Autonomous Supply Chain Planning Is Within Reach

By Elizabeth McGuire and Linda Plumley, Clarkston Consulting

Supply chain issues continue to dominate the news almost daily — the most recent being the last-minute avoidance of a rail strike by conductors and engineers and the potential impact it would have had on every one of us.¹ With so much volatility, how can your drug, therapy, or medical device supply chain stays ahead of the changing landscape to ensure no supply shortages and that company profitability is met?

There is genuine concern among many supply planning experts that, based on recent and unprecedented worldwide events, conventional demand and supply planning processes may not be adequate and could very well be on the verge of becoming obsolete. Today, many methods and models used within supply chains rely on historical data reviewed on a monthly cadence to predict what will happen tomorrow. However, the heightened speed of change is impeding the ability to effectively execute a response. This leaves supply chains faltering with over- and/or under-indexed inventories, expenses, and dissatisfied consumers.

While companies have begun executing at different levels of maturity within their integrated business planning (IBP)/sales and operations planning (S&OP) processes (there are many who have reaped the benefits of this), they are still struggling to adapt quickly enough to unprecedented events, such as the COVID-19 pandemic and the Russia-Ukraine conflict.² Some organizations have also added another business process, called control tower or sand operations execution (S&OE), in which a cross-functional team is expected to review real-time data and make quick data-driven decisions.

However, no matter how much decision-making authority, data integration, or improved scenario planning takes place, there continues to be a reduced level of confidence within the organization, and supply chains are not positioned to react quickly enough to the next domestic or global crisis. As a result, life sciences companies are looking at what more can be done to improve the speed of decision-making for their supply chains. Many are considering autonomous planning methodologies and leveraging other types of artificial intelligence (AI) and/or machine learning (ML) to meet that need and remain competitive.

In a fast-paced industry that is constantly evolving and innovating — from personalized cell and gene therapy treatments to neurotechnology to health wearables — life sciences companies must be prepared to adopt transformative technologies that can help them realize the capabilities necessary to meet the demands of the future of medicine.

What Is Autonomous Planning?

At the recent Gartner Supply Chain Symposium/Xpo 2022, autonomous supply planning was one of the critical topics of discussions.³ The premise is to combine system-generated data with the expertise and experience of people to come to a decision more quickly. The AI or ML can analyze the input and “learn” from various scenarios, so that in the future, the technology can quickly assess how to make a decision on its own — without human input — and therefore reduce the time for decision-making and taking action. Through autonomous planning, repetitive analysis can be reduced significantly, in addition to lowering the errors due to human bias.

Consequently, an IBP/S&OP process (which today takes 20 days of collective time) could be completed daily from start to finish, enabling planners to focus on exceptions or other activities. This methodology works well in volatile markets or when global disruptions present themselves. With data constantly being updated, decisions can be made with a global assessment versus current processes limited to a local focus, resulting in a healthier bottom line.

Are We Ready To Trust Algorithms, Complex Mathematical Formulas, Or Machines To Do Our Planning Roles Or Jobs?

For many of us, trusting machines to do our jobs screams science fiction, especially for making decisions about how much to make, when to make it, and where to make it or ship it. There’s an intense focus on quality and safety in life sciences supply chains, and that focus can make people hesitant to rely on technology for decision-making. Life sciences companies view it as their responsibility to keep patients safe, and it is a seemingly monumental change to now trust machines to make decisions that could significantly impact the supply chain.

However, these decisions are being made on a highly regular basis right now; the difference is many of us are not aware of where or how it impacts us today. Take for example your 401K; the trades that occur within the majority of your 401K are conducted by computers. The method used is called predictive analytics, where the computer uses ML, data mining, statistics, modeling, and AI to analyze current data to predict the future. This is what autonomous planning is built on.

Many biopharma companies are taking a phased-in approach to autonomous planning. They are assessing which aspects of their planning process should be automated first and what aspects still require the expertise of humans to make decisions. The challenge faced by many is that they have deeply entrenched planning methods using long-standing legacy processes and older technology with unclean data. The ability to work through the change management within their employee base will be a key success factor during the transition, followed closely by the robustness of their technology road map and a method for standardizing their data.

Building out a dual road map and ensuring effective employee and technology change, along with the subsequent upgrades to both, are critical to implementation success and will reduce the inherent burden of the larger projects. The result is a minimum viable product (MVP) with tools and process change identified with a laser focus on data integrity.

Great opportunities and benefits can be gained with the implementation of autonomous planning by increasing the operational efficiency for demand and supply planning teams and eliminating the routine nonvalue-added planning activities. It can also help contribute to quicker innovation in a fast-paced industry, where margins are razor thin and working capital can be scarce for budgeting.

Machine learning, artificial intelligence, and autonomous planning will be the future of supply chains and can enable better agility and visibility. The challenge for life sciences companies today is to prepare their organizations to embrace these changes. Imagine being able to view a live webcast where you can discuss a plan developed only a couple of hours earlier versus the days or weeks it takes to create and gain consensus today.

The Promise of Autonomous Supply Planning

• Tactical response to demand change requests down to hours, not days or weeks
• Detection of unusual demand patterns and identifying the source for faster recovery
• Provide scenarios and recommendations to maximize revenue and profits
• Improved materials requirement planning (MRP) and production planning
• Optimized inventory levels at the right place and at the right time

Faster, better decision-making and optimized operational and financial efficiency are just around the corner. Is your organization ready to leverage all that autonomous planning can offer?

References

1. Neuman, S. (2022, September 16). A deal to avert a rail strike is on track, but it won't fix U.S. supply chain issues. NPR. https://www.npr.org/2022/09/16/1123239993/rail-stike-supply-chain-food-gas-retail-prices
2. Shaw, S. (2022, February 28). Global Supply Chain Impacts of the Russian Invasion of Ukraine. Clarkston Consulting. https://clarkstonconsulting.com/insights/supply-chain-impacts-russia-ukraine/
3. Serebrianik, D. (2022, July 5). Key Takeaways from the Gartner Supply Chain Symposium/Xpo 2022. https://clarkstonconsulting.com/insights/gartner-supply-chain-symposium/

About The Authors:

Elizabeth McGuire is a director with Clarkston Consulting and has more than 25 years of diverse supply chain experience. She assists organizations with identifying and designing their supply chain transformation roadmap to ensure feasibility, sustainability, and change management are incorporated into the plan. McGuire has managed supply chain technology integration roadmaps, S&OP re-implementations, procurement and finance integration, ERP readiness, and product transitions and risk management. Prior to becoming a supply chain consultant, she spent 15 years leading various supply chains through challenging transformations, including complex multi-site and global ERP implementations.

Linda Plumley is a director at Clarkston Consulting, bringing extensive experience working with companies that span virtual start-ups to Big Pharma in the areas of pharmaceuticals, biologics, medical devices, combinations, cell and gene therapy (CGT), oncology, and clinical and commercial products. She has experience with mergers, acquisitions, and divestures. Plumley’s career started in manufacturing and continued in quality control and quality assurance until she found her passion in supply chain, where her focus has been for the past 20 years. Additional experience areas include designing standard ways of working for pre-clinical CGT execution and commercial launch, and ERP readiness.