Contribution Analysis (reference my most recent two postings below) can also be used effectively in Quality Assurance, including Vendor Quality Certification initiatives.

Let's say that your firm's QA (and Operations) team has determined that one of the major reasons for suboptimal customer service levels and stock-outs is unpredictable fluctuations in lead time. "Lead time", in this example, is defined as the length of time it takes to fill your stock from the moment you initiate a requisition. This allowance includes the time it takes to prepare the Purchase Order through to physical receipt of goods in your warehouse, and placing (posting) goods into available inventory. Naturally, if you place a PO with a supplier, the quantity in part is determined by the amount that you expect to sell within the lead time. If goods arrive three weeks late, and you do not have enough safety stock to compensate, you will likely run out of stock.

Now, your QA/Operations team might have come to this conclusion by using a variety of tools, such as the "Fishbone" (or Ishikawa) diagram, or even Pareto Analysis. But now we want to dig deeper into the problem of lead time variability.

Take a statistically significant sampling of inbound shipments that are arriving "late" (that is, beyond lead times that have been negotiated in procurement contracts). Determine what issue, or combination of issues, have led to the shipment arriving late. Such issues might include:

- supplier/vendor out of stock
- equipment breakdown in supplier's manufacturing process
- shipments delayed at point of consolidation at port of exit (for overseas suppliers)
- unreliable trucking company
- goods stuck in customs due to paperwork problems
- delays in rail yards
- and so on

Count the frequency of each issue within the sampling.

Rank the issues top-to-bottom.

Apply Pareto's Law and assign A, B, and C classifications. 

This will allow your QA team to focus upon those few issues that are contributing the most to your lead time variability problem (80%), and fix them first. This is the low-hanging fruit

It can be done!

Cheers    

In my last posting, I wrote about Pareto's Law and ABC Analysis (also called "Contribution Analysis".) Once the Inventory Manager understands the ABC's of his or her product portfolio, there are many practical applications.

One such practical application is with regard to Service Levels. Service Levels can be defined in myriad ways, but one of the most common is fill rate vis-a-vis customer orders. Another is a simple % in-stock calculation. In its simplest form, if customers, for example, order 100 widgets, and we are able to fulfill quantity 98 on time, our fill rate is said to be 98%. Now, there are a number of variants to this simple example that are designed to make the service level statistic more meaningful, but for the sake of illustration I have chosen the simplest example that I could conjour up.

Much to the chagrin of the Marketing and Sales Departments, it is statistically impossible to reach and maintain 100% service levels. The graphical relationship between service levels (on the horizontal axis) and inventory investment (on the vertical axis) is hyperbolic. It shows that as the firm approaches 100% service levels, the inventory investment required to achieve an extra 1% in service levels increases exponentially. The curve never reaches 100%. So, the firm that is obsessed with chasing the 100% service level Pot of Gold at the end of the rainbow can ruin its cash flow through over-investment in inventory. 

The idea behind ABC (Contribution) Analysis is to stratify service level goals by contribution code. Typically, the goal structure looks something like this:

"A" items =>Service Level Goal = 98%
"B" items => Service Level Goal = 95%
"C" items => Service Level Goal = 90%

This says, in essence, that the firm is willing to live with a 90% fill rate on "C" items as they are of little relative importance to the business as a whole. The firm will strive for high service levels for "A" items, due to thier importance to the business.

Firms employing this recognize the very real tradeoff that exists between financial needs and sales or customer service needs. It makes no sense to increase inventory investment by 50% in order to realize a 4% increase in sales. And believe me, I have sat in my fair share of Senior Management meetings where this arguement was made repeatedly. As the guardian of the inventory asset, the Inventory Manager cannot allow himself or herself to get talked in to this high-risk approach.

Firms ought to do some benchmarking, to try to determine what the competition is able to do. If best practice in the industry is that "A" items typically see a 99% service level, then you must judge whether or not that is achievable given current pricing structures and profitability scenarios. On the other hand, if you can achieve 98% due to superior inventory management expertise when the competition achieves 95%, and you can still beat or meet the competition on price, you have achieved a significant competitive advantage.

Moreover, the spirit of continous imprvement is that you can, and should improve service levels overall incrementally. But be careful not to blow your brains out in the process.

It can be done!

Cheers     

I cannot imagine a planner who manages tangible inventory who does not invoke Pareto's Law as a central tool in his or her analysis. Sadly, I know it happens.

Most will be familiar with Pareto's Law, which has also been known as the 80/20 Rule. It involves ranking the items within a product portfolio based on sales, value, or dollar value of usage. It is also used frequently in Quality Control to determine those issues that are most problematic. Once the ranking has been completed, a percent contribution is computed for each sku within the portfolio. Then, a "contribution code" such as A,B,C is assigned to each item. A items are those roughly 20% of items that contribute roughly 80% to overall sales, while B items are those 30% of items that contribute 15% to sales, then C items are, as Dr. Juran would say, "the trivial many"...50% of the items that contribute only 5% to sales.

The objective is to manage the A items with much more care and attention than the C items. In many cases, the inventory status of A items are reviewed daily or at a minimum weekly. Service level targets for A items are set much higher than C items. A items are cycle-counted in the warehouse more frequently than C items, to attain a very high level of inventory record accuracy. A items turn faster than C items. Expediting A items tends to be worth the effort.

I have worked for firms that insist on treating all items in a large product portfolio with equal reverance. The result was that, given limited time and resource, attention to the A items, the most important items, became diluted, and the lowly C  item that contributes 0.1% to annual sales got far more attention than it deserved.

So, let's incorporate Pareto's Law into every planner's tool kit. Use it, and work it.

(A little trivia....Vilfredo Pareto (1848-1923) actually received more credit than he deserved for Pareto's Law. It was really quality pioneer Dr. Joseph M. Juran who coined the term "Pareto's Principle", probably because it sounded better than "Juran's Principle"! While Paerto planted the seed, Juran popularized the notion!) 

Cheers!    

I have a colleague who works for a major player in the international financial services industry. This company is solid financially and is generally successful, but experiences the usual types of customer service issues that many companies face: failure to deliver some proportion of their service to a client or customer on time. These failures result in disappointed or irate customers, and frustrated customer service agents.

My colleague explained that while internal business process lead times involving exchange of information from person to person, or from department to department, are written in to policy, the lead times are frequently ignored. In the simplest of terms, documents sit on Individual A's desk for a week longer than they should, and others have to make up for the problem. The result is that tremendous pressure is put on downstream business processes to make up time for upstream failures. This pressure can come at great cost, both financially (overtime) and in human terms (stress). If the downstream work centre cannot deliver miracles, or they simply pay attention to the policy lead times that are standard for their section, the customer receives the service late.  Result = irate customers, and another series of problems with which the company has to deal.

It is not good enough to articulate workable lead times in policy. They must be monitored and enforced. Their importance must be underlined by Senior Management, even in industries that deliver no hard goods. It is interesting that such a best practice in Supply Chain and Inventory Management applies even to such an industry.

By applying good logistics principles, this company could achieve considerable improvement in customer service,

It can be done!