Stock Demand Trends and Forecast v.16
The tool to calculate stock demand trends and make predictions for future demand statistically
If you knew the stock demand trends of your warehouses, you would have a clue to decrease keeping costs and have a flawless supply chain. Regretfully, you can't know the future. However, you can predict it with a certain reliability. This is an Odoo tool for that goal. The app lets you construct stock demand by periods and forecast scientifically further demand based on the Python library statsmodels.
The scientific approach to forecasting
The app assumes applying statistical methods to calculate historical trends and make a forecast. Experiment with statistical models and parameters to achieve the most reliable prediction. Leverage the proven statsmodels methods
Innovative data analysis interface
The tool introduces a special interface to instantly calculate series. Flexibly switch between different reports and update settings in a few clicks. Control data in pivot, graph, or chart views. Export series as an Excel tabled
Focused analysis
Shrink considered stock operations flexibly or analyze global company inventory demand. Group data into various series
Topical data
Apply time frames for historical data and work with intervals that are of interest to your industry. Work with past and future periods.
Flexible report configuration
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Introduce as many analytic reports as you need. Each report is based on Odoo's stock moves' data
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Keep reports to re-use them, instantly switch between reports, or experiment with real-time changes without saving those
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Show historical and forecast series as a table, line graph, or bar chart, or export data as an Excel table
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Apply as many and as different statistical models as you want to check. Each stats model will peer its own data series and prediction if the latter is possible. See also the section "Statistical models"
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Define the data periods under analysis by entering a start and end. Only inventory data within this interval will be included in the series
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Split data series into years, quarters, months, weeks, and days depending on seasons assumed in your industry or business area
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Select an unlimited number of periods to predict future periods or check hypotheses
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Grant the right to forecast tools for any WMS user (the group Inventory > Stock Demand Forecast)
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Filter stock data by product variants, product templates, product categories, units of measurement, source or destination locations, warehouses, operation/picking types, stock rules, and companies (for multi-company regimes). Introduce your own criteria based on Odoo's dynamic domain constructor, so shrink analysis by any stock move storable fields
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Introduce a few data series simultaneously with grouping by a product variant, a unit of measurement, a source or destination location, a warehouse, a stock move state, an operation type, a stock rule, or a company. Take into that if the report assumes products with different units of measurement, the grouping will be possible only by the units or by variants
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Hide/show general statistical measurement of series: count, mean, standard deviation, minimum, maximum, 25%, 50%, and 75% factors
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Optionally compare predicted values to real historical data if any. See also the section "Hypothesis Testing"
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Make figures human readable (e.g. 192,217 as 200k) or show them precisely.
Statistical models
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The tool allows calculating inventory demand trends based on predefined statistical models introduced by the Python package statsmodels
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Methods have different complexity, and their own unique parameters, and might be suitable for a definite market or a specific business. The final choice of the model and related statistical parameters is only up to you. Nobody except you can better understand tendencies in your company inventory demand, and hence which methods and factors should be applied
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Available statistical models are Autoregression (AutoReg), Autoregressive Distributed Lag (ARDR), Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving-Average (SARIMAX), Holt Winter's Exponential Smoothing (HWES), Simple Exponential Smoothing (SES)
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For deep details about what each stats model means and which parameters might be applied, look through the tab "Help" on the stats model form
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Keep and use an unlimited number of stats models with various parameters. Check, compare, and update those flexibly through the forecast manager interface
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Often models with specific parameters will not result in any prediction or the prediction will be unrealistic. This is normal, only a deep understating of trends and much experimenting might give a viable forecast for your specific case.
Hypothesis Testing
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To get a proper prediction, it is usually needed to experiment a lot with statistical models and introduced parameters. To check the results, the app allows compare forecast data with real values
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To check a certain assumption with historical values, you may make an analysis and prediction for some past periods, and turn on the setting "Hypothesis testing"
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For example, you have stock moves from 2017 to 2022. Then, you can get report historical data for 2017-2021, and make the forecast for 12 months of 2022. In this way, you will get the prediction for the interval for which you already have actual values. By comparing forecast and actual values, you can decide whether a chosen model and factors are good enough
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The app allows checking the hypotheses for a few stats models simultaneously. In this way, it is possible to reveal what is actually the most appropriate, or whether experimenting should be continued.
Usage requirements
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To reveal trends, there should be enough historical stock moves data. It is senseless to make a forecast based on the last 5 days of operations since no trend cannot be retrieved. Take into account that data should be enough for the chosen filters and for each grouped series
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There should be not only enough stock moves but also there should be some actual trends. Chaotic data will not result in reliable predictions. To use the app, you should understand which assumptions (e.g. seasonal changes) you check
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To use the app, you either should have a clear understanding of how statistics works or a will to understand that. Regretfully, there is no way to make a "general" analysis, each specific case is unique and requires its own elaboration.
Konfigurations- og installationstips til Stock Demand Trends and Forecast Odoo v.16
Python dependencies
To guarantee the tool works correctly, a number of Python libraries are required: pandas, numpy, statsmodels, scipy, xlsxwriter. To install those packages execute the command:
pip3 install pandas numpy statsmodels scipy xlsxwriter
If you run Odoo on Python prior to or equal to version 3.7, please install the following versions of the packages pandas==1.3.5, statsmodels==0.13.1
Server memory limits
Running the statistical analysis and making the forecast is quite a resource-demanding operation. If users face the hanging interface, make sure that server memory limits and timeouts are enough. Otherwise, increase those settings.
Ofte stillede spørgsmål om Stock Demand Trends and Forecast Odoo v.16
Hvad er opdateringspolitikken for jeres vaerktojer?
Ifolge de gaeldende regler for Odoo Apps Store:
- Hvert modul kobt til version 12.0 eller tidligere giver adgang til alle versioner op til 12.0.
- Fra version 13.0 skal hver modulversion kobes separat.
- Uanset version giver kobet ret til alle opdateringer og fejlrettelser inden for en hovedversion.
faOtools-teamet styrer ikke disse regler. Ved sporgsmaal, kontakt Odoo Apps Store-representanterne direkte.
Hvordan installerer jeg jeres app pa Odoo.sh?
Den mest direkte tilgang er Odoo-butikkens indbyggede arbejdsgang:
1. Abn modulets side, og klik pa Deploy on odoo.sh
2. Du bliver omdirigeret til GitHub. Log ind, og klik pa Create a new repo, eller brug et eksisterende. Repot skal vaere privat. Apps under OPL-1 ma ikke offentliggores. Opret om nodvendigt et nyt repo til Odoo.sh-projektet
3. Ga derefter til odoo.sh, klik pa Deploy, bekraeft, og klik pa Continue. Installationen starter.
Disse trin installerer appen pa production-grenen. Til andre grene eller opdatering:
1. Download kildekoden fra Odoo-butikken
2. Commit modulet til GitHub-repot. Ingen app-mapper/filer ma sta i .gitignore. Repoer oprettet af odoo.sh kan ignorere vigtige stier (f.eks. /lib). Upload alle modulmapper og -filer
3. Deploy malgrenen i odoo.sh-projektet, eller vent pa det automatiske build, hvis indstillingerne angiver det.
Hvordan installerer jeg jeres app pa min egen server?
2. Genstart Odoo-serveren;
3. Sla udviklertilstand til (tekniske indstillinger);
4. Opdater applisten (Apps-menuen);
5. Find appen, og klik pa Activate/Install;
6. Folg retningslinjerne pa appsiden, hvis de findes.
Jeres app har ekstra tilfojelser. Kan jeg kobe dem senere?
Ja. Odoo laegger automatisk alle afhaengigheder i indkobskurven: udelad allerede kobte vaerktojer, sa du ikke betaler to gange.
Jeg deployede jeres app pa Odoo.sh og ser advarsel/fejl i testene
En rod/orange advarsel pavirker ikke appens funktioner. Nogle gange bestar vores moduler ikke de standard automatiske tests, fordi de forventer en adfaerd, der er i konflikt med appens mal. Vi aendrer f.eks. prisberegningen, mens Odoo-testene sammenligner slutprisen med standardalgoritmen.
Tjek forst funktionerne i den deployede database. Virker alt?
Hvis du stadig mener, at advarslen pavirker reelle funktioner, kontakt os, og send de fulde installationslogfiler og den fulde liste over deployede moduler (kerne og tredjepart).
Kan jeg kobe jeres app direkte hos jer?
Nej, vi distribuerer kun vaerktojerne via den officielle Odoo-appbutik.
Jeg vil gerne have rabat
Desvaerre har vi ikke tekniske midler til individuelle priser.
Kan jeg installere appen pa min Odoo Online (SaaS)-database?
Nej, tredjepartsapps kan ikke bruges pa Odoo Online. Odoo SaaS blokerer den mulighed.
Jeres vaerktoj afhaenger af andre apps. Skal jeg kobe dem?
Ja, vaerktojet kraver alle moduler markeret som afhaengigheder. Prisen pa appsiden inkluderer allerede disse afhaengigheder.
Hvad koster modulet i amerikanske dollar? Hvorfor aendrede den sig?
Prisen pa vores moduler er sat i euro. Odoo-butikken omregner til andre valutaer efter den interne valutakurs. Derfor kan prisen i amerikanske dollar aendre sig, nar kursen opdateres.
App- og webstedstekster på andre sprog end engelsk er AI-assisterede og afstemt med officielle Odoo-oversættelser. Hvis du opdager en fejl, send en sag om oversættelseskorrekthed så retter vi det.
Odoo-demodatabaser (livedemoer)
Til denne app kan vi tilbyde en gratis personlig demodatabase.
Du behøver hverken telefonnummer eller kreditkort for at kontakte os. Det rækker med en kort e-mail-tilmelding, der tager under 30 sekunder.
På din anmodning forbereder vi en individuel live-forhåndsvisningsdatabase, hvor du i ca. to uger kan teste og tjekke antagelser.
Fejl Rapportering
Hvis du støder på fejl eller inkonsistent adfærd, så kontakt os. Vi garanterer rettelser inden 60 dage efter køb og forbedrer værktøjerne også bagefter.
Du behøver hverken telefonnummer eller kreditkort for at kontakte os. Det rækker med en kort e-mail-tilmelding, der tager under 30 sekunder.
Medtag så mange detaljer som muligt: skærmbilleder, Odoo-serverlogs og en fuld beskrivelse af, hvordan problemet genskabes. Det tager som regel nogle hverdage at lave en arbejdsplan (hvis fejlen bekræftes) eller retningslinjer (ellers).
Offentlig funktioner Anmodninger Modul Idéer GRATIS Udvikling
Vi er stærkt motiverede til at forbedre vores værktøjer og taknemmelige for al feedback. Hvis kravene er til offentlig nytte og kan implementeres effektivt, tager teamet dem med på opgavelisten.
En sådan opgaveliste behandles regelmæssigt og indebærer ingen ekstra gebyrer. Selvom vi ikke kan love frister og endeligt design, kan det være en god måde at få ønskede funktioner uden investeringer og risici.
Du behøver hverken telefonnummer eller kreditkort for at kontakte os. Det rækker med en kort e-mail-tilmelding, der tager under 30 sekunder.
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