{"id":11069,"date":"2023-07-31T12:14:58","date_gmt":"2023-07-31T16:14:58","guid":{"rendered":"https:\/\/smartcorp.com\/inventory-optimization\/uncover-data-facts-and-improve-inventory-performance-copy\/"},"modified":"2023-09-05T08:46:05","modified_gmt":"2023-09-05T12:46:05","slug":"como-trata-su-sistema-erp-el-stock-de-seguridad","status":"publish","type":"post","link":"https:\/\/smartcorp.com\/es\/blog\/how-does-your-erp-system-treat-safety-stock\/","title":{"rendered":"\u00bfC\u00f3mo trata su sistema ERP el stock de seguridad?"},"content":{"rendered":"

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Is safety stock regarded as emergency spares or as a day-to-day buffer against spikes in demand? Knowing the difference and configuring your ERP properly will make a big difference to your bottom line.<\/p>\n

The Safety Stock<\/strong> field in your ERP<\/strong> system can mean very different things depending on the configuration. Not understanding these differences and how they impact your bottom line is a common issue we\u2019ve seen arise in implementations of our software.<\/p>\n

Implementing inventory optimization software starts with new customers completing the technical implementation to get data flowing.\u00a0 They then receive user training and spend weeks carefully configuring their initial safety stocks, reorder levels, and consensus demand forecasts with Smart IP&O.\u00a0 The team becomes comfortable with Smart\u2019s key performance predictions (KPPs) for service levels, ordering costs, and inventory on hand, all of which are forecasted using the new stocking policies.<\/p>\n

But when they save the policies and forecasts to their ERP test system, sometimes the orders being suggested are far larger and more frequent than they expected, driving up projected inventory costs.<\/p>\n

When this happens, the primary culprit is how the ERP is configured to treat safety stock.\u00a0 Being aware of these configuration settings will help planning teams better set expectations and achieve the expected outcomes with less effort (and cause for alarm!).<\/p>\n

Here are the three common examples of ERP safety stock configurations:<\/p>\n

Configuration 1.<\/strong> Safety Stock is treated as emergency stock <\/strong>that can’t be consumed. If a breach of safety stock is predicted, the ERP system will force an expedite no matter the cost so the inventory on hand never falls below safety stock, even if a scheduled receipt is already on order and scheduled to arrive soon.<\/p>\n

Configuration 2.<\/strong> Safety Stock is treated as Buffer stock<\/strong> that is designed to be consumed. The ERP system will place an order when a breach of safety stock is predicted but on hand inventory will be allowed to fall below the safety stock. The buffer stock protects against stockout during the resupply period (i.e., the lead time).<\/p>\n

Configuration 3.<\/strong> Safety Stock is ignored by the system and treated as a visual planning aid or rule of thumb<\/strong>. It is ignored by supply planning calculations but used by the planner to help make manual assessments of when to order.<\/p>\n

Note: We never recommend using the safety stock field as described in Configuration 3. In most cases, these configurations were not intended but result from years of improvisation that have led to using the ERP in a non-standard way. \u00a0Generally, these fields were designed to programmatically influence the replenishment calculations.\u00a0 So, the focus of our conversation will be on Configurations 1 and 2.\u00a0 <\/em><\/p>\n

Forecasting and inventory optimization systems are designed to compute forecasts that will anticipate inventory draw down and then calculate safety stocks sufficient to protect against variability in demand and supply. This means that the safety stock is intended to be used as a protective buffer<\/em> (Configuration 2) and not<\/u> as emergency sparse<\/em> (Configuration 3).\u00a0 It is also important to understand that, by design, the safety stock will be consumed<\/u> approximately 50% of the time.<\/p>\n

Why 50%? Because actual orders will exceed an unbiased forecast half of the time. See the graphic below illustrating this.\u00a0 A \u201cgood\u201d forecast should yield the value that will come closest to the actual most often so actual demand will either be higher or lower without bias in either direction.<\/p>\n

 <\/p>\n

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

 <\/p>\n

If you configured your ERP system to properly allow consumption of safety stock, then the on hand inventory might look like the graph below.\u00a0 Note that some safety stock is consumed but avoided a stockout.\u00a0 The service level you target when computing safety stock will dictate how often you stockout before the replenishment order arrives.\u00a0 Average inventory is roughly 60 units over the time horizon in this scenario.<\/p>\n

 <\/p>\n

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

 <\/p>\n

If your ERP system is configured to not<\/u> allow consumption of safety stock and treats the quantity entered in the safety stock field more like emergency spares, then you will have a massive overstock!\u00a0 Your inventory on hand would look like the graph below with orders being expedited as soon as a breach of safety stock is expected. Average inventory is roughly 90 units, a 50% increase compared to when you allowed safety stock to be consumed.<\/p>\n

 <\/p>\n

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

 <\/p>\n

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

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\u00bfSe considera el stock de seguridad como repuestos de emergencia o como un amortiguador diario contra picos en la demanda? Saber la diferencia y configurar su ERP correctamente beneficiar\u00e1 en gran medida su resultado final. Es fundamental comprender c\u00f3mo las configuraciones de su ERP afectar\u00e1n el tratamiento del stock de seguridad y las \u00f3rdenes de reabastecimiento\/sugerencias de trabajo de producci\u00f3n. Si lo hace, se asegura de que se puedan evitar los errores no deseados que causan la hinchaz\u00f3n y la escasez de inventario. <\/p>","protected":false},"author":123,"featured_media":11077,"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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