BE1025320B1 - Improved computer-implemented stock management - Google Patents

Improved computer-implemented stock management Download PDF

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Publication number
BE1025320B1
BE1025320B1 BE2017/5911A BE201705911A BE1025320B1 BE 1025320 B1 BE1025320 B1 BE 1025320B1 BE 2017/5911 A BE2017/5911 A BE 2017/5911A BE 201705911 A BE201705911 A BE 201705911A BE 1025320 B1 BE1025320 B1 BE 1025320B1
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BE
Belgium
Prior art keywords
computer
quantity
delivery time
expectation
delivery
Prior art date
Application number
BE2017/5911A
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Dutch (nl)
Inventor
Jan Justé
Original Assignee
Pneuvano Bvba
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Publication date
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Priority to BE2017/5911A priority Critical patent/BE1025320B1/en
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Publication of BE1025320B1 publication Critical patent/BE1025320B1/en

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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/08Logistics, e.g. warehousing, loading, distribution or shipping; Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G06Q10/087Inventory or stock management, e.g. order filling, procurement, balancing against orders
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0202Market predictions or demand forecasting
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/28Logistics, e.g. warehousing, loading, distribution or shipping

Abstract

The present invention relates to a computer-implemented method, a computer system, and a tangible non-transitory computer-readable information carrier comprising a computer program product for stock management. For each of a plurality of product types, a delivery time expectation is determined at least in part on the basis of a moving average of historical delivery times over a predetermined delivery time window. A purchase quantity for a type of product is calculated at least partially on the basis of a current stock quantity, an expectation of purchase, and the determined expectation of delivery time.

Description

IMPROVED COMPUTER-IMPLEMENTED STOCK MANAGEMENT

TECHNICAL DOMAIN

The present invention relates to computer-implemented stock management.

STATE OF THE ART US 6 341 271 describes an inventory management system that monitors available quantities and market prices for automated purchases. Furthermore, the document describes the prediction of prices based on historical price data, as well as the prediction of product purchase based on historical data.

However, the document does not describe provisions to take into account limited storage space and a diversity of products. Furthermore, the system does not take into account previous observed delivery times.

The present invention aims at least to find a solution to some of the above problems.

SUMMARY OF THE INVENTION

In a first aspect, the present invention relates to a computer-implemented method for stock management, according to claim 1.

In a second aspect, the present invention relates to a computer system for stock management, according to claim 13.

In a third aspect, the present invention relates to a tangible non-transitory computer-readable information carrier comprising a computer program product for stock management, according to claim 14.

The present invention is advantageous because it provides for an automated calculation of a purchase quantity, taking into account historical delivery times. For this purpose, a database can be provided, in which historical delivery data is kept for each type of product, the delivery data comprising an order time, an order quantity, and a delivery time. A historical delivery time can be determined based on a delivery time and an order time. By taking historical delivery times into account, the chance of having a stock shortage is limited to a minimum.

DETAILED DESCRIPTION

The present invention relates to a computer-implemented method, a computer system, and a tangible non-transitory computer-readable information carrier comprising a computer program product for stock management. The invention has been summarized in the section provided for this purpose. In the following, the invention is described in detail, preferred embodiments are explained, and the invention is illustrated on the basis of examples.

Unless defined otherwise, all terms used in the description of the invention, including technical and scientific terms, have the meaning as generally understood by those skilled in the art of the invention. For a better assessment of the description of the invention, the following terms are explicitly explained. "One", "de" and "it" in this document refer to both the singular and the plural unless the context clearly presupposes otherwise. For example, "a segment" means one or more than one segment.

The terms "include", "comprising", "consist of", "consisting of", "provided with", "contain", "containing", "withhold", "withhold" are "synonymous" and are inclusive or open terms that presence of what follows, and which do not exclude or prevent the presence of other components, features, elements, members, steps, known from or described in the prior art.

The term “input device” in this document refers to a device suitable for supplying input to a user device. A non-exhaustive list of examples of user devices includes a desktop, a server, a laptop, a smartphone, a tablet, a calculator, a music player, and a smartwatch. The input is not limited to a certain modality and may include mechanical movement, sound, images, and the like. The input can be discreet and / or continuous. The input is also not limited by the number of degrees of freedom. The input can relate to both direct and indirect input. When providing input with respect to a position or its change, such as an indicator on a screen, the input can be both absolute and relative. A non-exhaustive list of examples of input devices includes a keyboard, a computer mouse, a touchpad, a touch screen, a camera, a scanner, a joystick, a microphone, a light pen, a trackball, a projected keyboard, and a game controller.

In a first aspect, the present invention relates to a computer-implemented method for stock management. In a second aspect, the present invention relates to a computer system for stock management. The computer system comprises at least one central processing unit. In a third aspect, the present invention relates to a tangible non-transitory computer-readable information carrier comprising a computer program product for stock management. A person of ordinary skill in the art will appreciate that the computer system according to the second aspect is configured to perform the steps of the method according to the first aspect. Furthermore, one of ordinary skill in the art will also appreciate that the computer program product of the third aspect includes instructions for performing the method of the first aspect on a computer system of the second aspect. In what follows, therefore, a distinction is no longer made between the three aspects of the invention. The following description, embodiments, and examples may therefore relate to any of the three aspects.

For each of a plurality of product types, a delivery time expectation is determined at least in part on the basis of a moving average of historical delivery times over a predetermined delivery time window. For a type of product, a purchase quantity is calculated at least in part based on a current stock quantity, an expectation of purchase, and the determined expectation of delivery time. A historical delivery time is preferably determined on the basis of an order time and a delivery time. The order time is preferably an order date. The delivery time is preferably a delivery date. The delivery time is preferably determined on the basis of registering an arrival of an order via at least one input device. The at least one input device may include a barcode scanner, an RFID reader, or an NFC reader. Alternatively or additionally, the at least one input device may comprise a keyboard and / or computer mouse. Preferably, the expected decrease is determined at least in part on the basis of a moving average of historical decrease over a predetermined decrease time window. An order can then be placed based on a calculated purchase quantity for a type of product. Multiple types of products can be combined in one order, for example when the same supplier is involved. Accordingly, the method preferably also includes the step of placing at least one order based on at least one calculated purchase quantity. A quantity can be a number, a volume or a weight. Preferably an amount is an integer number. A delivery time window is characterized by a relative start time and one of a time window size and a relative stop time. A first example of a delivery time window is the past year, or the past twelve months. The relative start time is 12 months ago, the time window size 12 months, and the relative stop time is the current moment or 0 months ago. A second example of a delivery time window is a period of three consecutive months in which the middle month falls one year before the current moment. For October 2016 as the current moment, the corresponding delivery time window includes September, October and November 2015. As it concerns relative start times, the average progresses as the current moment evolves.

Suppliers often promise short delivery times to win customers, but suppliers exceed these. This can result in an undesired shortage of a product. The present invention is advantageous because it provides for an automated calculation of a purchase quantity, taking into account historical observed delivery times. By taking historical delivery times into account, the chance of having a stock shortage is limited to a minimum.

Preferably, for a type of product, said purchase quantity is determined at least partly on the basis of an open incoming quantity and an open outgoing quantity (in addition to the current stock quantity, the expectation of purchase, and the expectation of delivery time). The outstanding incoming quantity concerns orders that have not yet arrived. The outstanding outgoing quantity concerns the customer orders that have not yet been sent. By taking into account open incoming or outgoing product quantities, a more accurate picture can be obtained of the required order, which reduces the chance of a shortage or too large a stock.

In a preferred embodiment, a tangible non-transitory computer-readable storage medium comprising a database is provided. The database is preferably a relational database. More preferably, the database is a SQL database according to the ISO / IEC 9075 standard. The computer system preferably comprises the storage medium. Alternatively, a server may comprise the storage medium. In this case, the server and the computer system are configured for data communication via a network connection. The computer system can then request data from the storage medium via a network connection. The network connection can be a local connection, such as in a so-called local area network (LAN). The network connection can alternatively be an Internet connection. The database contains the current stock quantity for each of the plurality of product types; delivery data comprising an order time, an order quantity and a delivery time; and sales data comprising a sales time and a sales quantity. The current stock quantity can therefore be determined by reading in at least one quantity value from the storage medium (from the database). When an order is delivered, the arrival of the order can be registered via the above-mentioned at least one input device and the current stock quantity can be adjusted accordingly in the database. The delivery time of an order for a type of product is also added to the database. Alternatively or additionally, the current stock quantity can be determined via at least one weight sensor, at least one length, area or volume sensor; or at least one RFID reader. The sensors / readers can be read out via optical and / or electrically conductive cables. Alternatively, the corresponding sensor data and / or reader data is sent by the sensors / readers via wireless communication and this data is received for communication modules of the computer system. In this way the correct current stock quantity of a type of product can always be obtained. Furthermore, the expected delivery time can be determined at least in part based on the delivery data including an order time within the delivery time window. Furthermore, the expected purchase can also be determined at least in part on the basis of the sales data including a sales time within the purchase time window.

In a preferred embodiment, an expectation of delivery time is corrected in an exceptional period. Preferably, the expectation of delivery time is corrected in the exception period based on an average of historical delivery times over a corresponding exception delivery time window. This is advantageous because this way account can be taken of exceptional periods such as vacations, eg the summer holidays and the Christmas holidays.

In a preferred embodiment, historical price information is maintained for each of the plurality of types of products, e.g. in the database. Furthermore, current price information is monitored, for example by requesting current price information via the Internet. This can be at least one price for at least one purchase quantity from at least one supplier. The current price information can be added to the historical price information, for later use as historical price information. The purchase quantity can then be determined at least in part on the basis of the historical price information and / or the current price information. Preferably, an expectation of price is determined at least in part on the basis of a moving average of historical prices over a predetermined price time window. The purchase quantity is then determined at least in part on the basis of the expected price.

In one embodiment, a module for artificial intelligence can be provided. The module can be trained at least in part on the basis of historical stock quantities, historical delivery times, historical purchase, and / or historical prices. The module is hereby trained to determine optimum purchase quantities. The term "artificial intelligence" (AI) in this document refers to a field of expertise related to the mechanical realization of cognitive functions. The central issues in AI research include reasoning, knowledge, planning, learning, natural language processing, perception, and the ability to manipulate objects. Approaches include static methods, computational intelligence, and traditional symbolic AI. The AI field covers computer sciences, mathematics, psychology, linguistics, philosophy, neuroscience, artificial psychology, and many others. AI realizations include successfully understanding human speech, competing at a high level in strategic games, autonomous cars, intelligent routing in networks, interpreting complex data, face recognition, and the like. The AI field includes machine learning. A non-exhaustive example list of techniques used in machine learning includes decision tree learning, association regulation, artificial neural networks, deep learning (ind. Deep learning), inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning (eng. Reinforcement learning), representation learning , parable and metric learning, sparse dictionary learning, genetic algorithms, rule-based machine learning, and learning classification systems. A non-exhaustive example list of computer program products for machine learning includes Apache SINGA, Caffe, Deeplearning4j, Dlib, Keras, Microsoft Cognitive Toolkit, Microsoft Computational Network Toolkit, MXNet, Neural Designer, OpenNN, Pytorch, Scikit-learn for the Python programming language, TensorFlow, Theano, Torch, and Wolfram Mathematica.

In the following, the invention is described a.d.h.v. a non-limiting example illustrating the invention, which is not intended or may be interpreted to limit the scope of the invention.

EXAMPLE

A computer program product makes it possible to adjust the stock position in real time in function of the sales history of the last 12 months. For each product that a company sells, the computer program product determines the ideal stock position and the corresponding order position. This is done on the basis of an algorithm with a set of parameters: • A desired minimum stock is calculated on the basis of the moving normalized average of sales over the last 12 months and the moving average of the delivery time over the last 12 months. • A desired maximum stock is calculated based on the desired minimum stock and a stock buffer factor. • An order position is calculated based on the desired maximum stock, the current stock in stock, the outstanding customer orders and the outstanding orders with the supplier.

The moving normalized average of sales over the last 12 months is adjusted for months in which: • no sales were registered; • the sales amount to more than 2.5 times the unadjusted moving average of sales over the last 12 months; and • the sales are less than 0.25 times the unadjusted moving average of sales in the last 12 months.

In addition, the moving average of the delivery time over the last 12 months is calculated based on the delivery date and order date of the relevant item, adjusted for the July / August holiday period and the November / December end-of-year period.

Furthermore, items that were sold to fewer than 3 customers in the last 12 months are excluded as inventory items. Furthermore, items that have been sold for less than 4 months in the last 12 months are also excluded as inventory items.

Preferably, the computer program product comprises instructions in SQL source code.

Claims (14)

  1. CONCLUSIONS
    A computer-implemented method for stock management, comprising the steps of determining for each of a plurality of types of products: - an expectation of delivery time at least in part on the basis of a moving average of historical delivery times over a predetermined delivery time window; and - calculating a purchase quantity at least in part based on a current stock quantity, a decrease expectation, and the determined delivery time expectation, wherein the current stock quantity is determined via at least one weight sensor; at least one length, area or volume sensor; at least one RFID reader; or reading in at least one quantity value from a tangible non-transitory computer-readable storage medium.
  2. A computer-implemented method according to any one of the preceding claims, wherein the method also comprises the step of placing at least one order based on at least one calculated purchase quantity.
  3. A computer-implemented method according to any one of the preceding claims, wherein the method comprises the step of determining a historical delivery time based on an order time and a delivery time, preferably the order time an order date, preferably the delivery time a delivery date.
  4. A computer-implemented method according to any one of the preceding claims, wherein the expectation of decrease is determined at least in part on the basis of a moving average of historical decrease over a predetermined decrease time window.
  5. A computer-implemented method according to any one of the preceding claims, wherein an amount is a number, a volume or a weight, preferably an integer.
  6. A computer-implemented method according to any one of the preceding claims, wherein the purchase quantity of a type of product is calculated at least in part on the basis of an open incoming quantity and an open outgoing quantity.
  7. A computer-implemented method according to any one of the preceding claims, comprising the steps of: - providing a tangible non-transitory computer-readable storage medium comprising a relational database comprising the current stock quantity for a type of product of the plurality of types of products; delivery data comprising an order time, an order quantity, and a delivery time; and sales data comprising a sales time and a sales quantity; - determining the expected delivery time at least in part based on the delivery data including an order time within the delivery time window.
  8. A computer-implemented method according to the preceding claims 4 and 8, comprising the step of determining the expected purchase based at least in part on the sales data including a sales time within the purchase time window.
  9. A computer-implemented method according to any one of the preceding claims, wherein an expectation of delivery time is corrected in an exception period.
  10. The computer-implemented method according to the preceding claim 10, wherein the expectation of delivery time is corrected in an exception period based on an average of historical delivery times over a corresponding exception delivery time window.
  11. A computer-implemented method as claimed in any one of the preceding claims, comprising the steps of: - maintaining historical price information for each of the plurality of types of products; - monitoring of current price information; and - calculating a purchase quantity at least in part based on the historical price information and / or the current price information.
  12. A computer-implemented method according to any one of the preceding claims and to the preceding claim 8, wherein the relational database is an SQL database according to the ISO / IEC 9075 standard.
  13. A computer system for stock management, the computer system comprising at least one central processing unit, the computer system configured for each of a plurality of types of products: - determining an expectation of delivery time at least in part on the basis of a moving average of historical delivery times over a predetermined delivery time window; and - calculating a purchase quantity at least in part based on a current stock quantity, a decrease expectation, and the determined delivery time expectation, wherein the current stock quantity is determined via at least one weight sensor; at least one length, area or volume sensor; at least one RFID reader; or reading in at least one quantity value from a tangible non-transitory computer-readable storage medium.
  14. Tangible non-transitory computer-readable information carrier comprising a computer program product for stock management, the computer program product configured for execution on a computer system comprising at least one central processing unit, the computer program product comprising instructions for determining for each of a plurality of types of products: - determining an expectation of delivery time at least partly based on a moving average of historical delivery times over a predetermined delivery time window; and - calculating a purchase quantity at least in part based on a current stock quantity, a decrease expectation, and the determined delivery time expectation, wherein the current stock quantity is determined via at least one weight sensor; at least one length, area or volume sensor; at least one RFID reader; or reading in at least one quantity value from a tangible non-transitory computer-readable storage medium.
BE2017/5911A 2017-12-07 2017-12-07 Improved computer-implemented stock management BE1025320B1 (en)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070270990A1 (en) * 2006-05-16 2007-11-22 Kaan Kudsi Katircioglu System and process for supply management for the assembly of expensive products
US20100312611A1 (en) * 2005-09-02 2010-12-09 Flow Vision LLC Inventory management system

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100312611A1 (en) * 2005-09-02 2010-12-09 Flow Vision LLC Inventory management system
US20070270990A1 (en) * 2006-05-16 2007-11-22 Kaan Kudsi Katircioglu System and process for supply management for the assembly of expensive products

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Effective date: 20190121