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In-report contextual feedback - Salescast

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In-report contextual feedback

Salescast offers in-report contextual feedback about the number being gathered in the Excel spreadsheet. Those comments are intended to help you getting insights in the seemingly odds numbers are likely to appear once in a while in your reports.

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In-report contextual feedback
      Stock cover at 10,000 only reflects a zero forecasted demand.
      Horizon is shorter than lead-time.
      Extremely high service level.
      Extremely low service level.
      Extremely high order value in history.
      Extremely high stock on hand value.
      Large identical orders. Look like bulk purchase.
      Forecast horizon longer than history.
      Lead time looks short compared to aggregation period.
      Intermittent demand. Switch to quantiles.
      Flat forecasts are just fine.
      Strict seasonal forecasts are just fine.
      Spike observed among orders.
      No stock. No stock availability negatively impacts sales.
      Short history. Low accuracy forecasts expected.
      Very low accuracy anticipated for classic forecast.
      History is not twice longer than forecast horizon.
      Short time-series should have tags.
      Lead time is expressed in days.
      More than 8 tags.
      Do you have lot multiplier?
      Stock on hand available, but no stock on order
      Classic forecasts are going far in the future
      Lead times should be measured
      Switch to quantiles in case of bulk orders
      Zeroes found in classic forecasts
      Zeroes found in classic forecasts
      With lead time and service levels you could get reorder points
      High service level, reorder point can be very high
      Lead time shorter than period
      Flat last value forecast
      Quantile crossing
      Stock cover over 1 year
      Stock cover longer than horizon
      Stock cover cannot be computed when lead demand equals zero
      No sales over a year, dead inventory?


Stock cover at 10,000 only reflects a zero forecasted demand.

to be completed

Horizon is shorter than lead-time.

to be completed

Extremely high service level.

to be completed

Extremely low service level.

to be completed

Extremely high order value in history.

to be completed

Extremely high stock on hand value.

to be completed

Large identical orders. Look like bulk purchase.

to be completed

Forecast horizon longer than history.

to be completed

Lead time looks short compared to aggregation period.

to be completed

Intermittent demand. Switch to quantiles.

to be completed

Flat forecasts are just fine.

to be completed

Strict seasonal forecasts are just fine.

to be completed

Spike observed among orders.

to be completed

No stock. No stock availability negatively impacts sales.

to be completed

Short history. Low accuracy forecasts expected.

to be completed

Very low accuracy anticipated for classic forecast.

to be completed

History is not twice longer than forecast horizon.

to be completed

Short time-series should have tags.

to be completed

Lead time is expressed in days.

to be completed

More than 8 tags.

to be completed

Do you have lot multiplier?

to be completed

Stock on hand available, but no stock on order

to be completed

Classic forecasts are going far in the future

to be completed

Lead times should be measured

to be completed

Switch to quantiles in case of bulk orders

to be completed

Zeroes found in classic forecasts

to be completed

Zeroes found in classic forecasts

to be completed

With lead time and service levels you could get reorder points

to be completed

High service level, reorder point can be very high

to be completed

Lead time shorter than period

to be completed

Flat last value forecast

to be completed

Quantile crossing

to be completed

Stock cover over 1 year

to be completed

Stock cover longer than horizon

to be completed

Stock cover cannot be computed when lead demand equals zero

to be completed

No sales over a year, dead inventory?

to be completed

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What people say

Classical solutions require too much manpower and don't scale correctly over hundreds of thousands of products. Lokad and Windows Azure were exactly the solution my business needed. Pierre-Noël Luiggi, CEO of Oscaro
The Lokad forecasting solution allows us to precisely forecast our sales and to optimize our inventory accordingly. The result is there: we are maintaining a 99% customer satisfaction level and deliver food that is often fresher than what can be found at local pet stores. Anthony Holloway, CEO at k9cuisine
Lokad improved the accuracy of our planning process significantly. The immediate impact was a stock reduction of almost 1 million € at a monthly cost of 150€. It was almost frightening to see our inventory levels getting so low! But what impressed me most is the ease of implementation and use. The integration was painless, and now it takes only a the click of a button and within 10 minutes I receive my forecast. The time saving for me is significant. Thomas Brémont, Head of Supply Chain Bizline

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