Filtering by Tag: inventory optimization

# The illustrated stock reward function

Published on by Joannes Vermorel.

The stock reward function is a key ingredient to make the most of probabilistic forecasts in order to boost your supply chain performance. The stock reward is used for computing the return on investment for every extra unit of stock to be purchased or manufactured.

The stock reward function is expressive and can be used like a mini-framework for addressing many different situations. However, as a minor downside, it’s not always easy to make sense of the calculations performed with the stock reward function. Below you’ll find a short list of graphs that represent the various transformations applied to the forecasts.

The first graph - entitled Future demand - represents a probabilistic demand forecast associated with a given SKU. The curve represents a distribution of probabilities, with the total area under the curve equal to one. In the background, this future demand is implicitly associated with a probabilistic lead time forecast, also represented as a distribution of probabilities. Such a distribution is typically generated through a probabilistic forecasting engine.

The Marginal fill rate graph represents the fraction of extra demand that is captured by each extra unit of stock. In other words, this graph demonstrates what happens to the fill rate as the stock increases. Since we are representing a marginal fill rate here, the total area under the curve remains equal to one. The marginal fill rate distribution can be computed with the fillrate() function.

The Demand with backorders graph is identical to the Future demand graph, except that 8 units have been introduced to represent a back order. The backorder represents guaranteed demand since these units have already been bought by clients. As a result, when backordered units are introduced, the probability distribution of demand is shifted to the right as the backordered units being guaranteed demand. The shift operator >> is available as part of the algebra of distribution to compute such a transformation over the initial distribution.

The Fill rate with backorders graph is also very similar to the original Marginal fill rate graph, but has also been shifted 8 units to the right. Here, the plotted fill rate is only associated with the uncertain demand, hence the shape of the distribution remains the same.

The Margin graph represents the margin economic reward as computed by the stock reward function taking the Demand with backorders as input. The stock reward can be visualized as a distribution, but this is not a distribution of probabilities: the area under the curve is not equal to one but is instead equal to the total margin which would be captured with unlimited inventory. On the left of the graph, each backordered unit yields the same margin, which is not surprising as there is no uncertainty in capturing the margin given that the units have already been bought.

The Stockout penalty represents the second component of the stock reward function. The shape of the distribution might feel a bit unexpected, but this shape merely reflects that, by construction of the stock reward function, the total area under the curve is zero. Intuitively, starting from a stock level of zero, we have the sum of all the stockout penalties as we are missing all the demand. Then, as we move to the right with higher stock levels we are satisfying more and more demand and thus further reducing the stockout penalties; until there is no penalty left because the entire demand has been satisfied. The stock-out penalty of not serving backorders is represented as greater than the penalty of not serving the demand that follows. Here, we are illustrating the assumption that clients who have already backordered typically have greater service expectations than clients who haven’t yet bought any items.

The Carrying costs graph represents the third and last component of the stock reward function. As there is no upper limit for the carrying costs - it’s always possible to keep one more unit in stock thus further increasing the carrying costs - the distribution is divergent: it tends to negative infinity on the right. The total area under the curve is negative infinity, although this is a rather theoretical perspective. On the right, the carrying costs associated with the backordered units are zero: indeed, as those units have already been bought by clients they won’t incur any carrying costs, since those units will be shipped to clients as soon as possible.

The final stock reward - not represented above - would be obtained by summing the three components of the stock reward function. The resulting distribution would be interpreted as the ROI for each extra unit of stock to be acquired. This distribution typically starts with positive values,the first units of stock being profitable, but converge to negative infinity as we move to higher stock levels given the unbounded carrying costs.

The term support classically refers to the demand levels associated with non-zero probabilities. In the graphs above, the term support is used loosely to refer to the entire range that needs to processed as non-zero values by Envision. In particular, it’s worth mentioning that there are multiple calculations that require the distribution support to be extended in order to make sure that the final resulting distribution isn’t truncated.

• The shift operation, which happens when backorders are present, requires the support to be increased by the number of backordered units.
• The margin and carrying cost components of the stock reward function have no theoretical limits on the right, and can require arbitrarily large extensions of the support.
• Ordering constraints, such as MOQs, may require having inventory levels that are even greater than the ones reached by the shifted distributions. Properly assessing the tail of the distribution is key for estimating whether the MOQ can be profitably satisfied or not.

In practice, the Envision runtime takes care of automatically adjusting the support to make sure that distributions aren’t truncated during the calculations.

Categories: Tags: insights inventory optimization

# Local management of inventory settings

Published on by Joannes Vermorel.

As far as data management is concerned, Lokad's philosophy is to keep data centralized in the original business app whenever this is possible. We do not want to be an ABC management software (replace ABC by commerce, warehouse, store or entreprise), because our clients already have this software in place. Yet, it is not always possible - or practical - to stick to this principle; so we need to adjust.

More specifically, in order to generate an inventory forecasting report, Lokad needs domain-specific settings such as the desired service level and the applicable lead time, which are typically not built-in within most business apps. Certain apps like Brightpearl or Linnworks already support the notion of custom properties to enrich existing data with domain-specific settings, but many other apps do not support such a feature.

So far, companies using TradeGecko, Unleashed Software or Vend could only rely on the Salescast default inventory settings, which are rather crude as they apply to all of the data if no alternative settings are provided.

Yesterday, we have released an updated version of Salescast which now offers the possibility to manage the inventory settings within your Lokad account by uploading a specific Excel sheet acting as the repository of your settings. It is now possible to manage fine-grained inventory settings for all of our supported apps.

Categories: Tags: release salescast inventory optimization technical

# Mitigating supplier stockouts

Published on by Joannes Vermorel.

Most inventory optimization processes are approximate in the sense that the propensity of the suppliers to face a stockout is not modeled. This approximation simplifies a lot the analysis, and as long suppliers have service levels that are substantially higher than the target service levels of the downstream retailer, distortions introduced in the inventory analysis are minimal. However, if the retailer seeks service levels higher than the ones offered by its supplier, then things get more complicated, and a lot more expensive inventory-wise too. Let’s briefly review how to mitigate supplier stockouts.

From a pure inventory control perspective, associated with the quantile forecasting insights, assuming there is only a single supplier available, the correct way to model the supplier stockouts consists of adjusting the lead time. Indeed, when the stock is not readily available on the supplier-side, the retailer needs to wait until the inventory is renewed to get its next replenishment under way. Thus, in order to account for the potential supplier stockouts, the applicable lead time is not anymore the ordering delay plus the shipping delay, but the same plus the supplier’s own lead time.

Frequently in practice however, the lead time of the supplier is much larger than the typical lead time of the retailer. Such situations happen for example when the supplier is a wholesaler importing from Asia. In such conditions, trying to achieve a service level greater or equal to the one of the supplier proves to be a costly exercise because the lead time can be increased several times to match the one of the supplier. As a result, it’s not infrequent to observe that the stock would need to be more than doubled as well as a direct consequence of this increase of lead time.

One typical way to mitigate supplier stockouts without resorting to drastic inventory increase consists of introducing some redundancy, either within the offering itself, or by diversifying the suppliers.

Redundancy in the offering happens when some goods being sold are similar enough to be considered as substitutes. The presence of substitutes, even imperfect ones, mitigate the supplier’s stockouts - as well as the retailer’s own stockouts – by reducing the damage as a certain fraction of the demand can be redirected to the substitute products when the other one is missing. One drawback of this approach is that, frequently, unless when dealing with quasi-perfect substitutes, it’s hard to assess whether two distinct products will indeed be perceived as actual substitutes by the clients. Ideally, this would require a statistical analysis of its own. Also, too many substitutes can clutter the offering, making it less appealing to the clients in the end.

Redundancy on the supplier-side typically involves secondary suppliers selling at higher prices because overall purchase volumes are smaller. Those suppliers serve as back-up if the primary suppliers cannot readily serve the products. The primary benefit of this approach is an extra available obtained for the exact product that clients seek. Then, one potential major drawbacks lie in the correlation that exists between the levels of inventory of the various suppliers. Simply put, if one supplier goes out of stock for a given item, then chances are that the market demand for the product has been surprisingly large and as a consequence most of the other suppliers will go (or have already gone) out of stock too.

Categories: Tags: insights inventory optimization

Published on by Joannes Vermorel.

In retail, many companies don’t have much control over their service levels. In fact, many companies don’t monitor the service level where it matters the most: the physical store. Indeed, measuring in-store service level is a tedious exercise. Some companies - mostly panelists - specialize in doing this sort of measurements, but the cost is steep as there is no workaround for the extensive manpower involved in the process.

Taking a step back, why do we even need to measure the service level?

Wouldn’t it be more convenient if the service level was something obtained by design and defined through explicit settings within the inventory optimization software? This would certainly be a lot more practical. Service levels certainly don't need to be an afterthought of the inventory optimization process.

It turns out that, historically, the need to measure the service levels came from early inventory optimization methods such as the safety stock analysis that offer about no control on actual service levels. Indeed, the underlying models rely on the assumption that the demand is normally distributed and this assumption is so wrong in practice that most retailers gave up on this assumption in favor of ad-hoc safety stock coefficients.

Those ad-hoc safety stock coefficients are not bad per se: they are certainly better than relying on abusive assumptions about the future demand. However, the quantitative relationship between the safety stock and the service level is lost. Hence, retailers end-up measuring their service levels and tweaking coefficients until inventory stabilizes somehow. At the end, the situation is not satisfying because the inventory strategy is inflexible: safety stock coefficients can’t be changed without exposing the company to a myriad of problems, repeating the tedious empirical adjustments done originally.

However, with the advent of quantile forecasting, it’s now possible to produce forecasts that very accurately drive the service levels, even if the quantile forecasts themselves are not accurate. All it takes is unbiased forecasts, and not perfectly accurate forecasts.

Indeed, quantile forecasts directly and very natively address the problem of producing the reorder quantities it takes to cover target service levels. If a new and better quantile forecasting technology is found, then this technology might be capable of achieving the same service levels with less inventory, but both technologies deliver the service levels they promise by design.

This behavior is very unlike the case of classic forecasting allied to safety stock analysis where an improvement of the accuracy, while being desirable, leads to erratic results in practice. For example, for many low volume products, as observed in stores, shifting to a dumb forecasting model that always returns zero usually improves the accuracy defined as the absolute difference between the actual sales and the forecasted sales. Obviously, shifting toward zero forecasts for half of the products can only end with dismal business results. This example might appears as anecdotal but it is not. Zero forecasts are the most accurate classic forecasts in numerous situations.

Thus, in order to take control of your service levels, it takes an inventory optimization methodology where such a control is built-in.

Categories: Tags: quantiles inventory insights inventory optimization

# Whitepaper: Quantitative Inventory Optimization

Published on by Joannes Vermorel.

A few weeks ago, Brightpearl, one of the most impressive commerce management software companies, was posting their success story about online merchants benefiting from better stock levels through Salescast.

If your company happens to use Brightpearl, check-out the recently revised tutorial to get started. In particular, Salescast now benefits from special features that make it much easier to extract your historical data from Brightpearl.

Today, as part of the Commerce Accerelation 101 initiative of Brightpearl, we are jointly publishing a whitepaper about Quantitative Inventory Optimization.

The paper details the notion of quantitative performance of the inventory. Indeed, there is a widespread misconception in supply chain, that stock levels and demand forecasts would be different things. They are not. The combination of stock on hand and the stock on order represent the true "forecast" made by your company. Hence, this is the quality of this anticipation that needs to be measured.

This is a rather opinionated paper. However, we firmly believe that the classic vision (median forecasts, safety stock analysis, etc) is, simply put, nonsense when it comes to the long tail and most of the online commerce.

Categories: partners, accuracy Tags: whitepaper brightpearl inventory optimization

# An exciting vision cast into a new product: Introducing BIG DATA PLATFORM [Infographic]

Published on by Joannes Vermorel.

It seems to me that as we grow, our pace of innovation continues to accelerate. We are currently short of somewhat of a frenzy.  More clients means much more exposure to high priority problems in eCommerce and retail, which is our food for innovation.

The latest addition to our portfolio of Big Data Commerce solutions is a cloud based BIG DATA PLATFORM. It is a truly exciting vision that has been cast into concept and product: Make the capturing, storing and exploiting of all of your company's transactional data in a fast, reliable and agile data platform simple, efficient and low cost. Combine this with smart applications that exploit this data in order to make smarter, faster operative decisions that address specific problems in the company.

Couponing, inventory optimization, pricing, store assortment optimization and personalization of online and offline customer communication are all examples of what can be accomplished with such as system in an efficient and low cost manner. Customer satisfaction, rapid ROI and extreme profitability are the core of what makes us so excited.  Enough said, we chose to use this announcement to try our luck on our very first.... INFOGRAPHIC.

Do you share the excitement of this vision? Like or hate our infographic? Please get in touch or post in the comments.

Categories: Tags: bigdata cloud computing inventory optimization pricing

# ROI = Return On Inventory?

Published on by Joannes Vermorel.

ROI stands of course for return on investment. However, the idea that every euro or dollar ‘invested’ in inventory brings a certain return in terms of profits is a powerful thought. When looking at inventory from this angle, two questions arise:

• Are profits earned by a product versus the capital invested in stocking it (i.e. ROI) similar over the product portfolio?
• If the ROIs of the various products are heterogeneous – how can the overal ROI delivered by the inventory be maximized?

As you will have guessed, the answer to the first question is a clear ‘NO’. The return generated by a product for the euros invested in stocking it differs vastly from product to product. Two aspects have a major impact.

First, the gross margin of a product directly affects the ROI. This is obvious, and product managers naturally try to build a portfolio with high margins in mind. However, many other considerations come into play such as the coverage of the product portfolio for example. Adding more products to the portfolio is often also a strategy for growth.

The second aspect is more subtle, but equally important: The amount of stock that is required to provide a desired availability (i.e. service level) varies significantly among products. The main driver here is the volatility of demand - the higher the uncertainty of the future demand, the higher the inventory level required to assure a given service level.

As an example, lets imagine two products with the same gross margin that sell the same number of units during the year, thus generating the same amount of annual gross profit. However, product A sells the same amount each week with a high certainty, while product B is much more erratic with no sales for weeks and large orders in others. To achieve the same availability or service level for both products, the safety stock for product B will be a lot higher than for product A, where very little uncertainty and therefore safety stock requirement is given. As a result, product B requires much more units in stock than product A to achieve the same annual profit. The ROI for product A is therefore significantly higher.

Businesses continually work on maximizing their return on capital employed (ROCE). Inventory is a significant part of the capital invested by most retailers, and therefore an important opportunity for optimization. The good news is that the potential for optimizing the ROI of your inventory by taking advantage of the heterogeneous ROIs across your catalog is large.

The latter is done by finding the optimal service level at which an SKU produces the best ROI within the constraint of a set inventory budget. As a result availability and revenue can be increased for a given inventory budget, or cash can be released from inventory while maintain the overall availability.

The analysis behind this optimization however is not trivial given the non-linear correlation between service level and inventory levels. Additionally, a set of ‘strategic’ constraints such availability goals for certain products need to be taken into account.

This is a challenge which we plan to take on in the near future, the goal being a fully automated ROI optimization over the product portfolio for a given working capital sum. As an output, the system will determine service levels per SKU that give the highest availability (and therefore revenues) for a chosen inventory budget.

Excessive inventory reduces the return on invested capital, but too little inventory diminishes profits as well. The optimal inventory level is therefore a trade-off between stock-out cost and inventory cost, and falling too far on either side of the optimum will negatively impact your business. Our plans for service level optimization will leverage our forecasting technology. Stay tuned.