Thank you.

Finance is more interested in dollar-based measures of inventory. One example is the end-of-year total dollar investment in inventory, which is useful for creating financial statements. In today’s lean production environment, however, the finance department also may want to determine the inventory asset’s level of productivity. The formula for inventory turnover is (=Cost of Goods Sold/Average Annual Inventory Investment). It is an indicator of how well inventory is being used by a business. How many dollars of sales, at cost, are being supported by each dollar of inventory? That is what the formula tells you. Higher ratios are considered better, but this can be overdone in any given situation.

However, purely dollar-based measures of inventory are not particularly useful to operations. A customer pays a bill in dollars but buys specific items. From an operational perspective, items must be planned and controlled to match supply and demand. One measure of inventory at the item level is units on hand. This yields unambiguous quantity information item by item, which is useful for planning and control purposes. But it does not reveal whether the amount on hand is an appropriate amount. For that, you need a yardstick of some sort.

Traditionally, an appropriate amount was established by using independent demand inventory methods, such as Order Point/Order Quantity systems. But these methods failed to anticipate future demand and didn;t coordinate the availability of related items needed for joint use, for example, assembly.

Dependent demand planning methods, such as MRP, addressed both of these shortcomings by coordinating the planning of quantity and timing for related items and by driving that planning from known and projected requirements. With realistic lead times and realistic forecasts, the resulting inventories should have been “optimal” for the circumstances. Lack of realism in these areas, however, undermined the operation of MRP systems, yielding less-than-desired service and more-than-desired inventory.

Days of supply is an example of a time-supply measure of inventory. A common use of time-supply measures in inventory planning systems is to reduce the risks of excess and obsolete inventory by placing an upper limit on the time-supply of an item that can be stocked. One rule-of-thumb is “no more than one-year supply of C-items.”

Days supply could be calculated by inverting the inventory turnover figure and adjusting for units of measure. For example, a turnover of 5 (times per year) would be equivalent to a time-supply of 1/5 year or 1/5*365=73 days supply.  However please note that the turnover ratio is based on the cost of sales for the past year and on the average annual inventory investment in dollars. Aggregate dollars and a historical view aren’t very useful to operations, which needs to match supply and demand of specific items in future.

Days supply shoould be calculated at the item level, based on inventory currently on hand in units vs, known requirements and forecast demand in units per day for a specified period:

Days Supply= Inventory Currently On Hand/(Known Requirements+Forecast Demand) in Units/Day

Because time-supply and turnover are inversely related, the above result coould be inverted to project an inventory turnover figure for the future, which could be based on units not dollars. Generally, smaller days supply figures are considered indicative of a leaner, more productive use of inventory, but  again this can be carried too far. Fragile systems have “crashed” for lack of supply e.g. auto industry strikes at supplying plants shut down assembly plants rather quickly if there is not much buffer.

After the annual shutdown, a group of concerned employees was waiting for me in my office. The buyer informed me that MRP failed to generate the requirements for the new product and suggested MRP “may work for some products but just doesn’t work for ours.” One of the planners also explained that MRP had generated requirements for old part numbers but the newly created part numbers were missing from the MRP run. 

“Are you certain you loaded these new numbers into the part master?” I asked the planner. Without a word, she handed me the part master maintenance log for the last workday before the annual shutdown, which showed that all the new part numbers were loaded properly.

Perhaps we entered the wrong buyer code on these parts or perhaps purchasing failed to load any buyer code at all,” I said. But the planner handed me the maintenance record for the item vendor file with all the buyer codes highlighted. “I entered them myself because I was afraid purchasing would forget,” she explained.

I had one more possible explanation for the missing requirements. “Did we fail to load a lead time?” I asked the group. “A blank in the lead time field functions as a zero, and MRP will tell us to order a part in the same time period as is required if no lead time is loaded.” The planner held out another piece of paper to prove the lead times were loaded. She also assured me the dates in the master schedule and MRP were in sync and every data element she knew to check were all loaded correctly.

Far in the back of my mind, I struggled with another explanation, something I had ome across years before. But my memory was fuzzy.

“Given how rushed we were to begin our annual vacations, I’m surprised all the data got loaded properly,” said the IT Manager.

“Describe the daily MRP run,” I asked eagerly.

“We run MRP on third shift after all the second shift transactions are completed,” he explained. “After the preliminary jobs, we launch the MRP run itself, and it usually takes about four hours.”

He then described a series of preliminary jobs that updated the data MRP needed to function. Because these jobs were launched by a computer operator in a predetermined sequence, I suspected that our lone third shift computer operator, the last person to start his annual vacation, had taken a shortcut.

The IT Manager completed his list of preliminary jobs. “After updating effective dates for engineering changes, we run the low level calculation,” he explained.

“That’s it!” I said, “I’ll bet the missing parts have no low-level circulation. The field is blank; telling MRP not to search for requirements because they don’t appear on any level BOM.”

“I suppose it would work that way if a part ended up with no low-level calculations.” said the IT Manager. “But why would the program fail on just the new part numbers?” he asked.

“Because we didn’t run the program the last time we ran MRP,” I replied. “Our third shift operator dropped it to get a jump on his holiday.”

“But if he hadn’t run this preliminary program, why did MRP generate requirements for anyhting?” asked the buyer. “Why aren’t all parts missing requirements?”

“Because if you fail to run the program, it will use whatever number it has from the time before,” the IT Manager answered. “But for these brand new part numbers, there was no time before. This scenario would seem to fit the facts.”

He began searhing the computer files for confirmation, and soon announced, “I’ve checked the job log for the last day before the annual shutdown, and the low level calculation program was not run.” The mystery was solved.

“So does this mean that I should just dump today’s MRP output?” the buyer asked. “No,” I replied. “Your output is correct for the part numbers it contains. It’s just missinga few. The missing ones will be there tomorrow, right?” I asked, looking at the IT Manager. The others filed out of my office.

“Right,” he replied and added, “If there are no new BOMs loaded to cchanges to existing ones, you can drop the low-level calculation without affecting MRP.”

“Of course,” I replied. “I’m sure that’s what our third shift operator was counting on. He didn’t figure on BOM maintenance happening the last day before the annual shutdown.”

As he left, I realized I had gained an ally. The inventory planning group had always supported me and now IT was behind me, too. You never know how alliances will be forged. When people are involved, you just never know.

In this instance, the manufacturer needs to understand that inventory accuracy is only an indicator of performance and is not a true measure of the actual process. By definition, performance measures track a process, but something must have been performed in order to have a performance measure. Cycle counting is a process and has a performance measure related to its effectiveness.

Traditionally, companies judge a cycle-counting process based upon a measure of inventory accuracy. The more the process confirms the accuracy of the company’s inventory, the higher the process’ measure of effectiveness - or so it would seem.

Challenging The Traditional Measures

Consider the results of the cycle counts shown below. The mix of counts is representative of the category mix in order to suggest a level of inventory accuracy across all parts. Compare the total calculated accuracy of 98.7% with the following weighted calculation (there is more than one way to calculate accuracy):

Weighted Inventory Accuracy=[(99.9% * 1,000) + (99.0% * 3000) + (97.5% * 16,000)] / 20000 = 97.8%

[caption id="attachment_155" align="alignnone" width="300" caption="Click to enlarge"][/caption]

The major problem with the traditional process is that the measure confirms the inventory is already accurate. In fact, the highest measure is obtained only by not finding any inaccurate parts. The company’s inventory accuracy measure is actually penalized if inaccurate parts are found. Everyone knows more inaccurate parts exist than are being found, but there is no incentive to identify the causes of inaccuracy.

Consider the following scenario: Management has set a goal of 99.0% accuracy by count for category B. For an inventory containing 3,000 items, a minimum of 2,970 parts must be 100% accurate by count (or 990 0f 1,000 when counted each month to meet a quarterly cycle.) If an inventory manager discovers more than 10 inaccurate parts during any month, the B-item accuracy level will fall below 99%. Therefore, it is to this manager’s benefit to ensure that the cycle count team never discovers more than 1% inaccurate parts based on the number of parts counted.

In turn, the lack of incentive to find more than a given number of inaccurate parts then restricts production’s ability to schedule effectively; from experience, production knows that more than 10 inaccurate B-item parts are on hand at any given time. Some companies would rather delude themselves about their inventory accuracy level rather than acknowledge their processes are out of control.

Using dollar accuracy targets - rather than item accuracy counts - can mitigate the situation. When dollar overages (too much inventory) offset shortages (too little inventory), invemtory control can locate and correct more incorrect parts than is possible with an absolute count measure - as long as the dollars offset. The problem with this logic is that parts with an excess quantity don’t cause scheduling problems, but parts with shortages do. The inventory manager knows this but cannot risk exceeding the minimum boundary established by the measure. In effect, the measure restricts the number of inaccurate parts that can be discovered and fixed during any counting period. The measure drives the process - to the detriment of the company.

The real goal of an accuracy improvement program (as opposed to the cycle couonting program) is not to achieve a management-specified minimum level of inventory accuracy by ABC category. The goal is to achieve 100% accuracy of all parts by eliminating the possibility of inaccuracy in the first place. This means systematically identifying and eliminating all causes of inaccuracy.

With a traditional counting program, a 99% B-item inventory accuracy measure implies that 99% of the B-item parts are 100% accurate, based on the number of B-item parts samples counted during the previous period. In reality, that 99% is less a measure of inventory accuracy and more a measure of how diligent the inventory manager is at translating job performance measurements into a perception of inventory accuracy.

Finding The Causes

To maximize inventory accuracy, companies must measure the process itself. This requires establishing a performance measure that evaluates the inventory manager based on his or her ability to identify and eliminate causes of inventory accuracy. The more the causes of inaccuracy that are found and eliminated, the higher the performance of the process.

The performance measure must focus on identifying and resolving problems, crossing functional areas and even organizational boundaries in the process. Therefore, a team made up of a few hourly employees who do cycle counting everyday is inappropriate; the causes of errors are not restricted to the storeroom.

Actively identifying the causes of inaccuracy and tracking down their effect on inventory is a key difference between a performance measure and an indicator of inventory accuracy. The former focuses directly on the problems, while the latter simply monitors the results of counting a sample each period. When the emphasis is placed on identifying and eliminating errors, rather than the actual counting process itself, inventory accuracy becomes a byproduct of the process, not the sole measure of its effectiveness.

Changing the emphasis of a cycle counting program can uncover conflicting views of inventory accuracy within the company itself, as identified in the table below.

As shown, production benefits the most from an aggressive process aimed at eliminating inventory inaccuracies, compared to a traditional cycle counting process. As far as production is concerned, any accuracy level less than 100% means they never know which parts are likely to be inaccurate of all of those permitted to be inaccurate. In such an environment, all parts become suspect.

In contrast, as the first table indocates, 1000 counts per month are required for the B-item parts. No more than 10 inaccurate parts can be detected in order to maintain a B-item inventory accuracy level of 99%. At six minutes per count and $15 per operator per hour, the cost to not find more than 10 inaccurate B-item parts during the month and still meet the cyclical counting requirements is $1,500 (1,000 counts @ $15 per hour /10 counts per hour). The company is spending $150 ($1,500/10 inaccurate parts) to find and correct each B-item inaccuracy. This is the equivalent of investing 10 hours per incidence of inaccuracy.

Put another way, the company is wasting $4,934 each month (3,289 counts @ $15 per hoour/10 counts per hour) to simply confirm what it believes to be true. This calculation points to the ineffectiveness of the traditional process. Senior management should view this as a dismal indicator of the company’s effectiveness at spending valuable resources to identify and correct causes of inaccuracy.

A Real Measure

As these numbers demonstrate, indiscriminate counting is not the answer to permanently improving inventory accuracy. Counting is simply too random to be cost-effective once a company’s accuracy reaches a level that supports ERP and MRPII.

Measuring the effectiveness of the process itself is much more indicative of how effectively the inventory manager is targetting parts for counting. The lower the value, the more the company is wasting time and money because inaccurate parts are being missed.

Eliminating the causes of inaccuracies must be the true focus, and control groups are the means by which a company can identify those causes.  Imagine the progress that could be made by counting 3,334 parts in a month’s time, all of which were associated with control groups that targetted particular causes of inaccuracy.

Improving the inventory inaccuracy reduction measure is the target of an inventory accuracy improvement process, and is calculated as follows:

Inventory Inaccuracy Reduction= Inaccurate Parts Corrected/100% of Parts Counted

This measure works because the inventory manager is forced to concentrate on inaccurate parts and make a commitment to identifying and eliminating the causes of inaccuracy. It is in sharp contrast to counting known accurate parts, such as A-items or stockout parts that provide a false measure of accuracy. When done correctly, the inventory accuracy process will improve and sustain itself under normal operating conditions, at what may be a fraction of the cost of a cyclical counting program.

Risk Management Now a Core Capability of SCOR 

As enterprises’ operations have become increasingly global and complex, the potentialrisks to which companies are exposed have grown accordingly. The need for risk management capabilities within SCOR was brought to the Council’s attention by multiple SCC member organizations in government, industry, and academia.

In 2006 theSupply-Chain Council organized a volunteer team from various industries in the UnitedStates, Europe, Asia, and Latin America to determine how to include risk management in the SCOR model and, thus, provide an essential new tool for addressing risk and risk management in global supply chain operations. The result of those efforts are a set of new capabilities in SCOR 9.0 that enable a company to systematically identify, assess, and quantify potential supply chain disruptions to control exposure to risk or reduce its negative impact on supply chain performance. 

SCOR 9.0 has a comprehensive set of risk calculation metrics covering all levels of the supply chain, which enables a company to more effectively balance risk impact and costs of risk mitigation with overall supply-chain management costs. For example, the two new higher-level risk metrics included in SCOR 9.0 are “Value at Risk” and “Risk Mitigation Cost.” Value at Risk is the sum of the probability of risk events multiplied by the monetary impact of the events for all the supply chain functions (e.g. plan, source, make, deliver and return). Risk Mitigation Cost is now a component of Total SupplyChain Management Cost and provides the total of these costs across all the processes. 

Building a “Green” Supply Chain 

In addition to risk, environmental responsibility has grown steadily as a corporate concern for the past decade. Today, increasing governmental regulations—coupled witha broader recognition that developing more eco-friendly operations is “the right thing to do”—has put sustainability at or near the top of most companies’ agendas. To help organizations build a “greener” supply chain, SCOR 9.0 has incorporated the capabilities of GreenSCOR, which SCC introduced as a standalone reference model in2003.

These capabilities include the following: 

• Industry best practices for making the supply chain more environmentally friendly, such as collaborating with partners on environmental issues, reducing fuel and energy consumption, and minimizing and reusing packaging materials  
• Metrics to measure the effects of greening, including carbon and environmental footprint, emissions costs per unit, energy costs as a percent of production costs, waste produced as a percent of product produced, and returned products disposed of versus remanufactured.  
• Processes to address waste management, such as how to collect and manage waste produced during production and testing (including scrap metal and nonconforming product). 

The green capabilities within SCOR 9.0 not only make it easier for companies to comply with governmental and other regulations, but they also can improve overall supply-chain performance by identifying ways to reduce consumption and waste. 

Other Updates Include Expanded Metrics and Excel Format for Importing 

Accompanying the preceding new features are other important changes to SCOR 9.0.The entire metrics section of the reference model has been updated and clarified, and now benefits from the inclusion of metrics performance attributes needed for benchmarking and a new metrics coding system. The entire manual has been completely reformatted for easier use and readability. 

In addition to hard copy, lists of the Metrics, Best Practices, and Inputs/Outputs will be available in Excel format for easy importing into individual and company-specific programs. These lists include the name, definition, and process(es) where the item occurs, as well as coding and performance attributes for metrics. The new lists will make using current SCOR much easier on a global basis. 

“Version 9.0 marks a significant evolution in the SCOR model,” said Sean Simmons, executive director and CEO of the Supply-Chain Council. “The major additions—expanded risk management and “green” capabilities—enable enterprises to address twoof their most pressing operations challenges, while the updated metrics section gives companies stronger guidance for gauging the effectiveness of their supply chains. As a result, SCOR continues to be the standard upon which leading organizations base their critical initiatives for substantially improving supply chain performance.”

SCOR

The Supply Chain Operations Reference (SCOR) model in 1996 by the Supply Chain Council, an independent, not-for-profit organization of member companies. The SCOR model is now published in its ninth major revision and has gained wide acceptance by over 900 member companies as a leading edge business process reference model. The model itself builds itself around the linkages between process elements, metrics, best practices and features associated with spply chain execution. Over the last several years, SCOR practitioners in a variety of industries have evolved a SCOR deployment methodology which leverages the SCOR model to identify and prioritize supply chain metrics, design material, work, and information flow changes, and scope and quantify critical improvement opportunities in supply chains involving a single company or between multiple trading partners.

Lean

Lean is an evolution of waste elimination and process streamlining techniques that were founded in the Just In Time, Toyota Production System, 5S, concepts and applications from as far back as the early 1950s. One of the mottos of Lean is to do what is needed, when it's needed, in exactly the right quantities, with a minimum amount of resources. The goal of Lean is to maximixe process flow and flexibility in order to achieve breakthrough financial impact on the company. Lean deployment methodology had evolved over the last few decades into a highly capable, well-defined multi step approach that can be applied to administrative processes as effectively as production processes.

Six Sigma

Six Sigma is one of the most powerful business improvement approaches that has evolved over the last decade. Six Sigma is both a business improvement program and a powerful set of statistically based improvement tools. As a business improvement program, Six Sigma emphasizes the development of a very structured and disciplined infrastructure designed to translate strategic and operational opportunities into resourced, well scoped executable projects, to train, coach and mentor highly skilled product and process improvement experts, and to ensure project accountability and tracking of bottom-line  financial results. A problem solving tool called DMAIC (Define, Measure, Analyze, Improve, Control) structures the use of these tools to achieve optimal results.

The Move to Convergence

The relationship of SCOR, Lean, and Six Sigma principles have only recently been understood and recognized. Treated separtely, they each  have value; but merging all three into a comprehensive productivity plan could garner benefits ten-fold of what you might gain from any one of them.

But converged, or independent, they all require active executive sponsorship and championing. Companies take action when Senior Leadership realizes action is required. And what leads senior management to take action is: Visioning, Critical Need, and Peer Direction.

Critical need and peer direction are the most common, but not necessarily the most effective. Visioning is how organizational culture is changed, but it needs a proven execution tool to be effective. Today, the convergence of SCOR, Lean and Six Sigma requires vision. Culture, left to the masses, will always seek the familiar path and not change. This paper seeks to understand the power of this convergence so that readers will understand the methodologies and the reason to converge them.

Using SCOR, Lean and Six Sigma Effectively

SCOR's strengths in organizations is focusing results on how the SCORcard, or corporate measurement systems are affected. While not designed to deploy efficient processes, SCOR will help outline the effective projects to help achieve the desired results.

The strength of Lean and Six Sigma is producing results. There are no better methodologies to gain efficiency and construct valuable processes because these disciplines, by their nature, look down into finite areas, cells, or processes to improve. They can't look upwards at organizational goals and select the best projects on which to work. The projects are typically chosen because of burning platforms or executive directive.

The effectiveness of this convergence is achieved by using the inherent strengths of the three disiciplines to balance the deficiency of the others. (see Table 1 below).

Using any of the three will deliver good results. All are considered best practices when implementing continuous improvement processes. However greatness can only be achieved by realizing the strengths of all the methodologies as the inherent weaknesses are then overcome.

Lean and Six Sigma provide SCOR the infrastructure and the execution mechanism to make good SCOR-based supply chain process design a reality. The top-to-bottom organization impact of a Lean and Six Sigma program can be extended to include SCOR so that awareness and application of the model is threaded throughout the entire company. The prgram infrastructure, roles and responsibilities, training and development capability, and execution oriented tools of Lean and Six Sigma are ideal are idela for taking the output of SCOR i.e. the Project Portfolio and ROI analysis, and concerting it into real cost reductions, revenues and competitve advantage.

A few years ago, I accepted a consulting assignment for a company having problems moving product through the shop floor. Customers were’nt receiving their shipments on time even though the work-in-process (WIP) inventory was everywhere, and those customers were beginning to look elsewhere. To top it off, the company had recently acquired a new product line for which both tooling and machines had been sabotaged by the employees of the previous manufacturer. The acquisition had the company seriously in debt with nothing to show for it except piles of scrap, retooling, expenses, and engineering changes.

The company had a system. It wasn’t a great system, but it was capable of more than just tracking history, which was all that it was used for. All order entry, manufacturing control, and shipping functions were done manually and put in the system retroactively. Chaos ruled!

An example of the order entry process will illustrate. Sales immediately converted a new customer order into a shop order with a lead time of two weeks. And the order was always for the entire amount. Thus, if the customer order was a blanket order for 12,000 units to be delivered 1,000 per month, the shop order would be for 12,000 units due in two weeks. When the shop fell behind, sales reacted by doubling the quantity and reducing the leadtime to make sure the shop floor understood the urgency. The owner thought this was a perfectly reasonable way for sales to communicate the relative urgency of orders. And he had recently “helped” manufacturing by purchasing additional capacity. The fact there was insufficient capacity downstream meant inventory levels were limited only by space available, but at least sales could now tell angry customers their order was in process.

I was given a promising young man to mould into a materials manager. The two of us were given carte blanche to do whatever was necessary to commit to reasonable customer order ship dates, meet those commitments, and establish performance measurements throughout the organization to maintain our progress. For the next several months, we fixed, implemented, educated, begged, and threatened. Eventually, the employees embraced the new disciplines and customer service improved until on-time shipments exceeded 97 percent. WIP was reduced until inventory turns were nearly 15, and throughput soared.

The company had completed a great metamorphosis, but one problem remained. The acquired product had drained the company of cash and the owner of energy. He decided to sell.

The new owners had the resources to get finances into better shape but proved to have no management talent outside the accounting area. We assumed that anyone with a modicum of intelligence would see the advantages of running with lean inventories and quick customer response times. We assumed our inventory control procedures, with accuracy hovering around perfection, would be eagerly accepted by anyone who saw it.

We were wrong. The new owners had their old way of doing things, and what we accomplished was viewed with suspicion. When our physical inventory turned out to be 99.13 percent accurate, seven auditors came in to find out how we had cheated. Finding nothing, they left bitterly disappointed. Over our protests, the new owners insisted we adopt their old methods. These managers were so far back from the leading edge of manufacturing that when they replaced throughput and on-time shipment goals with old-time machine efficiency measurements, they thought they were teaching us something new.

Needless to say, the company began to backslide. WIP was increased by direct mandate because the owners feared running out of work in the shop. On-time shipments began to suffer as machine operators cherry-picked orders to meet their efficiency goals. The slide continued until the company was again sold.

The new managers had viewed with derision what the old management team saw as progress. progress is always in the eyes of the manager.