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Demand Driven Data & Effective Collaboration – Thoughts from Gartner’s 2014 Magic Quadrant for Supply Chain Planning System of Record

*Gartner, Inc. recently released the 2014 Magic Quadrant Report for Supply Chain Planning System of Record (SCP SOR). You can access a copy of the research here.

In addition to Gartner’s view of the competing players in the supply chain planning software market, the recently released Supply Chain Planning Magic Quadrant report includes interesting observations about the direction of the industry.

Retailers and manufacturers alike stand to gain substantially from one key observation – the movement toward more closely integrated supply chains and the use of demand-driven data to quickly respond to trends and/or disruptions.

Gartner Magic Quadrant for Supply Chain Planning System of Record 2014

*This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Blue Ridge.

According to Gartner, “In the future, the deployment of an SCP SOR will have to be increasingly in the context of multienterprise supply chains, along with the convergence of planning and execution capabilities, to facilitate more demand-driven, responsive and agile planning — especially in the short-term horizon (respond planning category) — across extended value chain.”

It is a visionary idea. Perhaps, you are not so far from seamlessly integrating the whole of your supply chain, establishing a single version of demand truth by analyzing every consumer interaction, translating precise demand needs at each supply chain tier, configuring execution plans across internal departments and with outside trading partners, all the while maintaining real-time visibility to capitalize on every emerging trend and prevent disruptions?

Ok, that’s probably a stretch; and if it is, keep reading…Retailers and suppliers, can benefit from a collaborative environment more than ever with the technology available today. And, they can do so without revamping the entire supply chain or worrying about data management woes.

As the story goes, suppliers have faced limitations in getting accurate demand signals. In some cases, the data for an accurate demand signal has not always been available; and in other cases, the capability to coordinate data from retailers and other consumer outlets has been restricted.  The result is a muddled demand signal, which leads to inaccurate production data, and sub-par demand fulfillment for the retailer – not an ideal situation for either party.

There are two reasons why collaboration initiatives are much simpler and more effective today:

  • There is more data available around consumer behavior than ever.
  • The Cloud provides a scalable, centralized platform for data integration.

Responding to the availability of big data, game-changing technologies capitalize on the power of the Cloud to deliver actionable consumer analytics, precise demand prediction and translation of consumer demand into upstream supply chain demand.

By sharing what is known about the consumer, retailers and suppliers can establish a relationship that will increase sales and net margin for both parties:

  • Suppliers gain- improved supply chain effectiveness through translation of consumer demand into upstream supply chain demand. As opposed to driving POS demand up the supply chain, proper translation gives suppliers the most accurate demand signal, enabling optimal production, operations and logistics planning.
  • Retailers gain- assurance that products are available where demand will occur, more loyal and satisfied customers, plus insights into the effectiveness of promotions and other demand-driving activities.
  • Both gain- The ability to measure their own effectiveness relative to their trading partners…e.g. suppliers can see how well they’re fulfilling retailer’s orders, and retailers can see how effective they are in penetrating the market with the supplier’s products. This opens dialogue to improve both parties’ effectiveness with the insights into the other.

While effective integration and collaboration has been seen as a daunting project for many companies, Blue Ridge and Liaison technologies have partnered to make collaborative commerce easier and more effective.

On April 10, you can join Blue Ridge and Liaison in a webinar that will explore how companies can enable collaborative commerce and use of analytics to optimize processes such as S&OP, freight management, logistics and more. We will cover data integration and harmonization, as well as real-time reporting.

The entire Gartner Supply Chain Planning Magic Quadrant report has much more information and analysis to offer- be sure to read the full report here.

*The Gartner Magic Quadrant for Supply Chain Planning System of Record was published by Tim Payne on March 06, 2014. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

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Hardgoods Industries Face a Unique and Shockingly Expensive Challenge: 
Long and Erratic Lead Times

Long lead times frustrate buyers, jeopardize service levels, and in the end, steal profits.  For inventory professionals in hardgoods industries, the lead-time component can be known as the sleeper component, the missing component, and often the killer component.

Lead Time:  The Sleeper Component

Let’s face it. Many times, companies put the vast majority of time,  resources and advanced math in forecasting customer demand.  After all those resources are invested, when it is time to forecast our suppliers and their lead times, they typically shoot from the hip.  This leads to missed service, expensive lead time ‘cushioned’ inventory, and overall frustration for customers and stakeholders.

To add insult to injured profits, blame is placed on the demand forecasts. When actually, the sleeper component has again reared its ugly head.

An argument can and should be made that items deserve as sophisticated a lead-time forecast as they do a demand forecast.  Food industries have short and consistent lead times, which are often less than 7 days.  Hardgoods industries don’t have that luxury. If your supplier is shipping to you at a rate of 89%, and you have to turn around and deliver to your customers at a rate of 94%, you need lead time forecasting support! It’s time to wake the sleeping giant that is Lead Time Forecasting.

Lead Time:  The Missing Component

Lead Time is part of any replenishment strategy and buying calculation. Today’s reality is that most buyers still use a ‘worst case scenario’ when determining a lead-time forecast.  If their experiences in recent months range from lead times of 21 to 38 days, you can bet that 38 will be the number! Just imagine if your demand forecasting always used a worst-case scenario.

There is typically at least a 10% inventory cushion from exaggerated lead times, but the problem does not stop there. There is also missed service from several areas. Long import lead times are screaming for attention in many ways. First, a lead time of 60 days or longer actually becomes your number one safety stock driver. The analysis across the Hardgoods industries shows an incredible average safety stock of 47 days for items with lead times above 60 days, where a nice 7 day lead time typically averages only 5 days of safety stock required. This means that you can forecast the lead time perfectly and still miss service due to the fact that there is a large void in safety stock.

Long lead times can be your number one safety stock driver

In addition, most inventory professionals are not including the critical factor of Lead Time Deviation into the mix.  Some of your import suppliers have long but consistent lead times, while others can deviate a month of more!  This lead-time deviation is a critical measurement that again plays into your safety stock.

Lead Time:  The Killer Component

Lead-time measurement is a critical responsibility of a strong buying strategy. Profits will suffer on long lead time items if you don’t monitor the margins and make smart service level goal decisions. Requesting to be in stock 98% of the time is much less costly on a 7-day lead time than a 90 day lead time.  In addition, you have to make sure your calculations understand how to adjust your safety stock to even achieve something in the high 90s.  Simply adding 2 or 3 weeks for safety stock does not cut it on those items.

Throughout the Hardgoods industries, long lead times in general require a deep investment just to maintain day-to-day service levels.  It’s the nature of our items.

Killer Warning:  If you were to suddenly insert our long lead times to any food industry distributor, without adjusting their margins up and their service goals down, they would be out of business before the next season.

Does your buying system or buying strategy put the required resources into lead-time forecasting and lead-time management?  Do your category management professionals understand the profit impact of long and erratic lead times and utilize that information when making decisions on the item mix, the import or domestic source, the service level goal, and the sales price to your customers?

If not, educate yourself and your team, then get real about your need for an advanced replenishment solution that gets it!

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Petco Manager ‘Scoops’ Award for Surprisingly Seasonal Merchandise

Do dogs in the North eat more in the early spring? Or do they just poop more outdoors? How else would you explain the surge in demand for poop scoopers in cold-weather states in March and April?

Julie Knox, Inventory Operations Manager at Petco Animal Products in San Diego, has a simple explanation. As the snow melts, she says, the winter’s accumulation of Fido’s daily deposits looks more like the output of an entire sled-dog team.

Pooper Scoopers Are a Surprisingly Seasonal Item in the North

With weather warming, the thought of walking on the lawn or of kids playing in the yard sends many a dog owner looking for a convenient way to clean up.

This insight won Julie a new iPad Mini in the first Surprisingly Seasonal Items contest, sponsored by Blue Ridge Inventory. We congratulate her.

Tennis, Fido?

Grace Gil, another inventory manager at Petco, also reports that one of the company’s most popular seasonal items is the “dog tennis ball.” Sales spike in summer months when the canine lawn-tennis leagues get into full swing.

Contestants from across the United States shared other weather-related oddities. For example, you’d expect motorcycle batteries to sell more in the spring and summer, when the weather’s mild and more people are riding their bikes, says Keith Nesbitt, inventory manager for seasonal and agricultural products at Tractor Supply Company in Nashville. But how do you explain the big winter lift for motorcycle batteries in northern states? It’s because some of the same batteries also power the starter motors of snowmobiles, he says.

Winter ice-cream cakes in Wisconsin

Mike Eash, a member of the Purchasing group at Valley Bakers Cooperative Association in Greenville, Wisconsin, notes that ice-cream cakes experience a big lift in sales in January. Why? He has no idea. Do you have a theory?

Amy Jones reports that demand for white cotton balls spikes in the spring, summer and winter. White cotton balls? Really? Ms. Jones, Purchasing Coordinator at Xpedx, a distributor of packaging, supplies and paper products near Cincinnati, says stores buy them for customers who try on makeup at cosmetics counters. Demand spikes in the spring for the prom/wedding season, then in the late summer for back-to-school season, and again in winter for the holiday-party season.

Eye makeup for Halloween and high-school prom

A similar pattern might drive the sales spike for fake eyelashes in late October and then in March, April and May. But why does only one type of eyelash spike so much more than the others? Halloween may help explain its appeal in October, but what about the springtime, wonders Kristin Montoya of Associated Food Stores in Salt Lake City. Is there a prom/wedding season for big eyelashes?

Getting into the holiday spirits

Tim Clarey, Inventory Control Manager at Wirtz Beverage in St. Paul, sees a 200-percent jump in sales of Aquavit, a Scandinavian spirit, during November and December. Aquavit, a vodka-like liquor flavored with caraway, dill and other flavors, is the perfect complement to pickled herring, lutfisk, and caviar on the traditional holiday tables of the Upper Midwest’s descendants from Norway, Finland, Denmark and Sweden.

To package all that Aquavit and other bottled liquor for the holidays, brown paper bags also see a spike during the holiday season, says Stacy Revinski, a replenishment buyer at Xpedx in Camp Hill, Pennsylvania. But there’s an even bigger surge in April. Can you guess why? It’s for Easter season and spring break, she says.

Food for the birds

Sales of birdseed also rise from September through February. Cindy Brasic, a brand manager at Ace Hardware Corporation in Oakbrook, Illinois, says fall and winter are good seasons for backyard bird watching. Birds have less food available from other sources, so it’s easier to attract them to feeders. With the leaves off the trees, the birds are also easier to see.

Speaking of birds, Brandon Meredith at Tractor Supply Company in Nashville, reports that black oil sunflower seeds are especially popular for bird feeders because they have higher nutrition content than striped sunflower seeds, and their shells are easier for birds to crack. As sales of black oil seeds climb during the colder months, the supply dwindles. Short supplies extend their lead times.

Pink slurp suckers

Kristie Hoffman offers what may be this year’s best quiz question. She’s a buyer at Henry Schein, Inc., of Melville, New York, a distributor of supplies to doctors, dentists, and veterinarians. Why might a pink saliva ejector have a strong seasonal demand pattern? It’s because Anything pink sells big in August, September and October for breast-cancer awareness month, she says – including those slurpy hooks that suck the water from your mouth as the dentist tells you to open wider, please.

Ms. Hoffman also reports that spot bandages and ¾-inch bandage strips surge before and during the flu-vaccination period.

A veteran of buying inventory for drugstores, Ms. Hoffman also reports that sales of pregnancy tests typically surge after power blackouts. She wonders how many of the kits druggists will sell in New York City in the months following Hurricane Sandy.

Thanks to all the contestants who took time to join the fun and share their insights. And our season’s best wishes to all the replenishment buyers who are still discovering surprisingly seasonal items.

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Is Inventory the Hidden Clue to Retailers’ Future Share Value?

David Berman is a hedge-fund manager who CNN says is like a “patient predator.” He watches retail inventories with the focus of a hungry jaguar. Specifically, he watches for retailers whose inventories are growing faster than sales.

Inventory, Mr. Berman says, is a little-known leading indicator of the rising or falling value of retail stocks. If inventory is rising faster than sales, and if gross margins are unusually strong, earnings and stock value are probably headed for a fall.

Why should retailers care what David Berman thinks?

Because a lot of investors care. CNN, Bloomberg and CNBC have all interviewed him on the topic of financial outlook for the retail industry.

Inventory predicts earnings and share price

Few investors seem to recognize the relationship between inventory, gross margin, earnings and share price, Mr. Berman says. Retail managers are inclined to manipulate operating margins in the short term, he observes, by “playing around” with inventories.

“If a retailer’s inventories are growing much faster than sales,” he says, “then gross margins would be higher than they normally should be, as the retailer has not taken markdowns that a solid, disciplined retailer should take.…

“You don’t want to be an investor when sales slow and when markdowns of bloated inventory finally need to be taken to move the goods.”

Inventory levels, considered in relation to trends in sales and gross margins, are likely to predict future earnings. You can learn more about this relationship through a free article from the University of Chicago Booth School of Business, which you can download here as a PDF document.

Mr. Berman is so focused on the effect of inventory, in fact, that he pays researchers to check store clearance racks for evidence of inventory discipline before his fund invests in a retail stock.

Inventory discipline is key to shareholder value

Here’s what David Berman’s insights can mean to retailers, even if their stock is not publicly traded:

Inventory discipline is not only key to your cash flow and balance sheet, but also to your P&L and share value.

To achieve inventory discipline, you have to manage a lot more than inventory turns. If you don’t exercise consistent vigilance, you won’t have much control over earnings.

What are David Berman’s credentials? He’s a CPA who spent his early career evaluating the finances of retail companies. He worked as an auditor for Arthur Anderson and then as a portfolio manager and analyst for two Wall Street firms.

He graduated in the top 10 percent of his class from the Harvard Business School.

More recently, he is founder and president of David Berman Capital and founder and general partner in Durban Capital, L.P. Durban Capital is a fund with $100 million in assets. The firm specializes in retailing and consumer goods.

David Berman knows a lot about how to spot retailers who lack of inventory discipline, but it’s not his business to teach retailers how to achieve it.

Blue Ridge will help you achieve inventory discipline

Helping retailers achieve inventory discipline is the business of Blue Ridge Inventory. Over the years, Dan Craddock and I have worked with this partial list of current and former clients:

CVS, Rite Aid, Eckerd Drug, Duane Reade, Dollar Stores, REI, Mountain Equipment Coop, AcuSport, Sport Chalet, Sports Authority, Gander Mountain, Dick’s Sporting Goods, Petco, Staples, Office Depot, Tractor Supply, Mill’s Fleet Farm, Northern Tool & Equipment, Ace Hardware, Home Hardware, True Value, Michael’s Stores, Best Buy, Provigo, Williams-Sonoma, Pep Boys Auto, Advance Auto, O’Reilly Auto, Guitar Center, Coldwater Creek, Stein Mart, Unified Grocers, Associated Food Stores, Northgate Gonzalez Market, Associated Grocers of New England, Europris, URM Stores, Dick Blick and Procurator.

For a free diagnostic consultation during January 2013, please contact me at

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Surprisingly Seasonal Inventory Items: Insights from Forecasters, Planners and Replenishment Buyers

As shoppers prepare for the holidays, demand forecasters, inventory planners and replenishment buyers are hard at work anticipating what the rest of us will buy and when. Some items are obvious and easy to predict. Others take even the pros by surprise.

It’s a safe bet that grocers will sell more turkeys in the weeks before Thanksgiving than during the summer months. And you don’t need an inventory analyst to predict that sales of gift-wrapping paper will peak in the weeks before and immediately following Christmas.

Inventory planners and replenishment buyers can offer plenty of example of unexpected seasonal demand.

(For a complete list of fun and unusual seasonal items that friends, colleagues and customers have shared over the years, go here.)

Back-to-school season for condoms and birth-control pills

The seasonal appeal of condoms and birth-control pills is no news to many college students and their pharmacists. Chemical mace and pepper spray also enjoy a back-to-school surge.

Collectively, these four inventory items represent three distinctly different strategies for prevention. The independent behavior of three separate but intersecting customer groups synchronizes their seasonal demand: female students, male students, and the worried parents of female students.

Suntan lotion a winter favorite in Minnesota

Forecasters also discovered the same merchandise item often has distinctly different seasonal demand in different parts of the country. Suntan lotion, for example, sells big in Florida all year. And its seasonal demand is strongest during winter months at drugstores near beaches. The population of Florida swells in the winter, and people use suntan lotion at the beach.

Yet central replenishment buyers for Eckerd Drug in Florida were surprised to see a similar winter demand pattern for suntan lotion in some of the chain’s most northern stores. Why would these drugstores sell so much suntan lotion in January, February and March? Is it to prevent sunburn for skiers and snowmobilers?

Maybe so. But no one could use enough suntan lotion on their hands and face to explain the strength of demand in some northern locations.

Detective work revealed the strongest winter demand in affluent areas. Well-heeled Yankees, the replenishment buyers learned, often stock up on suntan lotion – and also travel-sized tubes of toothpaste — before leaving for winter trips to warmer climates.

Cat litter – not just for cats

In cold-weather regions, demand for cat little increases in both summer and winter. Strong demand for cat litter seems to make sense during the coldest months when cats are likely to spend more time indoors. But why do cat-litter sales also rise during summer?

The answer is simple. Some demand for cat litter has nothing to do with cats. Do-it-yourself mechanics use the product to absorb motor oil spilled on pavement. Some people also use cat litter to harden leftover paint before dumping the cans in landfills. And some people use the grit for traction on snow and ice.

The “golden age of discovery” for surprisingly seasonal items began in the early 1990s. That’s when statistical demand-forecasting systems first improved to the point of spotting seasonal demand automatically.

Different seasonal demand in different parts of town

In another example of segmented seasonal demand, mid-winter sales of “resort wear” are likely to be stronger at higher-end department stores such as Nordstrom, Bloomingdales, Saks and Neiman Marcus than at lower-priced retailers like Dressbarn, Ross Dress for Less and Stein Mart.

A window into society

Such seasonal demand patterns can be interesting beyond a small circle of forecasting geeks, replenishment buyers and statisticians. Data on the way we buy can provide fresh insights into the way we collectively live, work and relax.

You can also get surprising insight into the way people think by studying changes in the volume of Google searches on various keyword phrases over time.

Despite a long and divisive political election, we collectively behave like the occupants of an ant colony or beehive – at least in our buying and consumption of mundane items.

Demand data, in the hands of curious people, can deepen our understanding and appreciation of the wonderful richness, complexity and variety of our economy and our society.

For more examples of surprisingly seasonal items that friends and colleagues have identified over the years, please see the tables provided here.

What additional information can you share about surprisingly seasonal items? For details of a how your insights can win you an iPad Mini for the holidays, please go here. But hurry. The contest deadline is December 7.

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Demand Forecasting Systems: Improve Forecast Accuracy With Higher Labor Productivity

Do you want to improve the accuracy of your demand forecasts for inventory planning? If so, you’re likely to achieve solid results with one or both of these approaches:

  1. Explore better forecasting methods.
  2. Make your forecast errors easier to manage.

This article focuses on ways to make forecast errors easier to manage. We’ll share important ways to increase forecast accuracy without creating more work.

Unfortunately, forecast error is inevitable

Probability theory tells us that when you flip a coin, the chance of it coming up as  “heads” is 50%. So you’d be smart to predict 1,000 heads and 1,000 tails in 2,000 flips.

But we all know from personal experience that in 10 flips it’s possible for a coin to come up heads eight times. And it’s only a little more likely to come up heads exactly five times. This is why statistical forecasting tends to be wrong, especially when the number of occurrences is small.

Statistical demand forecasting systems use statistics and probability theory to predict future demand. They do so by projecting demand forward, based on the history of prior demand. But statistical forecasting methods are blind to the effects of the many factors that may deviate from history.

Such deviations may include:

  • price changes or promotions
  • short supply of products
  • random changes in buyer behavior
  • changes of weather and
  • other factors that can occur between the moment you generate a statistical demand forecast and the time you record actual demand.

Any such factor can destroy the accuracy of a statistically generated forecast. This is why all statistical forecasting systems are built on the assumption that the forecast will often be wrong.

Exception management improves forecast accuracy

To manage inevitable forecast errors, many demand forecasting and inventory planning systems use a process called “exception management.”

(Some demand-forecasting systems ignore exception management, as if their mathematics were so sophisticated that they don’t need it.)

The exception-management process first identifies system-generated forecasts that are inaccurate. Many do so by looking at the forecast for each item in each location. Then the system compares these forecasts against actual demand for the same period.

When the system finds a significant difference between forecast and actual demand, it flags the items as exceptions.

The system then presents the exceptions to users (demand planners, forecast analysts or replenishers) whose job it is to assess factors that may have led to forecast inaccuracy.

The users may override the forecast, change the forecast model or identify the forecast as correct despite the exception. Users may also adjust the demand history if they think it’s an anomaly.

Exception management enables knowledgeable analysts to correctively nudge the system. They use their knowledge and experience to correct a forecast that would otherwise be based only on history.

Exception management comes at a cost

As valuable as it may be to invite humans fix the errors of statistical forecasting systems, we face a series of tradeoffs:

  • We can waste time wading through a sea of forecast exceptions that may not need adjustment.
  • We can review forecast exceptions that will have only a small effect on our demand and inventory plan.
  • We can incur high labor costs for doctoring the components of our forecasts.
  • We can ignore forecast exceptions at the expense of lower forecast accuracy

Studies have shown that “anomalous demand” generates more than 80% of exceptions. It’s pointless to adjust forecast errors that result from anomalous demand. They are, by definition, unlikely to occur again.

This means that as much as 80% of the time spent on forecast exceptions is wasted.

So how much unnecessary work do forecast exceptions create?

The short answer is, it depends. The labor required can be rather low or quite high, depending on the number of SKUs you carry and the number of locations where you carry them.

Forecast exceptions can add many labor hours a week

Let’s consider an example. Suppose you operate a single warehouse where you stock 40,000 items.

If your forecasting system normally generates forecast exceptions for about 10 percent of your items each week (the standard rate, based on decades of empirical evidence), that comes to 4,000 exceptions a week.

If your forecast analysts spend an average of 30 seconds per exception, that’s 120,000 seconds or 2,000 minutes, or 33 hours a week. If you employ three forecast analysts, that’s 11 hours per analyst every week.

While these exception volumes and labor hours may sound high, three analysts can easily manage the workload. The labor cost is probably worth paying for. You’re likely to achieve so much better forecast accuracy that you can improve in-stock performance or reduce inventory. Maybe you can do both.

Regardless of company size, forecast exceptions can be unmanageable

Let’s suppose you operate 700 stores, each with 15,000 items. That comes to 10.5 million SKU-store locations.

If your forecasting system still generates forecast exceptions at a rate of 10% per forecasting period, it will produce 1.25 million forecast exceptions a week.

Assuming the same level of labor productivity as before – 30 seconds per exception – the weekly labor requirement grows to about 10,400 hours.

How many replenishment analysts would you need to review those forecast exceptions? And what would be your labor cost?

True cost may be especially high for smaller organizations

In smaller organizations, the planner, analyst or buyer often has more duties and is less specialized than in bigger organizations. The effect of even a small amount wasted time may be big in a small organization – especially if it keeps inventory managers from pursing more profit-oriented activities.

Here’s the bottom line. Whether your organization is large or small, it will pay for you to use a forecasting system that…

  • produces fewer exceptions
  • manages some exceptions for you or
  • enables you to manage exceptions much more efficiently.

With good analytics, you can manage much bigger numbers

Many forecasting systems use the conventional approach to managing forecast exceptions.

Fortunately, good alternatives are available. For one, you can use your analytics system to help manage your forecast exceptions.

Here’s how we do it at Blue Ridge. Our CLARITY analytics system enables inventory planners and forecasters to focus only on the exceptions for items that have the greatest effect on their company’s key performance metrics.

For example, you can specify that if the total annual demand for an item in a location is less than $18 in revenue (or any other value you want to use), then remove it from the list of exceptions.

You can also specify that you don’t want to see exceptions for any items whose inventory value is less than, say, $50.

By layering the appropriate filters, you can reduce thousands or even millions of exceptions to the few that most need human attention.

With this filtering capability, you can achieve higher forecast accuracy by involving your analysts only with the right items. And you can do so without sending your labor costs through the roof.

Have you found better ways to manage forecast error? If so, please share your experience.

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Food Wholesaler Saves Its Own Bacon with New Demand Forecasting and Replenishment-Planning Systems

Company grows revenue by 12%, reduces inventory by 14%, improves cash flow by 300% and increases profit 181%.

Haakon Ekrheim felt his family-run business had come to the edge of a cliff – or to be more accurate, a fjörd. He was managing director for the third generation of one of Norway’s only five food wholesalers. And things were not looking good.

This is the story of how the company turned its business around.

Norway’s economic growth turned negative in 2009, as it did in many parts of the Western world. In addition, an important customer was threatening to take their business to one of Ekrheim’s competitors.

Profit and income for the business were already down, and the customer would be hard to replace.

Key customer demands better in-stock performance

Ekrheim’s dissatisfied customer had complained that the food wholesaler wasn’t filling orders reliably enough. Indeed, Ekrheim had seen customer-service levels decline across the board, even as inventories rose.

If the company didn’t fix this problem, Ekrheim knew it would cost them not just one important customer, but potentially their entire business.

As managing director of K. Ekrheim AS, a proud company whose origins date to 1924, Haakon Ekrheim was not going to let his family’s company decline on his watch.

Conventional remedies didn’t work

Haakon and his inventory-replenishment team tried to the conventional ways to improve customer service. But most of the methods increased inventory levels without improving order-fill rates.

The company’s inventory replenishment team was acting without focus, Ekrheim said. “When they were in doubt, they just bought more inventory.” This behavior put a serious strain on the company’s cash flow and profit. And it did nothing to satisfy unhappy customers.

Inaccurate demand forecasting was a big part of the problem, Ekrheim said. “We might as well have had no forecasting at all.”

Inventory Investment presents high-value, low-risk solution

Ekrheim turned for help to consulting firm in Oslo, Inventory Investment AS. The firm introduced Ekrheim to Blue Ridge.

Ekrheim evaluated the Blue Ridge software for demand forecasting, replenishment and replenishment planning. He also learned about Blue Ridge’s education and support for inventory his replenishment team and company executives.

Blue Ridge appeared to offer a solid way to transform the company’s business at low risk. Ekrheim liked that the system could be implemented in less than 90 days. He also liked that Blue Ridge provided software as a service, so his company would use it as easily as they use Google.

K. Ekrheim could use the software at reasonable cost. The company could avoid buying new computer hardware, operating systems or database software.

They could avoid the maintenance fees and reduce internal IT operating costs. And they could implement the new systems without spreading their IT staff any thinner.

Company faces a future of anemic growth

From Haakon Ekrheim’s perspective of 2009, the future looked uncertain at best. Norway’s economic growth rate had gone negative.  Despite the country’s bounty of North Sea oil, global demand for oil was down.

Even if the recession ended quickly, a return to economic growth wasn’t likely to make Ehrheim’s business thrive. Food consumption usually grows with population, and Norway’s small population wasn’t growing much.

With only about 4.7 million people, Norway has a population only a little bigger than that of the Boston-Cambridge metropolitan area. It’s a little bigger than Barcelona’s population and smaller than Milan’s. And for years Norway’s population has grown at an annual rate of only about one percent.

To build a stronger business, Ekrheim knew he would have to change the game. And he thought Blue Ridge offered a way to help him do it.

Executives bet on a turn-around plan

Ekrheim saw so much value in a relationship with Blue Ridge that he put everything on the line: He knew that the fate of his family’s company would be sealed and that he simply couldn’t stay around to see the end, if the company’s board didn’t approve his recommendation to move forward.

He got the green light he was looking for.

Working with Inventory Investment and Blue Ridge, K. Ekrheim implemented the CLARITY demand forecasting, replenishment and inventory planning system. They completed the work on schedule and on budget, in about 90 days.

Loss of important customer increases urgency

Of course, not everything went perfectly.

Soon after the company implemented CLARITY, they lost a major customer. It wasn’t the one that had threatened to leave; this one was even bigger. This customer left because of a change in market dynamics and because of K. Ekrheim’s strengthening relationship with the other major customer.

K. Ekrheim quickly found itself in a position where it could land with piles of excess inventory. And with the loss of revenue from the company’s big customer, cash flow would be under even more pressure.

With the intensity of people who knew their future was at stake, Ekrheim and his replenishment team set to work. They applied their skill and experience, powered by the Blue Ridge replenishment-planning system, to bleed inventories down without compromising service to their remaining customers.

Focus brings redemption

This story has a happy outcome, though it’s far from an ending. The company’s efforts worked, and they worked fast.

Forecast accuracy improved. Inventories came down and freed up cash. Service levels improved, and the company began growing much faster than Norway’s economy. Profit has increased dramatically.

Here are the details of what the company has achieved since 2009:

  • Revenue is up 12%, despite the loss of a major customer in 2009.
  • Inventory is down by 14%. Inventory turns increased from 21.4 to 24.6.
  • Cash flow went from negative to positive, then improved by 300%.
  • Service levels improved by 1.4 percentage points from 2009 to 2011, from 97 percent to 98.4 percent. Haakon’s new target is 99 percent.
  • Operating costs are down as a percentage of revenue.
  • Profit increased by 181%.
  • Both cash flow and profit have continued to improve each year from 2009 through 2011.
  • The company has more money available to invest in growing its business.

Haakon Ekrheim credits his company’s relationships with Blue Ridge and Inventory Investment for turning the business back from the edge of the cliff he felt he faced in 2009.

Even if your company doesn’t face similar threats, do you have similar opportunities to improve your business?






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Demand Forecasting: Look for the Next Big Wave of Innovation

The art and science of demand forecasting have both come a long way in the past 20 years. Yet as much as demand forecasting has improved, we believe there’s still plenty of opportunity to take it much further. We’re especially enthusiastic about the business results that future improvements will bring.

Today we share some of the remaining challenges of demand forecasting and the reasons for our optimism about overcoming them soon.

Although the topic of demand forecasting can be as dull as a rock, we trust you’ll share our interest when you consider how much demand forecasting can influence the amount of money your company makes.

Why care about demand forecasting?

When you forecast demand for finished goods, the financial consequences of inaccuracy can be huge. Forecast errors cause you to run out of stock. The result is missed sales and unhappy customers.Forecast errors can also cause you to carry too much inventory. You may have to borrow money to pay for the excess. You may also have to mark inventory down, donate it or even junk it just to get rid of it.

Forecast error reduces profit. It can also reduce cash flow and increase the need for capital. Excess inventories yield a lower return on assets.

Conversely, more accurate demand forecasts can reverse all of these problems. They can improve in-stock performance, increase revenue, improve customer satisfaction, reduce inventory investment, improve cash flow and improve return on assets.

What is demand?

Demand is the number of units your customers will buy if you have the product in stock. It’s often the same as sales, but not always. If your customers want to buy 23 units but you have only 20 in stock, your sales will be 20 units, but your demand is 23.

Some companies have a hard time forecasting demand simply because they can’t distinguish between sales and demand. In other words, they don’t know how much they could have sold if they’d had an item in stock.

(Fortunately, this is fairly easy to fix. Many demand-forecasting and inventory-planning systems have long since reduced or eliminated the problems that arise from the difference between demand and sales.)

Forecast accuracy is a measure of the difference between your demand forecast and your actual demand, usually expressed as a percentage.

What are the challenges of demand forecasting?

When demand forecasting works well, it can work really well. Modern demand-forecasting systems can deliver forecast accuracy of 99.99%. But such high levels of forecast accuracy are stubbornly elusive for many kinds of items.

Here are four primary causes of forecast error:

  • Statistical forecasting methods have inherent limitations because they use the history of demand to project forward. If history teaches us one thing, it’s that the future is often different from the past.
  • Statistical forecasting systems don’t understand the contextual or situational factors that influenced demand in the past or that will influence it in the future.
  • Demand for some kinds of items in some situations is inherently harder to forecast than for others.
  • Human judgment and memory are unreliable.

What are the best hopes for improved forecast accuracy?

Despite these challenges, we’re optimistic about the future of forecasting because of the convergence of these important trends:

  • New technologies have made it cost-effective for organizations to collect and manage huge amounts of data. Data storage is cheap. Even small computers have enormously more processing power than they did 10 years ago. The Cloud provides virtually limitless capacity for storing and processing data inexpensively.
  • You can easily tap into many sources of detailed contextual data, including information about weather, insights from social media, consumer interactions with websites and other digital media, POS market-basket data, information from customer-loyalty programs, and so on.
  • The growth of software as a service (SaaS) or Cloud-based demand-forecasting systems is changing the way you can generate and manage forecasts.
  • The fields of neuroscience and behavioral economics have taught us a great deal about how the human mind works. We know a lot more about decision making than we did just 10 years ago. The new insights can improve the way we design forecasting systems.

Collectively, these trends mean you’re likely to continue seeing big improvements in forecast accuracy. And we think the improvements are especially likely for items that are still hard to forecast. Stay tuned here for details.

Do you see any other signs that demand forecasting is likely to improve soon? If so, what are your hopes and expectations?

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How SKU Rationalization Can Further Improve Your Inventory Performance

No matter how well you manage your inventory today, you’re likely to benefit from what you might think of as a periodic review of the “junk in your closets.”

Does the number of items you carry in inventory seem to grow every year? For many wholesalers and retailers, items accumulate in stores or distribution centers like wire coat hangers in your closets.

Clothes hangers in a tangle

SKUs accumulate in inventory like wire coat hangers in a closet.

Each additional item increases your cost of doing business — sometimes without a corresponding contribution to revenue and profit.

Find the hidden items that drain your profit

Many of the SKUs you carry, whether new or old, are unprofitable for a variety of reasons that aren’t always obvious. If you haven’t evaluated all of your items in a while, many may be reducing your company’s total profit. It may be time for SKU rationalization.

Like the empty hangers in your closet, some under-performing items are easy to spot. If they haven’t sold in months, out they go. But many other items are more challenging to evaluate. Plenty of SKUs have at least one important customer who someone says will take their business elsewhere if you drop them.

Other items appear to be profitable because they have good gross margins, but their acquisition and carrying costs more than offset the profit.

Limit the damage of unprofitable items you can’t stop selling

For each item you feel you must carry despite its low profitability, you still have a choice: you carry it only in the locations where it’s most profitable. But what are the most profitable locations? In which locations would you choose to stock each item?

You can’t make such decisions by rule of thumb — not unless you’re willing to leave a potentially big chunk of profit on the table. To do it right, you have to analyze the unique economics of each item in each location.

This is one reason why SKU rationalization is much more challenging than cleaning out your home closet. And here’s another: Not many third-party software applications can help you with SKU rationalization. And even with good software, the process can be labor intensive.

Learn from a top global healthcare distributor

Despite the inclination to let those hangers multiply in the closet, the process of SKU rationalization was certainly worthwhile for Henry Schein, Inc. The company is the biggest supplier in the world for health care practitioners who work in offices or small clinics rather than hospitals.

Schein’s customers include dentists, physicians, veterinarians, school-based health centers, community-health centers, government offices, and dialysis and renal-care facilities.

The company generates more than $7 billion in annual sales, serving more than 400,000 customers worldwide. It has operations in 20 countries.

(If you’ve had your teeth fixed in the United States or Canada, chances are good that Henry Schein provided products that help you chew your food today.)

Schein became the leader in their industry by providing a broad selection of items and first-rate customer service. When customers place an order, they can be confident they’ll get what they need fast.

Cut low-profit inventory without sacrificing service

Schein’s customer-service strategy has been expensive for the company. To fulfill more than 94% of customer orders completely, they carry nearly 250,000 items. Before SKU rationalization, inventory had grown to more than $160 million in five U.S. distribution centers.

Company executives saw they needed to increase profit and reduce inventory investment to free up cash for other uses.

Because Schein has managed inventory carefully for many years, the company had little room to further improve replenishment practices without compromising in-stock performance. And Schein executives were unwilling to reduce on customer service.

To generate more sales from fewer stocking locations, they chose to optimize the product mix for each location.

Scrutinize the profitability of each item at every location 

By analyzing both demand and the economics of carrying and handling inventory for each item in every location, the company could be sure to have the right products in each. They could also achieve higher inventory profit by eliminating items with low demand. Through SKU rationalization, Schein executives hoped to improve in-stock performance while at the same time reducing item count and inventory.

To help achieve their goal, Schein chose Blue Ridge to implement CLARITY SKU Rationalization. This is Blue Ridge’s SKU rationalization suite for retail and wholesale. CLARITY analyzes the demand for each location. It determines the items that meet sales and inventory-profitability goals without adversely affecting customer service.

Save a ton of money in a few months

The result? Schein cut 10,000 unprofitable, slow-moving items and improved service by three percentage points. They did so while supporting a 14% increase in sales. And at the same time, they reduced base inventory by 8%.

(“Base inventory” is the inventory Schein maintains to supply customer demand. It doesn’t include any special inventory categories Schein may buy on deal or for promotions.)

These performance improvements reduced Schein’s carrying cost by $600,000 a year and reduced freight cost by 9%, for total freight savings of more $1.4 million. The company sent all these savings to the bottom line within six months after implementation.

To maintain their momentum, they also created policies and processes to help them manage SKU rationalization regularly in the future.

What’s been your experience with SKU rationalization? If you haven’t done it yet, why not?

For information about Blue Ridge’s SKU Rationalization solution, please look here.

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S&OP for Retail – Thoughts on Gartner MIOE Maturity Model

forecasting and replenishment identified as foundation for MIOE

Let me guess what came to your mind first

“What is MIOE?”

I can tell you that was my first thought when I read the recent research article from Gartner Research VP, Mike Griswold.  The article, “Retailers Embrace an MIOE Framework for Reduced Inventory and Greater Revenue,” is the culmination of Mike’s long-held vision that while retail and manufacturing organizations face fundamentally different challenges; demand and supply analysis, coordination and planning on the order of what manufacturers conduct is necessary for retailers.  I whole-heartedly agree, and I’m glad someone has taken up the mantle to offer some guidance and rules to live by for retailers.

All right, you’ve waited patiently, so I’ll give it up…MIOE stands for merchandising, inventory and operations execution.  Doesn’t exactly roll off the tongue like S&OP, but it’s very applicable to retail.  I’m an old retailer, so color me biased, but to me the key difference is the operative word of each handle that makes them applicable to their constituencies.  The manufacturing operator is “planning” vs. the retail, ”execution.”  This strikes me as an appropriate differentiator considering the inherent differences between retailing (or distribution…truly, anyone who sells to the end consumer) and manufacturing.  The biggest differences being:

  • Retailers connect directly with the consumer, often via personal contact of some sort, and influence & react to consumer-centric demand on a micro level
  • Manufacturers connect to consumers through the retailer & broad market outreach, while attempting to influence aggregate demand on a macro level
  • Retailers sell thousands, or even hundreds of thousands of products from hundreds or thousands of suppliers, out of dozens to thousands of sales outlets
  • Manufacturers sell as few as a dozen products from as few as a single production/distribution outlet, to thousands of products from dozens of outlets
  • Retailer’s supply chain demand is driven by the consumer and how consumer’s respond to demand drivers
  • Manufacturer’s supply chain demand is driven by the retailer and how they replenish their sales outlets…despite the widely-espoused view that retail POS demand is the answer

Since they are removed from the primary demand, manufacturers face significant limitations today in getting accurate demand signals.  So often, ”planning” is as good as it can get.  Mostly due to the data available from retailers and other consumer outlets, manufacturers face a gap in their ability to tie their production to consumer and store/DC demand.  Varying capabilities of retailers, along with varying levels of coordination with these consumer connections, creat vast differences in the quality of demand data that manufacturers receive.  The lack of consistency leads to muddled demand signals.  Muddled demand signals lead to innaccurate production data, and ultimately to sub-par demand fulfillment for the retailer.

What we as retailers have seen and Griswold has made a career of studying, is that even with primary and precise understanding of demand, a lack of coordination leads to difficulties in executing assortment optimization, demand planning, inventory replenishment and  therefore, on-shelf availability.  Fortunately, as supply chain solutions have evolved, the precision of demand signal available at the consumer level has improved dramatically.  However, this precision at the retail tier is difficult to project even internally to the merchandising, operations, finance or other critical areas within the enterprise.  Nearly all retailers struggle to translate retail demand effectively enough just to communicate to their own distribution tier for fulfillment of store needs, and do it without carrying excessive or ill-timed inventory in their supply chain.  As difficult as it is for a retailer to meet their own needs, this lack of coordination makes it nearly impossible to predict their demand to facilitate effectiveness at the manufacturing production tier or above.

Griswold’s call for a more defined process with clear-cut stages of maturity provides a sound and necessary foundation and growth path for a retailer’s internal planning and coordination. The MIOE blueprint will ultimately drive improved execution with retailer benefits such as increased inventory productivity, on-shelf availability, customer satisfaction, cash flow and gross-margin.  With better demand signals, item selection, store operations and supply chain effectiveness, retaliers can engage their trading partners in a new narrative that will improve the lot of both and ultimately increase sales and net income for both parties in an increasingly complex consumer relationship framework.

The entirety of the article is enlightening and good reading for anyone looking to get a better grasp on product availablity…and isn’t that everyone?  I will share that Mike suggests, and rightly so, that retailers must first gain control over their demand forecasting, planning and replenishment activities as a foundation.  This ability to effectively and predictably manage inventory productivity is crucial to the effectiveness of the more advanced levels of maturity.

Some retailers seem to have the tail wagging the dog with their price and markdown optimization for products poorly placed in the firs place, or their assortment optimization based on an ancient tops-down method of determining SKU mix.  With the plethora of demand data available, and the transactional and daily granularity of that data from advanced solutions (like you know who), a new vision of inventory and assortment optimization, along with merchandise, financial, logistics and store operations planning is emerging.  It’s the foundation for a more customer-centric and profitable retail enterprise, and the retailers that recognize that first will have a distinct advantage in this changing retail marketplace.

Griswold’s article, ”Retailers Embrace an MIOE Framework for Reduced Inventory and Greater Revenue,” can be accessed through your Gartner subscription at  Look out for future information on the MIOE process, and more detail on the maturity path in this and future publications.

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