There are tons of inventory apps out there that look good and yet fail to deliver any added-value compared to a nicely organized Excel sheet. If your app does not deliver an inventory performance that would be impossible to achieve through Excel, you should really question why you need such an app in the first place.
Joannes Vermorel, Founder of Lokad
3 Sprouts designs unique and modern products for babies. Since 2007 toys and nursery products can be found online or at retailers across the world.
We needed a solution at 3 Sprouts that would help us manage our inventory as we grew rapidly. Lokad helped us forecast inventory for all markets and created a system that generates purchase orders for our various warehouses. Lokad took a daunting process and made it simple and more accurate.
Banu Khurana, Co-Owner
Stock levels are a balance: too little and your customers are not properly served, and too much and your carrying costs skyrocket. As soon as an inventory management software is in place, there is an opportunity to start optimizing the stock levels. Lokad delivers this through a predictive optimization, which means lower stock levels, better service, less dead inventory, and increased productivity. This is particularly critical in smaller companies that cannot afford a large clerical staff to deal with mundane operations such as replenishments. In particular, in young, dynamic companies, time liberated from tedious tasks tend to fuel an even faster growth.
Usually, we start by addressing the problem of “when and how much to purchase”, by generating a daily report containing the suggested quantities to reorder. This exercise may entail many subtleties, such as ordering schedules, supplier MOQs, price breaks, inconsistent lead times or multi-sourcing. Lokad accommodates all these constraints and more.
However, depending on your needs, Lokad also addresses a variety of inventory-related problems such as:
Lokad delivers a “software + service” combo. When you subscribe to our managed services, a Supply Chain Scientist is assigned to your account. They take care of turning your historical data into actionable figures such as suggested reorder quantities. Under the hood, this expert leverages our webapp and Envision, a domain-specific programming language dedicated to predictive supply chain optimization and makes sure you get the most out of Lokad’s technology, without you having to turn into an AI/IT expert.
You remain in the driver's seat but have someone who translates your business expertise into code. Critical knowledge is automatically turned into automatic decisions delivered by a system that can be further refined by multiple contributors over time.
Forecasts looking only at a single averaged future work poorly for SMBs. Inventory costs are concentrated on extreme situations: stock-outs happen when the demand is severely under-estimated, and conversely dead inventory happens when the demand is severely over-estimated. In between, inventory rotates gently.
Yet, the vast majority of the software products on the market completely miss the point, opting for classic time-series forecasts, which are unfortunately not suitable to cope with the problems faced by SMBs.
Lokad features a probabilistic forecasting technology: we assign a probability to every single possible future. We forecast not only demand, but also lead times and returns whenever it’s relevant. More generally sources of uncertainty need to be forecast.
Probabilistic forecasting vastly outperforms outdated approaches like classic safety stock calculations, oversimplify reality and work poorly when demand is either intermittent or erratic, which is often the case for SMBs. Assigning a probability to each possible future - i.e. quantifying the harm that extreme scenarios could bring -, is the first step towards performing a predictive inventory optimization.
The second step consists of looking at all possible options, for example, every single quantity to be reordered - unit by unit. We do not think of replenishment through a min/max policy per SKU. Instead, we look at all SKUs, that is, we look for the one single extra unit of stock that will bring the most return on investment for the company while accounting for operational constraints as MOQs and batch sizes.
Which brings us to the final step: the economic prioritization. The opportunity to buy every single extra unit of stock should be assessed in dollars or euros of profits and losses. We refer to these factors as the economic drivers: gross margin, carrying costs, stock-out penalties, etc. The final result of the optimization is reorder quantities that are completely aligned with the uncertain futures and the strategy of your company.
For replenishments, the Lokad webapp delivers a tabular report that gives you exactly the quantities that you need reorder today, plus, the KPIs, in dollars or euros, that explain why those quantities.
This report can be accessed through a web dashboard, downloaded as an Excel spreadsheet, or even scheduled for automatic import into your ERP.
Lokad delivers numbers that do not need any further post-processing, and no further manual tweaks of any kind. Achieving this feat is a twofold challenge; it requires:
Indeed, many classic planning solutions are the opposite of lean: historical data needs manual “cleaning”, forecasting models need manual “tuning”, ordered quantities needs manual “tweaking”, etc. All those operations treat your staff like consumable resources.
Lokad delivers the opposite: efforts are invested and capitalized into bespoke numerical recipes, that are just right for your company.
| Classic solution | Issues with the classic solution | Solution adopted by Lokad |
|---|---|---|
| Classic forecasts (i.e. daily, weekly, monthly averages) | Doesn’t work for erratic or intermittent demand | Probabilistic forecasts that embrace uncertainty |
| Forecasting model tuning and forecasts editing | Very time consuming, payback is very poor | Self-calibration of machine learning models |
| ABC analysis | Crude categorization of SKUs, tons of edge cases | Embraces the full complexity of every single SKU |
| Min/Max inventory method | Ongoing generation of dead inventory | Prioritizes every single extra unit of stock against its ROI |
| Safety stocks | Unsafe approach that does not ensure fill rates | Robust optimizations reflecting direct stock-out penalties |
| Configurable lead times | Doesn’t work on varying lead times, and time-consuming | Learns and forecasts lead times with probabilistic forecasts |
| Configurable stock covers (i.e. days of stock) | Alternative unsafe approach that does not ensure fill rates | Economically optimize stock covers per SKU |
| MOQs, price breaks, multi-sourcing | Not supported, time consuming manual overrides | Native support through bespoke logic |
| Configurable per-item prices | Demand is assumed to be independent from price | Demand forecasts that leverage product prices |
