In the second of a series of progressive articles on this topic, I will continue expanding on the blending of current and future Supply Chain Management (SCM) with Lean Six Sigma (LSS) best practices.
During design and planning of the supply chain, there are many intersect of LSS, and we will continue examining some of them here. I delved into “voice of the customer” (VOC) in the first article in this series and will continue under the heading of “uncovering risks and opportunities–SWOT and the basis for competition.” LSS offers many tools to help in this area. The tool that first comes to mind is called Quality Function Deployment (QFD), which is also referred to as the “house of quality.” This tool has many components that assist in structuring a better understanding of the competitive environment. As I promised in the last article of this series, this tool helps us think critically about our competition as well.
I am including here a sample provided courtesy of the Quality Council of Indiana. This is an excerpt from section 10 the Lean Six Sigma Primer (LSS Primer) I helped the author (more at www.qualitycouncil.com) that was published early in 2007.
I am not going to attempt to teach you everything you need to know about QFD in this short article. However, I do want to call your attention to some of the important components of this model that can be morphed to fit almost any situation, including issues to consider in designing our supply chains. If a deeper understanding is needed, then additional research will need to follow. This particular model I have shared is an instructional example utilizing the LSS primer book itself as the “product” in question.
We will now examine each of the four areas I have highlighted in the figure using circles with letters in them for this discussion to help you understand the application to the subject at hand.
A – Customer Needs
The example here is for a given product, but for our supply chain, we can provide points that relate to both the products AND the means to fulfillment. For example, some of the points for customer needs might be the speed of fulfillment of stock orders versus custom-made orders, packaging options, delivery options, locations, services offered, etc. Actually, a number of models could be constructed for each major part of a supply chain–product/service design, manufacturing, warehousing, outbound logistics, reverse logistics and others.
Note that the attributes are given relative rankings. Where does the data come from for that? As I mentioned in the last installment of this department, the LSS technique of Kano is a great source for what to include here and the relative rankings of importance.
B – Design Features
The next area focuses on features related to the subject being modeled. For our book example, we have adaptable content, durability, etc. For our supply chain, we would add features to correlate with various key customer needs. For example, for logistics, will customers want various options (features) for how the goods are packaged? The method of conveyance–the trade-off of cost versus speed? The degree of customization available in the product or goods? The degree of “eco-friendliness” of our supply chain?
Each of the features is given a relative ranking of its importance against each of the customer needs identified. Again, going into the marketplace with a KANO approach promises to provide the vital input. There are many formulas to calculate the relative ranking of each of the features to choose from, but the result–as we see in this example–is that we can give each feature a ranking of importance. For example, the features of Adaptable Content and “Current BoK (Body of Knowledge)” had the two highest ratings of 65 and 70, respectively. Of all the features in the model, these two would get the most focus in the ongoing design of our product–or supply chain.
C – Competitive Assessment
The right-hand side of this model gets into the SWOT (Strengths, Weaknesses, Opportunities, Threats) assessment for a product, service or supply chain. For each correlation of a customer need and feature, we evaluate our ability (or our planned ability) to perform against our competition. The model shown here is admittedly a simple one–comparing our book to a single competitor or the market in general. For our purposes, we would want to compare our current/future supply chain against those of our competitors–most likely performing this analysis for at least the top two to four competitors.
This useful exercise identifies what we are doing (or will do) well, and where we are only keeping up or behind the curve. Where we are better than the competition, we have basically two considerations. First, this provides some assurance we are or will be doing the right things. Secondly, we can ask ourselves if the cost and energy to be best is worth it. In some markets (commodities for example), being best might also translate to more costly, impacting market share.
Where we are worse, there needs to be some clear thinking about what we will do about it.
If being worse is ok because our market will be attracted to our low prices, maybe we don’t worry about it. More often, being worse or even the same is a call to action, identifying where to focus improvements and innovation. Frankly, this is when we will reach out to LSS consultants or our internal LSS BlackBelts to bring in the arsenal of LSS tools to find ways to elevate this area of the supply chain.
D – Target Values
This part of the model gets down a layer deeper with respect to attribute values comparing our product/service/supply chain to the competition. This gets more technical than we need to discuss here and may not be needed, depending on the subject being examined.
A work about models...
One last point about all the foregoing that I think is important is the nature of models. I will share a quote here attributed to one of the fathers of Six Sigma: Mr. George Box. He put it this way: "All models are wrong. Some are useful." I encourage you to not get too caught up in the absolute accuracy of these kinds of models and instead use them to make an educated hypothesis for testing ideas in the future. I want to add my own axiom: “any model is better than no model at all.” You can quote me on that.
LSS offers two areas of application–complexity in the actual products/services and in the systems design.
With respect to products and services complexity, there are many LSS considerations in product/service design and in Product Lifecycle Management (or PLM). PLM is a strategic business approach and process that manages the entire lifecycle of a product or service–from its conception, through design, production and later for service and disposal. PLM is increasingly impacted by global issues such as being eco-friendly, taking in logistics issues and applying a consistent set of IT applications. The IT applications provide solutions to support the collaborative creation, management, dissemination and using product/service definition information–across the supply chain from concept to end of life–integrating people, processes, and information. But I digress…
With respect to PLM, the key impacting LSS tools are Design Failure Mode Effect and Analysis (DFMEA), Design in context FMEA, Process Failure Mode Effect Analysis (PFMEA) and Define Measure Analyze Design Verify (DMADV). Modular design–the concept supporting mass customization strategies―is directly supported by Quality Function Deployment (QFD), Design for Six Sigma (DFSS) (the idea being to design-in quality performance and reduced variation upfront) and Design for Manufacture/Assembly (DFMA ). All of these tools support the need for requirement-driven design, specification-managed validation and design for distribution, reuse, recycle and repair.
Whew! I’ll bet it has been a while since you were given this many acronyms in a single paragraph. I apologize… it seems we live in an acronym world. At the onset of this article, I explained the elements of QFD, and, between you and me, most of the other LSS tools listed above have some similar attributes in their design and function. For example, PFMEA examines each process in a system and provides a way to identify each element criterion to categorize the level of capabilities inherent in each.
By utilizing levels of risk and probability rankings, a team identifies where to focus their efforts to make the weak process elements more robust (utilizing yet other LSS tools). For the process steps that will continue to be high risk and likely to break down despite our best efforts to design them out upfront, the organization can now build the containment and control points needed to minimize their effect on the process when it goes into action. For a supply chain PFMEA, this might be the policies and procedures for our employees to follow on a daily basis to manage risks and trouble spots dynamically–a good thing to do until a better solution can be developed.
In the next installment of this department, we will continue our examination of supply chain design considerations and how LSS tools impact them.
Below is a listing of key areas in which LSS impacts the initial implementation of supply chain activities:
After the supply chain is up and running, LSS continues to support and improve the supply chain’s performance in many ways, including:
If, by now, your head is hurting a little, that’s ok, it is a large body of knowledge to take in all at one time. The full list of things one might consider is longer, but this will serve our purposes for this series of articles. I will next begin diving a little deeper into how LSS directly supports each of the major phases I have identified.
One of the key things the professional must ascertain early on in planning the future supply chain is to clearly understand the basis for competition now and in the future and reliably plan to deliver the volumes of goods and services the market will demand. Few organizations have excess resources they can afford to waste in a poorly conceived supply chain strategy that has too much capacity and inventory or not nearly enough–leading to other problems.
Since most of us rely on customers to pay for the goods and services we offer or will be offering, it makes sense to start with looking for the Voice of the Customer (VOC). Or better, both VOC and the Voice of the Prospect (VOP), as we like to emphasize for the organizations we help with their marketing and sales processes. This is important because there may be important differences between what customers who you are currently doing business with are thinking versus those prospective customers who don’t yet do business with you. In most cases, finding out why you are not doing business with someone is even more important that knowing why existing customers are buying from us.
I’ll add a bit more complexity by adding that in a B2B (business-to-business) marketing and sales situation, there are special considerations that don’t apply in a B2C (business-to-consumer) scenario. For example, collecting data on consumers can be fairly straightforward in the form of post-sale surveys, random surveys of targeted consumers and test marketing response programs. In a B2B situation, it is never that easy, as the economic decision-makers we need to reach are hidden behind a veil of protection of gatekeepers, e-mail, voicemail and just plain purposeful misdirection to keep marketing and sales people away from them.
Buyers of our product/service can be further stratified based on other factors such as geography, industry type, age, and socioeconomic status. Lean Six Sigma data analysis tools, such as Pareto Analysis and ANOVA, can be very helpful in taking a bunch of data and separating it into clear groups of customers with a common set of attributes–if this is needed.
After we are down to the specific defined set of potential customers, we can then put into use a form of Quality Function Deployment to begin to isolate important kinds of information for our purposes. Let’s pretend that we are trying to make sure we understand the features and functions of the product/service that will be most in demand in the future–so that we can design our supply chain to handle delivery of the right things to market at the right volume levels. To keep it simple, we might have the following features/benefits that may be offered to three customer groups for fictional products–bike accessories:
Bike Accessories
| Likelihood of buying | |||
|
Features/Products: |
Customer Group A |
Customer Group B |
Customer Group C |
| Integrated water bottle | High | Medium | Medium |
| GPS unit/distance meter device/ emergency response | High | High | Medium |
| Nighttime lighting package | High | Low | Low |
| Camping equipment holding module | High | Low | Low |
The question here is: where does the data come from to establish the likelihood of these groups’ future buying behaviors? This is where the technique known as KANO comes in. After narrowing down your targeted audiences, you still need to find out which things are really important to the market–which things they will really care about and buy.
KANO is a scientific technique to design and collect customer/prospect survey data that develop insights into the relationship between customer needs and the value of a particular feature–both in dollars and in relative volumes. The negative/positive pairings of questions separate specific features into categories and further quantifies them. For example, features can exhibit certain characteristics such as “must be” attributes–meaning having the feature does not excite them; they basically expect it to be there. Brakes on the bike would be a good example. Most buyers would not be excited if the feature was brake pads that last a million miles, as long as they will stop the bike for the time they own it.
Other attributes can be one-dimensional in nature–basically meaning the more of it they get, the better they like it. The classic example here would be fuel economy in cars. The Holy Grail is finding the things that are highly attractive and exciting. For example, if the GPS feature is truly innovative, and the perceived value and “coolness” is high, it will translate into higher demand. Its potential volume can be calculated with reasonable certainty based on its “attractiveness” in conjunction with prices, which also will vary depending on the socioeconomic status of the target market. Remember, stratifying the target market into sub-groupings is important. Something that excites one market may be a reverse satisfaction feature in another market with different tastes.
All of this acquired data about the things and features that will be offered leads to clarity on design objectives and solutions for the development team to focus on. This is also true for the supply chain team in terms of what will be demanded of the supply chain so that it can be designed appropriately upfront to deliver on what marketing and sales bring to the organization. Properly designed systems continue to collect this data to provide a future indicator that is then compared against point-of-sales data later (trailing indicator data) to adjust the expected demands on the supply chain.
In the next installment of this series, I will move into examining the next important part of planning after we understand what the market will demand: the competition.
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