Why is Supply Chain the Rockstar of Consumer Pharma?

Why is Supply Chain the Rockstar of Consumer Pharma?

The obsession of drug makers to re-evaluate and minimize Operational costs is the New Black. Nowadays Consumer Health (over-the-counter or GSL drugs) has a profit formula quite similar than the Retail business, but unfortunately delivering safety and quality are simply not enough.

The decrease trend on sales of the biggest US drug retailers (WBA, MCK, CVS) is due to the consumers shift from traditional to online shopping “the Amazon effect”.

As a consequence, because of discounts and pricing pressures, the profit strategy is getting lower. Moreover fighting against Amazon is not a solution, it’s just like a patient who tries to cure himself using a Placebo.

So what?

Industry really needs a Turn the ship around, which implicates radically changing Supply Chain Strategy from Deliberate to a more Emergent one.

Indeed, the future is getting harder to read and winning strategies that used to work in the past are not effective anymore … this is the point that differentiates you from the rest.

In particular, if we analyze the evolution journey of Retail and FMCG to an Agile Supply Chain Management, we will obtain a present-day version of the 3 pillars of Organizational Transformation: People, Processes and Technology.

  • Companies like Walmart Unilever and P&G are hiring the best talents where market skills and in-depth technical knowledge are the priorities to ensure creativity coming from top-rated professionals.
  • Their excellence in processes goes from the latest up-to-date Lean methods (TOC, 6Sigma …) to the most innovative Customer Segmentation, Supply Chain Resiliency and Circular models. 
  • They invest in technology. Advanced Analytics such as: machine-learning and AI enhances and even automate, decision making by reinventing operational models.

… and this is just the beginning.

Supply Chainers who are able to adapt its company value chain, make good products even better and at the same time improving Profit Margin, will ensure that the company model will not be disrupted in the future.

References:

  1. Bezos Starts the online drug war https://chiefexecutive.net/bezos-starts-the-online-drug-war/
  2. Gartner Top 25 Supply Chain list http://www.argentus.com/gartner-global-executive-partner-speaks-about-2017s-top-25-supply-chains-list/
  3. Deliberate versus emergent strategy https://hbswk.hbs.edu/item/the-business-of-life
  4. 2018 Trends to disrupt pharma supply chains https://www.pharmalogisticsiq.com/regulations/news/2018-trends-to-disrupt-pharma-supply-chains
  5. Beyond Supply Chains Empowering Responsible Value Chains http://www3.weforum.org/docs/WEFUSA_BeyondSupplyChains_Report2015.pdf
Advertisements

What role will Analytics and Artificial Intelligence play

What role will Analytics and Artificial Intelligence play

The 2018 SAS Health and Life Sciences Executive Forum is an event in which global leaders across the health care ecosystem are coming to discuss key issues and opportunities around next-generation analytics.

The main target of 2018 session held at Madrid was to get a deeper understanding of new analytical technology applications in pharma and health care and their potential benefit to pharmaceutical companies, healthcare providers, and patients and communities worldwide.

For more information visit the SAS Executive forum agenda.

Tearing Up the Rulebook: How Millennials are Changing Concepts of Forecasting

Tearing Up the Rulebook: How Millennials are Changing Concepts of Forecasting

… In the past, software was not powerful enough to provide a reliable forecast, but now we have tools and resources that provide insight like never before.

The question is, are you getting the most out of them?

Everyone knows that the present and future is changeable, so why then are we still using the same forecasting structures and assumptions? Why don’t we leverage technology to model different scenarios and build adaptive forecasts?

During my session, I stressed the importance of avoiding complacency when it comes to our forecasting capabilities, and how we should never regard our strategy as the “right” strategy.

We should never regard our strategy as the “right” strategy …

If you’re achieving forecast accuracy now, then you temporarily have a good strategy that must evolve with changing market conditions.

In my session at the Amsterdam IBF convention, I invited everyone to start thinking about how we can plan for all eventualities and base our analysis and conclusions on Forecast Accuracy Simulation, specifically in terms of what-if scenarios, hierarchy, and product segmentation.

What’s more, we discussed how to set up a separate business unit for Disruptive Innovation to develop the resources and processes needed to deliver improvements in forecast accuracy and greater operational efficiency.

Some of the Key Takeaways were:

  • The importance of leveraging software to build adaptive forecasts and develop a continually evolving approach
  • How to start adding real value to your forecast through demand driven models
  • How to organize for innovation and embrace different perspectives on forecasting

My deepest gratituted to all the IBF institute team.

The Institute of Business Forecasting & Planning (IBF) is a membership organization recognized worldwide as the premier full-service provider of demand planning, forecasting, business analytics and S&OP education. Having some of the world’s most well-known global companies as its members, the IBF is constantly finding and disseminating better ways to manage demand, improve organizational efficiency, and company performance. It has been said that no other organization on the globe has as much depth in its educational content for Demand Planning & Forecasting as the IBF. 

La Matriz Portfolio de Proyectos

La Matriz Portfolio de Proyectos

 

¿Cómo mantener el orden de tus proyectos?

¿Tienes problemas a la hora de trabajar en varios proyectos a la vez? Es posible que te estés convirtiendo en un ‘slasher’ … del inglés ‘slash’ que significa barra alta. El termino fue acuñado por una famosa escritora del New York Times, el cual describe al creciente número de personas que somos incapaces (me incluyo…) de responder en una frase a la pregunta ¿Cómo te ganas la vida?

Imagina que eres un Ingeniero/Musico/Consultor/Escritor. Algo muy común hoy en día, pero ¿cómo coordinas todos esos trabajos durante tu día a día? Y aún más importante, ¿cómo controlas tus ingresos/gastos regulares durante el mes/año?

Lo fundamental es tener una vista de pájaro en la cual puedas clasificar todos tus proyectos, ya sea de trabajo o tu vida personal con la ayuda de una Matriz de Portfolio de Proyectos de acuerdo al Tiempo y el Coste invertido en ellos.

Para ello piensa en el coste no sólo como el dinero invertido también debes añadir todos los recursos implicados como amigos, tecnología, energía, nivel de stress, etc.
Ahora bien, Tiempo y Coste son sólo un par de ejemplos, pero puedes utilizar cualquier tipo de parámetros que consideres relevante a tu situación, además el número de cuadrantes también es optativo (2, 4, 6 …).

En los casos de debajo verás algunas de las matrices más conocidas y utilizadas en el mundo empresarial, las cuales son configuradas después de analizar millones de resultados en complejas bases de datos y que nos permiten de esta manera presentar de una forma sencilla y rápida nuestros principales proyectos.

Veamos algunos ejemplos:

Figura 1. La Matriz BCG (Boston Consulting) es la más famosa y ayuda a las empresas a manejar su Portfolio.

  1. Estrellas: Productos atractivos con alta popularidad en mercados en crecimiento.
  2. Incógnitas: Productos en mercados en crecimiento, pero sin una alta popularidad.
  3. Perros: Bajo crecimiento o baja popularidad.
  4. Vacas de Dinero: Productos muy populares, pero en mercados sin mucho crecimiento.

Figura 2. Matriz de Proyectos de Investigación y Desarrollo (R&D) que clasifica los proyectos en:

  1. Pan y Mantequilla: Excelente potencial de éxito y buen beneficio en caso de éxito (mejoras evolutivas).
  2. Perlas: Los mayores productores de éxito y beneficio. Idealmente querremos tener solamente Perlas.
  3. Ostras: Proyectos recientes con la habilidad de conseguir ventajas estratégicas para la compañía.
  4. Elefantes Blancos: Grandes consumidores de recursos y tiempo, pero no generan grandes beneficios.

Figura 3. Matriz GE (Análisis Multifactorial) de McKinsey para el análisis cualitativo de productos/proyectos.

La matriz se divide fundamentalmente en las siguientes cuatro secciones:

  1. Invest & Growth: Requieren enfocar la mayor inversión posible para alcanzar un rápido crecimiento.
  2. Selective growth: Se recomienda mantener la inversión aunque dependen de un crecimiento muy selectivo.
  3. Selectivity: Merece la pena invertir, aunque de manera muy selectiva.
  4. Harvest / divest: Débiles y en mercados menos rentables, se recomienda vender o desinversiones progresivas 

 Figura 4. Otra versión de la Matriz de Mckinsey (fuente: Wikipedia en español)

 

Nota: Originalmente las matrices representan el eje X de mayor a menor, en lugar de menor a mayor …  Esto se debe a la tendencia de Negocio de ver siempre lo más positivo en el primer cuadrante (Superior-Izquierdo), no obstante, siempre podremos encontrar una versión modificada donde el segundo cuadrante (Superior-Derecho) representa el óptimo.

Big Data and 21st century Supply Chains

Big Data and 21st century Supply Chains

Data is everywhere and manufacturing companies today are collecting increasingly massive amounts of data with the help of digital technologies.

New strategies, improved skills and more powerful tools are needed to make sense of that data and crunch the numbers, and find useful insights that are buried in the data. This situation is elevating the importance of Big Data analytics as a critical business capability.

To share few statistics about the amount of data:

  • More than 90% of data in the world today has been created in the last two years, with 80% of it being unstructured, such as images, audio, video, social media, web pages and emails.
  • 1.8 trillion gigabytes of new data were created in 2011.
  • Data is expanding at a rate that doubles every two years.
  • By 2020, the digital universe will be 40 trillion gigabytes.
  • Most U.S. companies have at least 100 terabytes stored.
All companies understand the importance of big data and acknowledge that data analytics of the huge digitized data can help their supply chain process, but the challenge is how to implement it. However, the increased understanding of big data analytics is leading to action, and is becoming a reality.

The trend to implement analytics is on its way and companies have serious plans to incorporate role of analytics in their supply chains.

Optimized supply chain– i.e. delivering the right amount of product to meet market demand while minimizing production, inventory and transportation costs–is a smoothly functioning, comprehensive proposition sought after by all companies. Advances in data analytics, combined with proliferation of data acquisition mechanisms and huge volume of data points, is generating a plethora of possibilities for improving efficiencies in this integrated view of the supply chain.

Earlier, most of the companies sought methods to centralize data to help run their businesses via ERP systems. Now, the concentration is shifting to analytics in to effective decisions with respect to predict customer demand, supplier availability, inventory management, delivery route, etc.

Big Data extends the ability to respond, to predict and, in some cases, even recommend subsequent action, based on insights retrieved from these sources. This takes companies a step ahead in increasing -efficiency in the supply chain. Consequently, the focus is evolving from “supply to replenish” through “supply to forecast” to “supply to prediction based on dynamic pattern analysis”.

Big Data analytics capabilities even have the capacity to reduce supply-side disruptions. For instance, process industries have plant control systems that capture thousands of data points a second. With Big Data techniques, it is possible to proactively adjust parameters in order to improve yield and reduce waste. By identifying potential bottlenecks ahead of time, planners now can account for alternative scenarios and maximize payoff. Moreover, this capability can be used to predict, prevent, or even adapt to equipment failures in transportation, logistics, and warehousing.

References: