Toyota Kata : Managing People for Improvement, Adaptiveness and Superior Results Read online

  Figure A2-15. Current capacity limit of a 1×1 flow process

  Insufficient machine capacity is a show-stopper issue, which is why there is a smile/frown check in this step in Figure A2-9. If machine capacity is insufficient, then you must address this first, before going on and making other improvements, because in that situation other improvement efforts will not stick. We must provide the factory floor with a process that is capable of supporting the planned cycle time.

  To check machine capacity, draw a machine capacity chart as in Figures A2-16 through A2-19.

  Figure A2-16. Step 1

  Figure A2-17. Step 2

  Figure A2-18. Step 3

  Figure A2-19. Step 4

  Figure A2-20. Example of a machine capacity chart

  Interpretation of the machine capacity chart. The first thing a machine capacity chart shows you is if you have any equipment that currently cannot support the planned cycle time. As you can see in the example in Figure A2-20, machine 90 has a total cycle time that is too long for the planned cycle time. This must be addressed before going on. Tactics for dealing with this obstacle fall in three successive categories, the first category being preferable to the next, and so on.

  1.Category 1: True improvement. Work hard to achieve this before going on to the next category.

  Shorten unload and load time.

  Reduce the gap between takt time and planned cycle time, which makes the Pc/t slower.

  Find capacity in the machine cycle. For example, reduce empty machine cycle time, such as “cutting air.” How much of the machine’s cycle time is actually spent processing?

  Split up multifunction machines, if this can be done inexpensively. Single function machines have more capacity.

  Make machine and unload/load times occur in parallel. For example, put the part fixtures on a turntable so the operator can unload and load while the machine is running and processing another part.

  Speed up the machine (quality cannot be compromised).

  2. Category 2: Compensating. Not true improvement.

  Add a small standard work-in-process buffer up- and downstream of the machine, to isolate its “personality” from the rest of the 1×1 flow. This only works if the total machine cycle time is at or below the planned cycle time.

  Move work to other processes, which slows down the takt time and planned cycle time for this process.

  3. Category 3: Buy more capacity. The last resort option.

  A Toyota person once told me, “If we are resourceful and creative we can almost always find ways to get more capacity out of a machine.”

  A machine capacity chart can also help you see the current natural capacity level of a process. In Line A, Figure A2-21, there is a capacity problem, but it only involves two machines. If we can reduce the total machine cycle time for these two machines, the planned cycle time can be met. There is capacity available in this line, and perhaps, with some creativity, additional products can be added to it.

  Figure A2-21. Line A is not yet at its natural capacity limit

  In Line B, Figure A2-22, two machines also cannot currently meet the planned cycle time. However, most of the other machines here are near their current capacity limit. There are of course things we can do to free up more capacity in this line, but increasing capacity in this process would involve nearly all the machines. Line B is close to its current natural capacity limit.

  Figure A2-22. Line B is close to its current natural capacity limit

  How many shifts? In conjunction with checking machine capacity, you should also consider the number of shifts. The clearest way to see what the options are is to prepare a table, as shown in Figure A2-23.

  Figure A2-23. Consider the options for number of shifts

  Is the Process Stable?

  When you start applying the improvement kata to a production process, as well as again and again after process changes are made, the target condition often includes establishing cycle stability. Process stability, or lack of it, is another show-stopper issue.

  If a process is not stable, you will need to address this before trying to make other improvements, because without a stable process, further improvements will often not stick.

  Whenever production processes are unstable, especially pacemaker processes, the entire organization (shop floor, administration, planning, logistics, sales and after-sales service, customers, etc.) will experience waves of fluctuation, variation, and extra activities. The total extra effort and cost generated by this variation in production is called the “hidden factory.” The extra expense is not measurable because there are too many intangibles, but such variation has been estimated to add 20 to 30 percent to cost. The more stable and level you can get your processes, the leaner the entire organization can be.

  Note that a stable process does not mean there are no problems, but that the process operates in a consistent manner from cycle to cycle.

  Time 20 to 40 cycles of each operator’s work. You can check process stability by measuring individual cycles, hourly output, and daily output. The most revealing of these measures is individual cycles, from one piece to the next, because it is a process metric that makes process details visible (Figure A2-24). Fluctuation in hourly output is also interesting, but is determined after the fact, and fluctuation in total output from day to day is only an outcome metric, that is, simply too coarse and too late for process improvement.

  To check process stability, time 20 to 40 successive cycles of line output and do the same for each operator’s work. Graph the results as shown in Figure A2-24, including lines for the takt time and planned cycle time. Time full cycles: select a single reference point in the cycle for starting and stopping your stopwatch and let the stopwatch run until the operator returns to this point in the cycle. Distinguish between work cycle time and waiting time as much as possible, and graph the work cycle time. Finally, do not use averages, because they conceal instability.

  Figure A2-24. Measuring process stability

  On this graph you should also note the lowest repeatable work cycle time for each operator, which is a figure you will use in the next step. In the graph above, for example, the lowest repeatable operator work cycle time seems to be 24 seconds.

  What Is the Necessary Number of Operators If the Process Were Stable?

  The more unstable a process, the more extra operators it will need in order to make target output. Unfortunately, overstaffing a process leads to even greater inconsistency, as lightly loaded operators naturally (and with the best intentions) assist one another with problems, work ahead to build batches, and work differently from cycle to cycle. Such increased variability actually generates more problems and makes understanding the causes of problems even more difficult. A vicious cycle.

  Keep in mind, however, that if you operate even a stable process with the correct number of operators, you will need to have a way of responding quickly from outside the line when problems occur (see Chapter 7). Problems will happen.

  Calculate the number of operators. Determining the necessary number of operators for a process involves measuring the total operator work time required to process one piece from start to finish. This can be done by watching and timing each operator’s work, and adding the times together. (Avoid standard timetables here, as they take you away from observing the real situation.)

  There is also a quicker and simpler way, which is sufficient for this process analysis: Simply use the lowest repeatable operator work cycle times from the 20 to 40 cycle graphs of the previous step. In this process analysis the initial operator times you use do not need to be exact, because you will quickly notice imbalances, overlooked wait times, and problems, and adjust as you work toward the target condition and carry out PDCA cycles. Do not waste time trying to obtain and agree upon perfectly accurate operator times now, up front, because the situation will change anyway as soon as you start taking steps toward the target condition.

  The theoretically necessary number of operators for a pro
cess is determined with the formula in Figure A2-25.

  Figure A2-25. Number of operators required

  Figure A2-26 is an example of this calculation.

  Figure A2-26. Example calculation to determine necessary number of operators

  Currently, the process has four operators, and the calculation shows 3.2 operators. So four operators are necessary today. Since four operators are underutilized, however, one stretch aspect of a target condition for this process, if it is stable, could be to run with three operators.

  Summarizing the Current Condition

  One purpose of the process analysis is to make you spend time observing the real situation at the process, and the information and data you have obtained at this point may be sufficient for outlining a first target condition for this process. You may see what would be an appropriate next target condition and be anxious to start working toward it. However, be sure to make a simple written summary of the current condition before you start to define the next target condition.

  Figure A2-27 is one example of a current-condition summary in a one-page format from a German company. I encourage you to develop your own format.

  Figure A2-27. Current condition summary in one-page format

  Notes

  1. For more on value stream mapping see: Mike Rother and John Shook, Learning to See (Cambridge, Massachusetts: Lean Enterprise Institute, 1998), and www.lean.org.

  Bibliography

  Although this book is largely based on hands-on research, there was also considerable secondary research. The following publications were particularly helpful or influential.

  Austin, Robert D. Measuring and Managing Performance in Organizations. New York: Dorset House Publishing, 1996.

  Austin, Robert, and Lee Devin. Artful Making, What Managers Need to Know About How Artists Work. Upper Saddle River, New Jersey: Financial Times Prentice Hall, 2003.

  Biggs, Lindy. The Rational Factory, Architecture, Technology, and Work in America’s Age of Mass Production. Baltimore: Johns Hopkins University Press, 1996.

  Carse, James P. The Religious Case Against Belief. New York: Penguin Press, 2008.

  Cusumano, Michael A. The Japanese Automobile Industry, Technology & Management at Nissan and Toyota. Cambridge, Massachusetts: Harvard University Press, 1985.

  DeMente, Boye Lafayette. Behind the Japanese Bow, an In-Depth Guide to Understanding and Predicting Japanese Behavior. Chicago: Passport Books, 1993.

  Deming, W. Edwards. Out of the Crisis. Cambridge, Massachusetts: MIT Press, 2000. (Originally published in 1986.)

  Dewey, John. Human Nature and Conduct. New York: Prometheus Books, 2002. (Originally published in 1929.)

  ———. The Quest for Certainty. New York: Perigee Books, 1980. (Originally published in 1922.)

  Gilbert, Daniel. Stumbling on Happiness. New York: Alfred E. Knopf, 2006.

  Henry Ford Tax Case Manuscript Collection. National Automotive History Collection, Detroit Public Library. Transcripts of Testimony of Peter E. Martin (vol. II, pp. 846–904), Fred H. Colvin (vol. II, pp. 929–47), Edward Grey (vol. III, pp. 1230–50), and Fay Leone Farote (vol. III, pp. 1158–1229, 1250–69, 1387–1400).

  Hounshell, David A. From the American System to Mass Production, 1800–1932. Baltimore: Johns Hopkins University Press, 1984.

  Johnson, H. Thomas. “Lean Dilemma: Choose Systems Principles or Management Controls, Not Both.” Unpublished paper, 2006. (Later published as: H. Thomas Johnson, “A Systematic Path to Lean Management,” The Systems Thinker, vol. 20, no. 2 [March 2009], pp. 2–6).

  Johnson, H. Thomas, and Anders Bröms. Profit Beyond Measure, Extraordinary Results Through Attention to Work and People. New York: The Free Press, 2000.

  Kleiner, Art. The Age of Heretics, a History of the Radical Thinkers Who Reinvented Corporate Management, 2nd edition. San Francisco: Jossey-Bass, 2008.

  Malone, Patrick M. “Little Kinks and Devices at Springfield Armory, 1892–1918,” Journal of the Society for Industrial Archeology (1988): 14:1.

  Mintzberg, Henry. Managers Not MBAs. A Hard Look at the Soft Practice of Managing and Management Development. San Francisco: Berrett-Koehler Publishers, 2004.

  Ohba, Hajime, and Cindy Kuhlman-Voss. “Leadership and the Toyota Production System,” presentation at Association for Manufacturing Excellence Conference, Chicago, Nov. 2002.

  Pascale, Richard Tanner. “Perspectives on Strategy: The Real Story Behind Honda’s Success,” California Management Review, Spring 1984.

  Perrow, Charles. Normal Accidents. Living with High-Risk Technologies. New York: Basic Books, 1984.

  Popper, Karl R. The Logic of Scientific Discovery. London: Hutchinson & Co., 1968. (Originally published in 1959.)

  Shewhart, Walter A. Statistical Method from the Viewpoint of Quality Control. New York: Dover Publications, 1986. (Originally published in 1939.)

  Shimizu, Koichi. “Reorienting Kaizen Activities at Toyota: Kaizen, Production Efficiency, and Humanization of Work,” Okayama Economic Review, vol. 36, no. 3, December 2004, pp. 1–25.

  Spear, Steven J. “The Toyota Production System: An Example of Managing Complex Social/Technical Systems. 5 Rules for Designing, Operating, and Improving Activities, Activity-Connections, and Flow-Paths,” Ph.D. dissertation, Harvard University Graduate School of Business Administration, 1999.

  Watts, Alan. The Way of Zen. New York: Vintage Books, 1989. (Originally published in 1957.)

  Womack, James P., Daniel T. Jones, and Daniel Roos. The Machine That Changed the World: The Story of Lean Production New York: HarperPerennial, 1991.

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  Index

  Locators with “n” refer to note.

  A

  A3 document

  Abnormalities, responses to (See Problem-solving focus)

  Accounting concerns, and the factory floor

  Achievement

  reaching target condition

  sense of, kata behavior replication

  subsequent target conditions

  Act, in PDCA

  Action-item list approach

  Adaptation, organization, and long-term success

  Adaptive persistence, improvement kata

  Advance group, kata behavior replication

  Andon pulls

  Aristotle

  Assembly loop, value-stream mapping

  Aulinger, Gerd

  Austin, Robert

  Automatic machine capacity

  planned cycle time (Pc/t)

  process analysis

  B

  Bacon, Roger

  Bandura, Albert

  Beginning challenge, mentor/mentee dialogue

  Behavioral routines (See Coaching kata; Improvement kata; Kata behavior, challenge of replicating)

  Benchmarking

  Bibliography

  Blanchard, Thomas

  Block diagram, process analysis

  Bohr, Niels

  Boretz, Benjamin

  Bullwhip effect, heijunka

  Business philosophy/direction

  company as self-benchmark

  cost/benefit analysis (CBA)

  direction

  philosophy, Toyota vs. non-Toyota thinking

  production operations

  target conditions

  vision and direction

  C

  California Management Review

  Capability levels, training

  Capacity analysis, Pc/t

  Carse, James P.

  CBA (cost/benefit analysis)

  Centralized decision making, General Motors (GM)

  Change, as constant

  Changeover time, Pc/t

  Check, in PDCA

  Cho, Fujio

  Classroom t
raining, as ineffective for behavior change

  Coach (See Mentor/mentee dialogue, coaching kata)

  Coaching cycle

  Coaching kata

  defined

  as invisible

  kata behavior replication

  leaders as teachers/coaches

  to teach improvement kata

  who makes process improvements

  (See also Improvement kata; Mentor/mentee dialogue, coaching kata)

  Competition, continuous

  improvement and adaptation

  Contiguous flow experiments, Ford

  Continuous improvement and adaptation

  (See also Improvement kata)

  Cost/benefit analysis (CBA)

  Countermeasures

  Creative destruction

  Current condition

  coaching cycle

  in establishing target condition

  kata behavior replication

  long-term success, defining

  mentor/mentee A3 form

  plan creation, kata behavior replication

  process analysis

  (See also Target condition)

  Customer demand assessment, process analysis

  Cusumano, Michael

  D

  Deming, W. Edwards