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Returning From Paradise to Solve Demand Forecasting Issues

As June passes we think ‘vacation’. A tropical island is my vacation dream. My northern friends often ask how can I stand the extreme heat of the tropic. The answer to that question is also the highlight of our content this month – I adapt fast. The human body of is highly adaptable. “Our bodies, highly amenable machines that they are, can acclimatize to cold environments and warm environments, alike.” The internet of things (IOT) has been battled by the movie business longer than anyone. VCR, Netflix, Redbox and even YouTube have put the heat on the movie business. The movie business has done a great job “ADAPTING” by cutting locations, changing products and adjusting even things like length of movie and credits all to entice the customer to go to a movie. While many consultants and bloggers write about the end of brick and mortar retail, this is far from certain. I leave you with a quote to think about as you read this month’s cover stories.

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Haunted house in creepy foggy background with tree silhouettes.

Visiting a Haunted House this time of year was a rite of passage for me as a kid. To this day I love a good haunted house, forecasting what will pop up where and when? What room has the chainsaw, when will the plastic skeleton pop up, how bad will the sound effects and music really be as we wander into the darkness. Oddly, I can easily connect with the scary things that happen to companies using old (legacy) Demand Forecasting & Replenishment software that had core development more than 5 years ago. Then you bring in the consultant who cannot even explain the differences between demand and sales forecasting and you have a really scary (expensive) house of horror. A great example of the changing time is the Ken Bone costume that was born from a presidential debate in a matter of hours. Ken Bone rose to stardom from his question at a debate. Did you know a Ken Bone Halloween costume was made and being shipped to retailers all within the next 36 hours? That’s just a small part of the Omni-channel retail environment we live in today. Most forecasting and legacy software lack the tools to spin as the market spins within days. The issues in our supply chain today were never considered when the base code was written and consequently, no amount of ‘updates’ can create the fixes need in legacy software…scary, right?!?! So this month in our newsletter let’s take a few minutes, relax and gather a few ideas we can use to impact our supply chain this holiday. I’ve included some video clips, a seasonal index kit download with all the math (press release), and a collection of ideas of which one or two are bound to help your business. Enjoy Your Season

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How do you plan for promotional inventories?

promotional-inventory-3-ideas-to-make-profits-soarThe goal is to increase inventory amounts at the right locations to meet service goals and maintain additional inventory until the end of the promotion.  After the promotion ends, the inventory should be back to the basic inventory level needed to meet service. The key piece being how to reduce inventory at the right time during the promotion to avoid lost sales and maintain service goals after the promotion without being overstocked. If you have a sales and ERP system that can track the actual life of the previous promotions and you have a event based forecast algorithms in your replenishment toolbox, then all is well in your world, maybe….there are some other pieces that can help your business and this month we highlight a few ideas for you.

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Seasonal Indexes: Are You Asking The Wrong Questions?

Many people are finishing their summer holidays and prepping for the new school year with ‘Back to School’ shopping. Retail and Wholesale companies are implementing their 2016 fall season and holiday sales plans based on demand planning and hopefully a few seasonal indexes. Surprising to me is the fact that blogs, how to articles, and speaking requests on seasonal indexing are the most popular material we provide. The other odd fact is how many different math methods people use to calculate a seasonal index; not just what data, but the math. Odd only because a seasonal index ‘SHOULD’ be basic algebra that most of us learned in middle school. Many of the odd methods seem to be more about someone writing a paper and less about getting right product to right location on time. Maybe someone is asking the     wrong questions?

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 6 Seasonal Product Mistakes You are Probably Making and How to FixHave you noticed the number of stores with Easter products still on the shelf? The irony is the result of demand planning and replenishment systems that do not understand seasonal events. Easter last year was in late April and this year in March. Inventory systems bought late and planned the inventory around a different time of year, April and a different weather set spring.

Seasonal Inventory is the second most asked about question when I speak at inventory management events. As online have driven down life cycles, many inventory systems cannot easily adjust to shorter cycles and seasonal adjustments. Seasonal products can make or break a sales operation. Does your replenishment system fail because of seasonality?

Read more about what to do and not to do when it comes to seasonal indexes.

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5019000003194020_zc_v31_internet_of_things_is_bound_to_make_an_impact_in_your_demand_forecastingThe number of retailers and wholesalers closing locations continues to grow. Today the customer wants new choices every month. In the old days we could change products and assortments 2 times a year and then later that changed to 4 times a year. The internet of things (IoT) has redefined what is new and more importantly the expectation of the customer to have new choices every month. The issue is S&OP software that was written even 7 years ago is inadequate to serve a modern retail or wholesale business. Many ‘demand’ software solutions are not calculating demand, rather using a sales forecast method which is painful to see. Forecasting algorithms that use time series and regression analysis are the wrong choice of math for slow and intermittent demand. The technology has base code based on some old S&OP ideas and those ideas do not work today. This type of software cannot have an upgrade to move from top down to bottom up, and no product location forecasting does NOT mean it is a bottom up system. Upgrades are impossible due to the older architecture making change a start over situation that is very expensive for the tech company and customers… what to do?!?!?!
Running antiquated demand forecasting software? Find out what you need to keep pace with the Internet of Things (IoT).

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5019000002026111_zc_v9_sales_forecasting_and_demand_forecasting1In our last newsletter, we talked about the differences between Demand Forecasting and Sales Forecasting. As a follow-on, this letter highlights several of our posts that reveal how Demand Forecasting can increase overall forecast accuracy (how’s 98% sound?) and how Sales Forecasting is not the way to go for replenishment. The first : “Does 98% Demand Forecast Accuracy Get Your Attention?” is a top web download. You’ll read how to improve your forecast accuracy to reduce inventory while at the same time increasing sales.

Next, our essay featured in the Retail Value Chain Federation’s (RVCF.com) newsletter talks about the differences between Demand Planning for Replenishment and Service Driven Replenishment. The differences while subtle have a huge impact on your bottom line due to the rapid swings in the marketplace today that don’t allow for the slower response times of legacy systems.

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One of the questions that we get quite frequently is what is the difference between Demand Forecasting and Sales Forecasting? It seems that often the terms are used interchangeably. But once you understand the benefits between them, you’ll realize how important the distinction is. Then the next logical question is how do you know which method your system uses?

In this letter, we review the difference between Demand Forecasting and Sales Forecasting for inventory replenishment. While at first glance they appear to be similar, their differences can mean significant disparity in forecast accuracy which dramatically impacts overall profitability resulting from over stock and out-of-stock scenarios. How do they differ? The difference, you’ll see below, is not necessarily in the mathematical algorithms, but rather in the actual data points used to calculate the forecast.

Bottom line? An inventory replenishment system that is based on a demand forecast (demand driven) can reduce the risk of lost sales while improving service. This in turn delivers higher sales by connecting inventory levels with demand forecast.

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