{"id":10737,"date":"2023-05-01T09:28:40","date_gmt":"2023-05-01T13:28:40","guid":{"rendered":"https:\/\/smartcorp.com\/forecasting\/three-ways-to-estimate-forecast-accuracy-copy\/"},"modified":"2023-05-01T10:29:36","modified_gmt":"2023-05-01T14:29:36","slug":"tipos-de-problemas-de-pronostico-que-ayudamos-a-resolver","status":"publish","type":"post","link":"https:\/\/smartcorp.com\/es\/forecasting\/types-of-forecasting-problems-smartforecasts-help-solve\/","title":{"rendered":"Tipos de problemas de pron\u00f3stico que ayudamos a resolver"},"content":{"rendered":"

[et_pb_section fb_built=”1″ _builder_version=”4.16″ _module_preset=”default” custom_margin=”0px||||false|false” custom_padding=”0px||||false|false” global_colors_info=”{}”][et_pb_row admin_label=”Row” module_class=”blog_glob” _builder_version=”4.16″ locked=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ saved_specialty_column_type=”2_3″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text content_last_edited=”off|desktop” admin_label=”CONTENT” module_class=”post-main_text” _builder_version=”4.20.4″ header_3_text_color=”#0c71c3″ header_3_font_size=”19px” background_size=”initial” background_position=”top_left” background_repeat=”repeat” z_index_tablet=”500″ text_text_shadow_horizontal_length_tablet=”0px” text_text_shadow_vertical_length_tablet=”0px” text_text_shadow_blur_strength_tablet=”1px” link_text_shadow_horizontal_length_tablet=”0px” link_text_shadow_vertical_length_tablet=”0px” link_text_shadow_blur_strength_tablet=”1px” ul_text_shadow_horizontal_length_tablet=”0px” ul_text_shadow_vertical_length_tablet=”0px” ul_text_shadow_blur_strength_tablet=”1px” ol_text_shadow_horizontal_length_tablet=”0px” ol_text_shadow_vertical_length_tablet=”0px” ol_text_shadow_blur_strength_tablet=”1px” quote_text_shadow_horizontal_length_tablet=”0px” quote_text_shadow_vertical_length_tablet=”0px” quote_text_shadow_blur_strength_tablet=”1px” header_text_shadow_horizontal_length_tablet=”0px” header_text_shadow_vertical_length_tablet=”0px” header_text_shadow_blur_strength_tablet=”1px” header_2_text_shadow_horizontal_length_tablet=”0px” header_2_text_shadow_vertical_length_tablet=”0px” header_2_text_shadow_blur_strength_tablet=”1px” header_3_text_shadow_horizontal_length_tablet=”0px” header_3_text_shadow_vertical_length_tablet=”0px” header_3_text_shadow_blur_strength_tablet=”1px” header_4_text_shadow_horizontal_length_tablet=”0px” header_4_text_shadow_vertical_length_tablet=”0px” header_4_text_shadow_blur_strength_tablet=”1px” header_5_text_shadow_horizontal_length_tablet=”0px” header_5_text_shadow_vertical_length_tablet=”0px” header_5_text_shadow_blur_strength_tablet=”1px” header_6_text_shadow_horizontal_length_tablet=”0px” header_6_text_shadow_vertical_length_tablet=”0px” header_6_text_shadow_blur_strength_tablet=”1px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” use_border_color=”off” global_colors_info=”{}”]<\/p>\n

Here are examples of forecasting problems that SmartForecasts can solve, along with the kinds of business data representative of each.<\/p>\n

Forecasting an item based on its pattern<\/strong><\/p>\n

Given the following six quarterly sales figures, what sales can you expect for the third and fourth quarters of 2023?<\/p>\n

\"Forecasting<\/a>

Sales by Quarter<\/p><\/div>\n

SmartForecasts gives you many ways to approach this problem. You can make your own statistical forecasts using any of six different exponential smoothing<\/a> and moving average<\/a><\/u> methods. Or, like most nontechnical forecasters, you can use the time-saving Automatic command, which has been programmed to automatically select and use the most accurate method for your data. Finally, to incorporate your business judgment into the forecasting process, you can graphically adjust any statistical forecast result using SmartForecasts’ “eyeball” adjustment<\/u> capabilities.<\/p>\n

 <\/p>\n

Forecasting an item based on its relationship to other variables.<\/strong>
<\/strong>
Given the following historical relationship between unit sales and the number of sales representatives, what sales levels can you expect when the planned increase in sales staff takes place over the final two quarters of 2023?<\/p>\n

\"Forecasting<\/a>

Sales and Sales Representatives by Quarter<\/p><\/div>\n

You can answer a question like this using SmartForecasts\u2019 powerful Regression<\/a> command, designed specifically to facilitate forecasting applications that require regression analysis solutions. Regression models with an essentially unlimited number of independent\/predictor variables are possible, although most useful regression models use only a handful of predictors.<\/p>\n

 <\/p>\n

Simultaneously forecasting a number of product items and their total<\/strong><\/p>\n

Given the following total sales for all dress shirts and the distribution of sales by color, what will individual and total sales be over the next six months?<\/p>\n

\"Forecasting<\/a>

Monthly Dress Shirt Sales by Color<\/p><\/div>\n

SmartForecasts’ unique Group Forecasting features automatically and simultaneously forecasts closely related time series, such as these items in the same product group. This saves considerable time and provides forecast results not only for the individual items but also for their total. “Eyeball” adjustments at both the item and group levels are easy to make. You can quickly create forecasts for product groups with hundreds or even thousands of items.<\/p>\n

 <\/p>\n

Forecasting thousands of items automatically<\/strong><\/p>\n

Given the following record of product demand at the SKU level, what can you expect demand to be over the next six months for each of the 5,000 SKUs?<\/p>\n

\"Forecasting<\/a>

Monthly Product Demand by SKU (Stock Keeping Unit)<\/p><\/div>\n

In just a few minutes, SmartForecasts’ powerful Automatic Selection can take a forecasting job of this size, read the product demand data, automatically create statistical forecasts for each SKU, and saves the result. The results are then ready for export to your ERP system leveraging any one of our API-based connectors or via file export.\u00a0 Once set up, forecasts will automatically be produced each planning cycle without intervention by the user.<\/p>\n

 <\/p>\n

Forecasting demand that is most often zero<\/strong><\/p>\n

A distinct and especially challenging type of data to forecast is intermittent<\/em> demand, which is most often zero but jumps up to random nonzero values at random times. This pattern is typical of demand for slow<\/em> moving<\/em> items, such as service parts or big<\/em> ticket<\/em> capital goods.<\/p>\n

For example, consider the following sample of demand for aircraft service parts. Note the preponderance of zero values with nonzero values mixed in, often in bursts.<\/p>\n

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

SmartForecasts has a unique method designed especially for this type of data: the Intermittent Demand forecasting feature. Since intermittent demand arises most often in the context of inventory control, this feature focuses on forecasting the range of likely values for the total demand over a lead time, e.g., cumulative demand over the period Jun-23 to Aug-23 in the example above.<\/p>\n

\u00a0<\/h1>\n

Forecasting inventory requirements<\/span><\/strong><\/p>\n

Forecasting inventory requirements is a specialized variant of forecasting that focuses on the high end of the range of possible future values.<\/span><\/p>\n

For simplicity, consider the problem of forecasting inventory requirements for just one period ahead, say one day ahead. Usually, the forecasting job is to estimate the most likely or average level of product demand. However, if available inventory equals the average demand, there is about a 50% chance that demand will exceed inventory, resulting in lost sales and\/or lost good will. Setting the inventory level at, say, ten times the average demand will probably eliminate the problem of stockouts, but will just as surely result in bloated inventory costs.<\/span><\/p>\n

The trick of inventory optimization is to find a satisfactory balance between having enough inventory to meet most demand without tying up too many resources in the process. Usually, the solution is a blend of business judgment and statistics. The judgmental part is to define an acceptable inventory service level, such as meeting 95% of demand immediately from stock. The statistical part is to estimate the 95th percentile of demand.<\/span><\/p>\n

When not dealing with <\/span>intermittent demand<\/span><\/a>, SmartForecasts estimates the required inventory level by assuming a bell-shaped (Normal) curve of demand, estimating both the middle and the width of the bell curve, then using a standard statistical formula to estimate the desired percentile. The difference between the desired inventory level and the average level of demand is called the safety stock because it protects against the possibility of stockouts.<\/span><\/p>\n

When dealing with intermittent demand, the bell-shaped curve is a poor approximation to the statistical distribution of demand. In this special case, SmartForecasts uses patented intermittent demand forecasting technology to estimate the required inventory service level. <\/span><\/p>\n

 <\/p>\n

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

 <\/p>\n

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Generar pron\u00f3sticos estad\u00edsticos precisos no es una tarea f\u00e1cil. Los planificadores deben mantener los datos hist\u00f3ricos continuamente actualizados, crear y administrar una base de datos de modelos de pron\u00f3stico, saber qu\u00e9 m\u00e9todos de pron\u00f3stico usar, realizar un seguimiento de las anulaciones de pron\u00f3stico e informar sobre la precisi\u00f3n del pron\u00f3stico. Estos son ejemplos de problemas de pron\u00f3stico que SmartForecasts puede resolver, junto con los tipos de datos comerciales representativos de cada uno.<\/p>","protected":false},"author":123,"featured_media":10743,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"

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Recently, I had an interesting conversation with an inventory manager and the VP Finance. We were discussing the benefits of being able to automatically optimize both reorder points and order quantities. The VP Finance was concerned that given their large supplier required minimum order quantities, they would not be able to benefit.\u00a0 He said his suppliers held all the power, forcing him to accept massive minimum order quantities and tying his hands. While he felt bad about this, he saw a silver lining: He didn\u2019t have to do any planning. He would accept a large inventory investment, but his customer service levels would the exceptional.\u00a0 Perhaps the large inventory investment was assumed to be the cost of doing business.<\/p>

I pushed back and pointed out that he was not as powerless as he felt. He still had control of the other half of the procurement process: while he couldn\u2019t control how much to order, he could control when to order by adjusting the reorder point. In other words, there is always room for careful quantitative analysis in inventory management, even when you have one hand tied behind your back.<\/p>

An Example<\/strong><\/p>

To put some numbers behind my argument, I created a scenario then analyzed it using our methodology to show how consequential it can be to use inventory optimization software even in constrained situations. In this scenario, item demand averages 2.2 units per day but varies significantly by day of week. Let\u2019s say the imaginary supplier insists on a minimum order quantity of 500 units (way out of proportion to demand) and fills replenishment orders in either three days or ten days in equal proportions (quite inconsistent). To spread the blame around, let\u2019s also suppose that the imaginary supplier\u2019s imaginary customer uses a foolish rule that the reorder point should be 10% of the minimum order quantity. (Why this rule? Too many companies use simple\/simplistic rules of thumb in lieu of proper analysis.)<\/p>

So, we have a base case in which the order quantity is 500 units, and the reorder point is 50 units. In this case, the fill rate is 100%, but the average number of units on hand is a whopping 330. If the customer would simply lower the reorder point from 50 to 15, the fill rate would still be 99.5%, but the average stock on hand would drop by 11% to 295 units. Using the one hand not tied behind his back, the inventory manager could cut his inventory investment by more than 10%, which would be a noticeable win.<\/p>

Incidentally, if the minimum order quantity were abolished, the customer would be free to arrive at a new and much better solution. Setting the order quantity to 45 and the reorder point to 25 would achieve a 99% fill rate at the cost of a daily on-hand level of only 35 units: nearly a 90% reduction in inventory investment: a major improvement over the status quo.<\/p>

Postscript<\/strong><\/p>

These calculations are possible using our software, which can make visible the otherwise unknown relationships between inventory system design choices (e.g., order quantity and reorder point) and key performance indicators (e.g., average units on hand and fill rate).\u00a0 Armed with this ability to conduct these calculations, alternative arrangements with the supplier may now be considered. For example, what if, in exchange for paying a higher price per unit, the supplier agreed to a lower MOQ. Using the software to conduct an analysis of the key performance indicators using the \u201cwhat if\u201d costs and MOQs would reveal the cost per unit and MOQ that would be needed to develop a more profitable deal.\u00a0\u00a0 Once identified, all parties stand to benefit.\u00a0 The supplier now generates a better margin on sales of its products, and the buyer holds considerably less inventory yielding a holding cost reduction that dwarfs the added cost per unit.\u00a0 Everyone wins.<\/p>

\u00a0<\/p>

\u00a0<\/p>

\"\"<\/a><\/p>

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