Achieving high performance in pharma through supply chain analytics

Soumendra Mohanty

The global pharmaceutical industry is weathering a storm due to unprecedented market conditions. Over the past five years, patent protection has expired on products accounting for more than $80 billion in annual sales and inspite 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, understand their customers’ demands better, and devise creative responses to the marketplace’s challenges. Supply chain analytics provide a key to progress in each of these areas.

Analytics matters

Analytics is 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 modelling and forecasting tools to develop insight into trends and then translate that insight into action. Analytics are critical for making and sustaining both operational and strategic improvements across the functional areas of the supply chain. Research reveals that stronger analytical capabilities (e.g., closer integration with customers on demand forecasting) help companies consistently deliver higher margins. Beyond these, analytics play a critical role in identifying and building on competitive strengths which will become increasingly important as pharma companies are forced to operate in a less blockbuster-driven model.

Delivering results with analytics

While one can list down all possible ways in which a pharma company can leverage analytics, it is best to look outside the pharma world for examples of how analytics has driven improved performance in other industries.

A leading big-box retailer in the US 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 helps them in advertising and product recommendations, based on users’ likely preferences, 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 which has enabled them to charge premium prices for the most basic of commodities.

A leading global beverage manufacturer relies on statistical modelling with weather inputs to determine the appropriate product mix and stock points before the fourth of July holiday in US.

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.

How can we get better?

Data availability, a must-have for strong analytic capabilities, is where the pharma industry continues to struggle. Supply chain data are typically scattered throughout a fragmented landscape of manufacturing execution systems, enterprise resource planning systems and laboratory information management systems which do not exchange information. The data landscape is further complicated by multiple instances or platforms for each type of system running within the same company.

In addition, most pharma companies have not been able to effectively pull information from outside the organisation. 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 organisation can produce reliable data (or perhaps 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 which 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 that 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 which 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. Moreover, 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.

Way forward

If the challenges facing the pharma industry are large, so are the opportunities. The recent wave of merger and acquisition activity offers tantalising opportunities for the consolidated companies. Improved analytics in the areas of business simulation, network optimisation 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.

(The author can be contacted at [email protected])