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7-Reasons-Your-Lead-Time-Is-Wrong-&-Its-Costing-You-MillionsDo You Make These Lead Time Mistakes

Shoppers use technology today at a dizzying level to gain leverage over retail. Mobile phones offer limitless shopping options for products at the right price and place. Key to the shopper process is deciding where to purchase the product.  This changing speed of retail decisions shortens the retail response times in a manner legacy technology cannot manage due to their base code designs in a time where mobile shopping and omni channel were not even words to consider 10 or more years ago.

One core component of inventory replenishment is lead time, how long from when the purchase order is placed until the goods are available to ship/sell to a customer. If you plan a lead time of 10 days and it takes 17 days you will have 7 days of lost sales.  If you plan a lead time of 20 days and goods are available in 12 days, you will be forced to hold 8 days of safety stock.  While everyone will admit lead time is a critical part of replenishment, most retailers are not using their lead time data in an effective manner, the costs are astronomical.

Attached is a link to my slide share on the 7 most common Lead Time mistakes you make when managing or ignoring our lead time. I am often asked to speak about lead time and the problems created in replenishment.  These 7 are the most common problems of managing lead time for inventory replenishment.   I can even share a startling fact: every one of our customers made their investment in our software back from corrected lead time mistakes alone, everything else was additional return on investment.   To help you we provide a link in the slide share to a lead time forecasting kit with more education materials. Think of the dollars you can recover with just one or two corrections in lead time.  Enjoy the slide share and read more at Data Profits blog.

Watch the Slideshare here and use the link for free download:


I look forward to your comments and idea on the lead time Component of replenishment


Slow Demand products makeup 35-40% of most retailer assortments and cannot be simply eliminated. In order to profitably sail the Slow Demand Product seas, you need to be equipped with a proper forecasting engine. By providing a forecasting engine that utilizes the correct algorithm for slow demand products, you have taken an important step in your destination to increase demand forecasting accuracy.
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Is Your Demand Forecasting Solution Actually Leading to Profits?

All retailers face the fact that demand forecasting products that move slowly and products that move intermittently during the year is required to grow profits. In fact 35-40% of most retailer assortments consist of slow and intermittent products, while this may be well known, it is less known that demand forecast and how the supply chain software uses the demand forecast are the keys to making your turn goals and maximizing your GMROI. Read More


Is Your Demand Forecasting Solution Actually Leading to Profits?

A seasonal index, or seasonal multiplier, is a figure that is used to adjust a demand forecast, either raising it or lowering it for a period of time. The result of the calculation (product base forecast x seasonal index) can be used to determine the inventory needed to support sales during that period of time. A holiday like Memorial Day, a season like spring, or an event like the Super Bowl is often better serviced by applying a seasonal index across the year.
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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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inventory-replenishment-is-your-big-data-creating-a-big-mess?Big Data? is a buzzword that means just what it says. With the boom of handheld devices, online shopping, and the Internet of Things, retailers now have access to nearly infinite amounts of data regarding their customers shopping habits. All that data requires the right resources to collect, sort, analyze, slice and dice to make the data usable. Companies invest heavily in Big Data resources in an effort to get ahead of their competition. But once you have all that data and you have reports on all that data, what do you do with it? How do you integrate Big Data into your inventory replenishment and your demand forecasting?
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