Lokad news - Integrated business forecasting

We are proud to announce that Lokad is now officially listed by Microsoft (in French) as part of their IDEES program. The IDEES program is rewarding the 25 (most) innovative French startups every year. You can also visit the US edition of the Microsoft Startup Zone.

Also, we are also proud to mention that Lokad has been selected by ScrewTurn - the open source ASP.NET wiki used for www.lokad.com - as the first reference for their category Who is using ScrewTurn? Granted, the Lokad technology has little to do with this second selection. :-)

In a previous post, I have been discussing the various worlds of forecasting software, outlining 3 main categories

• Deterministic simulation software
• Expert insights aggregation software
• Statistical forecasting software

Lokad is clearly a member of the third category. Although, those three categories are not really competing with each other since they are usually not suited for the same type of situations.

In the second category, insight aggregation software, prediction markets software seems to attract more and more interest. Jed Christiansen has a very interesting review of prediction markets software

The overview page of Inlink provides an insightful summary about prediction markets

Prediction markets enable a diverse group of people to predict the answer to a question by buying and selling shares in stocks representing the possible answers. Using a stock market-like mechanism allows people to express their opinion as a "weighted vote" over time in response to new information or a change of opinion. And unlike a poll, a prediction market is asking "what will happen?" vs. "what do you want to happen?"

For example, if we ask the question: "Who will win the singing contest?" The four contestants would be represented as stocks that people buy shares in. If "Contestant A" has a stock price of $56, that means "the crowd" thinks there is a 56% chance that contestant will win. When people buy shares in that contestant, the price goes up. When they sell shares in that contestant, the price goes down. The stock price of an answer represents the probability of that answer being correct, priced stock after a period of time is considered the groups answer to the question posed.

The main difference with classical insight aggregation software is that the participants are financially involved in getting the right forecast.

Compared to Lokad (or to any statistical forecasting software), the main benefits of markets prediction is the ability to rationally tackle a forecast that depends on (potentially) irrational customer desires even when no relevant data is available. The crowd is bringing a solution to the small group of experts bias that usually plagues classical prospective methods such as the Delphi method.

Yet, like any insight aggregation method, market predictions involve quite an expensive forecasting process to get a single question answered. For example, it would not seem a very practical approach for call centers that requires 96 quarter-hour forecasts on a daily basis to predict inbound call volumes. If meaningful historical data is available, then statistical forecasts should be as accurate (if not more) and way much cheaper.

In its own statistical ways, Lokad is also (somehow) using the wisdom of the crowd, except that instead of considering a panel of people, we are considering a panel of business time-series that we exploit to improve the overall forecasting accuracy. In both cases, leveraging larger input datasets to improve forecasting accuracy is a key idea.

Lokad has been designed to be integrated into 3rd party applications. Our ambition is not to become the next killer inventory or staff management application, but to stick to what we do best: statistical forecasting.

Thus, I have just published a case study Forecasting for scheduling software that involves a fictitious company named Scheduloo delivering a staff scheduling software in ASP.

Disclaimer: All characters depicted herein are fictional characters, and any similarities to actual persons and/or companies are purely coincidental.

In summary, the case study details how (and why) Lokad could be integrated as a forecasting component in a staff scheduling application in order to provide a staff optimization feature.

The case study has been kept simple and tractable, A priori, it should not contain any rocket science ideas. Yet, more ambitious approaches could also be considered.

For example, following the idea of the evidence-based scheduling that ships with Fogbuzz, the mapping sales2staff (i.e. converting turnover into staff level) could be inferred from the historical data as opposed to be made explicit by the staff manager.

Then, many businesses (such as retail) are very dependent from the weather. Lokad, although being accurate for business forecasts, will only perform poorly on weather forecasts. Thus, there is a nice mash-up to be made that would combine the Lokad technology with classical weather forecasts as provided by the National Weather Service for example.

Your business needs a Plan – no matter how small

Any company or business, no matter how small, needs to look towards its future, ie it needs to ‘forecast’ its future – it needs a strategic plan.

Lack of planning often leads to failure. That's why strategic planning is a key success tool for growing your business. While a business plan focuses on startup nuts and bolts, a strategic plan plots your company's success in small, logical, ongoing steps.

The strategic planning process can:

• Establish realistic growth goals and objectives
• Focus resources on key priorities
• Increase staff productivity
• Build strong teams
• Measure progress

I’m going to take the normal process to pieces and give you some Action Steps.

Importantly, first of all – Understand what goes into a strategic plan

Basics that should be included in a strategic plan include an executive summary, organizational description, mission and vision statements, goals and strategies. Additional sections may include action plans (specific responsibilities for meeting goals), a budget for reaching goals and instructions for reviewing and communicating the plan.

NB: Before you develop your strategic plan make sure you have a look at a few sample plans, so you have some idea of how its done. Preferably, you seek professional help with the process. Its just far too important an issue to ‘scrimp’ on.

Think carefully first, then spell out your purpose in the strategic plan

One crucial part of the strategic planning process is a grid that asks two questions: Do we have it? And Do we really want it? Or, you can use another tool called SWOT, which stands for strengths, weaknesses, opportunities and threats. This has been around for years yes, but the important thing is that it works.

No business is either too small, nor too big, not to take time out now and then to either go through this process for the first time, or at least every two years, revisit the process.

Create an action plan

Detail the actions to be taken in order to gain your business goals. Always, always make sure you specify who is responsible for doing what, and when they are to have it done by. Crucial – during the first 90 days, continually conduct reviews – so that members of your team know exactly what is expected of them.

You, and they, should always be prepared though to allow for a little shifting of the goal posts. Also designate someone who is responsible for measuring the plan at least every 3 months, to check its success.

Carry out your strategic plan

Its amazing how many people go to the trouble and/or cost of doing a strategic plan and/or forecast – and then proceed to ignore the process!

Break down the plan into small pieces. You will usually find that you will need to use a mixture of strategies and tools to reach your goals – such as various types of marketing, forecasting, accounting etc. These will certainly change as you go along, but should always be expressed in quantifiable targets.

Practical Business Forecasting is a relatively recent development.

Businesses must consider impartially how they, in their different businesses, can benefit from a systematic forecast of business conditions. Many may be somewhat skeptical (which in some ways of itself is a healthy attitude of mind), but any who are need to remember how skeptical they were of things such as costing, planning, routing, functional organization and other things which are now very much accepted as essential to the efficient conduct of industry.

The attitude of many towards business forecasting still may be, unfortunately, “You might as well try to forecast the weather!” Wouldn’t the practical and sensible response to this be that we do try to forecast the weather? Realistically, we know that, in most climates, the weather can never really be forecasted for any substantial period of time with any certainty. Yet, surely, a scientific forecast with a certain margin of error is better than no forecast at all?

Don’t’ most of us act on this belief by listening in to the forecasts? Farmers and other agricultural businesses have to rely on forecasts to harvest their crops. As a matter of fact many have mentioned that they are very agreeably surprised at the degree of accuracy of weather forecasting as a whole.

It is not my intention to suggest that at the present time the vast complex of business can be forecasted for very far in advance, nor either with complete accuracy. YET, every one in business must continually be forecasting, in a careful and systematic way — based on certain dependable principles. Forecasting is likely to be a much more certain guide than any feelings of the moment we may have.

The point is we must forecast. We cannot take any important action in our business lives without forecasting!

As a matter of fact I would go so far as to agree with the viewpoint that a man who does not forecast may be defined as a savage! He has very little sense of time. The past and the future invariably have very little meaning for him.

For anyone to ever say that because the future is difficult to foresee we should give up all and any attempts to foresee it seems absolutely ridiculous. To be sure any business man holding a view similar to this should retire immediately, while he may still have some money on which to do so.

FORECAST OR FAIL! Every man or woman in business must forecast!

We are proud to announce that Paypal has listed Lokad in their solution directory. Indeed, Lokad Desktop Sales Forecasting (and the setup instructions that are specific to Paypal) has been supporting Paypal since the version 1.1.3.

Because no matter how many Web 2.0 widgets you have in your eCommerce front-end, too much inventory is wasted money.

The choice of a forecasting technique is significantly influenced by the stage of the product life cycle and sometimes by the type of firm or the industry for which a decision is being made.

In the beginning of a product life cycle, relatively small expenditures are made for research and market investigation. During the product introduction phase, these expenditures start to increase. Whereas, In the rapid growth stage, because decisions involve considerable amounts of money, a high level of accuracy is desirable. And after the product has entered the maturity stage, decisions are much more routine, involving marketing and manufacturing. These are important considerations in choosing a sales forecast technique.

After evaluating the particular stages of the product along with firm and industry life cycles, a further probe is necessary. Instead of selecting a forecasting technique by using whatever seems applicable, decision-makers should determine what is most appropriate. Some techniques are quite simple and inexpensive; others are extremely complex, require significant amounts of time to develop, and may be quite expensive. Some are best suited for short-term projections; others are better prepared for inter-mediate or long-term forecasts.

The choice of technique or techniques depends on the following criteria:]

1. How much will it cost to develop the forecasting mode compared with the potential gains resulting from its use? The choice is one of benefit-cost trade-off.
2. How complicated are the relationships that must be forecast?
3. Is the forecast for short-run or long-run purposes?
4. How much accuracy is desired?
5. Is there a minimum tolerance level of error?
6. What data are available? Techniques vary in the amount of data they require.

Now, a few comments about one of the methods used:

The Qualitative Approach – the Qualitative or judgmental approach can be useful in formulating short-term forecasts and can also supplement projections based on the use of any of the qualitative methods.

Four of the better know qualitative forecasting methods are Executive Opinions, the Delphi Method, Sales Force Polling and Consumer Surveys.

Beginning with Executive Opinions – with this approach the subjective estimates of executives or experts from sales, production, finance, purchasing and administration are averaged to generate a forecast about future sales.

The Delphi Method – this method is a group technique in which a panel of experts is individually questioned about their perceptions of future events.

Sales Force Polling – some companies use as a forecast source sales people who have continual contact with customers. It is believed that sales people, who are closest to the customers, have significant insights into the future market.

Consumer Surveys – and yet again some companies conduct their own market surveys of consumer purchasing plans. These vary from telephone contact, personal interviews or maybe even questionnaires.

For more detailed information, see also Strategic Business Forecasting, by Jae K. Shim.

Business forecasting, of course, comes in all shapes and sizes.

What I want to talk about for the next few minutes is forecasting that contains a technological element. In a case like this, a forecast deals with the characteristics of the technology itself—such things as levels of technical performance, like the speed of a military aircraft, the power of a future possible engine, the accuracy or precision of a measuring instrument for example.

The forecast does not have to actually state how these characteristics are to be achieved.

Importantly of course, technological forecasting usually deals only with useful machines, procedures or techniques—excluding from this definition those commodities, services or techniques intended only (or mainly) for luxury or amusement purposes.

Recitation of Alternatives

There really is no alternative to forecasting, and that is the main purpose of the presentation of alternatives—to show just that.

The point is that if a decision maker has several choices open to him, he will choose among them on the basis of which provides him with the most desirable outcome—thus his decision is based on a forecast.

The only alternative open to him is whether the forecast is obtained by rational and unambiguous methods or by intuitive means.

The virtues of using rational methods of forecasting are as follows:

1. They can be taught and learned
2. They can be described and explained
3. They provide a procedure able to be followed by anyone who has absorbed the necessary training, and in some cases,
4. These methods are even guaranteed to produce the same forecast regardless of who uses them!

The main benefit of using a rational method to forecast is that those forecasts can always be reviewed by others (including at any subsequent time in the future), and they can be checked for consistency.

Commonly adopted, and accepted, methods of technology forecasting include the Delphi method, forecast by analogy, growth curves and extrapolation.

Normative methods, such as relevance trees, morphological modes and mission flow diagrams are also used.

Studies of past forecasts have shown, not surprisingly, that that the most frequent reason why a forecast goes wrong is that the forecaster has ignored related fields.

Hence the value and wisdom of combining forecasts. The point is that a given technical approach may fail to accurately forecast because it has been superseded by another technical approach—which the forecaster ignored.

Yet another problem that often occurs is that of inconsistency between forecasts. Therefore it makes sense to combine the forecasts of different technologies rather than trying to select and utilize just one. What happens then is that the strengths of one method may balance out, and compensate, for the weaknesses of the other.

In same way that μISV (micro independent software editor), the eCommerce has its own wave of μCommerce (pronounced micro-commerce) where a single person without any funding starting selling goods online through his/her own webshop.

I have recently met the people from 42stores, a very interesting hosted solution for μCommerce. Basically, 42stores is a blog platform that has been extended with shopping cart features. The idea is pretty nice because the biggest barrier to enter the eCommerce market is not the amount of features in your shopping cart, but getting the community interest that you desperately need. For μCommerce, blogging looks a really cheap yet efficient way to generate sales through highly relevant traffic.

I have been asked whether there was anything to do with forecasting for a μCommerce like 42stores? Obviously, the average per-product sales are completely negligible (a couple of sales at most); thus forecasting won't be very accurate with such limited history. Yet, if the sales get aggregated on a webshop basis (summing up all product sales), then even a couple sales a month become sufficient to actually get some accurate forecasts after 3 or 6 months of activity. Such aggregated sales forecasts would not provide much help for inventory optimization; but it might give some business visibility to the retailer (how much revenue should I expect in 3 months?)

Then, web traffic itself could be subject to forecasting. I suspect that for many μCommerce, the volume of sales is directly proportional to its "organic" traffic (i.e. everything but paid traffic). Since traffic has an important impact on the value of the μCommerce, forecasting the traffic would help forecasting the future business value of the μCommerce.

In summary, for a μCommerce platform, I would suggest to restrict the forecasting operations to

• monthly forecast for the total number of orders.
• monthly forecast for the total value of orders.
• monthly forecast for the total number of web hits.

Through those forecasts, the retailer gets visibility on his/her emerging business.