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Model of the Automated Warehouse Management and Forecasting System in the Conditions of Transition to Industry 4.0
9th IFAC Conference on Manufacturing Modelling, Management and 9th IFAC Conference on Manufacturing Modelling, Management and Control 9th IFAC Conference on Modelling, Management and Available online at www.sciencedirect.com Control 9th IFAC Conference on Manufacturing Manufacturing Modelling, Management and Berlin, Germany, August 28-30, 2019 9th IFAC Conference on Manufacturing Modelling, Management and Control Berlin, ControlGermany, August 28-30, 2019 Control Berlin, Germany, Germany, August August 28-30, 28-30, 2019 2019 Berlin, Berlin, Germany, August 28-30, 2019
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IFAC PapersOnLine 52-13 (2019) 78–82
Model Model of of the the Automated Automated Warehouse Warehouse Management Management and and Forecasting Forecasting ModelSystem of the Automated Warehouse Management and Forecasting in the Conditions of Transition to Industry 4.0 Warehouse in the Conditions of Transition to Industry 4.0 ModelSystem of the Automated Management and Forecasting System in the Conditions of Transition to Industry to Industry 4.0 4.0 * Transition ** System in the Conditions V. Lototskyof *, R. Sabitov**,
V. Lototsky *** *, R. Sabitov**, **** G. Sirazetdinov **, ****, ***, *B. V. Lototsky ,, R. Sabitov V. Lototsky Sabitov ,, ******** G. Smirnova Smirnova , **B. Sirazetdinov , ** ***** V. Lototsky ,, R. R. Sabitov *** **** N. Elizarova Sh. Sabitov *** ****, ***** ****** , B. Sirazetdinov G. Smirnova G. Smirnova Sirazetdinov ***, B. ****, N. Elizarova , Sh. Sabitov *** **** G. Smirnova , B. Sirazetdinov ***** ****** ***** ****** , Sabitov N. Elizarova N. Elizarova Sh. Sabitov *****,, Sh. ****** ***** ****** * N. Elizarova , Sh. Sabitov *V.A. Trapeznikov Institute of Control Sciences, Moscow, Russia V.A. Trapeznikov Institute of Control Sciences, Moscow, Russia * * (tel.: + 77 495 334 9201, [email protected]) *V.A. Trapeznikov *e-mail: Institute of Control Sciences, Moscow, Russia Trapeznikov Institute of Control Sciences, Moscow, Russia *V.A. e-mail: [email protected]) (tel.: + 495 334 9201, * **, ***, ****, ***** V.A. Trapeznikov Institute of Control Sciences, Moscow, Russia * Kazan National Technical University *Research **, ***, (tel.: ****, ***** + 7 495 334 9201, e-mail: [email protected]) (tel.: + 7 495 334 9201, e-mail: [email protected]) *Research Kazan National Technical University * (tel.: + 7 495 334 9201, e-mail: [email protected]) **, ***, ****, ***** named after A.N. Tupolev – KAI, Kazan, Russia **, ***, ****, *****Kazan National Research Technical University National Research Technical University **,** ***, ****, ****, *****Kazan named after A.N. Tupolev –*** KAI, Technical Kazan, Russia **, ***, ***** Kazan National Research University [email protected]), (e-mail: [email protected]) **(e-mail: *** named after A.N. Tupolev – KAI, Kazan, Russia named after A.N. Tupolev – KAI, Kazan, Russia [email protected]), (e-mail: [email protected]) **** **(e-mail: ***** named after A.N. Tupolev – KAI, Kazan, Russia *** [email protected]), [email protected]) **(e-mail: ***(e-mail: ****(e-mail: ***** [email protected]), (e-mail: [email protected]) [email protected]), (e-mail: [email protected]) ** *** (e-mail: [email protected]), [email protected]) **(e-mail: ***(e-mail: ****** (e-mail: [email protected]), (e-mail: [email protected]) **** ***** Kazan Federal University, Kazan, Russia ****(e-mail: ***** ****** [email protected]), (e-mail: [email protected]) (e-mail: [email protected]), (e-mail: [email protected]) **** ***** Kazan Federal University, Kazan, Russia **** ***** ****** (e-mail: [email protected]), (e-mail: [email protected]) ****** (e-mail: [email protected]) ****** ****** Kazan Federal University, Kazan, Russia Federal University, Kazan, Russia ******Kazan (e-mail: [email protected]) ****** Kazan Federal University, Kazan, Russia ****** ****** [email protected]) (e-mail: ******(e-mail: ****** (e-mail: [email protected]) [email protected]) Abstract: of the the automated automated warehouse warehouse management management and and forecasting forecasting system Abstract: Dynamic Dynamic model model of system in in the the conditions of Industry 4.0 is discussed. Usage of inductive knowledge base allows to predict the appearance Abstract: Dynamic model of the automated warehouse management and forecasting system in the Abstract: Dynamic model of the automated warehouse management and forecasting system in conditions of Industry 4.0 is discussed. Usage of inductive knowledge base allows to predict the appearance Abstract: Dynamic model ofactually the automated warehouseknowledge management and forecasting system in the the of bottlenecks in advance and turns the warehouse management system into an expert with all conditions of Industry 4.0 is discussed. Usage of inductive base allows to predict the appearance conditions of Industry 4.0 is discussed. Usage of inductive knowledge base allows to predict the appearance of bottlenecks in advance and actually turns the warehouse management system into an expert with all the conditions of Industry 4.0 is discussed. Usage of inductive knowledge base allows to predict the appearance necessary integral experience, which helps to reduce significantly the financial costs of excessive inventory of bottlenecks in advance and actually turns the warehouse management system into an expert with all the of bottlenecks in advance and actually turns the warehouse management system into an expert all necessary integral experience, helps reduce significantly the financial costs inventory of bottlenecks in advance and which actually turnsto the warehouse management system intoof anexcessive expert with with all the the and to prevent certain components shortage possibility. Copyright © 2019 IFAC necessary integral experience, which helps to reduce significantly the financial costs of excessive inventory necessary integral experience, which helps to topossibility. reduce significantly significantly the financial costs of of excessive excessive inventory inventory and to prevent certain components shortage Copyright the © 2019 IFAC necessary integral experience, which helps reduce financial costs and to prevent certain components shortage possibility. Copyright © 2019 IFAC Keywords: Dynamic models, Industry 4.0, Forecasting, manufacturing executing and to certain components shortage possibility. Copyright © IFAC © 2019, IFAC (International Federation of Automatic Control) Hosting byIntegrated Elsevier All rights reserved. and to prevent prevent certain production components shortage possibility. Copyright © 2019 2019 IFAC Ltd. Keywords: Dynamic production models, Industry 4.0, Forecasting, Integrated manufacturing executing systems, Manufacturing control, Identification. Keywords: Dynamic production production models, Industry Industry 4.0, 4.0, Forecasting, Forecasting, Integrated Integrated manufacturing manufacturing executing executing Keywords: Dynamic models, systems, Manufacturing control, Identification. Keywords: Dynamic production models, Industry 4.0, Forecasting, Integrated manufacturing executing systems, Manufacturing control, Identification. systems, Manufacturing control, Identification. control, Identification. systems, Manufacturing 1. 2. 1. INTRODUCTION INTRODUCTION 2. DESCRIPTION DESCRIPTION AND AND MODELING MODELING OF OF WORK WORK PROCESSES AT AUTOMATED WAREHOUSE 1. INTRODUCTION 2. DESCRIPTION AND MODELING OF WORK 1. INTRODUCTION 2. DESCRIPTION AND MODELING OF WORK PROCESSES AT AUTOMATED WAREHOUSE In recent years, there has been an increase in integrated 1. INTRODUCTION 2. DESCRIPTION AND MODELING OF WORK In recent years, there has been an increase in integrated PROCESSES AT AUTOMATED WAREHOUSE PROCESSES AT AUTOMATED AUTOMATED WAREHOUSE technologies across the entire production and distribution Modern theory of production logistics management PROCESSES AT WAREHOUSE In recent years, there has been an increase in integrated In recent years, there been an integrated technologies across thehas entire andin distribution Modern theory of production logistics management considers considers In recent years, there has beenproduction an increase increase inand integrated value chain. This is the interconnection of digital physical the most effective stock management strategy in the form technologies across the entire production and distribution Modern theory of production logistics management considers technologies across the entire production and distribution Modern theory of production logistics management considers value chain. This is the interconnection of digital and physical the most effective stock management strategy in the form of of technologies across the entire production and and distribution Modern theory ofinproduction logistics management considers systems, known as Industry 4.0 (Bartodziej, 2017). It building a chain a “bottom-up” way, where demand-driven value chain. This is the interconnection of digital physical the most effective stock management strategy in the form of value chain. This is the interconnection of digital and physical the most effective stock management strategy in the form of systems, known as Industry 4.0 (Bartodziej, 2017). It building a chain in a “bottom-up” way, where demand-driven value chain. This isasthefrom interconnection of digital and physical the most effective stock management strategy in the formThe of combines all stages product design and planning to supply chain is put at the forefront (Dolgui et al., 2010). systems, known Industry 4.0 (Bartodziej, 2017). It building a chain in a “bottom-up” way, where demand-driven systems, known as Industry 4.0 (Bartodziej, 2017). It building a chain in a “bottom-up” way, where demand-driven combines all stages from product design and planning to supply chain is put at the forefront (Dolgui et al., 2010). The systems, known as from Industry 4.0 (Bartodziej, 2017). in It building a chain in a “bottom-up” way,System, where demand-driven supply manufacturing. Industry 4.0 technologies, Automated proposed for combines alland stages product design and planning to supply is forefront et 2010). combines all stages from design planning to supply chain chainWarehouse is put put at at the theManagement forefront (Dolgui (Dolgui et al., al., 2010). The The supply chain chain and manufacturing. Industry 4.0and technologies, in Automated Warehouse Management System, proposed for combines all stages fromofproduct product design and planning to supply chain is put at the forefront (Dolgui et al., 2010). The addition to the processes designing and producing goods, development, is characterized by the presence of an intelligent supply chain chain andprocesses manufacturing. Industry 4.0producing technologies, in development, Automated Management System, for supply and manufacturing. Industry technologies, in addition to the of designing and4.0 goods, is characterized by the presence an intelligent Automated Warehouse Warehouse Management System,of proposed proposed for supply chain and manufacturing. Industry 4.0 technologies, in Automated Warehouse Management System, proposed for can also influence the way which the products are moved, over the WMS, which, along with the coordination of addition to the the processes processes ofin designing and producing goods, add-on development, is characterized by the presence of an intelligent addition to of designing and producing goods, development, is characterized by the presence of an intelligent can also influence the way in which the products are moved, add-on over the WMS, which, along with the coordination of addition todistributed. the processes of designing and producing goods, development, is(within characterized by the presence ofasanwith intelligent stored and data exchange the warehouse, as well MES, can also influence the way way in in which which the the products products are are moved, moved, add-on over the WMS, which, along with the coordination of can also influence the add-on over the WMS, which, along with the coordination of stored and distributed. data exchange (within the warehouse, as well as with MES, can also influence the way in which the products are moved, add-on over the WMS, which, along with the coordination of ERP, etc.), will help to optimize warehouse and other stored and distributed. data exchange (within the warehouse, as well as with MES, stored and distributed. data exchange (within the warehouse, as well as with MES, ERP, etc.), will help to optimize warehouse and other Industry 4.0 technologies allow warehouse facilities to adapt stored and4.0 distributed. exchange (withininthe warehouse, as well as with MES, Industry technologies allow warehouse facilities to adapt data production time will to optimize warehouse ERP, etc.), etc.),processes will help help to real optimize warehouse and and other other production processes in the the real time mode. mode. to changes their processes. Recently, there has been aa ERP, Industry 4.0 in technologies allow warehouse facilities to adapt ERP, etc.), will help to optimize warehouse and other Industry 4.0 technologies allow warehouse facilities to adapt to changes in their processes. Recently, there has been production processes in the real time mode. Industry 4.0 technologies allow warehouse facilities to adapt production processes in the real time mode. migration to high-speed operations. This led to an increase in Conceptually and functionally, such a warehouse management to changesto in in their processes. processes. Recently, there has been inaa production processes in the realsuch timeamode. to changes their Recently, been migration high-speed operations. This ledthere to anhas increase and functionally, warehouse management to changes in their processes. Recently, there has been a Conceptually the number products at the expense physical belongs to the class of so-called advanced control migration to of high-speed operations. Thisof ledthe to same an increase increase in system Conceptually and functionally, such aa warehouse management migration to high-speed operations. This led to an in Conceptually and functionally, such warehouse management the number of products at the expense of the same physical system belongs to the class of so-called advanced control migration to of high-speed operations. Thisof ledtheto same an increase in Conceptually andto functionally, such a warehouse management assets while reducing overall costs. Warehouses are an systems of the production processes (which include, for the number products at the expense physical system belongs the class of so-called advanced control the number of products at the expense of the same physical system belongs to the class of so-called advanced control assets while reducing overall costs. Warehouses are an systems of the production processes (which include, for the number of reducing products at the expense of the same physical system belongs to the Process class processes ofControl so-called advanced control important component of supply chain infrastructure and are example, the Advanced System (APC) and the assets while overall costs. Warehouses are an systems of the production (which include, for assets while reducing overall costs. Warehouses are an systems of the production processes (which include, for important component of supply chain infrastructure and are example, the Advanced Process Control System (APC) and the assets while reducing overall costs. but Warehouses are are an systems of the production processes (which include, for increasingly seen not as cost centers, rather as strategic Advanced Planning System (APS)). The main difference important component of supply chain infrastructure and example, the Advanced Process Control System (APC) and the important component of supply chain infrastructure and are example, the Advanced Process Control System (APC) and the increasingly seen not as cost centers, but rather as strategic Advanced Planning System (APS)). The main difference important component of supply chainal., infrastructure and are between example, thePlanning Advanced Processthe Control System (APC) models and the tools for (Lu 2016). usage of increasingly seen not notadvantage as cost cost centers, but rather as as strategic strategic Advanced System (APS)). main increasingly seen as centers, but rather Advancedadvanced Planningsystems Systemis (APS)). The main difference difference tools for competitive competitive advantage (Lu et et al., 2016). between advanced systems is the usageThe of predictive predictive models increasingly seen not as cost centers, but rather as strategic Advanced Planning System (APS)). The main difference for information support of control. tools for competitive competitive advantage (Lu et et al., al., 2016). between advanced systems is tools for advantage (Lu 2016). between advanced systems is the the usage usage of of predictive predictive models models for information support of control. Industry 4.0 can an automated-robotic tools for competitive advantage (Lu etto advanced systems is the usage of predictive models Industry 4.0 technologies technologies can move move toal., an2016). automated-robotic between for information support of control. for information support of control. warehouse. This allows automated systems to more effectively Proposed system differs from all other systems of this for information support of control. Industry 4.0 technologies can move to an automated-robotic Industry 4.0 technologies can move to automated-robotic warehouse. allows automated systems more effectively Proposed system differs from all other systems of this class, class, Industry 4.0This technologies can with move to an an to automated-robotic solve problems when working people. Technologies such presented nowadays on market by the following features: warehouse. This allows automated systems to more effectively effectively Proposed system differs from all other systems of this class, warehouse. This allows automated systems to more Proposed system differs from all other systems of this class, solve problems when working with people. Technologies such presented nowadays on market by the following features: warehouse. This allows automated systems to more effectively Proposed system differs from all other systems features: of this class, as low-cost sensors, computer vision, augmented reality (AR), solve problems when working with people. Technologies such presented nowadays on market by the following solve problems when working with people. Technologies such presented nowadays on market by the following features: as low-cost sensors, computer vision, augmented reality (AR), • The system implements (at pace of real production solve problems when working with people. Technologies such presented nowadays on market by following • The system implements (at the pace of realfeatures: production drones, Internet of (IoT) (Norbert Jesse, as low-cost sensors, computer vision, augmented realityrobotic (AR), as low-cost sensors, computer augmented reality (AR), drones, Internet of things things (IoT)vision, (Norbert Jesse, 2016), 2016), robotic informational interaction and decision support as low-cost sensors, computer vision, augmented reality (AR), •• process) The system implements (at pace of real production The system implements (at pace of real production process) informational interaction and decision support preventiveness, robot security, analytics, and highdrones, Internet Internet of ofrobot things (IoT) (IoT) (Norbert Jesse, 2016), 2016), robotic system implements (at pace of decision real systems production drones, things (Norbert Jesse, preventiveness, security, analytics, and robotic high- • The with other corporate control and information drones, Internet of things (IoT) (Norbert Jesse, 2016), robotic process) informational interaction and support process) informational interaction and decision support with other corporate control and information systems performance computing provide ample opportunities to preventiveness, robot security, analytics, and informational interaction and decision support performance computing provide ample opportunities to • process) preventiveness, robot security, analytics, and highhighAccording to its architecture, the system provides aa preventiveness, robot security, analytics, and highwith other corporate control and information systems • According to its architecture, the system provides with other corporate control and information systems improve management efficiency. At the same time, they allow performance computing provide ample to other corporate control andembedding information systems performance computing provide ample opportunities to • with improve management efficiency. At the sameopportunities time, they allow promising opportunity for into future According to its architecture, the system provides performance computing provide ample opportunities to • promising According to to its architecture, architecture, the system system into provides opportunity for embedding futureaaa the use new intelligent automation, which can help improve the they its the provides improve management efficiency. At the same same time, time, they allow the use of ofmanagement new types types of ofefficiency. intelligentAt automation, which canallow help • According integrated production control systems by the principle of promising opportunity for embedding into future improve management efficiency. At the same time, they allow promising production opportunity for systems embedding future integrated control by the into principle of processes during warehouse operations (Sabitov et the use of new types of intelligent automation, which can help promising opportunity for embedding into future the use of new types of intelligent automation, which can help improve processes during warehouse operations (Sabitov et flexible digital production, also on the basis of internet of integrated production control systems the the use ofprocesses new typesduring of intelligent automation, which can help integrated production control systems by theofprinciple principle of flexible digital production, also on the by basis internet of al., 2015). improve warehouse operations (Sabitov et integrated production control systems by the principle of improve processes during warehouse operations (Sabitov et al., 2015). things and cloud technologies. flexible digital also of improve processes during warehouse operations (Sabitov et flexibleand digital production, also on on the the basis basis of of internet internet of things cloudproduction, technologies. al., flexible digital production, also on the basis of internet of al., 2015). 2015). things and cloud technologies. al., 2015). things and cloud technologies. things and cloud technologies. 2405-8963 © Copyright © 2019, 2019 IFAC IFAC (International Federation of Automatic Control) 80 Hosting by Elsevier Ltd. All rights reserved. Copyright 2019 responsibility IFAC 80 Control. Peer review©under of International Federation of Automatic Copyright © 80 10.1016/j.ifacol.2019.11.137 Copyright © 2019 2019 IFAC IFAC 80 Copyright © 2019 IFAC 80
2019 IFAC MIM Berlin, Germany, August 28-30, 2019
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V. Lototsky et al. / IFAC PapersOnLine 52-13 (2019) 78–82
Algorithms for dynamic state assessment and forecasting are based on Data mining and so-called inductive knowledge-patterns that are extracted and updated by information generated during production process.
̂ 𝑚𝑚 (𝑡𝑡) = 𝑦𝑦
79
𝐾𝐾
𝐿𝐿
∑ ∑ 𝑎𝑎𝑖𝑖𝑖𝑖 𝑦𝑦̂ 𝑚𝑚𝑚𝑚 (𝑡𝑡 − 𝑖𝑖) +
𝐾𝐾
𝑘𝑘=1 𝑖𝑖=1 𝑛𝑛 𝑅𝑅
+ ∑ ∑ ∑ 𝑏𝑏𝑗𝑗𝑗𝑗 𝑥𝑥𝑟𝑟𝑟𝑟𝑟𝑟 (𝑡𝑡 − 𝑗𝑗),
3. PREDICTION OF DEVIATIONS IN ACTUAL DELIVERY TIMES, SHORTAGES AND ILLIQUID STOCK
𝑘𝑘=1 𝑗𝑗=1 𝑟𝑟=1
where 𝐾𝐾 is the number of suppliers involved. The possible factors of the order execution time deviation (from the planned time) are: risks of delayed transportation, possible shifts in other orders execution time, production risks predicted by experts.
The task is to form and dynamically adjust the optimal stock management plan at warehouse, according to the minimization criterion of total costs of required inventory items storing and transportation directly from suppliers (without storage) and costs associated with their shortage. In modern production plan optimization systems the dynamics of change of production situation, should be taken into account, for example: there is a need for unscheduled use of inventories that were intended for use later. This is necessary, for example, when you need to urgently complete a higher priority order. Alternatively, it was be found that certain components from certain suppliers are not suitable for a number of reasons to fulfill this order, etc. The question arises: what is more profitable (the criterion can be multiple): take advantage of current stocks in the warehouse or order components from another supplier (the delivery will be fast and inexpensive, and the price is lower, but the deadline for the order will still move)? Decision support subsystem is being created, which will interactively provide decision options based on a statistical forecast using a knowledge base and simulation modeling (Fig. 1). The accepted solution then automatically transferred to ERP and MES (Smirnova et al., 2014). In particular, the main criterion for the selection of inventories from a particular supplier may be the minimum delivery time. To predict this indicator, an identification prediction model is used. Taking into account the interdependence of factors (model input variables), it is advisable to get the forecast problem solution by means of associative search method (Bakhtadze and Sakrutina, 2015). For the delivery dates forecasting task model may look in the following way (Bakhtadze et al., 2012a, 2012b):
̂𝑚𝑚 (𝑡𝑡) = 𝑦𝑦
𝐿𝐿
𝑛𝑛
𝑅𝑅
Fig. 1. Functional diagram for solving the problem of forecasting delivery dates.
∑ 𝑎𝑎𝑖𝑖 𝑦𝑦̂𝑚𝑚 (𝑡𝑡 − 𝑖𝑖) + ∑ ∑ 𝑏𝑏𝑗𝑗𝑗𝑗 𝑥𝑥𝑟𝑟𝑟𝑟 (𝑡𝑡 − 𝑗𝑗) , 𝑖𝑖=1
𝑗𝑗=1 𝑟𝑟=1
4. FORECASTING DEMAND DYNAMICS AND POSSIBILITY OF BOTTLENECKS. ALGORITHM DEVELOPMENT FOR INVENTORY ITEMS GROUPING AND REDISTRIBUTION IN STORAGE LOCATIONS.
̂𝑚𝑚 (𝑡𝑡) is the remaining (at the moment of time t) actual where: 𝑦𝑦 margin of time to complete the 𝑚𝑚-th inventory unit delivery order (while executing past supply orders for this item from the same supplier), 𝑥𝑥𝑟𝑟𝑟𝑟 (𝑡𝑡), 𝑟𝑟 = 1, . . . , 𝑅𝑅 is the factors of possible deviation of the order execution time from the contractual value. The values of the coefficients 𝑎𝑎𝑖𝑖 and 𝑏𝑏𝑖𝑖 are determined when updating the predictive model.
For the procurement planning purposes “Sales and Demand Forecasting” Subsystem implements an online forecasting and stock management support for WMS, using the database of generated and updated production tasks, as well as the expected changes in market conditions. The forecast model of the 𝑚𝑚-th inventory item demand will have the following structure:
If for a certain period of time this inventory item has been supplied from different suppliers, the model becomes more complicated:
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2019 IFAC MIM 80 Berlin, Germany, August 28-30, 2019
𝑦𝑦 ̂𝑚𝑚 (𝑡𝑡) =
𝐿𝐿
V. Lototsky et al. / IFAC PapersOnLine 52-13 (2019) 78–82
𝑛𝑛
𝑅𝑅
∑ 𝑎𝑎𝑖𝑖 𝑦𝑦̂ 𝑚𝑚 (𝑡𝑡 − 𝑖𝑖) + ∑ ∑ 𝑖𝑖=1
𝑛𝑛
𝑏𝑏𝑗𝑗𝑗𝑗 𝑥𝑥𝑟𝑟𝑟𝑟 (𝑡𝑡 − 𝑗𝑗) +
placement monitoring and monitoring of demand state, the following are needed:
𝑗𝑗=1 𝑟𝑟=1
𝑃𝑃
•
+ ∑ ∑ 𝑏𝑏𝑗𝑗𝑗𝑗 𝑥𝑥𝑝𝑝𝑝𝑝 (𝑡𝑡 − 𝑗𝑗),
•
𝑗𝑗=1 𝑝𝑝=1
where: 𝑦𝑦̂𝑚𝑚 (𝑡𝑡 − 𝑖𝑖) is the actual demand for the m-th inventory item at the time (𝑡𝑡 − 𝑖𝑖); 𝑥𝑥𝑟𝑟 (𝑡𝑡 − 𝑗𝑗) is the price of the m-th inventory item at the 𝑟𝑟-th supplier at the time (𝑡𝑡 − 𝑖𝑖); 𝑥𝑥𝑚𝑚𝑚𝑚 (𝑡𝑡 − 𝑗𝑗) is the cost of transportation of the 𝑚𝑚-th inventory item at the 𝑝𝑝-th carrier. Respectively, forecast of the deficit (or surplus) level for the 𝑚𝑚-th inventory item will be determined as the difference between the demand forecast and the predicted level 𝑥𝑥(𝑡𝑡 − 𝑁𝑁).
• •
Modeling of goods placement in stock, taking into account various strategies, in particular, developing placement strategies to meet the unique requirements; Tasks creation for automatic replenishment of the selection zone; Usage of inventory characteristics for placement calculation and planning; Optimization algorithms development for placement and bundling of goods.
A vast number of methods and practical development are known for solving such problems as placement of goods and materials optimization in the warehouse and loaders movement optimization for removal – placement operations. RFID technology provides ample opportunity.
The forecast of the stock level is determined as follows: 𝐿𝐿
The most well-known routing algorithms used for warehouse logistics are: the Clarkе and Wright algorithm; wave algorithm (wave trace algorithm, Lie algorithm); Dijkstra algorithm (Dijkstra, 1959), heuristic and metaheuristic algorithms, as well as genetic algorithms.
𝑥𝑥(𝑡𝑡) = 𝑥𝑥(𝑡𝑡 − 𝑁𝑁) + ∑ 𝑢𝑢𝑚𝑚𝑚𝑚 (𝑡𝑡 − 𝑖𝑖) , 𝐿𝐿 ≤ 𝑁𝑁, 𝑖𝑖=1
where 𝑥𝑥(𝑡𝑡 − 𝑁𝑁) is the stock level at the moment 𝑡𝑡 − 𝑁𝑁, 𝑁𝑁 is the forecast horizon; 𝑢𝑢𝑚𝑚𝑚𝑚 (𝑡𝑡 − 𝑖𝑖) is the forecasted supply schedule of the 𝑚𝑚-th inventory item. The supply forecast, generally speaking, must take into account random factors that may lead to an adjustment (sometimes very substantial) of the formed plan. Random supply models can be useful at conditions of environment changing due to changes of external market factors. The process of optimal supply planning can be described in terms of a multidimensional Markov process. In this case, the system state on each step is characterized by a random vector
6. MODELING SUPPLY ACTION SCHEME FOR THE PART OF INVENTORY ITEMS WHEN SUPPLYING “FROM THE WHEELS”, WITHOUT MOVING TO THE WAREHOUSE The solution of this problem provides the optimal supply amount of inventory items for stock replenishment. The demand for inventory items can be predicted by presented above algorithms. The deficit forecast is defined as the difference between the projected demand for this type of inventory items and their actual amount available in the warehouse.
𝛼𝛼(𝑡𝑡) = {𝛼𝛼1 (𝑡𝑡), 𝛼𝛼2 (𝑡𝑡), 𝛼𝛼3 (𝑡𝑡)},
where: 𝛼𝛼1 (𝑡𝑡) is the total demand for components at time t, 𝛼𝛼2 (𝑡𝑡) is total supply from warehouse, 𝛼𝛼3 (𝑡𝑡) is current traffic volume.
It is natural to determine possible reservation scheme for scarce inventory items at warehouse in dynamics. Volumes of supplied inventory items can be either completely sent to the warehouse, or can be fully or partially supplied “from the wheels”, without moving to the warehouse. To determine the appropriate (optimal or suboptimal) portion of stored items, it is necessary to forecast (for a fixed period ahead) the storing costs for the given inventory items amount and to compare it with the transportation costs forecast, price growth forecast at the supplier, production and transport risks.
5. ALGORITHMS DEVELOPMENT FOR INVENTORY ITEMS COMBINING AND REDISTRIBUTION AT STORAGE SITES Placement optimization at warehouse is carried out by the “Slots optimization - placement options” subsystem, which is the fragment of a larger (at the corporate level) System – “Planning of coarse cutting” (implements general planning of production capacity loading based on the current data received from basic production planning). The “Placement optimization” subsystem (Slotting optimization) not only performs operations related to the movement of inventory items flow to the warehouse, taking into account the workflow, but also taking into account the planning, placement of inventory items in warehouse with usage of intelligent analysis algorithms. Also it forms automatic stock replenishment tasks based on the prediction algorithms described above.
For each 𝑖𝑖-th inventory item we set the item storage costs per unit of time as: 𝑎𝑎𝑖𝑖 .
The 𝑖𝑖-th inventory item transportation cost per unit of time by the 𝑗𝑗-th carrier is denoted as 𝑏𝑏𝑖𝑖𝑖𝑖 . We will assume the transportation time 𝑇𝑇 is fixed (it is possible to denote the maximum allowed delivery time). Suppose also that storage cost is constant (the same in both periods 𝑇𝑇 and 𝑇𝑇 + 1).
The forecast of the i-th inventory supply amount is denoted as 𝑥𝑥𝑖𝑖 (𝑇𝑇). Suppose, it is necessary to determine which part 0 ≤ 𝛼𝛼𝑖𝑖 (𝑡𝑡) ≤ 1 of the required replenishment amount for the deficient inventory item should be reserved in the warehouse, and which part, here (1 − 𝛼𝛼𝑖𝑖 (𝑡𝑡)) should be purchased and brought during 𝑇𝑇 time (Bakhtadze et al., 2012a, 2012b).
“Optimization of placement” subsystem allows increasing essentially the productivity and throughput of the warehouse, thereby reducing the cost of goods storage and warehouse logistics in general. On the basis of inventory unit types
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Then the total cost of deficit eliminating at the end of time 𝑇𝑇 will be: 𝑦𝑦(𝑇𝑇) = 𝑇𝑇[𝛼𝛼𝑖𝑖 𝑎𝑎𝑖𝑖 + (1 − 𝛼𝛼𝑖𝑖 )𝑏𝑏𝑖𝑖𝑖𝑖 ]𝑥𝑥𝑖𝑖 . Assuming that the storage cost is the same for all inventory items, further necessary to solve the minimum finding problem for this functional when changing αi (t) (0 ≤ αi (𝑡𝑡) ≤ 1) and all 𝑗𝑗 − 𝑠𝑠. If the storage costs are different, then the plan is being optimized: 𝑦𝑦(𝑇𝑇) = ∑ min 𝑇𝑇[𝛼𝛼𝑖𝑖 𝑎𝑎𝑖𝑖 + (1 − 𝛼𝛼𝑖𝑖 )𝑏𝑏𝑖𝑖𝑖𝑖 ]𝑥𝑥𝑖𝑖 . 𝑖𝑖
𝑗𝑗=1,..,𝐽𝐽 0≤𝛼𝛼𝑖𝑖 ≤1
This optimization problem is solved by means of well-known optimization methods. Determination of the deficient positions reserved amount, while taking into account the projected demand. Suppose that as a result for solving of optimization problem considered in this section, the value 0 ≤ 𝛼𝛼𝑖𝑖 (𝑡𝑡) ≤ 1 is determined exactly. Then the reserved amount is determined by forecasted demand for this inventory items: 𝑅𝑅𝑖𝑖 = 𝛼𝛼𝑖𝑖 𝑥𝑥𝑖𝑖 (𝑇𝑇). 7. DYNAMIC AUTOMATED WAREHOUSE MANAGEMENT SYSTEM BASED ON PREDICTIVE MODELS Dynamic automated warehouse management system based on predictive models interacts and exchanges data with other corporate systems at the pace of the actual production process. The system is characterized by the presence of an intelligent add-in (IA), which will conduce to integration and optimization of warehouse and other production processes in real time regime (Fig. 2). The features of an intellectual system consist primarily in the possibility of operational re-planning based on forecasting and tracking of inventories, taking into account both the current production situation and the intellectual analysis of statistical content. It can also be used to optimize production logistics (for example, reducing the time for completing the supply of components), to fulfill production orders by a certain date, orders for purchasing components by the time they should be used in production, etc.
Fig. 2. Functional diagram of a warehouse management and forecasting system based on a dynamic model. 8. MODEL EXAMPLE Integral brackets are molded at the Foundry, processed at other factories and sent to the assembly plant. The required number and types of brackets – the demand – reveals in 14 days before – and, besides, it can be adjusted daily either up or down. The program for the brackets production is also set 14 days before, but can no longer change. The number of brackets and their types, which can be produced daily, should not be less than the maximum daily requirement during normal operation. The following factors were used for forecasting: planned demand, actual demand, safety stock amount and the minimum volume of deliveries. As additional parameters for forecasting improvement hereby were used: demand factors for integral brackets in a certain time period (history); dynamics of sales and vehicles shipment; demand for bracket types in pieces; initial demand for items by type; waiting of a large urgent order – the probability and number of cars (expert assessment). The following random factors were taken into account: failure – production centers breakdown – output capacity reduction for integral brackets, raw materials prices changing.
The formation of an optimal scenario in a dynamic mathematical model under any change in the production situation is the choice of parameters of the dynamic process from the inductive base of production knowledge based on clustering methods and the method of associative search for analogues. This is an alternative to a request to MES and other information and control systems with the subsequent on-line recalculation of the optimal values of the parameters, which takes much longer time.
Demand prediction is carried out according to the associative search methodology based on the intellectual analysis of statistical data using the wavelet analysis method (which allows to take into account the non-stationarity influence as much as possible). 83
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The graphs (Fig. 3, Fig. 4) show the proposed algorithms calculations results for the integral brackets element for deliveries that are multiples of the standard quantity (400 units). From the charts, it can be seen that forecasting stabilizes the process (ensures its sustainability), allowing you to avoid both excess stocks and shortages. At the same time, while having the 30 days time lag, the number of deliveries was reduced from 9 to 8. The application of the developed algorithms for fixing bottlenecks in any processes occurring at the enterprise may be especially relevant.
optimization, 4 PL logistics implementation, planning optimization, etc. REFERENCES Bakhtadze, N.N. and E.A. Sakrutina (2015). “The Intelligent Identification Technique with Associative Search”, International Journal of Mathematical Models and Methods in Applied Sciences, vol. 9, pp. 418-431. Bakhtadze, N.N., Lototsky, V.A., Vlasov, S.A., and E.A. Sakrutina (2012a). Associative Search and Wavelet Analysis Techniques in System Identification, IFAC Proceedings Volumes, vol. 45, no. 16, pp. 1227-1232. Bakhtadze, N., Lototsky, V., and E. Maximov (2012b). Associative search method in system identification, Pr. 14th International Conference on Automatic Control, Modeling and Simulation, Saint Malo & Mont Saint Michel, France, pp. 49-57. ISBN: 978-1-61804-080-0. Bartodziej, C.J. (2017). The Concept Industry 4.0 An Empirical Analysis of Technologies and Applications in Production Logistics, Springer. ISBN 978-3-658-16501-7. Dijkstra, Е.W. (1959). A note on two problems in connexion with graphs, Numerische Mathematik, vol. 1, no. 1, pp. 269-271. Dolgui, A. and J.-M. Proth (2010). Supply Chains Engineering: Useful Methods and Techniques, Springer, ISBN: 978-1-84996-016-8. Jesse, N. (2016). Internet of Things and Big Data – The Disruption of the Value Chain and the Rise of New Software Ecosystems, IFAC-PapersOnLine, vol. 49, no. 29, рр. 275-282. Lu, W., McFarlane, D., Giannikas, V., and Q. Zhang (2016). An algorithm for dynamic order-picking in warehouse operations, European Journal of Operational Research, vol. 248, no. 1, pp. 107-122. Sabitov, R.A., Smirnova, G.S., Sabitov, Sh.R., Morozov, B.M., Sirazetdinov, В.R. (2015). Adaptive control and operational management system of machine-tool fleet of the manufacturing enterprise, IFAC-PapersOnLine, vol. 48, no. 3, pp. 1236-1241. Smirnova, G.S., Sabitov, R.A., Elizarova, N.Y., and Sh.R. Sabitov (2014). Operational management system of enterprise production processes “1C: MES: cloudy production management”, Automation in Industry, no. 8, pp. 48-51. (in Russian)
1000 800
pcs
600 400 200 0 -200
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Days Safety stock in fact Safety stock for forecast-adjusted supply
Fig. 3. Safety stock dynamics for integral brackets with the intellectual forecasting consideration.
pcs.
600 400 200 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Days Planned demand Demand in fact Demand, forecast Safety stock for forecast-adjusted supply
Fig. 4. Comparative analysis for the integral brackets demand dynamics. 9. CONCLUSIONS Usage of inductive knowledge base allows to predict the appearance of bottlenecks in advance and actually turns the warehouse management system into an expert with all the necessary integral experience, which helps to reduce significantly the financial costs of excessive inventory and, at the same time, to prevent certain components shortage possibility. This approach allows to use clustering capabilities for various processes and to achieve additional economic benefits due to the complex system approach to business processes organization, for example, suppliers quantity
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Model Model of of the the Automated Automated Warehouse Warehouse Management Management and and Forecasting Forecasting ModelSystem of the Automated Warehouse Management and Forecasting in the Conditions of Transition to Industry 4.0 Warehouse in the Conditions of Transition to Industry 4.0 ModelSystem of the Automated Management and Forecasting System in the Conditions of Transition to Industry to Industry 4.0 4.0 * Transition ** System in the Conditions V. Lototskyof *, R. Sabitov**,
V. Lototsky *** *, R. Sabitov**, **** G. Sirazetdinov **, ****, ***, *B. V. Lototsky ,, R. Sabitov V. Lototsky Sabitov ,, ******** G. Smirnova Smirnova , **B. Sirazetdinov , ** ***** V. Lototsky ,, R. R. Sabitov *** **** N. Elizarova Sh. Sabitov *** ****, ***** ****** , B. Sirazetdinov G. Smirnova G. Smirnova Sirazetdinov ***, B. ****, N. Elizarova , Sh. Sabitov *** **** G. Smirnova , B. Sirazetdinov ***** ****** ***** ****** , Sabitov N. Elizarova N. Elizarova Sh. Sabitov *****,, Sh. ****** ***** ****** * N. Elizarova , Sh. Sabitov *V.A. Trapeznikov Institute of Control Sciences, Moscow, Russia V.A. Trapeznikov Institute of Control Sciences, Moscow, Russia * * (tel.: + 77 495 334 9201, [email protected]) *V.A. Trapeznikov *e-mail: Institute of Control Sciences, Moscow, Russia Trapeznikov Institute of Control Sciences, Moscow, Russia *V.A. e-mail: [email protected]) (tel.: + 495 334 9201, * **, ***, ****, ***** V.A. Trapeznikov Institute of Control Sciences, Moscow, Russia * Kazan National Technical University *Research **, ***, (tel.: ****, ***** + 7 495 334 9201, e-mail: [email protected]) (tel.: + 7 495 334 9201, e-mail: [email protected]) *Research Kazan National Technical University * (tel.: + 7 495 334 9201, e-mail: [email protected]) **, ***, ****, ***** named after A.N. Tupolev – KAI, Kazan, Russia **, ***, ****, *****Kazan National Research Technical University National Research Technical University **,** ***, ****, ****, *****Kazan named after A.N. Tupolev –*** KAI, Technical Kazan, Russia **, ***, ***** Kazan National Research University [email protected]), (e-mail: [email protected]) **(e-mail: *** named after A.N. Tupolev – KAI, Kazan, Russia named after A.N. Tupolev – KAI, Kazan, Russia [email protected]), (e-mail: [email protected]) **** **(e-mail: ***** named after A.N. Tupolev – KAI, Kazan, Russia *** [email protected]), [email protected]) **(e-mail: ***(e-mail: ****(e-mail: ***** [email protected]), (e-mail: [email protected]) [email protected]), (e-mail: [email protected]) ** *** (e-mail: [email protected]), [email protected]) **(e-mail: ***(e-mail: ****** (e-mail: [email protected]), (e-mail: [email protected]) **** ***** Kazan Federal University, Kazan, Russia ****(e-mail: ***** ****** [email protected]), (e-mail: [email protected]) (e-mail: [email protected]), (e-mail: [email protected]) **** ***** Kazan Federal University, Kazan, Russia **** ***** ****** (e-mail: [email protected]), (e-mail: [email protected]) ****** (e-mail: [email protected]) ****** ****** Kazan Federal University, Kazan, Russia Federal University, Kazan, Russia ******Kazan (e-mail: [email protected]) ****** Kazan Federal University, Kazan, Russia ****** ****** [email protected]) (e-mail: ******(e-mail: ****** (e-mail: [email protected]) [email protected]) Abstract: of the the automated automated warehouse warehouse management management and and forecasting forecasting system Abstract: Dynamic Dynamic model model of system in in the the conditions of Industry 4.0 is discussed. Usage of inductive knowledge base allows to predict the appearance Abstract: Dynamic model of the automated warehouse management and forecasting system in the Abstract: Dynamic model of the automated warehouse management and forecasting system in conditions of Industry 4.0 is discussed. Usage of inductive knowledge base allows to predict the appearance Abstract: Dynamic model ofactually the automated warehouseknowledge management and forecasting system in the the of bottlenecks in advance and turns the warehouse management system into an expert with all conditions of Industry 4.0 is discussed. Usage of inductive base allows to predict the appearance conditions of Industry 4.0 is discussed. Usage of inductive knowledge base allows to predict the appearance of bottlenecks in advance and actually turns the warehouse management system into an expert with all the conditions of Industry 4.0 is discussed. Usage of inductive knowledge base allows to predict the appearance necessary integral experience, which helps to reduce significantly the financial costs of excessive inventory of bottlenecks in advance and actually turns the warehouse management system into an expert with all the of bottlenecks in advance and actually turns the warehouse management system into an expert all necessary integral experience, helps reduce significantly the financial costs inventory of bottlenecks in advance and which actually turnsto the warehouse management system intoof anexcessive expert with with all the the and to prevent certain components shortage possibility. Copyright © 2019 IFAC necessary integral experience, which helps to reduce significantly the financial costs of excessive inventory necessary integral experience, which helps to topossibility. reduce significantly significantly the financial costs of of excessive excessive inventory inventory and to prevent certain components shortage Copyright the © 2019 IFAC necessary integral experience, which helps reduce financial costs and to prevent certain components shortage possibility. Copyright © 2019 IFAC Keywords: Dynamic models, Industry 4.0, Forecasting, manufacturing executing and to certain components shortage possibility. Copyright © IFAC © 2019, IFAC (International Federation of Automatic Control) Hosting byIntegrated Elsevier All rights reserved. and to prevent prevent certain production components shortage possibility. Copyright © 2019 2019 IFAC Ltd. Keywords: Dynamic production models, Industry 4.0, Forecasting, Integrated manufacturing executing systems, Manufacturing control, Identification. Keywords: Dynamic production production models, Industry Industry 4.0, 4.0, Forecasting, Forecasting, Integrated Integrated manufacturing manufacturing executing executing Keywords: Dynamic models, systems, Manufacturing control, Identification. Keywords: Dynamic production models, Industry 4.0, Forecasting, Integrated manufacturing executing systems, Manufacturing control, Identification. systems, Manufacturing control, Identification. control, Identification. systems, Manufacturing 1. 2. 1. INTRODUCTION INTRODUCTION 2. DESCRIPTION DESCRIPTION AND AND MODELING MODELING OF OF WORK WORK PROCESSES AT AUTOMATED WAREHOUSE 1. INTRODUCTION 2. DESCRIPTION AND MODELING OF WORK 1. INTRODUCTION 2. DESCRIPTION AND MODELING OF WORK PROCESSES AT AUTOMATED WAREHOUSE In recent years, there has been an increase in integrated 1. INTRODUCTION 2. DESCRIPTION AND MODELING OF WORK In recent years, there has been an increase in integrated PROCESSES AT AUTOMATED WAREHOUSE PROCESSES AT AUTOMATED AUTOMATED WAREHOUSE technologies across the entire production and distribution Modern theory of production logistics management PROCESSES AT WAREHOUSE In recent years, there has been an increase in integrated In recent years, there been an integrated technologies across thehas entire andin distribution Modern theory of production logistics management considers considers In recent years, there has beenproduction an increase increase inand integrated value chain. This is the interconnection of digital physical the most effective stock management strategy in the form technologies across the entire production and distribution Modern theory of production logistics management considers technologies across the entire production and distribution Modern theory of production logistics management considers value chain. This is the interconnection of digital and physical the most effective stock management strategy in the form of of technologies across the entire production and and distribution Modern theory ofinproduction logistics management considers systems, known as Industry 4.0 (Bartodziej, 2017). It building a chain a “bottom-up” way, where demand-driven value chain. This is the interconnection of digital physical the most effective stock management strategy in the form of value chain. This is the interconnection of digital and physical the most effective stock management strategy in the form of systems, known as Industry 4.0 (Bartodziej, 2017). It building a chain in a “bottom-up” way, where demand-driven value chain. This isasthefrom interconnection of digital and physical the most effective stock management strategy in the formThe of combines all stages product design and planning to supply chain is put at the forefront (Dolgui et al., 2010). systems, known Industry 4.0 (Bartodziej, 2017). It building a chain in a “bottom-up” way, where demand-driven systems, known as Industry 4.0 (Bartodziej, 2017). It building a chain in a “bottom-up” way, where demand-driven combines all stages from product design and planning to supply chain is put at the forefront (Dolgui et al., 2010). The systems, known as from Industry 4.0 (Bartodziej, 2017). in It building a chain in a “bottom-up” way,System, where demand-driven supply manufacturing. Industry 4.0 technologies, Automated proposed for combines alland stages product design and planning to supply is forefront et 2010). combines all stages from design planning to supply chain chainWarehouse is put put at at the theManagement forefront (Dolgui (Dolgui et al., al., 2010). The The supply chain chain and manufacturing. Industry 4.0and technologies, in Automated Warehouse Management System, proposed for combines all stages fromofproduct product design and planning to supply chain is put at the forefront (Dolgui et al., 2010). The addition to the processes designing and producing goods, development, is characterized by the presence of an intelligent supply chain chain andprocesses manufacturing. Industry 4.0producing technologies, in development, Automated Management System, for supply and manufacturing. Industry technologies, in addition to the of designing and4.0 goods, is characterized by the presence an intelligent Automated Warehouse Warehouse Management System,of proposed proposed for supply chain and manufacturing. Industry 4.0 technologies, in Automated Warehouse Management System, proposed for can also influence the way which the products are moved, over the WMS, which, along with the coordination of addition to the the processes processes ofin designing and producing goods, add-on development, is characterized by the presence of an intelligent addition to of designing and producing goods, development, is characterized by the presence of an intelligent can also influence the way in which the products are moved, add-on over the WMS, which, along with the coordination of addition todistributed. the processes of designing and producing goods, development, is(within characterized by the presence ofasanwith intelligent stored and data exchange the warehouse, as well MES, can also influence the way way in in which which the the products products are are moved, moved, add-on over the WMS, which, along with the coordination of can also influence the add-on over the WMS, which, along with the coordination of stored and distributed. data exchange (within the warehouse, as well as with MES, can also influence the way in which the products are moved, add-on over the WMS, which, along with the coordination of ERP, etc.), will help to optimize warehouse and other stored and distributed. data exchange (within the warehouse, as well as with MES, stored and distributed. data exchange (within the warehouse, as well as with MES, ERP, etc.), will help to optimize warehouse and other Industry 4.0 technologies allow warehouse facilities to adapt stored and4.0 distributed. exchange (withininthe warehouse, as well as with MES, Industry technologies allow warehouse facilities to adapt data production time will to optimize warehouse ERP, etc.), etc.),processes will help help to real optimize warehouse and and other other production processes in the the real time mode. mode. to changes their processes. Recently, there has been aa ERP, Industry 4.0 in technologies allow warehouse facilities to adapt ERP, etc.), will help to optimize warehouse and other Industry 4.0 technologies allow warehouse facilities to adapt to changes in their processes. Recently, there has been production processes in the real time mode. Industry 4.0 technologies allow warehouse facilities to adapt production processes in the real time mode. migration to high-speed operations. This led to an increase in Conceptually and functionally, such a warehouse management to changesto in in their processes. processes. Recently, there has been inaa production processes in the realsuch timeamode. to changes their Recently, been migration high-speed operations. This ledthere to anhas increase and functionally, warehouse management to changes in their processes. Recently, there has been a Conceptually the number products at the expense physical belongs to the class of so-called advanced control migration to of high-speed operations. Thisof ledthe to same an increase increase in system Conceptually and functionally, such aa warehouse management migration to high-speed operations. This led to an in Conceptually and functionally, such warehouse management the number of products at the expense of the same physical system belongs to the class of so-called advanced control migration to of high-speed operations. Thisof ledtheto same an increase in Conceptually andto functionally, such a warehouse management assets while reducing overall costs. Warehouses are an systems of the production processes (which include, for the number products at the expense physical system belongs the class of so-called advanced control the number of products at the expense of the same physical system belongs to the class of so-called advanced control assets while reducing overall costs. Warehouses are an systems of the production processes (which include, for the number of reducing products at the expense of the same physical system belongs to the Process class processes ofControl so-called advanced control important component of supply chain infrastructure and are example, the Advanced System (APC) and the assets while overall costs. Warehouses are an systems of the production (which include, for assets while reducing overall costs. Warehouses are an systems of the production processes (which include, for important component of supply chain infrastructure and are example, the Advanced Process Control System (APC) and the assets while reducing overall costs. but Warehouses are are an systems of the production processes (which include, for increasingly seen not as cost centers, rather as strategic Advanced Planning System (APS)). The main difference important component of supply chain infrastructure and example, the Advanced Process Control System (APC) and the important component of supply chain infrastructure and are example, the Advanced Process Control System (APC) and the increasingly seen not as cost centers, but rather as strategic Advanced Planning System (APS)). The main difference important component of supply chainal., infrastructure and are between example, thePlanning Advanced Processthe Control System (APC) models and the tools for (Lu 2016). usage of increasingly seen not notadvantage as cost cost centers, but rather as as strategic strategic Advanced System (APS)). main increasingly seen as centers, but rather Advancedadvanced Planningsystems Systemis (APS)). The main difference difference tools for competitive competitive advantage (Lu et et al., 2016). between advanced systems is the usageThe of predictive predictive models increasingly seen not as cost centers, but rather as strategic Advanced Planning System (APS)). The main difference for information support of control. tools for competitive competitive advantage (Lu et et al., al., 2016). between advanced systems is tools for advantage (Lu 2016). between advanced systems is the the usage usage of of predictive predictive models models for information support of control. Industry 4.0 can an automated-robotic tools for competitive advantage (Lu etto advanced systems is the usage of predictive models Industry 4.0 technologies technologies can move move toal., an2016). automated-robotic between for information support of control. for information support of control. warehouse. This allows automated systems to more effectively Proposed system differs from all other systems of this for information support of control. Industry 4.0 technologies can move to an automated-robotic Industry 4.0 technologies can move to automated-robotic warehouse. allows automated systems more effectively Proposed system differs from all other systems of this class, class, Industry 4.0This technologies can with move to an an to automated-robotic solve problems when working people. Technologies such presented nowadays on market by the following features: warehouse. This allows automated systems to more effectively effectively Proposed system differs from all other systems of this class, warehouse. This allows automated systems to more Proposed system differs from all other systems of this class, solve problems when working with people. Technologies such presented nowadays on market by the following features: warehouse. This allows automated systems to more effectively Proposed system differs from all other systems features: of this class, as low-cost sensors, computer vision, augmented reality (AR), solve problems when working with people. Technologies such presented nowadays on market by the following solve problems when working with people. Technologies such presented nowadays on market by the following features: as low-cost sensors, computer vision, augmented reality (AR), • The system implements (at pace of real production solve problems when working with people. Technologies such presented nowadays on market by following • The system implements (at the pace of realfeatures: production drones, Internet of (IoT) (Norbert Jesse, as low-cost sensors, computer vision, augmented realityrobotic (AR), as low-cost sensors, computer augmented reality (AR), drones, Internet of things things (IoT)vision, (Norbert Jesse, 2016), 2016), robotic informational interaction and decision support as low-cost sensors, computer vision, augmented reality (AR), •• process) The system implements (at pace of real production The system implements (at pace of real production process) informational interaction and decision support preventiveness, robot security, analytics, and highdrones, Internet Internet of ofrobot things (IoT) (IoT) (Norbert Jesse, 2016), 2016), robotic system implements (at pace of decision real systems production drones, things (Norbert Jesse, preventiveness, security, analytics, and robotic high- • The with other corporate control and information drones, Internet of things (IoT) (Norbert Jesse, 2016), robotic process) informational interaction and support process) informational interaction and decision support with other corporate control and information systems performance computing provide ample opportunities to preventiveness, robot security, analytics, and informational interaction and decision support performance computing provide ample opportunities to • process) preventiveness, robot security, analytics, and highhighAccording to its architecture, the system provides aa preventiveness, robot security, analytics, and highwith other corporate control and information systems • According to its architecture, the system provides with other corporate control and information systems improve management efficiency. At the same time, they allow performance computing provide ample to other corporate control andembedding information systems performance computing provide ample opportunities to • with improve management efficiency. At the sameopportunities time, they allow promising opportunity for into future According to its architecture, the system provides performance computing provide ample opportunities to • promising According to to its architecture, architecture, the system system into provides opportunity for embedding futureaaa the use new intelligent automation, which can help improve the they its the provides improve management efficiency. At the same same time, time, they allow the use of ofmanagement new types types of ofefficiency. intelligentAt automation, which canallow help • According integrated production control systems by the principle of promising opportunity for embedding into future improve management efficiency. At the same time, they allow promising production opportunity for systems embedding future integrated control by the into principle of processes during warehouse operations (Sabitov et the use of new types of intelligent automation, which can help promising opportunity for embedding into future the use of new types of intelligent automation, which can help improve processes during warehouse operations (Sabitov et flexible digital production, also on the basis of internet of integrated production control systems the the use ofprocesses new typesduring of intelligent automation, which can help integrated production control systems by theofprinciple principle of flexible digital production, also on the by basis internet of al., 2015). improve warehouse operations (Sabitov et integrated production control systems by the principle of improve processes during warehouse operations (Sabitov et al., 2015). things and cloud technologies. flexible digital also of improve processes during warehouse operations (Sabitov et flexibleand digital production, also on on the the basis basis of of internet internet of things cloudproduction, technologies. al., flexible digital production, also on the basis of internet of al., 2015). 2015). things and cloud technologies. al., 2015). things and cloud technologies. things and cloud technologies. 2405-8963 © Copyright © 2019, 2019 IFAC IFAC (International Federation of Automatic Control) 80 Hosting by Elsevier Ltd. All rights reserved. Copyright 2019 responsibility IFAC 80 Control. Peer review©under of International Federation of Automatic Copyright © 80 10.1016/j.ifacol.2019.11.137 Copyright © 2019 2019 IFAC IFAC 80 Copyright © 2019 IFAC 80
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Algorithms for dynamic state assessment and forecasting are based on Data mining and so-called inductive knowledge-patterns that are extracted and updated by information generated during production process.
̂ 𝑚𝑚 (𝑡𝑡) = 𝑦𝑦
79
𝐾𝐾
𝐿𝐿
∑ ∑ 𝑎𝑎𝑖𝑖𝑖𝑖 𝑦𝑦̂ 𝑚𝑚𝑚𝑚 (𝑡𝑡 − 𝑖𝑖) +
𝐾𝐾
𝑘𝑘=1 𝑖𝑖=1 𝑛𝑛 𝑅𝑅
+ ∑ ∑ ∑ 𝑏𝑏𝑗𝑗𝑗𝑗 𝑥𝑥𝑟𝑟𝑟𝑟𝑟𝑟 (𝑡𝑡 − 𝑗𝑗),
3. PREDICTION OF DEVIATIONS IN ACTUAL DELIVERY TIMES, SHORTAGES AND ILLIQUID STOCK
𝑘𝑘=1 𝑗𝑗=1 𝑟𝑟=1
where 𝐾𝐾 is the number of suppliers involved. The possible factors of the order execution time deviation (from the planned time) are: risks of delayed transportation, possible shifts in other orders execution time, production risks predicted by experts.
The task is to form and dynamically adjust the optimal stock management plan at warehouse, according to the minimization criterion of total costs of required inventory items storing and transportation directly from suppliers (without storage) and costs associated with their shortage. In modern production plan optimization systems the dynamics of change of production situation, should be taken into account, for example: there is a need for unscheduled use of inventories that were intended for use later. This is necessary, for example, when you need to urgently complete a higher priority order. Alternatively, it was be found that certain components from certain suppliers are not suitable for a number of reasons to fulfill this order, etc. The question arises: what is more profitable (the criterion can be multiple): take advantage of current stocks in the warehouse or order components from another supplier (the delivery will be fast and inexpensive, and the price is lower, but the deadline for the order will still move)? Decision support subsystem is being created, which will interactively provide decision options based on a statistical forecast using a knowledge base and simulation modeling (Fig. 1). The accepted solution then automatically transferred to ERP and MES (Smirnova et al., 2014). In particular, the main criterion for the selection of inventories from a particular supplier may be the minimum delivery time. To predict this indicator, an identification prediction model is used. Taking into account the interdependence of factors (model input variables), it is advisable to get the forecast problem solution by means of associative search method (Bakhtadze and Sakrutina, 2015). For the delivery dates forecasting task model may look in the following way (Bakhtadze et al., 2012a, 2012b):
̂𝑚𝑚 (𝑡𝑡) = 𝑦𝑦
𝐿𝐿
𝑛𝑛
𝑅𝑅
Fig. 1. Functional diagram for solving the problem of forecasting delivery dates.
∑ 𝑎𝑎𝑖𝑖 𝑦𝑦̂𝑚𝑚 (𝑡𝑡 − 𝑖𝑖) + ∑ ∑ 𝑏𝑏𝑗𝑗𝑗𝑗 𝑥𝑥𝑟𝑟𝑟𝑟 (𝑡𝑡 − 𝑗𝑗) , 𝑖𝑖=1
𝑗𝑗=1 𝑟𝑟=1
4. FORECASTING DEMAND DYNAMICS AND POSSIBILITY OF BOTTLENECKS. ALGORITHM DEVELOPMENT FOR INVENTORY ITEMS GROUPING AND REDISTRIBUTION IN STORAGE LOCATIONS.
̂𝑚𝑚 (𝑡𝑡) is the remaining (at the moment of time t) actual where: 𝑦𝑦 margin of time to complete the 𝑚𝑚-th inventory unit delivery order (while executing past supply orders for this item from the same supplier), 𝑥𝑥𝑟𝑟𝑟𝑟 (𝑡𝑡), 𝑟𝑟 = 1, . . . , 𝑅𝑅 is the factors of possible deviation of the order execution time from the contractual value. The values of the coefficients 𝑎𝑎𝑖𝑖 and 𝑏𝑏𝑖𝑖 are determined when updating the predictive model.
For the procurement planning purposes “Sales and Demand Forecasting” Subsystem implements an online forecasting and stock management support for WMS, using the database of generated and updated production tasks, as well as the expected changes in market conditions. The forecast model of the 𝑚𝑚-th inventory item demand will have the following structure:
If for a certain period of time this inventory item has been supplied from different suppliers, the model becomes more complicated:
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𝑦𝑦 ̂𝑚𝑚 (𝑡𝑡) =
𝐿𝐿
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𝑛𝑛
𝑅𝑅
∑ 𝑎𝑎𝑖𝑖 𝑦𝑦̂ 𝑚𝑚 (𝑡𝑡 − 𝑖𝑖) + ∑ ∑ 𝑖𝑖=1
𝑛𝑛
𝑏𝑏𝑗𝑗𝑗𝑗 𝑥𝑥𝑟𝑟𝑟𝑟 (𝑡𝑡 − 𝑗𝑗) +
placement monitoring and monitoring of demand state, the following are needed:
𝑗𝑗=1 𝑟𝑟=1
𝑃𝑃
•
+ ∑ ∑ 𝑏𝑏𝑗𝑗𝑗𝑗 𝑥𝑥𝑝𝑝𝑝𝑝 (𝑡𝑡 − 𝑗𝑗),
•
𝑗𝑗=1 𝑝𝑝=1
where: 𝑦𝑦̂𝑚𝑚 (𝑡𝑡 − 𝑖𝑖) is the actual demand for the m-th inventory item at the time (𝑡𝑡 − 𝑖𝑖); 𝑥𝑥𝑟𝑟 (𝑡𝑡 − 𝑗𝑗) is the price of the m-th inventory item at the 𝑟𝑟-th supplier at the time (𝑡𝑡 − 𝑖𝑖); 𝑥𝑥𝑚𝑚𝑚𝑚 (𝑡𝑡 − 𝑗𝑗) is the cost of transportation of the 𝑚𝑚-th inventory item at the 𝑝𝑝-th carrier. Respectively, forecast of the deficit (or surplus) level for the 𝑚𝑚-th inventory item will be determined as the difference between the demand forecast and the predicted level 𝑥𝑥(𝑡𝑡 − 𝑁𝑁).
• •
Modeling of goods placement in stock, taking into account various strategies, in particular, developing placement strategies to meet the unique requirements; Tasks creation for automatic replenishment of the selection zone; Usage of inventory characteristics for placement calculation and planning; Optimization algorithms development for placement and bundling of goods.
A vast number of methods and practical development are known for solving such problems as placement of goods and materials optimization in the warehouse and loaders movement optimization for removal – placement operations. RFID technology provides ample opportunity.
The forecast of the stock level is determined as follows: 𝐿𝐿
The most well-known routing algorithms used for warehouse logistics are: the Clarkе and Wright algorithm; wave algorithm (wave trace algorithm, Lie algorithm); Dijkstra algorithm (Dijkstra, 1959), heuristic and metaheuristic algorithms, as well as genetic algorithms.
𝑥𝑥(𝑡𝑡) = 𝑥𝑥(𝑡𝑡 − 𝑁𝑁) + ∑ 𝑢𝑢𝑚𝑚𝑚𝑚 (𝑡𝑡 − 𝑖𝑖) , 𝐿𝐿 ≤ 𝑁𝑁, 𝑖𝑖=1
where 𝑥𝑥(𝑡𝑡 − 𝑁𝑁) is the stock level at the moment 𝑡𝑡 − 𝑁𝑁, 𝑁𝑁 is the forecast horizon; 𝑢𝑢𝑚𝑚𝑚𝑚 (𝑡𝑡 − 𝑖𝑖) is the forecasted supply schedule of the 𝑚𝑚-th inventory item. The supply forecast, generally speaking, must take into account random factors that may lead to an adjustment (sometimes very substantial) of the formed plan. Random supply models can be useful at conditions of environment changing due to changes of external market factors. The process of optimal supply planning can be described in terms of a multidimensional Markov process. In this case, the system state on each step is characterized by a random vector
6. MODELING SUPPLY ACTION SCHEME FOR THE PART OF INVENTORY ITEMS WHEN SUPPLYING “FROM THE WHEELS”, WITHOUT MOVING TO THE WAREHOUSE The solution of this problem provides the optimal supply amount of inventory items for stock replenishment. The demand for inventory items can be predicted by presented above algorithms. The deficit forecast is defined as the difference between the projected demand for this type of inventory items and their actual amount available in the warehouse.
𝛼𝛼(𝑡𝑡) = {𝛼𝛼1 (𝑡𝑡), 𝛼𝛼2 (𝑡𝑡), 𝛼𝛼3 (𝑡𝑡)},
where: 𝛼𝛼1 (𝑡𝑡) is the total demand for components at time t, 𝛼𝛼2 (𝑡𝑡) is total supply from warehouse, 𝛼𝛼3 (𝑡𝑡) is current traffic volume.
It is natural to determine possible reservation scheme for scarce inventory items at warehouse in dynamics. Volumes of supplied inventory items can be either completely sent to the warehouse, or can be fully or partially supplied “from the wheels”, without moving to the warehouse. To determine the appropriate (optimal or suboptimal) portion of stored items, it is necessary to forecast (for a fixed period ahead) the storing costs for the given inventory items amount and to compare it with the transportation costs forecast, price growth forecast at the supplier, production and transport risks.
5. ALGORITHMS DEVELOPMENT FOR INVENTORY ITEMS COMBINING AND REDISTRIBUTION AT STORAGE SITES Placement optimization at warehouse is carried out by the “Slots optimization - placement options” subsystem, which is the fragment of a larger (at the corporate level) System – “Planning of coarse cutting” (implements general planning of production capacity loading based on the current data received from basic production planning). The “Placement optimization” subsystem (Slotting optimization) not only performs operations related to the movement of inventory items flow to the warehouse, taking into account the workflow, but also taking into account the planning, placement of inventory items in warehouse with usage of intelligent analysis algorithms. Also it forms automatic stock replenishment tasks based on the prediction algorithms described above.
For each 𝑖𝑖-th inventory item we set the item storage costs per unit of time as: 𝑎𝑎𝑖𝑖 .
The 𝑖𝑖-th inventory item transportation cost per unit of time by the 𝑗𝑗-th carrier is denoted as 𝑏𝑏𝑖𝑖𝑖𝑖 . We will assume the transportation time 𝑇𝑇 is fixed (it is possible to denote the maximum allowed delivery time). Suppose also that storage cost is constant (the same in both periods 𝑇𝑇 and 𝑇𝑇 + 1).
The forecast of the i-th inventory supply amount is denoted as 𝑥𝑥𝑖𝑖 (𝑇𝑇). Suppose, it is necessary to determine which part 0 ≤ 𝛼𝛼𝑖𝑖 (𝑡𝑡) ≤ 1 of the required replenishment amount for the deficient inventory item should be reserved in the warehouse, and which part, here (1 − 𝛼𝛼𝑖𝑖 (𝑡𝑡)) should be purchased and brought during 𝑇𝑇 time (Bakhtadze et al., 2012a, 2012b).
“Optimization of placement” subsystem allows increasing essentially the productivity and throughput of the warehouse, thereby reducing the cost of goods storage and warehouse logistics in general. On the basis of inventory unit types
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Then the total cost of deficit eliminating at the end of time 𝑇𝑇 will be: 𝑦𝑦(𝑇𝑇) = 𝑇𝑇[𝛼𝛼𝑖𝑖 𝑎𝑎𝑖𝑖 + (1 − 𝛼𝛼𝑖𝑖 )𝑏𝑏𝑖𝑖𝑖𝑖 ]𝑥𝑥𝑖𝑖 . Assuming that the storage cost is the same for all inventory items, further necessary to solve the minimum finding problem for this functional when changing αi (t) (0 ≤ αi (𝑡𝑡) ≤ 1) and all 𝑗𝑗 − 𝑠𝑠. If the storage costs are different, then the plan is being optimized: 𝑦𝑦(𝑇𝑇) = ∑ min 𝑇𝑇[𝛼𝛼𝑖𝑖 𝑎𝑎𝑖𝑖 + (1 − 𝛼𝛼𝑖𝑖 )𝑏𝑏𝑖𝑖𝑖𝑖 ]𝑥𝑥𝑖𝑖 . 𝑖𝑖
𝑗𝑗=1,..,𝐽𝐽 0≤𝛼𝛼𝑖𝑖 ≤1
This optimization problem is solved by means of well-known optimization methods. Determination of the deficient positions reserved amount, while taking into account the projected demand. Suppose that as a result for solving of optimization problem considered in this section, the value 0 ≤ 𝛼𝛼𝑖𝑖 (𝑡𝑡) ≤ 1 is determined exactly. Then the reserved amount is determined by forecasted demand for this inventory items: 𝑅𝑅𝑖𝑖 = 𝛼𝛼𝑖𝑖 𝑥𝑥𝑖𝑖 (𝑇𝑇). 7. DYNAMIC AUTOMATED WAREHOUSE MANAGEMENT SYSTEM BASED ON PREDICTIVE MODELS Dynamic automated warehouse management system based on predictive models interacts and exchanges data with other corporate systems at the pace of the actual production process. The system is characterized by the presence of an intelligent add-in (IA), which will conduce to integration and optimization of warehouse and other production processes in real time regime (Fig. 2). The features of an intellectual system consist primarily in the possibility of operational re-planning based on forecasting and tracking of inventories, taking into account both the current production situation and the intellectual analysis of statistical content. It can also be used to optimize production logistics (for example, reducing the time for completing the supply of components), to fulfill production orders by a certain date, orders for purchasing components by the time they should be used in production, etc.
Fig. 2. Functional diagram of a warehouse management and forecasting system based on a dynamic model. 8. MODEL EXAMPLE Integral brackets are molded at the Foundry, processed at other factories and sent to the assembly plant. The required number and types of brackets – the demand – reveals in 14 days before – and, besides, it can be adjusted daily either up or down. The program for the brackets production is also set 14 days before, but can no longer change. The number of brackets and their types, which can be produced daily, should not be less than the maximum daily requirement during normal operation. The following factors were used for forecasting: planned demand, actual demand, safety stock amount and the minimum volume of deliveries. As additional parameters for forecasting improvement hereby were used: demand factors for integral brackets in a certain time period (history); dynamics of sales and vehicles shipment; demand for bracket types in pieces; initial demand for items by type; waiting of a large urgent order – the probability and number of cars (expert assessment). The following random factors were taken into account: failure – production centers breakdown – output capacity reduction for integral brackets, raw materials prices changing.
The formation of an optimal scenario in a dynamic mathematical model under any change in the production situation is the choice of parameters of the dynamic process from the inductive base of production knowledge based on clustering methods and the method of associative search for analogues. This is an alternative to a request to MES and other information and control systems with the subsequent on-line recalculation of the optimal values of the parameters, which takes much longer time.
Demand prediction is carried out according to the associative search methodology based on the intellectual analysis of statistical data using the wavelet analysis method (which allows to take into account the non-stationarity influence as much as possible). 83
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The graphs (Fig. 3, Fig. 4) show the proposed algorithms calculations results for the integral brackets element for deliveries that are multiples of the standard quantity (400 units). From the charts, it can be seen that forecasting stabilizes the process (ensures its sustainability), allowing you to avoid both excess stocks and shortages. At the same time, while having the 30 days time lag, the number of deliveries was reduced from 9 to 8. The application of the developed algorithms for fixing bottlenecks in any processes occurring at the enterprise may be especially relevant.
optimization, 4 PL logistics implementation, planning optimization, etc. REFERENCES Bakhtadze, N.N. and E.A. Sakrutina (2015). “The Intelligent Identification Technique with Associative Search”, International Journal of Mathematical Models and Methods in Applied Sciences, vol. 9, pp. 418-431. Bakhtadze, N.N., Lototsky, V.A., Vlasov, S.A., and E.A. Sakrutina (2012a). Associative Search and Wavelet Analysis Techniques in System Identification, IFAC Proceedings Volumes, vol. 45, no. 16, pp. 1227-1232. Bakhtadze, N., Lototsky, V., and E. Maximov (2012b). Associative search method in system identification, Pr. 14th International Conference on Automatic Control, Modeling and Simulation, Saint Malo & Mont Saint Michel, France, pp. 49-57. ISBN: 978-1-61804-080-0. Bartodziej, C.J. (2017). The Concept Industry 4.0 An Empirical Analysis of Technologies and Applications in Production Logistics, Springer. ISBN 978-3-658-16501-7. Dijkstra, Е.W. (1959). A note on two problems in connexion with graphs, Numerische Mathematik, vol. 1, no. 1, pp. 269-271. Dolgui, A. and J.-M. Proth (2010). Supply Chains Engineering: Useful Methods and Techniques, Springer, ISBN: 978-1-84996-016-8. Jesse, N. (2016). Internet of Things and Big Data – The Disruption of the Value Chain and the Rise of New Software Ecosystems, IFAC-PapersOnLine, vol. 49, no. 29, рр. 275-282. Lu, W., McFarlane, D., Giannikas, V., and Q. Zhang (2016). An algorithm for dynamic order-picking in warehouse operations, European Journal of Operational Research, vol. 248, no. 1, pp. 107-122. Sabitov, R.A., Smirnova, G.S., Sabitov, Sh.R., Morozov, B.M., Sirazetdinov, В.R. (2015). Adaptive control and operational management system of machine-tool fleet of the manufacturing enterprise, IFAC-PapersOnLine, vol. 48, no. 3, pp. 1236-1241. Smirnova, G.S., Sabitov, R.A., Elizarova, N.Y., and Sh.R. Sabitov (2014). Operational management system of enterprise production processes “1C: MES: cloudy production management”, Automation in Industry, no. 8, pp. 48-51. (in Russian)
1000 800
pcs
600 400 200 0 -200
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Days Safety stock in fact Safety stock for forecast-adjusted supply
Fig. 3. Safety stock dynamics for integral brackets with the intellectual forecasting consideration.
pcs.
600 400 200 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Days Planned demand Demand in fact Demand, forecast Safety stock for forecast-adjusted supply
Fig. 4. Comparative analysis for the integral brackets demand dynamics. 9. CONCLUSIONS Usage of inductive knowledge base allows to predict the appearance of bottlenecks in advance and actually turns the warehouse management system into an expert with all the necessary integral experience, which helps to reduce significantly the financial costs of excessive inventory and, at the same time, to prevent certain components shortage possibility. This approach allows to use clustering capabilities for various processes and to achieve additional economic benefits due to the complex system approach to business processes organization, for example, suppliers quantity
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