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.
Petua konfigurasi dan pemasangan untuk 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.
Soalan lazim tentang Stock Demand Trends and Forecast Odoo v.16
Apakah dasar kemas kini alat anda?
Mengikut dasar Odoo Apps Store semasa:
- Setiap modul yang dibeli untuk versi 12.0 dan sebelumnya memberi akses ke semua versi sehingga 12.0.
- Bermula versi 13.0, setiap versi modul perlu dibeli berasingan.
- Tanpa mengira versi, pembelian memberi hak kepada semua kemas kini dan pembetulan pepijat dalam versi utama.
Sila ambil perhatian bahawa pasukan faOtools tidak mengawal dasar itu. Untuk soalan, hubungi terus wakil Odoo Apps Store.
Bagaimana memasang aplikasi anda di Odoo.sh?
Pendekatan paling terus ialah menggunakan aliran kerja terbina dalam kedai Odoo:
1. Buka halaman modul dan klik butang Deploy on odoo.sh
2. Selepas itu anda akan dihalakan ke halaman GitHub. Log masuk ke akaun, kemudian klik 'Create a new repo' atau guna yang sedia ada. Pastikan repositori bersifat peribadi. Tidak dibenarkan menerbitkan aplikasi di bawah lesen OPL-1. Jika perlu, cipta repo baharu untuk projek Odoo.sh
3. Kemudian pergi ke odoo.sh dan klik butang Deploy. Dalam tetingkap pop timbul, hantar keputusan dan klik 'Continue.' Tindakan ini akan mencetuskan pemasangan.
Langkah ini akan memasang aplikasi pada cabang pengeluaran projek. Jika anda mahu men-deploy aplikasi ke cabang lain atau mengemas kini modul, lakukan tindakan berikut:
1. Muat naik kod sumber aplikasi dari kedai Odoo
2. Commit modul ke repositori GitHub yang diperlukan. Pastikan tiada folder/fail aplikasi diabaikan (iaitu tidak termasuk dalam .gitignore repo). Repositori dicipta secara automatik oleh odoo.sh dan secara lalai mungkin termasuk item penting (cth., /lib). Muat naik semua direktori, subdirektori dan fail modul tanpa pengecualian
3. Deploy cabang sasaran projek odoo.sh atau, jika tetapan anda mengandaikan demikian, tunggu sehingga ia dibina secara automatik.
Bagaimana memasang aplikasi anda pada pelayan saya sendiri?
2. Mulakan semula pelayan Odoo;
3. Hidupkan mod pembangun (tetapan teknikal);
4. Kemas kini senarai aplikasi (menu aplikasi);
5. Cari aplikasi dan tekan 'Activate'/'Install';
6. Ikuti garis panduan pada halaman aplikasi jika ada.
Saya perasan aplikasi anda ada add-on tambahan. Boleh beli kemudian?
Ya, sudah tentu. Pada masa yang sama Odoo secara automatik menambah semua kebergantungan ke troli, jadi anda harus kecualikan alat yang sudah dibeli untuk elak bayaran berganda.
Saya menyebarkan aplikasi anda di Odoo.sh dan nampak amaran/ralat dalam ujian
Amaran merah/oren tidak mempengaruhi ciri aplikasi. Malangnya, modul kami kadang-kadang tidak lulus ujian automatik standard kerana ujian itu mengandaikan tingkah laku yang bercanggah dengan matlamat aplikasi. Contohnya kami mengubah pengiraan harga, manakala ujian modul Odoo standard membandingkan harga akhir dengan algoritma standard.
Pertama sekali, semak ciri pangkalan data yang dilancarkan. Adakah semuanya berfungsi dengan betul?
Jika anda masih menganggap amaran mempengaruhi ciri sebenar, hubungi kami dan hantar log pemasangan penuh serta senarai penuh modul yang dilancarkan (termasuk teras dan pihak ketiga).
Bolehkah saya membeli aplikasi terus daripada syarikat anda?
Tidak, kami hanya mengedarkan alat melalui kedai aplikasi Odoo rasmi.
Saya ingin mendapat diskaun
Malangnya, kami tidak mempunyai cara teknikal untuk memberikan harga individu.
Bolehkah saya memasang aplikasi pada pangkalan data Odoo Online (SaaS) saya?
Tidak, aplikasi pihak ketiga tidak boleh digunakan di Odoo Online. Malangnya Odoo SaaS menyekat kemungkinan itu.
Alat anda bergantung pada aplikasi lain. Patutkah saya membelinya?
Ya, alat memerlukan semua modul yang ditanda dalam kebergantungan untuk berfungsi dengan betul. Harga pada halaman aplikasi sudah merangkumi semua kebergantungan yang diperlukan.
Berapakah harga modul dalam dolar AS? Mengapa ia berubah?
Harga modul kami ditetapkan dalam euro. Kedai Odoo menukar harga ke mata wang lain mengikut kadar pertukaran dalamannya. Jadi harga dalam dolar AS mungkin berubah apabila kadar dikemas kini.
Teks aplikasi dan laman web dalam bahasa selain Inggeris dibantu AI dan diselaraskan dengan terjemahan rasmi Odoo. Jika anda nampak kesilapan, hantar tiket ketepatan terjemahan dan kami akan membetulkannya.
Pangkalan data demonstrasi Odoo (pratonton langsung)
Untuk aplikasi ini, kami mungkin sediakan pangkalan data demo peribadi percuma.
Anda tidak memerlukan nombor telefon atau kad kredit untuk menghubungi kami. Hanya pendaftaran e-mel ringkas yang tidak mengambil lebih daripada 30 saat.
Atas permintaan anda, kami sediakan pangkalan data pratonton langsung individu, di mana anda boleh menguji dan semak andaian selama kira-kira dua minggu.
Pelaporan pepijat
Jika anda menemui pepijat atau tingkah laku tidak konsisten, sila hubungi kami. Kami jamin pembetulan dalam 60 hari selepas belian dan masih berminat menambah baik alat selepas tempoh itu.
Anda tidak memerlukan nombor telefon atau kad kredit untuk menghubungi kami. Hanya pendaftaran e-mel ringkas yang tidak mengambil lebih daripada 30 saat.
Sertakan sebanyak mungkin butiran dalam permintaan: tangkapan skrin, log pelayan Odoo, penerangan penuh cara menghasilkan semula masalah, dan sebagainya. Biasanya mengambil beberapa hari bekerja untuk menyediakan pelan kerja (jika pepijat disahkan) atau memberi garis panduan apa yang perlu dilakukan (jika tidak).
Permintaan ciri awam dan idea modul (pembangunan percuma)
Kami sangat bermotivasi untuk menambah baik alat kami dan berterima kasih atas sebarang maklum balas. Jika keperluan anda berguna secara awam dan boleh dilaksanakan dengan cekap, pasukan akan memasukkannya ke senarai tugasan kami.
Senarai tugasan sebegini diproses secara berkala dan tidak melibatkan yuran tambahan. Walaupun kami tidak dapat menjanjikan tarikh akhir dan reka bentuk muktamad, ini mungkin cara baik untuk mendapat ciri yang diingini tanpa pelaburan dan risiko.
Anda tidak memerlukan nombor telefon atau kad kredit untuk menghubungi kami. Hanya pendaftaran e-mel ringkas yang tidak mengambil lebih daripada 30 saat.
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