The global company manufactures and markets  quality power tools and accessories, hardware and home improvement products, and technology-based fastening systems. Its products and services are sold in more than 100 countries and the company has manufacturing operations in 11 of those. Black & Decker recently has focused on implementing inventory optimization, using the PowerChain solution from Optiant. Martin led this implementation, which is helping Black & Decker define a methodology and set targets for component and finished goods safety stock to support customer fill rates.

Q: We know you have been at Black & Decker for some time. How did you become involved in the supply chain?

Martin: Yes, I have been with Black & Decker for 20 years. I started working here as a temporary worker when I was going to school at night. I think I gravitated toward the supply chain because I like being in the middle of the action. I always say that in the supply chain you have accountability for everything and complete control over nothing. A lot of people have trouble with that disconnect, but I am comfortable with it.

Currently, I have responsibility for global supply chain strategy for power tools and accessories. This involves developing new metrics and reporting, exploring systems enhancements and driving business process improvements. Our big focus lately has been leading the implementation of inventory optimization.

Q: Give us a profile of Black and Decker's supply chain.

Martin: We build product in regional low-cost locations like Mexico, the Czech Republic and China and also in the U.S. More than half of everything that we sell in a given region is made within that region. For example, more than half of what we sell in the U.S. is made in either the U.S. or Mexico. This helps us balance low costs with shorter lead times.

Our largest markets are the U.S. and the European Union. In the U.S., we have three major distribution centers, in North Carolina, Tennessee and California. In Europe, a major DC in Belgium serves most of the EU, with a few forward-stocking locations in other countries. 

Q: So short order-cycle times are a priority?

Martin: Quick order-to-delivery cycle times are very important to our customer base. But we have to contend with very elongated supply chains. Even when we are making products in the U.S. or Mexico for the U.S. market, we have components coming in from China or Europe or other places and these imports have very long lead times. So, we have to create buffers within the supply chain to enable us to meet customer demands. 

Q: What were the key issues that led you to an inventory optimization project?

Martin: We wanted to improve our inventory turns to support corporate financial targets. As with every company, our corporate executives set targets every year to improve performance. Inventory management was one area where we felt there was opportunity to increase our cash flow and make better use of capital. There is always a trade-off between inventory and service - how do you support higher fill rates without a heavy inventory investment or how do you maintain good fill rates with less capital investment? These are the issues we were trying to balance. Where we had poor fill rates, we wanted to see what we needed to invest to give us the return we wanted; where we had high fill rates, we felt there was a significant opportunity to take inventory out. 

Q: What was behind your choice of the Optiant solution?

Martin: Black & Decker had purchased Optiant before this area came under my scope of responsibility and had been using it with mixed results in smaller areas. It had not really been rolled out. Even though I was not involved in the initial search I am familiar with the process. I know that after an extensive search the decision was made to go with Optiant because of the good, solid science behind their product. What really sealed the deal was talking to some of Optiant's clients and seeing the success they had achieved with the software.

Q: What did you do to get wider use of the software at Black & Decker?

Martin: I think early attempts to implement the software at Black & Decker ran into problems because they tried to accomplish too much too quickly. There were really bright people working on it who understood how much the software could do, but the rest of the business saw the solution as a black box and didn't really understand where it fit and what it was supposed to accomplish. So it never really got off the ground - it was used in a couple of areas but it didn't really stick.

When this came under my area, I had the benefit of having been with the company a long time and of having been a supply planner and a demand planner, as well as having worked in a bunch of other areas. So I was very aligned with the businesses we were supporting and I knew what they needed.  And because I knew the players involved, it was pretty easy to sit down with them and say, 'OK, here is what the software does, here are the primary inputs, here are a couple of the decision points you have and here is what the output will look like.'

Also, we initially took small steps and focused just on a single stage, setting targets only for finished goods. And we worked on simplifying some of the inputs. We focused on the service levels that we wanted to achieve and set one inventory target for that service level. Then we focused on the demand variability or forecast error variability and on getting good data for those inputs, because it is really your coefficient of variance that drives how much buffer you need in your safety stock. For some of the other inputs we used approximations - good guesstimates - and that simplified the process a lot. We picked something that was between the best-case and worst-case scenarios and went with it.

When we involved the planners, we made it clear they didn't need to understand all the math or algorithms behind the software. We didn't want them to think of it as a black box, but we did want the benefit of their experience and expertise to guide those input variables and to make sure outputs were aligned with real-world observations. So we set some caps or put up some guardrails around some outputs, but within those parameters we took the results as gospel. We made it clear that we believed in the numbers and were willing to stand up in front of our commercial organization and say, 'these are the right targets.'

As we got some success and traction with finished goods and we then were able to move into components and raw materials that the plants were bringing in. The next goal for us will be to do this at multiple levels and become truly multi-echelon with our implementation, because it is at that level that you really get the balance between the cost of inventory investment and the ability to execute. 

Q: Explain what "truly multi-echelon" would look like.

Martin: To date, we have been working on what we call 'single stage' targets, such as finished goods. We know the lead time is X, the forecast error or demand variation is Y, and the desired service level is, say, 98 percent. So we basically are just doing a calculation of inventory for one stage -our finished goods at the DC. The next step was to do a different stage - components coming into manufacturing locations. We took the lead time of a component and the variability and the desired service level and we calculated a target at that single stage. In theory, this is just a calculation and not an optimization. If you have been doing nothing before, you will see significant improvements. But if you already are using an APICS methodology, for example, you will not see tremendously different results.

Multi-echelon is true optimization because you really map the entire flow through your supply chain from supplier to plant to DC or forward stocking location and on to the customer. Multi-echelon is where you take all the stages you have in your supply chain and you model them all together with the right inputs, variables and decision points, such that you are balancing the lowest cost of pushing goods through the supply chain with the fastest response to customers at the end of your chain.

Let's say we are making something in the U.S. and the U.S. plant might have a one- or two-week lock for the finished goods schedule. But the components that go into that product could be coming in just-in-time or on a consignment basis or have a 10-week lead time from China. So while we say we can change the schedule in two weeks, that is dependent on our having that material with a 10-week lead time in place. Multi-echelon tells you what buffers you need at each stage to be able to support a two-week lead time on the finished good.

Q: Typically, do the inputs needed for these calculations already exist in an enterprise system?

Martin: Yes, all the data you would need to do this is either in your ERP system or in your planning system - or, in some cases, you rely on someone's brain and body of experience. You are taking data that you already use every day or decisions that one or two key people can sit down and come up with fairly quickly.

Q: Have you quantified any benefits?

Martin: We definitely have gotten benefits in every area. We have improved service levels where that was needed and have reduced inventory and increased inventory turns. It is difficult to give dollar amounts because of what has been going on with the economy, but we definitely have identified millions of dollars of opportunity. We have used the solution to make a lot of strategic decisions about our network and about the trade-offs involved in reducing or increasing lead times.

This solution also contributed to our being able to manage the economic downturn better than we might have otherwise. In the last two downturns we were beaten up by Wall Street because we did not respond fast enough with our inventory. This time around we got pats on the back. A lot of that was because we had this really robust methodology around how we set our targets, which drives our inventory. We were able to use that in our sales and operations planning process to say to the organization, these are the right targets and this is why, so we were able to react.

Resource Link:
Optiant, www.optiant.com