By Mustafa Bayülgen | Supply Chain Quarterly| Quarter 2 2012 issue

As the year 2008 came to a close, the global economy was struggling. Even so, we at the Mercedes-Benz bus plant where I work in Hosdere, near Istanbul, Turkey, were determined that we would not simply weather the economic downturn but would also increase production capacity by 30 percent. We knew it would be a huge challenge because of economic conditions and the increasingly complex nature of supply chains. Nevertheless, we went ahead with the project, which ultimately proved successful.

Thanks to the efforts of a logistics task-force team that Mercedes-Benz Türk A.Ş. (MBT) formed to tackle that challenge, the plant was able to meet its goal. The team achieved success by aligning all logistics operations with production operations through a project called “Hosdere 2010—New Logistics Concept.” This project applied a concept that can best be described as “evidence-based supply chain practice” (EBSCP).

Simply put, EBSCP is the incorporation of proven practical or research findings into daily supply chain management practices. It involves taking what has already been discovered—examining best-practice examples and current research—and then implementing it and enhancing it in actual practice. This concept facilitates both everyday operational decisions and long-term strategic plans.

By drawing on established knowledge, EBSCP lets supply chain managers succeed in today’s challenging business environment, and it prevents mistakes stemming from inexperience or individual preferences. Critical to its success is employing people with appropriate skills and knowledge.

EBSCP allows supply chain managers to avoid the kind of “crash tests” companies often undergo when implementing new initiatives in complex, intertwined supply chains. Rather than innovate without guidance, they can take advantage of widely available information. For example, they can learn from journal articles and books published by supply chain researchers around the world who are rigorously working to establish principles and find solutions for problems. They can also learn from practitioners who share their experiences through case studies in journals and business publications, as well as through other means, such as professional associations and professional networking sites on the Internet.

In short, with so many complex challenges facing supply chain practitioners today, it makes sense to learn from and take advantage of already published, tested information and solutions to problems. By doing so, companies will strengthen their supply chains, and that will eventually lead to greater profitability and an improved quality of life around the world as products and services arrive where and when needed.

The logistics team begins its work

Mercedes-Benz Türk A.Ş. was founded in 1967 under the name Otomarsan A.Ş. in Istanbul, Turkey, by Daimler-Benz AG and its Turkish partners, Mengerler T. A.Ş. and Has Otomotiv A.Ş. The company’s initial production in Hosdere was 0.6 buses per day. By 1970, it was already exporting buses, mostly to the Middle East and North Africa. In 1986, the company added a truck production plant to its portfolio, in Aksaray, Turkey, and changed its name to Mercedes-Benz Türk A.Ş. in 1990.

Over the years, MBT, now a subsidiary of Daimler AG, evolved into a manufacturer of complete buses and trucks. An increase in export activities led MBT in 1995 to add a second bus plant in Hosdere and to convert the first plant to manufacture bus chassis and bodies for the new facility. A few years later, the new plant was expanded to include chassis and body operations, and the original plant was shut down. Today the Hosdere factory produces four basic bus models: Travego, Tourismo, Intouro, and Conecto. It has a production capacity of 14 buses per day, and it exports vehicles to Europe, Asia, and Africa. MBT also markets and sells Mercedes-Benz automobiles and light commercial vehicles in Turkey.

In 2008, in order to boost efficiency and to get the bus plant ready for a potential capacity increase, project “Hosdere 2010″ was launched. The first thing the management decided to do was to bring highly qualified and talented individuals together to form a logistics project team. (For more about the human resources considerations involved in EBSCP, see the sidebar “Invest in talent to make EBSCP a success.”)

The logistics team found that the plant was facing such problems as material shortages, excessive inventory, and high transportation costs. Previous attempts to solve them had not been entirely successful, and sometimes the outcomes of those attempts were not what managers had expected. Clearly more information was needed. Recognizing that practitioners and researchers around the world have confronted similar issues, the team decided to adopt an “evidence-based” approach, making use of published research findings to help it address those problems.

Since then, Mercedes-Benz Türk A.Ş. has implemented several supply chain practices that the logistics team developed while consulting research findings, case studies, and other published best practices. (The notes throughout this article refer to the published information sources that provided some of the ideas we incorporated into our solutions.) The following are just two examples of how we applied evidence-based supply chain practices to achieve significant cost and efficiency improvements that we might not have been able to achieve otherwise.

1. Matching material flow with production
One area where the logistics team applied EBSCP was in adapting the assembly parts warehouse and in-plant material-flow operations to the increased rate of production. As part of that effort, the team set out to devise a new process in the warehouse, since the existing process limited how quickly the warehouse could perform such tasks as picking, sorting, kitting, staging, and delivery to the assembly line. An associated problem was that the on-time availability of components at the assembly line was 99.38 percent. Although this might seem good at first glance, it’s actually on the poor side for an assembly plant and has considerable cost implications.

The team began by brainstorming several ideas, looking for ways to increase not only throughput but also parts availability and overall efficiency. The questions then were, which of the ideas would work, and what would be the best way to implement them? To answer those questions, the members consulted some books, articles, and other documents, and then combined that information with their past experiences and the theoretical knowledge provided by their formal educations. Using all of those resources, the team developed the following solution.

Approximately 20,000 components and raw materials were being stored in the warehouse. But only 0.5 percent of those items were delivered just-in-time to the assembly line after having been picked from a dedicated storage zone. For the rest, a pull system was used, where assembly workers issued internal orders in the MRP (materials resource planning) system in batches (usually in full pallets); warehouse workers picked those materials from their storage locations and then delivered them to the line. This practice resulted in a significant amount of inventory positioned in the assembly area but provided little protection from stock-outs.

The master production schedule was fixed for three days out. The logistics team hypothesized that if the data in the MRP system was 100-percent correct, then it was theoretically possible to pick materials in single pieces from the warehouse, sort them in the right order, place them on trolleys in kits, and deliver them to the corresponding assembly station just-in-time and in perfect synch with the three-day, fixed master production schedule. This would greatly reduce, if not eliminate altogether, the inventory of components located next to the production line. The strategy would also provide more space for production-related activities and reduce the non-value-added time that production workers spent walking back and forth between assembly stations and material storage locations, some of which were not optimally positioned. In addition, since almost all of the inventory would be in the warehouse area at any given time, it would be much easier to anticipate a probable stock-out a few days in advance just by looking at the current stock level in the warehouse and in-transit from the supplier. If there should be a high probability of a stock-out for a critical assembly item used in, say, the Conecto bus model, then the scheduled assembly date for Conecto buses could be pushed back a few days before it entered the three-day “frozen” zone, and other bus models for which materials were available could be brought forward.

Taking the above arguments into consideration, the team decided to increase the number of parts picked from the dedicated just-in-time zone in the warehouse. This basically meant that the team would have to balance the trade-off between the cost of adding some more warehouse space and the benefits of improved speed of delivery to the line. Since this proposed system would allow for almost no margin of error in the MRP data, critical parameters entered into the MRP system, such as pallet capacities, batch sizes, and delivery frequencies, were carefully checked and corrected when necessary.

After implementing this plan, the percentage of materials picked from the dedicated zone and delivered just-in-time to the assembly line increased from 0.5 percent to nearly 15 percent. Because we learned from our reading that a reduction in the time to pick and deliver was a direct function of warehouse configuration,⁴ we made some modifications to the configuration of the warehouse that helped to increase picking and delivery speed. Another renovation was the conversion of the small-parts storage area to an automated storage and retrieval (AS/RS) system.

2. Supply base localization
As we approached the year 2008, MBT became interested in acquiring more parts from local Turkish suppliers. In 2008 we established a group within our logistics department that was assigned to deal with localization of supplies. One of our aims was to decrease the company’s total costs by reducing logistics-related costs. (Our thinking was similar to the total-cost-of-ownership approach described in the article “Time to come home?” in CSCMP’s Supply Chain Quarterly.⁵) Over the last four years, the division has successfully localized more than 5 percent of the components that originally had been imported from suppliers located outside Turkey.

Using more local suppliers was also a way to perform risk management by addressing both supply side risks and catastrophic risks, two of the five main risk categories outlined in an article on that subject by Stephan and Bode (2008).⁶ We also put forward a hypothesis that we needed to balance both prevention programs and response preparedness in our risk management practices.⁷ Proof that this hypothesis was correct came a couple of years later, when many firms that were stronger in prevention programs had difficult times during such disruptions as the massive earthquake and tsunami in Japan and the eruption of the Eyjafjallajökull volcano in Iceland. No manager would have thought to include such unusual, extreme events in a prevention program or to develop contingency plans for them.⁸

Meanwhile, many companies that were stronger in response preparedness continued spending time, effort, and money on response activities due to the disruptions, even though at least some of those efforts could have been avoidable if they had paid more attention to prevention. It should also be noted that the volcano eruption and the earthquake led managers around the world to realize that diversifying their supplier base might be beneficial. However, managers should not have needed those supply chain disruptions to come to that realization, since the principle that diversifying the supplier base by splitting orders among multiple suppliers could indeed be beneficial (depending on certain factors) was already widely evidenced in literature.^9, 10 As discussed in those articles, the expected reduction in shortage costs could make order-splitting worthwhile for some very critical components.

With those thoughts in mind, we decided to perform cost-benefit analyses for new components. If risks were high, then we would source the components dually, from one foreign and one local supplier. This would diversify the supply base in terms of both alternative suppliers and geographical locations.

Thanks to our decision to implement the dual or multiple sourcing strategy wherever possible, we avoided supply disruptions during such events as the eruption of the Icelandic volcano and the earthquake and tsunami in Japan. We also had some experiences closer to home. For example, in 2011 a very trusted local supplier of ours had a fire that incapacitated its plant for more than a month. Despite that disruption we kept the material flowing in, with one portion coming from the foreign supplier and the other portion from another local supplier. And in January 2012 we faced another potential disruption when a foreign supplier of a critical assembly part was unable to deliver it because of the extreme winter conditions in Europe. Trucks could not move, railways were covered with ice, ships couldn’t leave ports due to extremely high winds, and rivers flooded very near the Turkish border. We were able to manage despite those weather conditions because we had already split the order between a local and a foreign supplier. The local supplier, located just two hours away from our plant, sent its quota of parts just-in-time, and that quantity was enough to keep production going until the shipment from the other supplier arrived. Looking back at those situations, it is clear that the costs we would have incurred if we had experienced disruptions would have far outweighed the costs associated with placing orders with the second, local supplier.

Proof positive

Thanks to the two implementations above, as well as to many others that are outside of the scope of this article, MBT saw a steep rise in efficiency indicators. As shown in Figure 1, the on-time-delivery service level of parts to the assembly line rose from 99.38 percent in 2008 to 99.81 percent in 2011. Although this 0.43-percent increase may seem like a small nuance, it actually represents a significant cost reduction. That’s because when parts are available where and when needed, there is less overtime work and no penalties for late deliveries of buses to customers. As of February 2012, the on-time rate had reached 99.85 percent.

The drop in raw material and component inventories is also a strong indicator of how EBSCP has contributed to the cost savings Mercedes Benz Türk A.Ş. has achieved so far. Figure 2 compares each year’s average raw material and component inventory levels with that of 2008. We are aiming to continue the trendline shown in the chart in the coming years.

Figure 3 shows late deliveries of buses as a percentage of the daily production rate, from the assembly line to the respective sales organizations that deliver the buses to the end customers. The abrupt fall from the year 2008 to 2009 was mostly attributable to the impact of the economic crisis. During that time we had more production capacity then demand, which meant we could deliver buses with more ease than in a normal year. As the demand gradually increased in 2010, the backlog also increased as expected in 2010 through 2011. The rate of the backlog increase in 2011 was less than that of 2010 because the effects of the EBSCP implementations had started to kick in. We have recently begun to see the full impact of the implementations, as demonstrated by a backlog of just 0.29 percent as of February 2012—despite a record-high demand and daily production rate.

Enlarge image

As a result of the implementations described above and other Hosdere 2010 projects, MBT has achieved a significant drop in costs and has sustained profitability despite the global economic downturn. This success has attracted the attention of upper-level management at Daimler AG. As a result, the bus plant has hosted several visits and received recognition from the parent company’s management. In fact, Daimler AG’s top-level management meeting was held in Turkey in July 2011, and during the event executives had a chance to see the implementations’ results firsthand.

Top management has been so impressed with our accomplishments that it is considering implementing some of our ideas elsewhere in the company. For example, an extension of the just-in-time delivery to the line strategy, better known by the German term Spezialgutabwicklung (or the Turkish term set sevkiyat among MBT employees), is now being considered for incorporation into an ongoing process-harmonization initiative for other bus plants in Europe. The lean in-plant logistics system we developed is about to become a companywide benchmark and, with minor local variations, has the potential to be implemented in any of Daimler’s bus plants.

We believe it is important to share our knowledge and ideas with others. Accordingly, our plant has hosted visits by many logistics- and manufacturing-related groups and logistics specialists from all over Europe, as well as by faculty and students from local universities.

The MBT logistics team can point to many achievements. Yet even while we work on current projects, the fast-changing nature of business means that already there are new challenges that must be addressed. As we deal with them, the evidence-based supply chain practices concept that has proven so successful in the past will continue to be our guide.

Notes:

1. Marcia MacLeod, “Making the Warehouse Visible,” Automotive Logistics, July-September (2011).

2. A. E. Ellinger, M. Natarajarathiram, G. A. Frank, J. B. Gray, D. Hofman, and K. O’Marah, “Supply Chain Management Competency and Firm Financial Success,” Journal of Business Logistics 32.3 (2011): 214-226.

3. C. Eroglu and C. Hofer, “Inventory Types and Firm Performance: Vector Autoregressive and Vector Error Correction Models,” Journal of Business Logistics 32.3 (2011): 227-239.

4. G. Ghiani, G. Laporte, and R. Musmanno, Introduction to Logistics Systems Planning and Control, John Wiley & Sons (2004).

5. Harry Moser, “Time to Come Home?,” CSCMP’s Supply Chain Quarterly, Q4 (2011).

6. M. W. Stephan and C. Bode, “An Empirical Examination of Supply Chain Performance Along Several Dimensions of Risk,” Journal of Business Logistics 29.1 (2008): 307-325.

7. Bruce Arntzen, “Global Supply Chain Risk Management Part 1: Differences in Attitudes,” Massachusetts Institute of Technology Center for Transportation and Logistics White Paper (2010).

8. James A. Cooke, “Supply Chain versus the Volcano,” CSCMP’s Supply Chain Quarterly Q2 (2010).

9. D. J. Thomas and J. E. Tyworth, “Pooling Lead-Time Risk by Order Splitting: A Critical Review,” Transportation Research Part E 42.4 (2006): 245-257.

10. P. D. Berger, A. Gerstenfeld, and A. Z. Zeng, “How many suppliers are best? A decision analysis approach,” The International Journal of Management Science 32.1 (2004): 9-15.

11. E. A. Silver, D. F. Pyke, and R. Peterson, Inventory Management and Production Planning and Scheduling, John Wiley & Sons (1998): 50.

12. Thomas H. Davenport and Jerry O’Dwyer, “Tap into the Power of Analytics,” CSCMP’s Supply Chain Quarterly Q4 (2011).

13. J. Blackhurst, K. S. Dunn, and C. W. Craighead. “An Empirically Derived Framework of Global Supply Resiliency,” **italic{Journal of Business Logistics} 32.4 (2011): 374-391.

14. Tim M. Stratman, “Why You Should Never “Graduate’, “ CSCMP’s Supply Chain Quarterly Q4 (2011).

By Kimberly Knickle | IDC Manufacturing Insights community

Big Data – “Nothing new”.  That’s how I’d summarize the reaction of (hopefully) a minority of the consumer goods manufacturers that attended Consumer Goods Technology (CGT)’s Sales and Marketing conference in June.   Yes, I admit, I was surprised by that comment.  But if I can judge interest in the topic based on how many attendees came to the Big Data panel session, then I can tell you it wasn’t the opinion of all attendees.  I don’t think we should dismiss the “nothing new” reaction without considering why it does or doesn’t make sense.  I’d also like to share some of the Big Data-related discussions that took place at the CGT event and some of our research on how manufacturers will use big data technology in general.

We expect companies to spend a tremendous amount of money on big data technologies.  In fact, IDC forecasts the Big Data technology and services market will grow to $16.9 billion in 2015 across the technology stack including infrastructure, software, and services.   In our current forecasts, the growth rate for this market is about seven times that of the overall information and communication technology (ICT) market.  Spending those funds wisely is going to be very important to manufacturers, starting with identifying the right use cases for those investments.

Almost every manufacturer will tell you they have too much data, or at least a large quantity of data that doesn’t deliver a sufficient quantity of value; from that perspective big data is nothing new.  (Of course, most also want better and more data, too).  Unfortunately, most people that are skeptical of big data technology focus in too much on the word “big”.  The way we’re using the term Big Data today does represent something new, at least following the “Four V’s” definition, as we define each of the references by the following:

• Volume – Large quantities of data that could reach up to petabytes (and more), but again, size is not the only thing that matters.
• Variety – Multiple types of data, from structured to unstructured or semi-structured data, in combination, meaning companies could analyze a mix of data from web logs with customer information stored in a database with sensor data that provides real-time information on inventory or shipments.
• Velocity – The speed that data arrives and the speed of analysis, ranging from batch to streaming data, and keeping in mind the speed of both business processes and users’ decision requirements.
• Value – More affordable technology than ever before, including open source software and decreasing hardware prices, as well as increasing business value that manufacturers can generate from the use and analysis of Big Data.

Given this definition, big data tools and technology are for more than just analyzing a large volume of data.   A manufacturer could also be analyzing data from a variety of sources, delivering analysis at a greater velocity.  This combination of the V’s can enable manufacturers to use data as a means of making decisions faster or driving analysis that was too complex to do affordably in the past.   We also make note of the fact that not all applications of Big Data technologies are for analysis of data; they also include operational workloads and information access applications.

At the CGT Sales & Marketing conference in June, I participated in a Big Data panel led by Michael Forhez from TCS, and I was joined by CIOs from Energizer and Conair. Given the conference’s emphasis on sales and marketing challenges for consumer products manufacturers, many of the use cases we discussed focused on understanding the consumer better and customer service.  Specific examples include trade promotion optimization, SKU optimization, and modeling marketing campaigns; more use cases in this industry segment include sentiment analysis based on behavior in ecommerce websites or comments on social media forums (blogs, Facebook, twitter, etc).

We also raised examples that were related to the supply chain as well, such as knowing when product shipments could be delayed based on ocean carrier data or other information sources for inventory in-transit.  Ultimately, the value would come from knowing in advance if the current supply chain performance would impact customer orders.  Our recent IDC Manufacturing Insights supply chain survey of U.S. manufacturers shows a significant interest in applying big data; 52.7% of all manufacturers (not just consumer products) consider big data tools to be important or very important to their supply chain.   That number increases to 62.2% among those with supply chain titles.  On a broader scale, we’ve identified additional use cases in manufacturing as warranty analytics, enterprise asset management, and monitoring equipment performance.

Although we see evidence that manufacturers are taking the first step to creating value from big data technology by identifying the best fit use cases, manufacturers will stay in learning mode for some time.  Challenges will relate to managing data quality and data governance, selecting technology options and IT partners, and finding people with the right skills or “an appetite for analytics” as one of the CGT panel CIOs commented.   We also suggest that manufacturers explore options such as big data appliances and outsourcing big data skills or even technology through the cloud.   Today our research shows that each use case requires a different combination of software, hardware, and services to be most effective, but we believe that manufacturers will find that the investment in big data tools for volume, variety, and velocity is ultimately worth the “4th V” – value.