A legacy of siloed functions in pharma and biotech organizations inhibits innovators from developing and commercializing therapies with speed and efficiency. Manufacturing organizations rarely have access to key insights and information from research and development (R&D), quality, supply chain, sourcing, procurement, and logistics functions and systems. This is particularly true for product and process data stored across enterprise applications. R&D professionals may use Digital Knowledge Management (DKM) systems to manage product and process data, including formulation and packaging information, but often this information is not easily accessible to the manufacturing organization that has to make daily production decisions based on available supply and capacity. Demand forecasting, sales and operations (S&OP) and supply planning functions may use purpose-built solutions and enterprise resource planning (ERP) systems to align demand, supply, capacity, sourcing events and purchase orders, but when they run into supply constraints or demand fluctuation, manufacturing cannot react quickly, preventing them from making proactive decisions on how to best meet production demand.
Manufacturing facilities frequently use manufacturing execution systems (MES) to manage daily production and track key performance indicators (KPIs). Often, each manufacturing plant has its own MES instance, and companies lack an aggregated view across all lines and facilities. This makes it nearly impossible to make the best possible choices when it comes to balancing capacity, available supply and production demand.
What if you could collect all the data from these purpose-built systems and provide information to key production stakeholders that will help optimize operations?
With information on what’s going to be manufactured, as well as when and how to manufacture it, machine operators, production supervisors and plant managers can make better-informed decisions.
Enter the Manufacturing Control Tower (MCT)
A manufacturing control tower (MCT) captures structured and unstructured data across an organization’s manufacturing lines, equipment, and facilities. It facilitates the creation of virtual dashboards that provide key insights into the status and performance of manufacturing processes, including their dependencies on raw materials or intermediates suppliers - ultimately impacting order fulfillment, logistics and transportation. By collecting the information from heterogeneous systems and visualizing it in a single ”pane of glass”, decision makers have an aggregated view into the manufacturing process performance at any level of the organization
A manufacturing control tower is more than a single piece of technology.
Gartner defines a control tower as “a concept that results in combining people, process, data and organization, facilitated by appropriately combined technology elements to drive smarter decision making.”1
Enterprise applications are just one part of the landscape that makes MCTs.
There are three levels of control towers:
MCT for a single facility’s lines and associated equipment.
MCT for a collection of lines and equipment from multiple facilities with the ability to drill down by facility.
An enterprise-level control tower that brings MCTs together with a supply chain tower, logistics tower, and others that may exist across an organization.
Who are the users of a MCT?
They generally fall into four categories:
Machine operators: At the machine operator level, the MCT provides simple dashboards and the ability to create ad-hoc reports. This is typically at the level of a single line or a group of lines within one facility.
Production schedulers/planners: A production scheduler uses the MCT to view data on both a single line or on all lines across a facility. They leverage analytics to perform diagnostic analyses and identify imbalances among the production plan, facility/line capacity and available supply.
Data scientists: The MCT provides a data scientist with an entirely new data set, pulling information from previously disparate sources and allowing them to leverage advanced analytics and machine learning to provide not only predictions on emergent bottlenecks, but also prescriptive intelligence on how to optimize the production plan based on available capacity, supplies and workers.
Logistics planners: The MCT enables logistics planners to better understand and optimize the lane management process, based on the visibility into the expected fulfillment of work orders being executed in manufacturing and visibility enabled in the MCT.
One of the key benefits of a MCT is increased visibility into manufacturing processes at an aggregate level to monitor key dependencies on the ability to execute a production plan. In a single dashboard, a MCT provides visibility into process performance information at the machine operator, manufacturing line facility and enterprise levels spanning all four layers of the ISA 95 reference hierarchy2. Today, this requires collecting data from multiple source systems3 such as DKM, ERP, MES and often multiple instances of each system across the organization.
The enhanced visibility provided by a MCT enables timely and financially-sound decision making and provides intelligence on tradeoffs. When decision makers have access to real-time manufacturing data aggregated from across the organization, they can extract insights previously unavailable to them. Furthermore, when artificial intelligence (AI) is applied to the MCT, these decisions can be optimized without the need for manual human analysis that may introduce latencies or errors into key decisions that need to be made quickly. For example, imagine procurement learns that a supplier of an excipient used in the manufacturing of a drug substance is unable to make their shipment as planned. The MCT can automatically respond by sending instructions to the manufacturing floor to slow down production on the line of that drug product.
Finally, the implementation of a MCT leads to enhanced performance through improved KPIs, such as higher throughput, yield, reduced scrap and faster batch cycle times. With the availability of larger data sets, a MCT can enhance the ability of existing smart connected operations capabilities to improve overall equipment effectiveness (OEE).
Here are two examples:
First, with historical data from multiple lines and plants a MCT enables predictive maintenance by accurately predicting when a machine or line will require maintenance. This allows production and supply planners to plan accordingly. This is especially important for biotech manufacturing where an undersupply situation could lead to unused capacity or where an oversupply situation may lead to materials that perish before they can be processed.
Second, when manufacturing data is combined with data from other systems, such as product and process design data from DKM, and aggregated with work order information, planners can also more effectively predict upcoming utilization of a facility and plan for workforce and machine allocation accordingly.
How to Get Started
Identify high-priority use cases
Step one for realizing the benefits of a MCT is to identify the priority use cases that will deliver the highest value to your organization. It is best to focus on a small number of use cases where you can see immediate value before scaling to more applications. Would using a MCT to enable Predictive Maintenance or Lineside Next Best Action bring the most value to your organization?
Understand current system landscape
No two organizations’ information technology (IT) and operational technology (OT) landscapes are the same. MCT benefits come from combining data from across the organization’s IT and OT systems, so it’s important to first understand the scope of enterprise applications that could potentially be incorporated into its network. How many DKM, ERP and MES instances is your organization using, and which should be linked to the MCT first? Which MES systems govern your most critical processes and how can you leverage their historical data to make enterprise-wide decisions?
Assess the state of data
Without high-quality data and persistent data pipelines, it is impossible to realize the full benefits of a MCT. Before implementing a MCT, it may be necessary to enhance your organization’s data and data pipelines, a process that could be aided with the use of artificial intelligence and machine learning techniques, such as robotic process automation and natural language processing. In addition, plan for the effort to build or leverage existing integration frameworks and patterns that can systematically process real-time data and provide pertinent and relevant data to the MCT.
Decide which measurements and KPIs to track
Once you have all this information at your disposal, you have to decide how to best synthesize it into meaningful indicators for your organization. A MCT will not only give you more accurate and up-to-date visibility into the KPIs you are tracking today but may provide a view to enterprise-wide insights you hadn’t imagined. Which KPIs would you track if you had the data available?
Today’s value chains and enterprise solution landscapes are complex. Connected visibility through MCTs helps organizations achieve new efficiencies and make decisions not possible before. Fully realizing the value of a successful MCT will require investment and organizational change. However this investment in MCT and careful planning of its implementation and adoption will prepare your organization for the shifting competitive landscape of the future.
Sachin lives in Austin, Texas with his wife Michelle and their 2 dogs. He enjoys cooking Indian and other Asian cuisines, enjoys golf, and is an active industrial designer for charity and open source. He is a technology enthusiast and builds various devices in his spare time utilizing existing and emerging technologies such as IoT, Arduino, Particle and additive/subtractive manufacturing. Some of his designs can be found at http://thingiverse.com/Sachin/designs
Ryan is a passionate foodie, cook, and baker who is always in search of new flavor experience whether that's found by flipping through a cookbook exploring the restaurant scene in his home of Salt Lake City.