New Salescast pricing: Express (free) and Enterprise

Published on by Joannes Vermorel.

Following the release of our major upgrade of Salescast last week, we are introducing a new pricing plan.

FREE Express edition for inventory optimization

Under this new pricing, Salescast is completely free to use for fully self serving customers. The Express edition comes with three main limitations:

  • Only the Top 10,000 items (top sellers) get included in your Excel report.
  • Native database export is not supported.
  • No support from the Lokad team.

Beside those, the full power of our inventory optimization is made available the in Express plan. All plans benefit from the same analytic technology.

The Enterprise plan removes the limitation on the number of items, enables the database export and gives access to our support teams. It cost a montly subscription fee plus consumption. Finally, the partner plan is for customers that desire an even closer interaction and cooperation. 

Pricing 2007: 15 cents x number of forecasts2/3 

The historic pricing formula 15 cents x number of forecasts2/3 was introduced back in 2007, when it triggered a humorous post from a software blogger that was amused by the apparent complexity of our pricing. Yes, it was funny, but we stuck with the approach for a number of reasons: 

  • Transparency: It has been our conviction from the start that our prospects should know much we are charging for, and that transparency it is good business practice. 
  • Adequacy: It is important to 'track' the value that is created by our technology  (e.g. in reduced inventory, increased availability and saved time) and to use it to approximate their ability and willingness to pay. The number of forecasts as a function of products and refreshs have proven to be reasonably good proxys ranging from our smallest customers (<$50/month), to our biggest customers (> $10 000/month) in retail and Ecommerce. 
  • Simplicity: Well, the formula is not that simple. Yet, by providing a calculator on our website, we reduced the complexity to a minimum and customer feedback has been mostly positive.

However, whoever thought that this formula was as "complicated" it could become,  did underestimate us. With the introduction of quantile forecasting this spring, we outdid ourselves. Check out the blog post of Lokad's founder on 'bizarre pricing - does it matter?'

So why are we changing the pricing schema today?

Pricing 2012: Pricing according to customer's needs

As mentioned above, our past pricing tracked reasonably well the operative value we created for the client, but it was 'blind' to an important customer need: Support!

Salescast has been built from scratch with a self-serving customer in mind. Through the intermediate schema, a sound documentation in video and text and automated troubleshooting during integration, it is fast and simple for an IT savvy prospect to integrate with Salescast. We usually help iron out remaining technical issues, and the technical integration is hardly an obstacle. 

However, our clients vastly differ in their need for support when interpreting, using and generally understanding the optimization metrics we produce. Inventory optimization is a complex topic, and it is only natural that many of our clients are raising a lot of smart questions triggered by their interest to get the most of our forecasts.

In addition to publishing an extensive documentation (FAQs), we decided to invest in more support capacity and provide a plan that gives access to these teams for a monthly subscription fee.

In return, we decided to remove all cost for clients (below a certain size) that serve themselves autonomously, thus tracking more closely their need for support. This also draws a clear line between no support (free Express customer) and support (paying Enterprise customer). 

The 'Formula' is not dead!

In the Enterprise plan, customers are charged a monthly subscription of 500€ - or an equivalent amount in other currencies - plus the actual consumption per month. The consumption cost is calculated, you probably guessed it, according to our proven formulas.

We hope that with our new fremium pricing we are keeping the virtues of a transparent, 'adequate' and simple pricing while tracking more closely out client's needs. In terms of simplicity we most likely have most room for improvement. We promise to keep it in mind for our next pricing plan. 

Categories: subscriptions Tags: pricing subscription No Comments

The all new Salescast Forecasting & Inventory Optimizer

Published on by Joannes Vermorel.

salescast-logo.png

I was recently asked when the development of our software would be finished. While we generally like to finish tasks and move on to new challenges, this notion does not really apply to smart analytics software. Inventory optimization being one of our core areas of activity, we are continuously improving performance, extending functionality, and refining user experience in close cooperation with our clients. 

Over the last 12 months, under the lead of Rinat, our software development team has taken the existing version of Salescast completely apart and started to re-design and re-build the program from the ground up. We managed to never loose faith that they would be able to put it all back together, and we are excited that the new Salescast is going into production today!

Salescast project view.JPG

Salescast solution view: Manage and see settings and reports of an individual solution. 

Extending the vision from demand forecasting to inventory optimization

While we considered demand forecasting to be the major complexity when planning inventory, we have come to realize that this is only part of the challenge. A demand forecast is a rather theoretical value. Turning this data into an operative inventory policy is a big part of the challenge. The planner's decisions boil down to two 'simple' questions: when to order and how much to order.

The new release consolidates changes we have introduced in the past months. Effectively Salescast has evolved into an integrated inventory optimizer: 

  • Reorder points: The on-hand inventory level that should trigger an order (when to order).
  • Order quantity and lot multipliers: The optimal quantity that should be ordered (how much to order).  
  • Quantile forecasts: Our break-through forecasting technology that provides a major increase in planning accuracy and solving the issue of planning slow movers.  

Support of Big Data accounts

A completely new architecture has been implemented in order to increase the speed of data import and consolidation, which is now up to 300 times faster! Scalability of data import has never been an issue for our eCommerce clients, however for large retail networks this brings a major speedup gain.

Proactive, automated troubleshooting during integration

When integrating for the first time with Salescast, much more detailed error messages and feedback are now delivered, thus making it much easier to quickly iterate and iron out the integration. We have been keen on this feature, which will make the integration process again more efficient, and remove a potential headache for our client's DB administrators as well as our support team.

New features

No update without new features, of course. A few are still ‘hidden’ and will be released in the future. However one we can mention is a full and detailed history of any single project run (i.e. planning or forecasting). This makes it easier to fine-tune the forecast settings.

Also, we have now a public tiny REST API for Salescast. Through this API, it becomes possible to programmatically trigger a project run. This is handy to achieve complete automation of the inventory optimization process.

UI re-design

Alxander, our UI expert, has worked hard on improving the user interface again, after he released a major upgrade almost to the day one year ago. We will refrain from suing Twitter and Co. for stealing our designs even before we have created them.

Salescast account view.JPG

Salescast account view: See all activity and all your projects (i.e. forecasts that are set up)

New clients will start working with the new version as of today, our existing clients will be switched over successively in the next few months. We want to thank the whole team for the relentless work and great outcome!

Suggestions, feedback or rants concerning our new baby? We would love to hear from you directly or leave your comments below.

Categories: release, supply chain Tags: release salescast No Comments

ECR Tag 2012: "From data grave to goldmine - Big Data intelligence at the point of sale"

Published on by Joannes Vermorel.

Germany's largest conference concerning Efficient Consumer Response (ECR) gathers once a year top experts and decision makers from trade, suppliers and technology to discuss processes, solutions and applications for an efficient value chain with a particular focus on the consumer. 

Over 100 speakers are invited to present and discuss best practices and innovation and address top industry concerns. Among the speakers this year will be Alain Caparros, CEO of the REWE Group and Dr. Reinhard Schütte, member of the management board of EDEKA AG

Lokad is exhibiting at the ECR Village and CEO Matthias Steinberg will be presenting at the Business Intelligence and POS data mining forum: "From data grave to goldmine: Big Data intelligence at the point of sale". We are looking forward to interesting meetings and discussions. 

The ECRTAG 2012 is organized by GS1 Germany and will take place at the Rhein-Main-Hallen Wiesbaden September the 5th/6th. Please see the program for further details. 

Categories: Tags: press No Comments

Wired UK's 100 hottest startups in Europe

Published on by Joannes Vermorel.

Wired cover, September 2012A few days ago, an unnamed team-member entertained herself late at night by ego-Googling the term ‘Lokad’ when she made an unexpected discovery: According to Turkish tech blog webrazzi, Lokad was to be featured as one of Europe’s 100 hottest startups in the upcoming edition of Wired Magazine UK.

This was rather unexpected, and our Turkish is a bit rusty lately, so we kept our feet still. However, today we have a freshly minted Wired Magazine in our hands (or rather iPad) and must say we are thrilled to find the Turkish prediction come true.

“Lokad blends the cloud with algorithms to spot patterns in business data; send off a mess of historical stats and receive back a neat set of forecasts about demand, sales or workload. And its pay-as-you-go – cheaper than performing analysis in-house.”

We say thank you to the Wired team for their trust and the Lokad team for their great work. Oh, let’s not forget to mention that we feel in great company, with the likes of Criteo, Sensee and Vente Privee being featured as well. But now enough of the self-congratulations and back to work…

Categories: community Tags: startups 2 Comments

Spare Parts Inventory Management with Quantile Technology

Published on by Joannes Vermorel.

The management of spare and service parts is as strategically important as it is difficult. In a world where most equipment manufacturers and retailers are operating in fiercely competitive markets, a high service level to the existing customer base is a strategic priority for many players.

Not only does a high spare part availability help build a loyal base of customers, product/equipment companies have also discovered services as an often very profitable and recurring revenue stream that is typically more resilient to economic cycles than equipment sales.

However, managing a spare parts inventory efficiently still poses a huge challenge. Despite a forecasting and inventory planning technology industry that is several decades old, spare parts management has remained a difficult for a number of reasons:

  • Large number of parts: Even smaller equipment manufacturers can easily be confronted with managing more than a hundred thousand spare parts.
  • High service level requirement: Stock outs are often very costly, high to very high service levels are therefore paramount in many industries.
  • Infrequent demand: The demand for spare parts is typically sparse and intermittent, meaning that only very low volumes are required occasionally.

Why standard forecasting technology performs poorly

Unfortunately, the combination of these factors makes standard inventory and forecasting technology ill-suited for spare parts planning. In classic forecasting and inventory planning theory, a forecast is produced by applying models such as moving average, linear regression and Holt Winters and a great deal of attention is given to the forecasting error, which is optimized by measuring MAPE or similar indicators. The transformation into a suggested stock level is done in a second step via classic safety stock analysis.

In the case of sparse time series (also called slow movers: low unit and infrequent sales), this methodology fails. The main issue with forecasting slow movers is that what we are essentially forecasting are zeros. This is intuitively obvious when looking at the demand history of a typical spare parts portfolio on a daily, weekly, or even monthly basis: By far the most frequent data point is zero, which can in some cases make up more than 50% of all recorded data points.

The challenge of forecasting slow movers: Good statistical performance and good inventory practice are not the same.

When applying classic forecasting theory to this type of data set, the best forecast for a slow moving product is by definition a zero. A 'good' forecast from a statistical point of view will return mostly zeros, which is optimal in terms of math, but not useful in terms of inventory optimization.

The classic method completely separates the forecast from replenishment. The problem is, the situation can hardly be improved with a “better” forecast. What actually matters in practice is the accuracy of the resulting inventory level (reorder point ), which is not measured nor optimized.

Changing the vision from Forecast Accuracy to Risk Management

When dealing with slow movers, we believe the right approach is not to approach the problem as a forecasting issue and to try to forecast demand (which is mostly zero). Much rather, the analysis should provide an answer to the question how much inventory is needed in order to insure the desired service level.The whole point of the analysis is not a more accurate demand forecasts, but a better risk analysis. We fundamentally change the vision here.

Determining and optimizing directly the Reorder Point

Quantile forecasts allow the forecasting of the optimal inventory that provides the desired inventory level directly: A bias is introduced on purpose from the start in order to alter the odds of over and under forecasting.

Benchmarks against classic forecasting technology in food, non-food, hardware, luxury and spare parts consistently show that quantile forecasts bring a performance improvement of over 25%, that is either more than 25% less inventory or 25% less stock outs.

In our opinion, by solving the problem of forecasting intermittent and sparse demand in spare parts management, quantile technology not only provides a strong performance increase, but also makes classic forecasts plain obsolete.

Whitepaper spare parts management available for download

Download the whitepaper Spare Parts Inventory Management with Quantile Technology for an in-depth discussion of the topic. Further whitepapers and resources on quantile forecasting and inventory management are available on our resources page

Do you have comments, questions or experiences regarding spare parts management to share? Please participate in the comments below, your contribution is highly valuable to our team.

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Categories: Tags: bigdata forecasting quantiles slow movers spare parts whitepaper 1 Comment