agree with the statement that "supply chain analytics are a crucial part of our strategic priorities," their efforts are largely focused on a single dimen­sion: 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, participants who indicated stronger analytical capabilities (e.g. closer integration with customers on demand forecasting) also consistently demonstrated higher margins. Even beyond these quantitative benefits, analytics offer the keys to identifying and building on competitive strengths which will become increasingly important as pharma companies are forced to operate in a less blockbuster-driven model.

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, enterprise resource planning (ERP), and laboratory information management (LIM) systems that do not exchange information. Frequently, multiple instances or platforms for each type of system are running within the same company, further complicating the data landscape. Pharma companies' ability to pull information from outside the organisation 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.

Obstacles

But having the data, while necessary, is far from sufficient to develop strong analytics. Learning where the organisation 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, organisational factors that break the link between data and decisions are often the biggest obstacles to overcome. Too often, supply chain organisations in the pharma industry operate in disconnected functional silos, which 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. Traditional thinking, for example, might dictate a decision like the following:

We manufacture life-saving drugs. Stock-outs are intolerable, and we will work to maximise our delivery to customers' requested dates and quantities, building inventory if necessary to ensure that all orders are fulfilled.

Analytical thinking might suggest a very different approach:

Pharmacies and distributors both retain some stock level of our products. Given their inventory levels and patient demand, what level of order performance must we achieve to ensure patients have the drug when they need it?

As organisational 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.

Improvement

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.

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.

• 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 democratise 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 behaviour which allow them to direct advertising and product recommendations 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 that allows them to guarantee a 20-minute arrival window for perishable mixed cement, a capability which has enabled them to charge premium prices for the most basic of commodities.

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.

If the challenges facing the pharma industry are large, so are the opportunities. 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 prioritised to develop analytics capabilities in the long run. The recent wave of merger and acquisition activity offers especially tantalising opportunities for the consolidated companies. Improved analytics in the areas of business simulation, network optimisation and risk modeling 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.

About

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.