Process Improvement — Work Smarter, Not Harder
OF THE MANY insights that stand out in Eli Goldratt’s book, “The Goal,” one of the most memorable is the plant manager’s frustration with a machine operator reading a newspaper while on the clock. Like most of us, the plant manager thought that working harder or faster would naturally generate more money for the business. That’s pretty straight forward, right?
Not according to Goldratt. He makes it clear that running an operation at 100 percent rated capacity is not the objective. He reminds us that making money is the “Goal” of being in business. Goldratt’s “Theory of Constraints” teaches that you might actually make more money and offer faster ship dates when operators—who are not constrained—turn off their equipment. Perhaps, the only challenge greater than believing such a radical insight is to have the real-time information and frontline leadership necessary to act upon it.
Goldratt’s story of the idle operator and upset plant manager highlights the complexity of today’s manufacturing “systems.” Most businesses can be modeled as classic systems with inputs, such as labor and materials, and outputs, such as shipments and finished goods inventory. Unfortunately, manufacturing tends to fall on the complex end of system theory, where the relationship between cause and effect is not as intuitive as a simple system.
Consider a simple system, such as your car’s cruise control. You set the desired output speed; the speed control system monitors the actual speed, compares it to the target speed and makes the necessary adjustment on the gas pedal. This is referred to as a first-order system with a balancing feedback loop. First order basically means that a single cause (input = gas pedal) leads to a single, immediate effect (output = speed). Good feedback loops are critical to any well-behaved system.
Goldratt highlights that today’s manufacturing process is anything but a simple, first-order system. Perhaps, in the good old days of mass manufacturing and building to inventory—where a print run of, say, 500,000 units lasted several days—manufacturing was closer to the simple end of the spectrum. Controlling such a system to make more money was somewhat like setting a speed target in your car and adjusting the gas pedal as needed (faster = more gas, slower = less gas). There are minimal information and basic leadership skills required to optimize such a simple system. No one ever got fired for keeping operators busy running their machines.
Most printing companies in today’s developed economies, such as the United States, build to order, not to inventory. Supply chains are tighter than ever, meaning tighter deliveries. Run lengths are often shorter than the time required to makeready the machine.
Product families not only come in more than one color, size and material, they may be so customized that your manufacturing team is likely to be printing a new product for the very first time—every day they come to work.
Yet, in the midst of trying to manage such complexity, printers today basically have the same types of manual information systems as 25 years ago. “Islands of automation” that are semi-connected with paper-based, manually entered data. “Feedback loops” within these manufacturing systems not only lack accurate data, they often experience delays of hours, if not days, before “Actuals” can be compared to “Planned” to adjust to unexpected changes in the system.
Is it any wonder that printers today are often performing at less than one-third of their design manufacturing capacity? Sheetfed presses in some short-run environments might run less than five hours out of 24 hours fully crewed, yet orders still ship late in spite of outsourcing and overtime. There is no “pull signal” from finishing to close the loop with the pressroom for optimal output of materials into the plant.
This need for real-time visibility between “Planned” and “Actual” is a first step towards optimizing today’s printing systems. Closing the feedback loop between demand signals, starting with sales and moving across the entire manufacturing workflow, makes the goal far easier to achieve—especially within a dynamically constrained system. Isn’t this a key promise of the CIP4 standards around JDF/JMF? To help frontline leaders and managers get easier access to accurate, timely data so that they can make more money—ideally with less effort?
Good Data Is a Must
Managers within printing have heard for years about tools such as Six Sigma, Lean Manufacturing, TQM, SPC, TOC and the Continuous Improvement Philosophy. Why haven’t these proven techniques been widely adopted in the graphic arts industry? One likely root cause is the lack of easy access to accurate, timely process data that measures true process capability. Consider for a moment:
• How easy is it for your team to identify the current bottleneck in meeting customer demand?
• If a production process is over-producing, how quickly would operations managers know?
• If you were able to “flex” your operators to a constrained resource, who are the best qualified?
• If your job costing data identified profit leaders from losers, how fast could you change?
Today’s most effective process improvement techniques utilize the basic scientific method of “Plan—Do—Check—Act.” Good data is necessary for making good decisions. With JMF “feedback loops” tightly coupled to JDF demand signals, printers have access to a key enabler for moving from an often chaotic craft to the more predictable world of a manufacturing science.
Today, the paradigms are well-known, and the technologies are well within reach. The ultimate prize will go to those frontline leaders with the vision and management commitment to usher in a new era of print manufacturing excellence and profitability. PI
About the Authors
Jay Foster serves as president of SoftSolutions Inc. Peter Doyle is workflow manager for Muller Martini.