{"id":12127,"date":"2023-12-11T09:56:39","date_gmt":"2023-12-11T13:56:39","guid":{"rendered":"https:\/\/smartcorp.com\/forecasting\/everybody-forecasts-to-drive-inventory-planning-its-just-a-question-of-how-copy\/"},"modified":"2023-12-11T11:05:57","modified_gmt":"2023-12-11T15:05:57","slug":"estas-jugando-al-juego-de-adivinar-el-inventario","status":"publish","type":"post","link":"https:\/\/smartcorp.com\/es\/blog\/are-you-playing-the-inventory-guessing-game\/","title":{"rendered":"\u00bfEst\u00e1s jugando al juego de adivinar el inventario?"},"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=”4.23.1″ 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

Some companies invest in software to help them manage their inventory, whether it\u2019s spare parts or finished goods. But a surprising number of others play the Inventory Guessing Game every day, trusting to an imagined \u201cGolden Gut\u201d or to plain luck to set their inventory control parameters. But what kind of results do you expect with that approach?<\/p>\n

How good are you at intuiting the right values? This blog post challenges you to guess the best Min and Max values for a notional inventory item. We\u2019ll show you its demand history, give you a few relevant facts, then you can pick Min and Max values and see how well they would work. Ready?<\/p>\n

The Challenge<\/strong><\/p>\n

Figure 1 shows the daily demand history of the item. The average demand is 2 units per day. Replenishment lead time is a constant 10 days (which is unrealistic but works in your favor). Orders that cannot be filled immediately from stock cannot be backordered and are lost. You want to achieve at least an 80% fill rate, but not at any cost. You also want to minimize the average number of units on hand while still achieving at least an 80% fill rate. What Min and Max values would produce an 80% fill rate with the lowest average number of units on hand? [Record your answers for checking later. The solution appears below at the end of the article.]<\/p>\n

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

Computing the Best Min and Max Values<\/strong><\/p>\n

The way to determine the best values is to use a digital twin, also known as a Monte Carlo simulation. The analysis creates a multitude of demand scenarios and passes them through the mathematical logic of the inventory control system to see what values will be taken on by key performance indicators (KPI\u2019s).<\/p>\n

We built a digital twin for this problem and systematically exercised it with 1,085 pairs of Min and Max values. For each pair, we simulated 365 days of operation a total of 100 times. Then we averaged the results to assess the performance of the Min\/Max pair in terms of two KPI\u2019s: fill rate and average on hand inventory.<\/p>\n

Figure 2 shows the results. The inherent tradeoff between inventory size and fill rate is clear in the figure: if you want a higher fill rate, you have to accept a larger inventory. However, at each level of inventory there is a range of fill rates, so the game is to find the Min\/Max pair that yields the highest fill rate for any given size inventory.<\/p>\n

A different way to interpret Figure 2 is to focus on the dashed green line marking the target 80% fill rate. There are many Min\/Max pairs that can hit near the 80% target, but they differ in inventory size from about 6 to about 8 units. Figure 3 zooms in on that region of Figure 2 to show\u00a0 quite a number of Min\/Max pairs that are competitive.<\/p>\n

We sorted the results of all 1,085 simulations to identify what economists call the efficient frontier. The efficient frontier is the set of most efficient Min\/Max pairs to exploit the tradeoff between fill rate and units on hand. That is, it is a list of Min\/Max pairs that provide the least cost way to achieve any desired fill rate, not just 80%. Figure 4 shows the efficient frontier for this problem. Moving from left to right, you can read off the lowest price you would have to pay (as measured by average inventory size) to achieve any target fill rate. For example, to achieve a 90% fill rate, you would have to carry an average inventory of about 10 units.<\/p>\n

Figures 2, 3, and 4 show results for various Min\/Max pairs but do not display the values of Min and Max behind each point. Table 1 displays all the simulation data: the values of Min, Max, average units on hand and fill rate. The answer to the guessing game is highlighted in the first line of the table: Min=7 and Max=13<\/strong>1<\/sup>. Did you get the right answer, or something close2<\/sup>? Did you maybe get onto the efficient frontier?<\/p>\n

Conclusions<\/strong><\/p>\n

Maybe you got lucky, or maybe you do indeed have a Golden Gut, but it\u2019s more likely you didn\u2019t get the right answer, and it\u2019s even more likely you didn\u2019t even try. Figuring out the right answer is extremely difficult without using the digital twin. Guessing is unprofessional.<\/p>\n

One step up from guessing is \u201cguess and see\u201d, in which you implement your guess and then wait a while (months?) to see if you like the results. That tactic is at least \u201cscientific\u201d, but it is inefficient.<\/p>\n

Now consider the effort to work out the best (Min,Max) pairs for thousands of items. At that scale, there is even less justification for playing the Inventory Guessing Game. The right answer is to play it\u2026 Smart3<\/sup>.<\/strong><\/p>\n

1 <\/sup>This answer has a bonus, in that it achieves a bit more than 80% fill rate at a lower average inventory size than the Min\/Max combination that hit exactly 80%. In other words, (7,13) is on the efficient frontier.<\/p>\n

2<\/sup> Because these results come from a simulation instead of an exact mathematical equation, there is a certain margin of error associated with each estimated fill rate and inventory level. However, because the average results were based on 100 simulations each 365 days long, the margins of error are small. Across all experiments, the average standard errors in fill rate and mean inventory were, respectively, only 0.009% and 0.129 units.<\/p>\n

3<\/sup> In case you didn\u2019t know this, one of the founders of Smart Software was \u2026 Charlie Smart.<\/p>\n

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

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

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

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

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\u00a0<\/b>\"\"<\/a><\/p>\n

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Algunas empresas invierten en software que les ayude a gestionar su inventario, ya sean repuestos o productos terminados. Pero un n\u00famero sorprendente de personas juegan el juego de adivinar el inventario todos los d\u00edas, confiando en un \u201ctripa dorada\u201d imaginario o en la simple suerte para establecer sus par\u00e1metros de control de inventario. Pero, \u00bfqu\u00e9 tipo de resultados espera con ese enfoque? <\/p>","protected":false},"author":122,"featured_media":12142,"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>

[\/et_pb_text][et_pb_code module_class=\"comment_sect\" _builder_version=\"4.10.7\" global_colors_info=\"{}\"]jQuery(document).ready(function($) { \/\/ Stuff to do as soon as the DOM is ready. Use $() w\/o colliding with other libs$(window).on('load', function(){ if ($(window).width() < 2000 ) { $(\".comment_sect\").append($(\"#comment-wrap\")) } if ($(window).width() < 2000) { $(\"#et-main-area\").prepend($(\".blog_bg \")) } if ($(window).width() < 2000 ) { $(\".post-main_text\").prepend($(\".et_post_meta_wrapper\")) } }); });[\/et_pb_code][et_pb_code admin_label=\"Comment \" module_class=\"com-title\" global_colors_info=\"{}\"]<\/p>

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