The combination of new drugs, new treatments and advanced data analytics solutions creates superior opportunities for patients, making treatment more effective, more affordable and less intrusive, with a positive impact on quality of life. Distribution and drug consumption models are rapidly changing with the emergence of new business avenues around individualised medicine, combination therapies, device/drug convergence, etc. These new models create a superior patient-and-caregiver experience, but have major consequences for the supply chain in that they strongly increase its complexity.

Advantage through digital supply chain

Supply chain imperatives are evolving, moving from drug production and delivery that are limited in scope towards a versatile supply chain addressing the needs of multiple stakeholders (caregivers, patients, insurance companies, etc.). The traditional supply chain approach based on materials requirement planning (MRP2) has progressed to its limits.

From the patient perspective, the current supply chain creates frustration and complications in individualised treatments, or even fails to offer satisfactory performance with new treatment types (companion diagnostic, individualised dosage, combination therapies, etc.). From an industrial perspective, as new treatments become available, new types of supply chains need to be established, with new actors, as well as new technological and logistical systems. Managing these new treatments with traditional supply chain methods is costly and complex, with high risk of non compliance. Yet, while creating new challenges in terms of regulatory compliance and scaling, the incorporation of these players also provides significant opportunities in terms of data collection for predictive analytics.

The challenge for companies is to develop new capabilities by identifying useful and necessary initiatives, without putting the ongoing operations at risk by building service models on the basis of correlations that will not be sustainable in the future. The intelligent supply chain, based on predictive analytics and machine learning, is better at demand anticipation (SKU, quantity) and characterisation (localisation, service levels) by identifying and understanding the patterns influencing it, rather than projecting past demand. However, seeing patterns is not sufficient; understanding the “why” behind them is key.

Understanding the “why”

Moving beyond the world of descriptive statistics, which relies mainly on the extrapolation and (often poor) observation of recurring patterns, companies are entering the domain of predictive analytics, which requires deep data analysis to understand causal links. Leveraging those links, predictive analytics will be able to predict potential future states. The complexity of such models is to identify and select the links with predictive value.

To navigate through this data analytics environment, we have developed a maturity model to evaluate the current situation and development priorities in the supply chain. This methodology allows thorough testing and leveraging of existing analytics pilots to outline key causal factors to integrate into the predictive supply chain:

  1. The first step is to better understand methodologies applied to various pilots, confirm their underlying hypotheses, and identify whether other options are available.
  2. Secondly, pilot methodologies need to be tested on historical company data, and their results compared to actual history, in order to exclude most of the “false positive” methodologies and identify repeated correlations. Those methodologies should be tested in an exhaustive, systematic and automated manner.
  3. At this step, the correlations have proved to be resilient over time; hence, causality can be assumed, but is not yet proven. A large number of “false positives” have already been excluded, and a qualitative assessment of the repeated correlations is the best way to identify meaningful (with explainable links – no random coincidences) and useful causality links (where the variables can be measured).
  4. Ultimately, the stability of the methodology is pressure-tested, applying “derived laws” in Step 3 against all possible future scenarios to evaluate maximum variability and their implications through multi-variate analysis.

In spite of being a nascent technology, predictive analytics is a top priority for healthcare supply chain executives. Failing to prepare to leverage this technology is therefore not an option, and companies need to reflect on how to minimise risks linked to initiatives in this field.

Marc Herlant, partner, Arthur D. Little