Or, imagine that you’re at a continuous improvement meeting. Some team members are clustered around a large flat screen, while others are accessing the same information on their smartphones miles away. The entire team comes to an agreement quickly, because all the facts are clearly visible. Enterprise-, system- and machine-level data has been transformed into actionable information—making the meeting more collaborative and effective.

Neither scenario is far-fetched today. By applying demand-driven principles and synchronized systems, manufacturers are reaching new levels of communication, growth and customer responsiveness.

Demand-driven manufacturing incorporates the best principles of lean manufacturing, the theory of constraints, and Six Sigma. It describes production based on actual customer demand, with the aim of synchronizing everything (workforce, methods, materials, machines and information) to drive flow. This process is accelerated by software that automatically collects, analyzes and communicates data, and that connects every function within a manufacturing organization and its supply chain.

The Industrial Internet of Things

If the Internet is a global system of interconnected computer networks, the Internet of Things (IoT) is a network of devices—not computers, but “things”—embedded with electronics, software, sensors and connectivity, so that they can exchange data with their manufacturers, users and other connected devices. The IoT allows objects to be sensed and controlled remotely across existing network infrastructure, creating opportunities for more direct integration between the physical world and computer-based systems. This results in improved efficiency, accuracy and economic benefit.

Within manufacturing, this concept is referred to as the Industrial Internet of Things. The Industrial IoT gives manufacturers the ability to collect, analyze and share data about materials, machines and processes in the production environment, in real time.

The Industrial IoT will bring exponential value to those companies that know how to use it. According to a recent report from the McKinsey Global Institute, “The Internet of Things has the potential to unleash as much as $6.2 trillion in new global economic value annually by 2025. McKinsey also projects that 80 to 100 percent of all manufacturers will be using IoT applications by then, leading to a potential economic impact of as much as $2.3 trillion for the global manufacturing industry alone.”

When manufacturing equipment, products, planning and execution systems are more connected, managers can see what is really going on as it happens. With a synchronized factory enabled by the Industrial IoT, manufacturing management can visualize and overcome obstacles and bottlenecks that once prevented them from responding to their true priorities.

In a white paper published last year, the consulting firm Accenture summed up the potential of the Industrial IoT: “The Industrial Internet of Things has been heralded as a way to improve operational efficiency. But in today’s environment, companies can also use it as a tool for finding new—and unexpected—growth opportunities. Successful companies will use the Industrial Internet of Things to capture new growth through three approaches: boost revenues by increasing production and creating new hybrid business models; exploit intelligent technologies to fuel innovation; and transform their workforce.”

Connect, Collaborate, Compete

Research has shown that connected factories are using this new intelligence to become more demand driven, and thus increase their competitive advantage. They pull together data from machinery, inventory management, suppliers and customers—and transform it into actionable information. This information provides a big-picture view that identifies disruptions in real-time, coordinates a fast and effective response, and logs the cause and effect to direct continuous improvement efforts.

In manufacturing environments, this level of synchronization and integration allows teams to see information as it happens. For example, teams can:

• Collect tag and sensor data from machines, conveyors, HMIs and environment management systems.
• Collect transactions from RFID tags and capture business application information in a contextualized historical database.
• Analyze granular data to pinpoint causes of production variability, quality problems or wasted opportunities.
• Use data to support operator and resource metrics.
• Create workcell information boards to support gemba walks; plantwide performance indicators; and factory, business unit, and corporate gold standards.

The key is that this core data is automatically collected and used to evaluate and support decisions at all levels of the organization. Engineers and managers no longer have to work from different data sets or pull data from disparate business applications, spreadsheets and databases. Everyone works with the same real-time information which, depending on their system, they can visualize in many different ways.

According to Aberdeen Research, “one of the keys to a successful lean operation is getting the baseline data and then tracking improvements. A good…strategy can accelerate lean’s impact through:

• improved access to data.
• self-service analytical tools.
• alerts to out-of-control conditions.
• management tracking tools for prioritization of projects.”

Demand-driven factories can deliver on-time orders more reliably and gain capacity to meet future customer needs. As information is gathered by the software and placed in front of the people who need to act—they become more empowered and informed to improve the way things are done. The intelligent environment gives employees clarity about the No. 1 thing they should be doing to improve overall company performance and provides information for how best to approach that task.

Reactive vs. Proactive

A reactive environment is replaced with proactive thinking when technology enables predictive analytics—using information to predict trends and behavior patterns. When data links all the moving parts in a manufacturing process, it can be used to create predictive intelligence to prevent flow disruptions and bottlenecks from occurring before they affect production.

For example, process engineers are now using predictive analysis triggered by machine-level “warning states” to do preventive maintenance and repairs before the line goes down and compromises throughput. What’s more, they can identify the metrics for demand-driven success with more confidence and direct these new activities with more visibility.

Demand-Driven Metrics

The demand-driven factory is managed according to flow-based metrics visible to everyone on a shared technology platform in a connected enterprise. As outlined by management guru Eliyahu Goldratt, production leaders predict and plan using three primary demand-driven metrics:

Throughput. This is the selling price of the product minus variable costs per unit.

Investment. All the money currently tied up in the system. Investment refers to capital (equipment, fixtures, buildings), as well as inventory in the form of raw materials, work-in-process and finished goods.

Operating expense. All the money the organization spends in turning investment into throughput.

In addition, the demand-driven factory focuses less on unit-costing measures and more on global effectiveness, taking a more holistic view of the entire production system and supply chain.

While data silos are still commonplace, manufacturers with synchronized systems have more comprehensive, actionable knowledge about their production cycles. They gain transparency in managing customer orders, expenses and investment across the enterprise.

Real World Results

Based in Sabetha, KS, Wenger Manufacturing makes extruders, mixers and other food processing machinery. The company needed to reduce cycle time and get a handle on its production capacity to better meet customer needs. “We went through about three different software solutions before we selected Synchrono,” says Brad Wenger, vice president of manufacturing. “We didn’t like the scheduling packages that came with most ERP solutions. We simply had no idea how much production capacity was on the floor.”

With a mixed product line and an engineer-to-order environment, Wenger Manufacturing usually has 1,500 work orders on the floor at one time. “We see change orders daily, and it was a difficult environment to keep stable,” says Wenger.

Synchrono adaptive manufacturing software provides the dynamic scheduling and execution capabilities needed to respond to change minute by minute. This has allowed Wenger Manufacturing to increase throughput by putting an end to work-in-process imbalances, uncoordinated actions and off-target tasks.

“If a part sales order comes in midday, it’s immediately prioritized into the shop floor,” says Wenger. “Getting that order out the same day might mean $10,000 cash to the bottom line and a very happy customer.”

The new software enabled the company to:

• increase on-time delivery from 40 percent to more than 95 percent.
• reduce WIP by 15 percent in just the first few months after implementation.
• reduce stock-outs of parts by 25 percent.

Wenger returned to profitability for the first time in three years by gaining visibility of their flow and acting on these signals to meet customer demand.

Manufacturing Intelligence

Demand-driven manufacturers succeed when they use technology to gain capacity and solve customers’ most pressing business issues.

With clear objectives monitored by key performance indicators, everyone—from managers to shop floor personnel—becomes empowered to improve not only their individual performance, but the entire assembly plant. With real-time visibility into what is actually happening on the shop floor and in the extended supply chain, they know which metrics can best help them meet their objectives and they can act on them. In many cases, their compensation also becomes tied to these objectives.

This level of manufacturing intelligence is the key to enhancing value. As Aberdeen Research reported in January 2014, “manufacturers that effectively harness their manufacturing intelligence achieve meaningful results.”

Aberdeen used four key performance criteria to distinguish best-in-class manufacturers (the top 20 percent of aggregate performers) from “all others” (the bottom 80 percent). The best-in-class achieved the following performance metrics:

• 96 percent of best-in-class manufacturers were successful with new product introductions, vs. 82 percent for all others.
• 26 percent of best-in-class manufacturers surpassed corporate goals for operating margin, vs. 6 percent for all others.
• Best-in-class manufacturers posted an operating equipment efficiency of 94 percent, vs. 81 percent for all others.
• Best-in-class manufacturers posted 99 percent on-time complete shipping, vs. 90 percent for all others.

For more information on demand-driven manufacturing, visit

 www.demand-drivenmatters.com.