The document discusses inventory management models and concepts including:
1) Traditional inventory models make simplifying assumptions about demand, lead times, and costs that may not reflect reality.
2) Probabilistic models use statistical distributions to represent uncertain demand and lead times. Safety stock is calculated based on the probability of stockouts.
3) Service level goals can be set based on the probability of stockouts over an order cycle or per unit demanded. Imputed stockout costs are used to determine optimal reorder points.
Inventory Model with Different Deterioration Rates under Exponential Demand, ...inventionjournals
An inventory model with different deterioration rates under exponential demand with inflation and permissible delay in payments is developed. Holding cost is taken as linear function of time. Shortages are allowed. Numerical examples are provided to illustrate the model and sensitivity analysis is also carried out for parameters.
Data warehouse 21 other varients of star schemaVaibhav Khanna
The snowflake schema is a variant of the star schema.
Here, the centralized fact table is connected to multiple dimensions.
In the snowflake schema, dimension are present in a normalized from in multiple related tables.
The snowflake structure materialized when the dimensions of a star schema are detailed and highly structured, having several levels of relationship, and the child tables have multiple parent table.
The snowflake effect affects only the dimension tables and does not affect the fact tables.
Inventory Model with Different Deterioration Rates under Exponential Demand, ...inventionjournals
An inventory model with different deterioration rates under exponential demand with inflation and permissible delay in payments is developed. Holding cost is taken as linear function of time. Shortages are allowed. Numerical examples are provided to illustrate the model and sensitivity analysis is also carried out for parameters.
Data warehouse 21 other varients of star schemaVaibhav Khanna
The snowflake schema is a variant of the star schema.
Here, the centralized fact table is connected to multiple dimensions.
In the snowflake schema, dimension are present in a normalized from in multiple related tables.
The snowflake structure materialized when the dimensions of a star schema are detailed and highly structured, having several levels of relationship, and the child tables have multiple parent table.
The snowflake effect affects only the dimension tables and does not affect the fact tables.
r for data science 2. grammar of graphics (ggplot2) clean -refMin-hyung Kim
REFERENCES
#1. RStudio Official Documentations (Help & Cheat Sheet)
Free Webpage) https://www.rstudio.com/resources/cheatsheets/
#2. Wickham, H. and Grolemund, G., 2016.R for data science: import, tidy, transform, visualize, and model data. O'Reilly.
Free Webpage) https://r4ds.had.co.nz/
Cf) Tidyverse syntax (www.tidyverse.org), rather than R Base syntax
Cf) Hadley Wickham: Chief Scientist at RStudio. Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University
History and traditional system of medicine.ritamchoudhury
this is a topic based upon pharmacognosy.
here all of the tradional systems of medicine are discused,name of some contributors of pharmacognosy.all the systems like-kampoh system,ayurveda,aromatherapy,siddha,homeopathic,naturopathy,unani,bach flower remidies,yoga,yin-yang theory are also discused here.
r for data science 2. grammar of graphics (ggplot2) clean -refMin-hyung Kim
REFERENCES
#1. RStudio Official Documentations (Help & Cheat Sheet)
Free Webpage) https://www.rstudio.com/resources/cheatsheets/
#2. Wickham, H. and Grolemund, G., 2016.R for data science: import, tidy, transform, visualize, and model data. O'Reilly.
Free Webpage) https://r4ds.had.co.nz/
Cf) Tidyverse syntax (www.tidyverse.org), rather than R Base syntax
Cf) Hadley Wickham: Chief Scientist at RStudio. Adjunct Professor of Statistics at the University of Auckland, Stanford University, and Rice University
History and traditional system of medicine.ritamchoudhury
this is a topic based upon pharmacognosy.
here all of the tradional systems of medicine are discused,name of some contributors of pharmacognosy.all the systems like-kampoh system,ayurveda,aromatherapy,siddha,homeopathic,naturopathy,unani,bach flower remidies,yoga,yin-yang theory are also discused here.
An Inventory Management System for Deteriorating Items with Ramp Type and Qua...ijsc
The present paper deals with an inventory management system with ramp type and quadratic demand rates. A constant deterioration rate is considered into the model. In the two types models, the optimum time and total cost are derived when demand is ramp type and quadratic. A structural comparative study is demonstrated here by illustrating the model with sensitivity analysis.
International Journal of Mathematics and Statistics Invention (IJMSI)inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
Traditional model limitations
1.
2. WORKING AND SAFETY STOCK Safety Stock QUANTITY TIME B Q + S S Working Stock Working Stock
3. IDEAL INVENTORY MODEL B Q + S S QUANTITY Order Lot Order Lot Placed Received Placed Received Safety Stock Reorder Point Lead Time TIME
4. Q + S S Lead Time Lead Time Lead Time REALISTIC INVENTORY MODEL TIME B QUANTITY Stockout
5. SAFETY STOCK VERSUS SERVICE LEVEL .50 1.00 high SAFETY STOCK low SERVICE LEVEL (Probability of no stockouts)
6. STATISTICAL CONSIDERATIONS max M 0 M ) M ( M P 0 M d ) M ( M f CONTINUOUS DISCRETE VARIABLE DISTRIBUTIONS DISTRIBUTIONS M max M 1 B M ) M ( P ) B M ( B M d ) M ( f ) B M ( Quantity Stockout Expected max M 1 B M ) M ( P B M d ) M ( f max M 0 M ) M ( P 2 ) M M ( 0 M d ) M ( f 2 ) M M ( Variance Demand Time Lead 2 E(M > B) P(M > B) B = reorder point in units. M = lead time demand in units (a random variable). f(M) = probability density function of lead time demand. P(M) = probability of a lead time demand of M units. = standard deviation of lead time demand Demand Time Lead Mean Probability of a Stockout
7. PROBABILISTIC LEAD TIME DEMAND DEMAND DURING LEAD TIME (M) PROBABILITY OF A STOCKOUT, P(M>B) SAFETY STOCK REORDER POINT PROBABILITY P(M) 0 M B
8. NORMAL PROBABILITY DENSITY FUNCTION 2 ) ( 2 2 / 2 ) ( M M e M f Lead Time Demand (M) M = 1 - F(B) = P(M >B) f(M) f(B) B Area stockout a of probability B M P B F function distribution cumulative M d M f B F function density probability M f B = > = - = = = ) ( ) ( 1 ) ( ) ( ) (
9. POISSON DISTRIBUTION LEAD TIME DEMAND (M) PROBABILITY P(M) 0.00 0.10 0.20 0.30 0.40 0 4 8 12 16 20 24 M=2 M=4 M=6 M=8 M=10 M=1 P(M) = M M e - M M!
10. NEGATIVE EXPONENTIAL DISTRIBUTION LEAD TIME DEMAND (M) PROBABILITY DENSITY F(M) 0 1/M f(M) = e M/M M
11. NEGATIVE EXPONENTIAL DISTRIBUTION 0.0 0.5 1.0 1.5 2.0 2.5 0 2 4 6 8 10 12 LEAD TIME DEMAND (M) PROBABILITY DENSITY f(M) M=1 M=2 M=3 M=0.5 M=5 f(M) = e M/M M
12. INDEPENDENT DEMAND : PROBABILISTIC MODELS LOT SIZE : 2CR / H REORDER POINT : B = M + S I. KNOWN STOCKOUT COST A. Obtain Lead Time Demand Distribution constant demand, constant lead time variable demand, constant lead time constant demand, variable lead time variable demand, variable lead time B. Stockout Cost backorder cost / unit lost sale cost / unit II. SERVICE LEVEL A. Service per Order Cycle
13. Demand Probability Demand Probability Lead time Probability first week second week demand (col. 2)(col. 4) (D) P(D) (D) P(D) (M) P(M) 1 0.60 1 0.60 2 0.36 3 0.30 4 0.18 4 0.10 5 0.06 3 0.30 1 0.60 4 0.18 3 0.30 6 0.09 4 0.10 7 0.03 4 0.10 1 0.60 5 0.06 3 0.30 7 0.03 4 0.10 8 0.01 CONVOLUTIONS (variable demand/week and constant lead time of 2 weeks)
14. Lead time demand (M) Probability P(M) 0 0 1 0 2 0.36 3 0 4 0.36 5 0.12 6 0.09 7 0.06 8 0.01 1.00
15. INVENTORY RISK ( VARIABLE DEMAND, CONSTANT LEAD TIME ) J S 0 W Q + S -W B TIME QUANTITY L P(M>B) Q = order quantity B = reorder point L = lead time S = safety stock B - S = expected lead time demand B - J = minimum lead time demand B + W = maximum lead time demand P(M>B) = probability of a stockout J
16. SAFETY STOCK : BACKORDERING M B S M d M f M M d M f B M d M f M B S - = ) ( ) ( ) ( ) ( ) ( ) ( ) ( 0 0 0
17. BACKORDERING Cost Stockout Cost Holding TC S + = B M P Q AR H dB dTC S 0 ) ( B M E Q AR H M B ) ( ) ( M d M f B M Q AR SH ) ( ) ( ) ( B AR HR s P B M P ) ( ) (
18. TC s = (B - M)H + E(M > B) = B = 67 E(M > B) = = (68- 67).08 + (69- 67).03 + (70- 67).01 = .17 units TC s = (67- 65)(2)(.30) + = 1.20 + 2.04 = $3.24 B = 68 E(M > B) = = (69- 68).03 + (70- 68).01 = .05 units TC s = (68- 65)(2)(.30) + = 1.80 + 0.60 = $2.40 AR E(M>B) Q 2(3600)(.05) 600 2(3600)(.17) 600 + = - 70 1 68 ) ( ) 68 ( M M P M max 1 ) ( ) ( M B M M P B M + = - 70 1 67 ) ( ) 67 ( M M P M
19. B = 69 E(M > B) = = (70- 69).01 = .01 units TC s = (69- 65)(2)(.30) + = 2.40 + 0.12 = $2.52 + = - 70 1 69 ) ( ) 69 ( M M P M 2(3600)(.01) 600 Therefore, the lowest cost reorder point is 68 units with an expected annual cost of safety stock of $2.40.
20. SAFETY STOCK : LOST SALES ) ( ) ( 0 M d M f M B S B - = ) ( B M E M B S > + - = ) ( ) ( M d M f B M M B B - + - = ) ( ) ( ) ( ) ( 0 M d M f M B M d M f M B B - - - =
21. LOST SALES Cost Stockout Holding Cost TC S = HQ AR HQ s P B M P = = ) ( ) ( B M P H Q AR H dB dTC S = = 0 ) ( B M E Q AR H B M E M B = ) ( ) ( M d M f B M Q AR SH B - + = ) ( ) ( B M E H Q AR H M B = ) ( ) (
22. INVENTORY RISK (CONSTANT DEMAND, VARIABLE LEAD TIME) Q + S S B L m L QUANTITY TIME P(M > B) 0 L = expected lead time P(M > B) = probability of a stockout B - S = expected lead time demand Q = order quantity B = reorder point S = safety stock L m = maximum lead time
23. J S 0 Q + S - W B QUANTITY L m INVENTORY RISK (VARIABLE DEMAND, VARIABLE LEAD TIME) L TIME P(M >B) P(M > B) = probability of a stockout B - S = expected lead time demand B + W = maximum lead time demand Q = order quantity B = reorder point S = safety stock L = expected lead time L m = maximum lead time B - J = minimum lead time demand
24.
25. SERVICE PER ORDER CYCLE c c SL B M P B M P cycles order of no total stockout a with cycles of no SL = > = = 1 ) ( ) ( 1 . . 1
26. IMPUTED STOCKOUT COSTS ) ( ) ( / cost B M P R HQ A AR HQ B M P unit Backorder ) ( ) ( 1 ) ( / B M P R B M P HQ A HQ AR HQ B M P unit sales cost Lost
27. SAFETY STOCK : 1 WEEK TIME SUPPLY (Normal Distribution : Lead Time = 4 weeks) Weekly Demand Safety Stock D D 1000 100 1000 5.00 0 1000 200 1000 2.50 0.0062 1000 300 1000 1.67 0.0480 1000 400 1000 1.25 0.1057 1000 500 1000 1.00 0.1587 4 1000 D S Z S P(M>B)
28. PROBABILISTIC LOGIC Service Levels Service/units demanded, E(M>B) = Q(1 - SL U ) E(M>B) = E(Z) Convolution over lead time Multiply dist. by demand, M = DL, = D L Analytical Combination / Monte Carlo simulation Service/cycle, P(M>B) = 1 - SL c Variable demand, variable lead time Variable demand, constant lead time Constant demand, variable lead time Lost Sale, P(M>B) = HQ / (AR+HQ) Backordering, P(M>B) = HQ / AR Lead time demand distribution ? Known stockout costs ? No Yes Yes No Start
29. RISK : FIXED ORDER SIZE SYSTEMS FOSS Order Quantity (Q) Reorder Point (B) Set by Management EOQ EPQ Service Level Per Cycle Per Units Demanded Known Stockout Cost Lost Sale Backorder Per Outage Per Unit Per Outage Per Unit