MAX-MIN OR ROP – ROQ

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

MAX-MIN OR ROP – ROQ

by Philip Slater

This guest blog is authored by Philip Slater, Founder of SparePartsKNowHow.com the leading educational resource for spare parts management. Mr. Slater is a global leader and consultant in materials management and specifically, engineering spare parts inventory management and optimization. In 2012 Philip was honored with a national Leadership in Logistics Education Award. To view the original blog post, click here.

There are essentially two ways that companies express their inventory control settings: either as MAX- MIN (sometimes MIN-MAX) or ROP-ROQ.

Some people will say that it doesn’t really matter which you use, just as long as you understand the definitions and the pros and cons. However, in my experience it does matter and this is one aspect of spare parts inventory management that you really do need to get right.

Let’s Start With the Definitions for MIN, MAX, ROP & ROQ

 

MIN = short for minimum

There is, confusingly, two schools of thought about what is meant by the MIN. Most typically this is the point at which the need to order more stock is triggered. Sometimes, however, the MIN is seen as the minimum quantity that can be safely held to cover expected needs. In this case the need to order more stock is set so that the reorder point is one less than the MIN value. That is. MIN -1.

The key to managing when using a MIN setting is to understand the configuration of the computer system you use, as different definitions will change the resulting holding level, the re-order point, and perhaps even the actual safety or buffer stock.

MAX = short for maximum

This value is most typically the targeted maximum holdings of the item. Usually, in a MAX- MIN system, where the MIN is the reorder point, the quantity reordered after reaching the MIN is the quantity required to get back to the MAX. For example, if the MAX- MIN is 5-2, when the quantity in the storeroom reaches 2, procurement would need to order 3 to get back to the MAX.

ROP = Reorder Point

As the name suggests, quite simply, this is the stock level at which the need to reorder is triggered. This is calculated by determining the safety stock level and the stock required to service needs during the reorder lead time.

ROQ = Reorder Quantity

Again, as the name suggest, this is the quantity to be reordered when the ROP is reached. This is not the EOQ but rather the quantity that both makes economic sense and is commercially available.

MAX-MIN OR ROP – ROQThe Differences are Meaningful and Important

It is essential that every inventory manager understands that the MAX- MIN and ROP-ROQ approaches are not simply interchangeable.

For example, in general terms:

MIN can be equated with the ROP, except if you have a system set up for reordering at a point of MIN-1. In that case, there is no equivalence.

For slow moving items the MAX can in some circumstances be equal to the ROP + ROQ. This is because for slow moving items it is possible that there will be no additional demand before the newly ordered item(s) arrive in stock.

However, with all other items the MAX is UNLIKELY to be equal to the ROP + ROQ as items may be issued between the time of reaching the MIN and the newly ordered items arriving. In fact, there is a logic that says that the MAX would never actually be achieved.

Do these differences matter? I think that they do.

For example, what if you change IT systems? If you move from one type of MAX-MIN system to another but they define the MIN differently then you cannot just migrate your data. This may not seem obvious if everyone is using the language of MAX-MIN but is classic trap where words are used in different ways.

Similarly, if you are benchmarking your holding levels with another company or site then you need to be aware of the different definitions and the outcomes that each approach would achieve. Otherwise you are comparing ‘apples with pears’.

Or what about what happens when a new team members arrives at your company and their previous company used the terms MAX-MIN but with different parameters or meaning to that your company uses. There will likely be an assumption that the terms are used in the same way and this could lead to stock shortages or overstocks, depending on the differences in the definitions.

To add further confusion, some software systems use the term ‘Safety Stock’ to represent the MIN holding level, despite this not being the universal definition of safety stock. This different nomenclature leads some people to assume that holding less than the so-called ‘safety stock’ according to your IT system is ‘unsafe’ or risky, when in fact it may not be at all. They may even be holding an excessive level of stock because they don’t properly apply the term ‘safety stock’. Calling it safety stock does not make it so.

Pros and Cons

MAX-MIN

Pros:

• Conceptually simple to understand.

Cons:

• Terms can be misleading in terms of safety stock and actual maximums.

• Terms are used in different ways and so caution required to ensure a common understanding.

• Values often set using ‘experience’ or intuition.

• Often leads to overstocking while reporting misleading overstock data

ROP-ROQ

Pros:

• Meaning of each term is clear and consistent.

• Values set using auditable logic.

• Safety stock values clearly established.

• Holdings more likely to reflect the actual needs and commercial constraints.

Cons:

• Requires more work to determine the appropriate values.

You Need to Get This Right

The differences between MAX-MIN and ROP-ROQ are not trivial and the terms certainly are not interchangeable. In my experience, the ROP-ROQ approach produces greater transparency and is easier to manage because there is no confusion about the meaning of the terms. This approach also produces a more appropriate and auditable level of inventory.

This suggests that if spare parts inventory management is important to you then you really do need to get this right.

 

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Forecasting and the Rising Tide of Big Data

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

“Trillions of records of millions of people…Finding the useful and right information, understanding its quality and producing reliable analyzed data in a timely and cost-effective manner are all critical issues.”

Smart Software Senior Vice President for Research Tom Willemain recently had the opportunity to talk with Dr. Mohsen Hamoudia, President of the International Institute of Forecasters (IIF), to discuss current issues with, and opportunities for, big data analytics. The IIF informs practitioners on trends and research developments in forecasting via print and online publications and the hosting of professional conferences.

Dr. Hamoudia begins, by way of introduction:

In all industries, data availability is exploding in volume, variety and velocity. Big data analytics is playing an important role in identifying the data that is most important to the business.

Let me take the example of the Information and Communication Technology (ICT) sector. We are seeing literally exponential growth in the amount of data available to telecoms, Over-the-top (OTT) independent content distributors, government, regulators and other organizations.

Around the world, we are witnessing petabytes of data: trillions of records of millions of people—all coming from multiple sources. Among these sources: internet connections, sales, customer contact centers, social media, mobile and land lines data. Finding the useful and right information, understanding its quality and producing reliable analyzed data in a timely and cost-effective manner are all critical issues. ICT companies are increasingly looking to find actionable insights in their data. How they can increase their customer base and loyalty programs? How can they improve the quality of service (QoS) and reduce customer turnover? With the right big data analytics platforms in place, they can be more competitive and efficient, improving operations, customer service and risk management. Forecasting and predicting customer trends and directions are key for telecoms.

Forecasting skills, including mathematics, statistics and econometrics, form one of the most important “blocks” of required skills in managing Big Data. Some forecasting activities naturally form part of the big data debate.

In retail industries, forecasting addresses daily demand across thousands of products. Financial forecasting, whether considering customer behavior or financial data series, generates massive on-line data sets. As pointed out by Robert Fildes, Distinguished Professor at Lancaster University, as yet the academic forecasting community is not thoroughly engaged—with only a few exceptions. Hal Varian of Google has looked at some of the work that David Hendry and Jennifer Castle, at Oxford University, have undertaken on searching large data sets for data congruent meaningful models. Stock and Watson have also developed their own approaches to large macro data sets. But despite the attempt, at last year’s symposium on forecasting in Seoul, to explore the theme of big data and its forecasting applications, there remain few convincing applications of using on-line data on real forecasting problems.

Q. One hears a great deal about “predictive analytics” these days, yet the phrase rarely is linked with forecasting. Do you agree that forecasting lies at the heart of predictive analytics? Have you an explanation for why the link has been broken? Have you ideas about how to re-inject forecasting into the conversation?

The results of forecasting (the “what”) are perhaps now perceived as less important than the “how”. Consequently, the trust that users give to traditional forecasting has declined. Who indeed is challenging the accuracy or relevance of forecasting by comparing, a posteriori, the reality vs. forecast—making a case for metholodiges’ effectiveness and therefor building credibility?

With the current perception of “predictive analytics”, there is probably more space in the public imagination allocated to the “how” side of things, and therefor a more credible story to tell to partners, investors or customers.

Q. It appears that there is almost no link between traditional forecasting and mobile technology (smart phones, tablet computers). Is this true, or are some companies migrating forecasting to mobile devices? Do you see a path forward in which traditional forecasting algorithms would routinely reside on mobile devices?

First of all, I am really delighted to invite your readers to have a look at our latest issue of Foresight. An excellent paper on the subject, “Forecasting In the Pocket: Mobile Devices Can Improve Collaboration”, explains that “the increasing popularity of PDAs, smartphones, tablet computers and other mobile devices opens up new opportunities for communication and collaboration on business forecasts.” The authors tell us “mobile forecasting (m-forecasting) applications may streamline approaches to collaboration between retailers and suppliers, thus contributing to the provision and exchange of product information, especially since forecasts are strongly tied to local context knowledge.”

For example, on the ICT & OTT side, a large number of predictive projects, such as those of Google+ and Facebook, are happening thanks to the inclusion of the “user location” data in the OTT IT systems. In my opinion, and what I see in some sectors like retail and logistics, is that traditional forecasting and mobile forecasting (m-forecasting) are complementary. This latter could be seen as a bottom-up forecasting approach that will or will not confirm the top-down forecasting results.

Q. Some people argue that big data will facilitate the replacement of forecasting with “sense and react” systems. Practically speaking, how would you explain “sense and react”, and are there application areas where you think it is or is not likely to take hold?

It seems to me that “sense and react” is fully oriented to the short-term perspective. Forecasting extends this by addressing needs for a variable horizon: short-term and medium-term.

As a side effect of ATAWAD (Anytime, Anywhere, Any Device), the decision-making criteria are, more than ever, “short term”. Big data is a “weak signals” sensing system, which enable the near-real-time detection of business opportunities that would go unnoticed with traditional IT systems. There are not really preferred or non-priority applications for this, the question is more on the “when” side.

Big data is relevant when looking below the surface in difficult economic times, but I am less sure whether it is worth the effort in “normal” economic period. To conclude on this point: I will be happy to see an example on how accurate are forecasts which are based on “sense and react” versus forecast based on traditional models.

Q. I’m asking some big questions. To what extent do you see the IIF community shaping these discussions and outcomes? How can readers join in the dialogue?

We are expecting an increasing availability, and increasing usage, of huge amount of data in many industries—such as energy, transportation, health care, finance, telecommunications and tourism.

Many of the IIF’s members are engaged in different aspects of the big data “movement.” The IIF is doing some work in the forecasting activities that naturally form part of the big data debate. More generally, the IIF is actively participating in, and providing a forum for, the discussion of forecasting in the wider world.

The theme of our last International Symposium on Forecasting (ISF) held in Seoul was “Forecasting with Big Data” and a few presentations were related to health care and telecommunications. A relevant workshop has just been run by the European Central Bank (ECB). If these models are capitalized on, they have the potential to impact the economic policy of Europe quite quickly.

Readers can join in the dialogue by contributing papers to the IIF’s publications (The International Journal of Forecasting, Foresight and The Oracle). Foresight, for one, is an invaluable voice in bringing academics and practitioners together in an ongoing discussion.

Readers also can present papers at the annual conference (the aforementioned ISF). They also can suggest and organize specific workshops for specific applications of big data, like the one that was just organized by the ECB in Frankfurt. Another opportunity is to invite IIF’s members to attend any meeting related to forecasting with big data. All these opportunities form good platforms for networking and working together.

Mohsen Hamoudia, PhD, is the President of the International Institute of Forecasters. He also serves as Head of Strategy for Large Projects (Paris) for Orange Business Services (the former France Telecom).

Thomas Willemain, PhD, co-founded Smart Software and currently serves as Senior Vice President for Research. Dr. Willemain also serves as Professor Emeritus of Industrial and Systems Engineering at Rensselear Polytechnic Institute, and as a member of the research staff at the Center for Computing Sciences, Institute for Defense Analyses.

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Worst Practices in Forecasting

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Companies launch initiatives to upgrade or improve their sales & operations planning and demand planning processes all the time. Many of these initiatives fail to deliver the results they should. Has your forecasting function fallen short of expectations? Do you struggle with “best practices” that seem incapable of producing accurate results?

For ten years, the editorial team at Foresight: The International Journal of Applied Forecasting has been telling readers about the struggles and successes of forecasting professionals and doing all we can to educate them about methods and practices that really work. We do that with articles contributed by forecasting professionals as well as respected academics and authors of highly-regarded books.

As Founding Editor of Foresight, I’d like to invite you to join us for the upcoming Foresight Practitioner Conference entitled “Worst Practices in Forecasting: Today’s Mistakes to Tomorrow’s Breakthroughs.”

This 1.5-day event will take place in Raleigh, North Carolina, October 5-6. There we will take a hard look at common practices that may be inhibiting efforts to build better forecasts. Our invited speakers will share how they and others have uncovered and eliminated bad habits and worst practices in their organizations for dramatic improvements in forecasting performance.

Some of the topics to be addressed include:

• Use and Abuse of Judgmental Overrides

• Avoiding Dangers in Sales Force Input to Forecasts

• Improper Practices in Inventory Optimization

• Pitfalls in Forecast Accuracy Measurement

• Worst Practices in S&OP and Demand Planning

• Worst Practices in Forecasting Software Implementation

Foresight is published by the non-profit International Institute of Forecasters (IIF), an unbiased, non-commercial organization, dedicated to the generation, distribution and use of knowledge on forecasting in a wide range of fields. (Smart Software’s own Tom Willemain serves on Foresight’s Advisory Board.) Foresight is just one of the resources made available by the IIF. Additional publications, a host of online resources, an annual symposium and periodic workshops and conferences are available to all IIF members. The Smart Forecaster previously interviewed IIF past-president Dr. Mohsen Hamoudia. Visit the IIF site for information about joining.

(Len Tashman is the editor of Foresight: The International Journal of Applied Forecasting. The unusual practice-related conference he describes, upcoming in October 2016, will appeal to many of readers of The Smart Forecaster. For instance, those who have received Smart Software’s training have been alerted to the possibility that overriding statistical forecasts can backfire if done cavalierly. Two sessions at the conference focus on the use of judgement in the forecasting process. — Tom Willemain)

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Handling Extreme Supply Chain Variability at Rev-A-Shelf

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Pursuing best practices in demand planning, forecasting and inventory optimization

Does your extended supply chain suffer from extreme seasonal variability? Does this situation challenge your ability to meet service level commitments to your customers? I have grappled with this at Rev-A-Shelf, addressing unusual conditions created by Chinese New Year and other global events, and would like to share the experience and a few things I learned along the way.

First, let me explain our situation. We import 60% of the parts we use to build our kitchen and bath accessories from China and Europe. Most of the year we were able to plan our inventory needs using a spreadsheet-based min/max approach. But not during Chinese New Year, which drives the planet’s greatest annual population migration. Chinese New Year shuts down production for up to two months, creating significant supply risk as we strive to meet our three day order fulfillment commitment.

We solved our problem, introducing statistical demand forecasting with the flexibility to extend lead times when necessary, the ability to reliably establish safety stocks that achieve our required service levels and a continuous reporting system that lets everyone know exactly where we stand. However, success required much more than a new piece of software. We needed to change the way we view future demand, supply risk and safety stock. Here are a few key things we did that made all the difference.

Stakeholder education and buy-in

Regardless of the project, it’s always best to enlist the buy-in of all stakeholders. We knew we had to do something to solve our problem, but there was bound to be resistance. Senior managers, for example, had developed a healthy distrust of software and wondered whether demand forecasting software could help. Our buyers had developed their own perspectives and procurement methods, and felt personally at risk as we considered new approaches.

People came around as they developed a common understanding of the problem and how we would address it. Education was a big part of the solution. We explained how forecasting works and key factors we should all understand: how to analyze trends, how to use “what if” scenarios, impact of shifting lead times, how to relate service levels to supply risk and safety stock and key performance indicators like inventory turns. Going through this process together, we all became stakeholders in the solution.

Use the Right software

When you have lots of part numbers and any sort of supply or demand variability, you just cannot forecast effectively with a spreadsheet. With our min/max forecasting system, we were planning to an average, and it wasn’t working. Average usage has inherent flaws for planning purposes—it’s always looking backward!

You need software that looks ahead, recognizes seasonal patterns and enables you to determine how much stock you’ll need to meet required service levels over varying lead times.

Fine-tune processes

When the old ways don’t work, you need to be open to adjusting your assumptions. Think less about where you’ve been, and more about where you want to be. Take a look at your lead times and plan to your desired service level. Last year’s history may not be the best predictor of this year’s demand. The same forecast horizon may not be appropriate for all products or certain time of the year.

Make the Forecast Actionable

It’s not enough to produce an accurate forecast and estimated inventory stocking levels. You’ve got to develop a way to make the information actionable for those tasked with using it. We developed a set of reports that enabled buyers to leverage better forecast and safety stock information. Now, at the end of every month, we produce a forecast report that provides a clear picture of current inventory, safety stock, past usage, forecasted usage, incoming deliveries (PO’s) and recommended order quantities.

Validate Results

You can, and we did, test our new methods against our own demand history. Still, an authoritative outsider can make acceptance easier. We commissioned a study by a professor at Louisville University’s College of Business who set one of her graduate students to the task. Through them we were able to reinforce what we saw happening from our results, and feel comfortable that we were on a good path.

All of these factors helped Rev-A-Shelf transform its demand planning process, to great effect. Today we are exceeding our service level targets, and our fill rate, based on a three day ship cycle, is showing steady improvement, and trending up. Overall, units-in-stock have stayed flat while supporting a 13% increase in sales.

John Engelhardt is currently Director of Purchasing and Asian Operations for Rev-a-Shelf, LLC in Louisville, KY. He has held a variety of management positions both in private business and public organizations. At Rev-A-Shelf he held the position of International Sales Manager and Director of Sales Support before assuming his current position. He can be reached at johne at rev-a-shelf dot com.

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Truth in Forecasting—Practical Advice at Year’s End

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

At year’s end, we are often caught up in thinking and planning for the coming year. Did 2013 turn out the way you expected? Will 2014 be dramatically different? Are there other factors—things we are planning to do; things we think our competitors might do; outside forces like changing taste, demographics or economics—that might change the course of business in the coming year?

Most companies that do a formal forecast start out with a statistical projection of past sales patterns into the future. Your forecast model should detect and characterize any seasonality inherent in your markets and include that in the projection. But that’s just the first step.

The next thing to consider is product lifecycle. Nearly all products go through a predictable cycle of introduction, acceptance and growth, maturity (demand levels off) and finally decline to obsolescence. These cycles can be as short as weeks or as long as decades. Clothing fashions and consumer electronics would be on the shorter end of the scale, while products like plumbing fixtures and construction equipment would experience longer cycles. In specialized situations like bus fleet management, entire fleets may be replaced over defined transition periods. In any case, the demand forecast should be adjusted to reflect increasing or decreasing demand according to the product’s position in its lifecycle.

Now comes the hardest part—predicting the unpredictable. In general, the future is likely to look a lot like the recent past in a similarity to Newton’s first law of motion: a body in motion tends to stay in motion unless acted upon by an external force. But it’s those external forces that can send your carefully calculated forecast right into the gutter. A competitor might slash their prices to take away some of your market share. New technologies might obsolete your product before the end of its expected life span. Changing tastes or new regulations might stop sales in their tracks.

But good things might happen as well. You might be the one to slash prices or improve your product and take away a competitor’s business. Your product may catch the fancy of the market and sales will skyrocket. A competitor may abandon the business or go bankrupt, leaving you with more opportunity.

Should you plan for these kinds of things? Certainly, to the extent that you can. You may know when you’ll run promotions or phase in the next product line. But the future, by nature, is uncertain. History and your business knowledge of the past lay the foundation for your view of the future. Statistically-based tools can help you create a risk-adjusted forecast, with safety stock recommendations that correspond with the level of risk you are willing to take. Beyond this, your key to success is agility—the ability to adapt to changing conditions. Prepare the best forecast you can, build your plans around that forecast—then monitor sales and market conditions closely and continuously. Look for early warning that things may be going in a direction other than you predicted.

You must be willing to recognize and adapt to changing conditions—in other words, don’t fall in love with your forecast and ignore evidence that it may be wrong. Pride of authorship in this case can be deadly to the business.

It is also important to have contingency plans in place so you will be prepared to make the necessary changes to procurement, production and inventory to respond to the new estimate of demand. The best tools for this are the shortest possible lead times (both production and supplier), good supplier relationships and a clear view of the world and your markets.

Forecasting is difficult mainly because people know it is likely to be wrong and nobody likes to be publicly and visibly wrong. Nevertheless, a good forecast is necessary to position the resources necessary to satisfy customer demands. Just be open to the first signs of change and be prepared to react quickly and decisively.

Dave Turbide, CFPIM, CIRM, CSCP, CMfgE is a New Hampshire-based independent consultant and freelance writer. He can be reached via e-mail at dave at daveturbide dot com.

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A CFO’s Perspective on Demand Planning – “More Strategic Than You Think”

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Bud Schultz, CPA, Vice President of Finance for NKK Switches, presented his company’s experience with demand planning during a recent webinar. The following is a brief summary of Bud’s key points; view the complete webinar by clicking here.

Q: Tell us about NKK’s business and demand planning challenges.

NKK Switches, based in Scottsdale, Arizona, is a leading manufacturer and supplier of electromechanical switches. The business involves many different switch types—toggles, push-button, rotary, even some programmable switch types. We are known for our high quality, and for our ability to meet an exceptionally broad range of customer requirements on a turnkey (custom configuration) basis. NKK Switches produces customized solutions from component parts sourced exclusively from manufacturing facilities in Japan and China.

There are literally millions of possible switch configurations, and we never know what configured solutions our customers will order. This makes our demand highly intermittent and exceptionally difficult to forecast. In fact, until fairly recently we considered our demand unforecastable. We operated on a build-to-order basis, which meant that customer orders could not be fulfilled until their component parts were produced and then fashioned into finished goods by NKK. This resulted in long lead-times, painful for our customers and a competitive challenge for our sales organization.

Q: What did you expect to get from improved product demand forecasting?

When we began to investigate the value of demand forecasting software (SmartForecasts from Smart Software), we tried to view the decision from a Return on Investment (ROI) point of view. We did some capital budgeting, making assumptions about potential reductions in inventory levels, reduced inventory carrying costs and other potential savings. Although the capital budgets returned positive returns on investment, we nevertheless were unable to move forward based on that information. We lacked confidence in our assumptions, and we were worried that we wouldn’t be able to justify the safety stock and inventory levels that the software would suggest.

What we didn’t expect was a challenge from our parent company. In light of the capabilities of a newly implemented ERP system, they would consider a new approach. If we could produce demonstrably reliable demand forecasts, they would consider procuring raw materials and producing switch components on a build-to-forecast rather than build-to-order basis. This opened the door to a much more profound impact. We tracked actuals against forecasts over a twelve-month period and found that our forecasts, particularly in aggregate, were exceptionally accurate: actual demand was within 3% of forecast. Once we were able to prove the validity of our forecasts, we were able to move forward with the parent company’s plan to manufacture product based on those forecasts.

Q: How did accurate forecasts of product lines with intermittent demand data transform NKK’s operations?

From the many different combinations we manufacture to order, individual switch parts can show very intermittent demand (long periods with zero orders and then seemingly random spikes), but we can identify more consistent patterns across switch series. All of the part numbers in a given series have common components and raw materials, such as plastic housing, brackets and other hardware, gold, silver and LEDs.

Providing our manufacturing facilities with reliable forecasts ended up allowing us to make dramatic changes. Our manufacturing plants could start procuring raw materials that in the aggregate would eventually be used in production of different part numbers within that series, even if the specific part numbers to be produced were unknown at the time the forecasts were made. And in many instances, despite the irregular demand history data, it was even possible for the suppliers to manufacture specific part numbers based on the forecast.

Once the program is fully implemented, we anticipate our leads times will be reduced to half the time or even less. Shorter lead times will result in lower reorder points, resulting in higher service levels while reducing our inventory requirements.

Bud Schultz leads all finance and accounting functions at NKK. His background as a Certified Public Accountant, attorney, engineer and pilot for the US Air Force provide unique perspective on finances for engineering and manufacturing operations.

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