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Growth and Global Expansion Put Strains on �the Toyota Way�

Good story in the Wall Street Journal last week on how Toyota’s continued growth is putting some strains on the systems and methods that had made it the world leader in quality. It contains some points of interest not only for those in the automotive industry, but any interested in lean production methods.

Toyota has enjoyed tremendous success. As the article notes, the company has “nearly doubled its revenue in the past decade, and redefined competition in key parts of the automotive business.” Toyota’s profits last year exceeded those of GM and Ford combined.

But the growth, especially that driven by production outside of Japan, has come at a cost in terms of the quality that was key to the company’s market success and production efficiency. It’s Georgetown, KY plant has slipped badly in ratings from JD Power and Associates, and quality ratings on several individual models have also fallen from the top ranks.

What’s happened? The article suggests that with growth, there has been a watering down in both knowledge and belief in the famed “Toyota Production System” that not only led to the company’s own success but really is the foundation for much of today’s “lean” thinking across thousands of manufacturers.

Growth has sometimes put more focus on getting cars out the door than adherence to TPS principles. There has been a lack of TPS experts from Japan to train North American supervisors. Language barriers have played a role. There has been a much higher level of turnover in North America, both at the floor level, leading to training issues, as well as at the executive level as TPS experts went to Toyota rivals.

All this led executives from Japan to find that “some hourly workers began ignoring standardized work processes – considered one of the biggest sins inside Toyota plants because of the impact on the consistency and accuracy of manufacturing.”

The company is taking action, of course. More experts from Japan have been sent to Georgetown and other plants, and there is a new training program to develop more non-Japanese experts. Managers have been dragged down to the factory floor and supplier plants to gain a better understanding of real production issues. And in the company’s flagship operations in Toyota City in Japan, where a new generation of TPS innovations are delivering benefits, will soon be “exported” to other plants. These include, for example, having “kits” of parts delivered by suppliers and placed into the car as it moves down the assembly line, so that operators don’t have to look for the right parts in kanban bins – which both takes time and adds risk the wrong parts will be selected. Toyota calls it “error proofing,” or “poke yoke.”

As always with WSJ articles, we can’t provide a direct link, but please email us if you would like a copy of this article.

Are you surprised Toyota has had some troubles maintaining its TPS disciplines as it expands production globally? What lessons does the Toyota experience have for other manufacturers? Let us know your thoughts.

Insights to Improve Forecasting Success

The latest issue of The Journal of Business Forecasting contains a nice article from Naresh Sadarangani and John Gallucci, supply chain managers at Fuji Photo Film USA that contains some advice for working with sales in the forecasting process.

The article contains some commonsense advice for obtaining greater levels of sales participation in the forecasting process. This includes listening more closely upfront to sales issues and concerns, and quantifying the impact of forecast inaccuracy on the company and sales. The authors include some simple but effective models for calculating the effects of both under and over forecasting.

Sadarangani and Gallucci then argue that perhaps the biggest obstacle, the real or perceived impact on the sales organization’s time, can be addressed through focusing on a handful of “demand drivers” for each key account. They define demand drivers as “simply the account specific elements (such as promotional plans, inventory strategy, and forecasts of an account) that most impact the accuracy of the forecasts.” [Note: the use of “forecasts” as a demand driver for forecasts seems not right.].

Nonetheless, the authors make a good case that by focusing on relatively few demand drivers for a market, channel or account, the time requirements for sales are minimized, while forecast accuracy increases. They cite a mass merchandise customer that may have key demand drivers related to base forecast, displays, customer inventory strategy, and promotional events, while for an ad-driven drug retailer they might use base forecast, opportunity sales, ad/promotional lift, and new store openings.

When the collaboration of sales has been achieved, the nature of the forecasting process and demand planning technology begins to change. The key is evaluating the forecast error coming out of the planning tool with that of the sales team. In the authors’ experience:

	Planning tool yields the best forecast for A and B items with strong seasonality, with little promotional activity (modestly augment with sales input).
	The planning tool is a strong supplement to the sales input for items with strong seasonality and high promotional activity.
	The sales forecast is actually more accurate for non-seasonal A and B items, and most C items.

A more detailed chart illustrating this is included. Interestingly, in two of the five cases, the authors recommend ignoring the sales input, and in another two, advise ignoring the forecast coming from the demand planning tool.

At Fuji, this approach has led to improved forecast accuracy at the key account/product level from 43% to 25% in two years, the impact of which is certainly in the tens of millions of dollars in bottom line savings.

This article is not yet available on line, but send me an email if you would like a copy.

What have you found are the keys to getting sales participation in the demand planning process? Is the authors’ perspective on use of demand drivers the right way to both improve forecasts and reduce the time required from sales? Let us know your thoughts.

Measuring Plant Productivity

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Short article in a recent Industry Week magazine on measuring manufacturing productivity.

It gives a brief snapshot of furniture maker La-Z-Boy, for which “output is measured in terms of "equivalent units." This figure is normalized for complexity, or the amount of time typically required for assembly of a given product. An ottoman, for example, might be one quarter the value of a recliner or sofa. Total employment and the number of hours worked for a given period comes from his payroll department. The end result: equivalent units produced per day per employee. This figure is monitored over time, and can be compared among the eight plants in [the] division.

Regular adjustments are key to making sure they aren't fooling themselves, says Dave Layman, senior vice president of operations. “Buying more components will skew the equivalent units and drive productivity upward, but total costs might still remain unchanged. "I can drive that number down by taking the labor out and purchasing it, but the end result is it doesn't turn out," he says. “

New Balance Athletic Shoe measures productivity via pairs produced per person per day, with headcount including assembly and some maintenance personnel. To compensate for the different time required to make different styles (because some styles are easier to make than others), the company's accounting department defines a basket of styles that is supposed to reflect a standard amount of work.

Meanwhile, Bridgestone/Firestone's passenger and light-truck tire facility in Graniteville, S.C., measures plant productivity two ways -- pounds per production per man-hour and pounds per total number of people who work in the plant.

The article makes the point that as the amount of direct labor involved in production decreases for many products, manufacturers need to look at new ways to calculate productivity levels. It also briefly touches on what may be the much bigger issue – the disconnect between many plant measures that drive increased output to improve yield or cost figures at the price of producing inventory that no one is going to buy.

Are their changes in many manufacturing environments that should cause many manufacturers to change the way they measure productivity? How have you seen your company get these productivity measures right – or wrong? Let us know your thoughts.

More on CLM/CSCMP:

More on the Bullwhip Effect:

The bullwhip effect is only minimized when a businesses’ economies of scale match up those of the marketplace of their vendors and customers (a random occurrence unless a business is willing to cap sales at predetermined production levels which leave no residual inventories, in essence undersell your sales forecast, this will never happen).

The focus to real time tracking of information and the push to achieve JIT have given us greater visibility into the data that drives the "Bull Whip Effect", but controlling the effect in total is to control the marketplace and there are not any businesses that control their marketplace.

It is as simple today as it was 100 years ago … planning, planning, and more planning - leads to enhanced cost control - which leads to competitive edge and greater profitability.

An example would be the Wal-Mart and Costco models, which are somewhat in control of more of their marketplace, as they control more of the variables. Eventually though some of their suppliers will reach a point where it is less expensive for them to open their own retail outlets or take on retail partners versus incurring the excess costs that Wal-Mart may drive into their operating bottom line by their insistence on the "Wal-Mart way". Every model has a lifecycle and Wal-Mart's will eventually be challenged and or modified.

Karl C. Sliwinski
PENSKE Logistics


The question as to whether we've made real progress is more than fair. And, the answer is, "We haven't." Not really. Not a fraction of what's possible.

Actually, recognition of what is called the "bullwhip effect" goes back to the beginnings of The Beer Game, maybe 30 years ago. Since then, there have been any number of initiatives to balance, integrate, communicate, etc., ultimate demand with supply chain operations.

Remember Efficient Consumer Response (ECR) 15 or so years ago? Essentially, the same potential to take time, cost, and delay out of the supply chain is still there.

Perhaps a bare handful of leaders and visionaries are doing the right thing about demand communications and ordering/replenishment, but it's still a mystery to the mainstream. I frequently conduct a version of The Beer Game, and the participants universally acknowledge how the game reflects the realities of their companies' supply chain dynamics.

Art Van Bodegraven
The Progress Group, LLC