{"id":6474,"date":"2018-03-22T09:12:02","date_gmt":"2018-03-22T13:12:02","guid":{"rendered":"https:\/\/demand-planning.com\/?p=6474"},"modified":"2018-03-22T09:39:37","modified_gmt":"2018-03-22T13:39:37","slug":"big-data-chill-out-keep-it-old-school","status":"publish","type":"post","link":"https:\/\/demand-planning.com\/2018\/03\/22\/big-data-chill-out-keep-it-old-school\/","title":{"rendered":"Big Data? Chill Out & Keep It Old School"},"content":{"rendered":"

Over the past few years, the Demand Planning community has become quite starry eyed over advancements in predictive software and tools. The concepts of \u201cBig Data<\/a>\u201d and \u201cadvanced analytics\u201d are enough to make seasoned practitioners stand to attention \u2013 and even catch the interest of the Executive Team. But when many of us still struggle with the fundamentals, it is worth investing in new-fangled technology?<\/strong><\/p>\n

Admittedly, in a field where you know you will never be \u201cright\u201d, this fancy technology and impressive phrases are quite attractive \u2013 they bring to mind a picture of a utopian state where analytical horsepower and near infinite data points lead to a 100% accurate forecast. There may even be a unicorn there. For me, I can\u2019t help but be reminded of two much-loved colloquialisms that I urge Demand Planning professionals to consider as we journey into the future with new tools and ideas that may or may not usher in a new age of Demand Planning.<\/p>\n

The only thing we know about Advanced Analytics is that you must have clear, fully costed plan as to how it is going to provide a return.<\/p><\/blockquote>\n

\u201cIs The Juice Worth The Squeeze?\u201d<\/h2>\n

This is a phrase I say probably too frequently when considering new tools, methods, and processes to improve Demand Planning. Does the effort required to explore and\/or implement the new approach measure up to its expected return? For some organizations, intensified data collection (or data purchase) and the machine capability to chug through it may be cost prohibitive.<\/p>\n

Computing equipment and data aside, the organization may not have the human resources on hand to give these capabilities their due. Perhaps the organization already enjoys a high level of forecast accuracy<\/a>. Is the expenditure worth that extra percentage point? Maybe, maybe not. The only thing we know is that you must have clear, fully costed plan as to how this new tech is going to provide a return.<\/p>\n

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\u201c<\/strong>Don\u2019t Throw The Baby Out With The Bathwater\u201d<\/h2>\n

Or, \u201cdon\u2019t throw the fundamentals out when you get your shiny new tools\u201d. Even if your organization does decide to invest in Big Data<\/a> and\/or Advanced Analytics, it\u2019s important to not abandon some of the tried-and-true measures and methodologies of effective forecasting. If your organization decides not to invest in these buzzworthy tools, there is still a great amount of improvement that can be made using some tried-and-true Demand Planning basics. Additionally, these concepts can assist in answering the juice-vs-squeeze question of a potential upgrade or data investment if the organization chooses to entertain new solutions. Some of the most impactful are as follows:<\/p>\n

Put down the Big Data Kool Aid – FVA is great low-hanging fruit to pursue prior to making a new technology investment.<\/p><\/blockquote>\n

Forecast Value Add Analysis (FVA)<\/h2>\n

Whether or not Advanced Analytics and insights are in your future, the impact of a simple Forecast Value Add<\/a> (FVA) analysis cannot be overemphasized. FVA<\/a> is a measurement of your forecasting process \u2013 from the statistical models utilized, to the overrides added by analysts and the insights from salespeople. Each step in the forecasting process is measured to determine the added value the step brings to the overall process. Advanced Analytics or sophisticated tools could of course be an added forecasting layer to be measured, but I would caution that if steps in your process are continuing to devalue the forecast, there are things to look at first. Put down the Big Data Kool Aid – FVA is great low-hanging fruit to pursue prior to making a new technology investment.<\/p>\n

Keeping an eye on tracking signal is important no matter how sophisticated the forecasting methodology.<\/p><\/blockquote>\n

Tracking Signal<\/h2>\n

While somewhat reactive in nature, I love using tracking signal<\/a> as an indicator to let me know if my forecast needs a second look. Tracking signal<\/a> is simply a measure of consistent bias over time. In short, if actual demand has come in lower than forecasted for each of the last three months, you may want to reinvestigate your demand assumptions.<\/p>\n

Not only is consistent under- or over- forecasting a reliable indication to an analyst that their projections may be incorrect, it is also a great signal of potential inventory shortages or surpluses. Keeping an eye on tracking signal<\/a> is important no matter how sophisticated the forecasting methodology.<\/p>\n

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Forecast Accuracy<\/h2>\n

I\u2019m sure many of you are rolling your eyes at this point. Of course<\/em> we measure forecast accuracy<\/a>, this isn\u2019t even worth talking about! I challenge you to revisit and audit your metric. Most organizations are familiar with the debate on precisely when<\/em> forecast accuracy<\/a> should be measured – is it a month before the actuals are due to come in? Three months? A week<\/em> before the actuals come in? The answer is likely that a measurement at material lead time is the most appropriate. After all, this is the time in which the supply chain can, in a perfect world, respond appropriately and without expediting to the demand signal.<\/p>\n

Recent analysis in my own organization found that the traditional \u201cT (time) minus a generic lead time\u201d approach was not allowing us to gather the proper insights from our forecast accuracy metrics because lead times are so wildly disparate. As a result, a change to the metric was required and more insightful conversations are now being driven during the S&OP process.<\/p>\n

There\u2019s No Unicorn In Your Advanced Analytics Utopia<\/h2>\n

The latest and greatest technologies offer a very tempting vision of what the future could be; after all, who doesn\u2019t<\/em> want the powerhouse predictive analytics of an Amazon or Target? However, it\u2019s important to approach these decisions with a healthy dose of skepticism. Be mindful to evaluate the promises being made and ensure they are aligned with your needs. And, if the juice truly is worth the squeeze and you embark along the new frontier of Demand Planning, don\u2019t forget the babies floating in that bathwater.<\/p>\n

 <\/p>\n<\/span>","protected":false},"excerpt":{"rendered":"

Over the past few years, the Demand Planning community has become quite starry eyed over advancements in predictive software and tools. The concepts of \u201cBig Data\u201d and \u201cadvanced analytics\u201d are enough to make seasoned practitioners stand to attention \u2013 and even catch the interest of the Executive Team. But when many of us still struggle […]<\/p>\n","protected":false},"author":5362,"featured_media":6482,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[390,387],"tags":[395,255,69,133,394],"class_list":{"0":"post-6474","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-advanced-analytics","8":"category-models-and-methods","9":"tag-advanced-analytics","10":"tag-big-data","11":"tag-forecast-accuracy","12":"tag-fva","13":"tag-tracking-signal"},"_links":{"self":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/6474"}],"collection":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/users\/5362"}],"replies":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/comments?post=6474"}],"version-history":[{"count":0,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/6474\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/media\/6482"}],"wp:attachment":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/media?parent=6474"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/categories?post=6474"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/tags?post=6474"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}