{"id":2739,"date":"2015-01-21T09:10:46","date_gmt":"2015-01-21T14:10:46","guid":{"rendered":"https:\/\/demand-planning.com\/?p=2739"},"modified":"2015-01-21T09:10:46","modified_gmt":"2015-01-21T14:10:46","slug":"forecast-value-added-fva-series-2-interview","status":"publish","type":"post","link":"https:\/\/demand-planning.com\/2015\/01\/21\/forecast-value-added-fva-series-2-interview\/","title":{"rendered":"Forecast Value Added (FVA) \u2013 Series 2 Interview"},"content":{"rendered":"

\"Shaun<\/a><\/p>\n

Interviewer: Michael Gilliland, SAS<\/p>\n

This month\u2019s interview is with Shaun Snapp, founder and editor of SCM Focus, where he provides independent supply chain software analysis, education, and consulting.<\/p>\n

Shaun’s experience and expertise spans several large consulting companies and at i2 Technologies before staring SCM Focus. He has a strong interest in comparative software design, maintains several blogs, and has authored 19 books, including Supply Chain Forecasting Software<\/em> and most recently, Promotions Forecasting<\/em>. He holds an MS in Business Logistics from Penn State University.<\/p>\n

I asked Shaun about the application of FVA analysis with his clients.<\/p>\n

Mike<\/strong>: What forecasting performance metric are you using (e.g., MAPE, weighted MAPE, forecast accuracy), and at what level do you measure (e.g. by Item \/ Distribution Center \/ Week with a 3-week lag)?<\/strong><\/p>\n

Shaun<\/strong>: <\/strong>I really only use MAPE or weighed MAPE. In most cases I am comparing different\u00a0effects\u00a0on forecast accuracy<\/a>,<\/strong> so a relative measure is the most appropriate. As I have to export\u00a0forecasts\u00a0and actuals from systems to calculate global figures, weighed MAPE, while\u00a0certainly\u00a0the\u00a0most\u00a0accurate, is a bit more work to calculate, and of\u00a0course there are different ways of weighing MAPE, which brings up a separate discussion.<\/p>\n

I try to get companies to measure at the Item\/DC. I bring up the topic that the relevant duration\u00a0estimate is over\u00a0the\u00a0replenishment lead time. I don’t use any lagging.<\/p>\n

Mike<\/strong>:\u00a0Are you measuring forecast bias? \u00a0What are your findings?<\/strong><\/p>\n

Shaun<\/strong>: <\/strong>Yes very frequently. My finding is the same as the literature, sales\u00a0inputs\u00a0have a consistent bias — which in my clients is not addressed through anything but planner adjustment.\u00a0<\/strong><\/p>\n

Mike<\/strong>: Are you comparing performance to a na\u00efve model?<\/strong><\/p>\n

Shaun<\/strong>: <\/strong>No. I tend to compare the forecast of my clients against a\u00a0best fit. I do have an approximation of the percentage of\u00a0the\u00a0database which does not need very much forecasting energy, as I\u00a0know what percentage of the database has a level forecast applied — these are both highly variable items, and very stable items.\u00a0<\/strong><\/p>\n

My work pretty much stops at getting the system to generate a decent forecast. I don’t have any involvement in what the planners do after that. Most companies I work with have either walked away from the statistical forecast or only use a very small portion of the statistical forecast that are generated. The planners are free to make any adjustment or change the model applied.<\/p>\n

Mike<\/strong>: What are the steps in the forecasting<\/a> processes you see (e.g., stat forecast, analyst override, consensus meeting override, executive approval)? What FVA comparisons are you measuring?<\/strong><\/p>\n

Shaun<\/strong>: <\/strong>I do all of these\u00a0comparisons\u00a0for clients. I am trying to understand what the FVA is at each step so poor quality inputs can be de-emphasized and quality inputs can be emphasized.<\/p>\n

The bigger problem is impressing the importance of the FVA on clients. I can’t recall finding any work of this type done at clients before I arrive. I think this is\u00a0because\u00a0it does\u00a0take work, and demand planners are busy\u00a0doing other things.\u00a0Because so many manual\u00a0adjustments have to be made and because so many meetings are necessary with groups that provide\u00a0forecasting input\u00a0most demand planning departments seem overworked versus their staffing level.<\/p>\n

Most of the forecasting consulting that comes before me is of a system focused nature. Adding characteristics to a view, creating new data cubes, that sort of thing. There\u00a0seems to be a much smaller market for forecast input testing. It is something I bring to clients, but normally not something they ask for. Many\u00a0decisions are still very much made based upon opinions and “feel.” In fact I find it very rare for the attribute\/characteristics which is used to create a disaggregated forecast to have been proven to improve forecast\u00a0accuracy before it is implemented in the system.<\/p>\n

Mike<\/strong>:\u00a0Anything else you\u2019d like to say about FVA? Including advice for other companies considering the application of FVA?<\/strong><\/p>\n

Shaun<\/strong>: <\/strong>I have never seen any forecasting group that based its design upon FVA.<\/p>\n

This is not to say that lip service may not be paid to FVA. If you bring up the topic, most\u00a0people\u00a0will tend to agree it makes sense. However, really using FVA means being very scientific in how one measures different forecast inputs, and while businesses use math, businesses are generally not\u00a0particularly\u00a0aligned with\u00a0scientific\u00a0approaches.<\/p>\n

There are an insufficient number of people, either in companies or working as consultants that have an understanding of how to perform and document comparative studies. Documentation is a very important part of the process, and again this is a serious limitation for every company I have ever come into\u00a0contact with, from the biggest to the smallest\u00a0and the industry affiliation does not seem to matter very much in this regard.<\/p>\n

On a different topic, as the literature points out and as I can\u00a0certainly\u00a0attest, there\u00a0are some groups that have a negative interest in FVA. That is, some groups\u00a0want\u00a0to provide input to the forecast and don’t particularly care if they are right, and don’t particularly want to be measured. Some groups just want to ensure the in-stock position of their items.\u00a0These\u00a0groups are very powerful and exert great deal of pressure on the supply chain<\/a><\/strong> forecasting group to accept their forecasting input.<\/p>\n

Further, this\u00a0gets into the topic that there is not simply “one forecast.”\u00a0There are really\u00a0multiple forecasts, and while there is discussion of\u00a0unifying the forecasts, this is not in reality an easy thing to do, because\u00a0different groups have different financial and other incentives and see things through different lenses.<\/p>\n

I\u00a0would\u00a0say poor quality forecasting or inputs to the forecast which are entirely unregulated as a policy (but regulated by individual planners to a degree) is\u00a0the\u00a0norm.<\/p>\n

Willing to share your experiences with FVA?\u00a0\u00a0 Please contact the IBF at <\/em>info@ibf.org<\/em><\/a> to arrange an interview for the blog series.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Interviewer: Michael Gilliland, SAS This month\u2019s interview is with Shaun Snapp, founder and editor of SCM Focus, where he provides independent supply chain software analysis, education, and consulting. Shaun’s experience and expertise spans several large consulting companies and at i2 Technologies before staring SCM Focus. He has a strong interest in comparative software design, maintains […]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[33],"tags":[277,82,271,60,255,61,263,62,275,41,281,64,65,66,162,34,67,39,68,260,69,70,273,35,42,43,44,279,133,36,76,248,48,81,251,236,264,284,37,252,73,74,270,258,38,280],"class_list":{"0":"post-2739","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-forecasting-and-planning","7":"tag-analytics","8":"tag-apics","9":"tag-apo","10":"tag-best-practices","11":"tag-big-data","12":"tag-business-forecasting","13":"tag-certification","14":"tag-collaborative-forecasting","15":"tag-cpf","16":"tag-data-cleansing","17":"tag-data-mining","18":"tag-demand-forecast","19":"tag-demand-forecasting","20":"tag-demand-management","21":"tag-demand-planner","22":"tag-demand-planning","23":"tag-demand-planning-and-forecasting-conference","24":"tag-economic-forecasting","25":"tag-executive-sop","26":"tag-finance","27":"tag-forecast-accuracy","28":"tag-forecast-error","29":"tag-forecast-value-added","30":"tag-forecasting","31":"tag-forecasting-metrics","32":"tag-forecasting-models","33":"tag-forecasting-system","34":"tag-fundamentals","35":"tag-fva","36":"tag-ibf","37":"tag-ibp","38":"tag-innovation","39":"tag-institute-of-business-forecasting-and-planning","40":"tag-inventory-management","41":"tag-marketing","42":"tag-metrics","43":"tag-new-products","44":"tag-predictive-analytics","45":"tag-sop","46":"tag-sales","47":"tag-sales-operations-planning","48":"tag-sales-forecasting","49":"tag-sap","50":"tag-siop","51":"tag-supply-chain","52":"tag-training"},"_links":{"self":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/2739"}],"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\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/comments?post=2739"}],"version-history":[{"count":0,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/2739\/revisions"}],"wp:attachment":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/media?parent=2739"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/categories?post=2739"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/tags?post=2739"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}