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text:
Originally published on Wed December 26, 2012 9:40 am
An update on last weekend's rape of a student in New Delhi, an incident which provoked widespread outrage, and calls for a crackdown on sexual violence in India. Weekend Edition Saturday host Scott Simon talks with NPR's Julie McCarthy in India.
View the discussion thread.

subset: commoncrawl


text:
Rimmel Lash Accelerator Serum
69 reviews
73% would repurchase
Package Quality: 3.6
Price: $$
Package Quality: 3.6
Price: $$
INGREDIENTSWater (Aqua), Pentylene Glycol, PVP, Butylene Glycol, Glycerin, Hydroxyethylcellulose, Phenoxyethanol, Panthenol, Methylparaben, Disodium EDTA, PPG 26 Buteth 26, PEG 40 Hydrogenated Castor Oil, Arginine, Caffeine, Lithium Magnesium Sodium Silicate, Apigenin, Myristoyl Pentapeptide 17, Biotin, Hydrolyzed Keratin, Sodium Hyaluronate, Oleanic Acid, Biotinoyl Tripeptide 1, Sodium Sulfate, Potassium Sorbate, Bambusa Arundinacea (Bamboo) Leaf Extract, Sodium Benzoate
on 7/4/2013 6:07:00 PM
Age: 25-29
Skin: Combination, Tan, Warm
Hair: Black, Straight, Medium
Eyes: Brown
I've always had long lashes but they are not as thick as I would like them to be; they are generally thin and are long in the middle but are shorter at the outer and inner corners of my eyes.
I've read reviews of some different 'lash growth serums' and since this was the only lash growth treatment I could find and the price was okay (I think it was about $10 at Wal-Mart), I decided to give it a try. To date, I've been using this product for about 2 months (I'm almost done my first tube) on both my eyebrows and eyelashes (once a day, nights only). The applicator SUCKS - Rimmel could have made it more like a mascara wand (since it may be easier to apply that way) and the little brush at the tip could have been a bit larger. Be warned that the applicator can get really dirty if you still have makeup residue around your eyes and on your lashes after a wash - it's probably best to wipe the wand every single time you dip it back into the tube (it might also be a good idea to give it a proper cleaning regularly, similar to how you would clean makeup brushes).
I think this products WORKS insofar as it PREVENTS eyebrow/eyelash hairs from falling out FOR A TIME (i.e., they don't fall out as quick. That's what gives lashes the appearance of looking longer). I've noticed that I haven't really lost any lashes naturally or while removing eye makeup (same goes for my brows). My eyelashes do look a TAD BIT longer, definitely not a big noticeable difference though, and I didn't really see a difference in the thickness.
I tried wearing this during the day, underneath my makeup, but it weighs down my lashes too much and it leaves a sticky, white residue. Even wearing this before bed, I wake up with white residue on my brows, lashes, and lids but it doesn't bother me much.
Overall, I think this is a good product for me to PREVENT eyelashes from falling out due to the natural shedding process and/or trauma. Concerning thickness/growth/more lashes, there's probably a better product out there.
on 6/29/2013 11:48:00 PM
Age: 25-29
Skin: Combination, Fair, Not Sure
Hair: Other, Other, Other
Eyes: Other
When I was younger, I overplucked the inside of my brows, and lately I have been trying to encourage them to regrow. This product contained some of the same ingredients as more expensive lash and brow serums for a fraction of the price, so I decided it was worth a try. Within the first few days, I did notice a little regrowth in the overplucked areas, and existing hairs grew longer. One naturally bald spot in the arch of one brow filled in completely. But despite the early promising results, I didn't see any new growth after the first few weeks, and still couldn't leave the house without brow powder. After I stopped using the product, the bald spot in my arch reappeared. So my conclusion is that this product works more on the level of conditioning existing hair, rather than helping regrow hair. If you do see regrowth, you'll have to keep using the product to maintain it.
Since this didn't work as well as it hoped, I am trying Talika right now to see if I get better, more long-lasting results.
on 6/23/2013 4:04:00 AM
Age: 30-35
Skin: Acne-prone, Fair, Not Sure
Hair: Blond, Other, Other
Eyes: Green
I have been using this product for almost 2 months and I am starting to notice a difference with my lashes. I have very fine very blonde lashes. I wear mascara everyday and my lashes became very dry and were breaking off to the point where they were not growing back. I had used the ardel lash and brow serum and it did nothing for my lashes. I threw it out and decided to try the Rimmel serum. I am very impressed with this product. My lashes are getting longer and thicker. My eyelases are not falling out at a rapid rate anymore. Before I was loosing them all the time, but not anymore. For anyone who decides to use this product I would advise you to be patient. The packaging says you will see results after one month, but for me I am reaching two months and have just started noticing the difference. I use it every night before I go to bed and I just brush it along my lash line like and eyeliner. My lashes are definitely softer and stronger.
on 6/5/2013 3:37:00 PM
Age: 19-24
Skin: Normal, Fair, Cool
Hair: Red, Wavy, Fine
Eyes: Brown
Rimmel says their Lash Accelerator Serum with Grow Lash Complex will "help to support the natural growth of beautifully long, remarkably strong lashes. With a unique hybrid applicator for precise application to lashes, lash line, or brows. Around the clock conditioning and fortifying gel helps to support healthy lash and brow growth. Shapes, separates, and sets lashes and brows into place. Improves the look of lashes, making them look longer and fuller." I'd been meaning to try this lash growth serum for quite a while, I finally bought it after an accident at work that caused me to lose a significant chunk of lashes on my left eye. I bought this serum at Ulta in a value pack that included this product and a kohl eyeliner. This duo cost $9.99, which is the regular retail price of Lash Accelerator Serum. This product contains 11ml (.37 fl oz). Rimmel's Lash Accelerator Serum is ophthalmologist tested and safe for contact lens wearers and those with sensitive eyes. This serum was made in America. Rimmel products are tested on animals.
I don't have a lot to say about Lash Accelerator's packaging. This lash enhancing serum comes in a clear tube, with a metallic yellow cap. The tube features the product name as well as a description and directions. The applicator for Rimmel's lash serum certainly is interesting. Its a white plastic spoolie with no actual bristles, just spiraling grooves that catch the lashes. On top of the spoolie, if you want to call it that, is a small brush like you'd find in a liquid eyeliner. While I like the option of how to apply this product, I feel like the applicator is a little awkward to use, but I got the hang of it after a few applications.
Rimmel's Lash Accelerator Serum is a clear get that dries white. Rimmel says this serum can be worn as an overnight treatment or as a base for mascaras, I suggest only using it as an overnight treatment. Though Rimmel says this serum is fine for sensitive eyes, I found it kinda burny and irritating the first couple times I used it. I guess I've developed a tolerance to it or something though, but it still burns if it gets directly in the eye. I've been using Rimmel's Lash Accelerator Serum for about 6 weeks now and I've noticed some good (but not amazing) results. I've applied this serum to both my lashes and brows just about every night, and it took about a week for it to kick in. I don't think this serum directly makes your lashes longer so much as it fortifies and nourishes them, preventing premature lash loss and allowing them to grow longer on their own. Sadly, this serum has barely helped my injured lashes as much as my healthy ones. I feel like this serum thickened my lashes a fair amount. My lashes are also stronger and seem to hold a curl better lately. The enhancement is slight, but makes a difference. Rimmel's Lash Accelerator Serum was very effective for my brows. I don't really need any help in that department, I have very full brows naturally, but I was curious. This serum seems to have thickened my brows, I have to pluck way more often now, so I'm going to discontinue use on the brows except in areas I may over pluck.
Rimmel's Lash Accelerator Serum is good! For the most part it's non-irritating and has no negative side effects. Though the results aren't anything to be in awe of, this serum does what it promises. It definitely promotes brow growth better than it works for lashes, at least for me, but I feel like the results are definitely noticeable in both areas. It doesn't take too long to kick in either, which is great. I won't keep applying this serum to my brows, but Lash Accelerator has become part of my nightly routine. I'm not sure if I'll need to repurchase this serum or not, but I would. I do recommend Rimmel's Lash Accelerator Serum if you're looking for a product to subtly enhance your lashes and brows.
on 6/5/2013 2:07:00 AM
Age: 30-35
Skin: Combination, Fair-Medium, Not Sure
Hair: Blond, Wavy, Fine
Eyes: Blue
Pretty amazing stuff!! My lashes are longer my brows are growing in nicely ( over tweeze plus a bad wax job) I've been using it a week and can see a difference already!!
on 3/28/2013 12:08:00 PM
Age: 19-24
Skin: Oily, Olive, Not Sure
Hair: Brunette, Other, Other
Eyes: Other
This stuff is pretty good! Helped my eyebrows grow out. Didnt make my lashes longer- only made them stronger and thicker. You apply it like mascara! Will repurchase.
on 2/16/2013 8:57:00 PM
Age: 36-43
Skin: Very Oily, Fair-Medium, Not Sure
Hair: Brunette, Other, Other
Eyes: Blue
I use this product at night. When I first started using it I wound up with gummy or webby lashes in the morning. This stopped after a few days, I started working the product into my lashes like mascara instead of applying a quick coating.
I have absolutely no problem applying mascara or makeup in the morning. I have very sensitive skin and have noticed a slight irritation, sometimes, but I have no problems using this product on my lashes. I am wondering if there is a fragrance that irritates my eyes since the irritation only lasts a few minutes.
After 1 week I started noticing a difference in length and fullness. Loose eyelashes (from breakage or just falling out) had practically stopped. I did not have much eyelash loss to begin with.
After 2 weeks the length and fullness was very noticeable.
This seems to help stop breakage, I am not sure about new growth. I saw some reviews with different techniques on applying. I am leery on applying this directly to my skin though.
I have used this for almost 2 months and the bottle is still quite full. It appears the bottle will last 6 months (using on eyelashes only, once at night). I am very surprised at the results. I bought this on a whim, I did not know this type of product was available at stores nor did I know it was so inexpensive. I will continue to use this.
on 1/18/2013 12:02:00 AM
Age: 36-43
Skin: Acne-prone, Fair, Not Sure
Hair: Blond, Other, Other
Eyes: Blue
I waited a good long time to write this review because I wanted to be sure it was really working. Yes, it does for me, but it's subtle and you need to be VERY patient.
I use this on my eyebrows only. when I was younger, I overplucked the inside tips of my brows (the part closer to the bridge of the nose). Big regret! Fast forward to today, and I am constantly looking for ways to extend the length of my brows without it looking fake.
I decided to give this a go because of the low price. I used it for approx 3-4 months before I started to see results. The first month I was using it 2x per day, but then I decided to use it just at night only. So maybe if you are willing to use it 2x per day longer than I did, maybe it'll work faster? I don't know.
My brows are not dramatically different now. But where there were bald spots from overplucking, now there are baby hairs. It has not completely filled in (at this point it's been 6 months), but here is the big thing for me: there is finally enough hair for my eye pencil to "grab" on to and my brows just look so much better! plus, I sometimes feel like they look ok enough to go without brow pencil. again, not a miracle, but better than I expected! but if I ever come across a brow growing product that works better, I'll probably switch to it.
Note: I have a feeling this will only work for overplucked brows. I don't think it will grow hair where there was never hair before. also if anyone is curious, it did not make my brow hairs darker or longer.
Packaging: The applicator is really stupid. I find that a mascara brush works much better. Specifically, I use the cover girl lash perfection brush -- the cap fits perfectly. it's a little too short but by the time the product gets that low, it's pretty murky looking and you might feel like purchasing a fresh tube by then anyway.
on 1/4/2013 9:28:00 AM
Age: 30-35
Skin: Sensitive, Fair, Cool
Hair: Blond, Wavy, Fine
Eyes: Blue
After having success with the more expensive Physicians Formula Last Boost Serum, I realized that this Rimmel lash serum has the same active ingredient as the PF serum (Myristoyl Pentapeptide 17) plus some other goodies like caffeine, which is supposedly good for stimulating hair growth in general though I've only ever seen it used before in products made for the hair on your head.
Results: My lashes are longer and stronger. They don't fall out as often, which is great! It took a few weeks to notice a difference, but since it's so quick and easy to apply at night, and the product isn't too expensive (<$10), I kept at it. My tube has lasted me 4+ months, and I still have probably 1/5 left, so you get a lot for your money.
Compared to Physicians Formula Last Boost Serum: The PF serum, which I've used in clear, doesn't sting quite as often as the Rimmel can, but both have given me the same results. The PF is thinner, which is both good and bad - that means it can be used 2x a day including in the AM under makeup without issues (I never had any), but it also meant that I was never sure that my lash line was coated enough.
Both products are great, but for me, Rimmel has the edge due to the addition of ingredients like caffeine, biotin, sodium hyaluronate, and an additional peptide, Biotinoyl Tripeptide 1.
on 12/9/2012 10:17:00 PM
Age: 44-55
Skin: Dry, Fair, Cool
Hair: Blond, Wavy, Fine
Eyes: Blue
I had very bad false eyelashes applied and when I removed most of my real lashes were pulled out. I tried this and while results are not instant in a month my lashes were back and much fuller. Not a fan of the wand I only use brush and paint on a thin line just above my lash line. No residue left. I'm also using the mascara with the serum works for me.
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text:
Democratic Party, in the United States, one of the two major political parties, the other being the Republican Party.
The Democratic Party has changed significantly during its more than two centuries of existence. During the 19th century the party supported or tolerated slavery, and it opposed civil rights reforms after the Civil War in order to retain the support of Southern voters. By the mid-20th century it had undergone a dramatic ideological realignment and reinvented itself as a party supporting organized labour, the civil rights of minorities, and progressive reform. Since President Franklin D. Roosevelt's New Deal of the 1930s, the party has also tended to favour greater government intervention in the economy and to oppose government intervention in the private, noneconomic affairs of citizens. The logo of the Democratic Party, the donkey, was popularized by cartoonist Thomas Nast in the 1870s; though widely used, it has never been officially adopted by the party.
The Democratic Party is the oldest political party in the United States and among the oldest political parties in the world. It traces its roots to 1792, when followers of Thomas Jefferson adopted the name Republican to emphasize their antimonarchical views. The Republican Party, also known as the Jeffersonian Republicans, advocated a decentralized government with limited powers. Another faction to emerge in the early years of the republic, the Federalist Party, led by Alexander Hamilton, favoured a strong central government. Jefferson's faction developed from the group of Anti-Federalists who had agitated in favour of the addition of a Bill of Rights to the Constitution of the United States. The Federalists called Jefferson's faction the Democratic-Republican Party in an attempt to identify it with the disorder spawned by the "radical democrats" of the French Revolution of 1789. After the Federalist John Adams was elected president in 1796, the Republican Party served as the country's first opposition party, and in 1798 the Republicans adopted the derisive Democratic-Republican label as their official name.
In 1800 Adams was defeated by Jefferson, whose victory ushered in a period of prolonged Democratic-Republican dominance. Jefferson won reelection easily in 1804, and Democratic-Republicans James Madison (1808 and 1812) and James Monroe (1816 and 1820) were also subsequently elected. By 1820 the Federalist Party had faded from national politics, leaving the Democratic-Republicans as the country's sole major party and allowing Monroe to run unopposed in that year's presidential election.
During the 1820s new states entered the union, voting laws were relaxed, and several states passed legislation that provided for the direct election of presidential electors by voters (electors had previously been appointed by state legislatures). These changes split the Democratic-Republicans into factions, each of which nominated its own candidate in the presidential election of 1824. The party's congressional caucus nominated William H. Crawford of Georgia, but Andrew Jackson and John Quincy Adams, the leaders of the party's two largest factions, also sought the presidency; Henry Clay, the speaker of the House of Representatives, was nominated by the Kentucky and Tennessee legislatures. Jackson won the most popular and electoral votes, but no candidate received the necessary majority in the electoral college. When the election went to the House of Representatives (as stipulated in the Constitution), Clay—who had finished fourth and was thus eliminated from consideration—threw his support to Adams, who won the House vote and subsequently appointed Clay secretary of state.
Despite Adams's victory, differences between the Adams and the Jackson factions persisted. Adams's supporters, representing Eastern interests, called themselves the National Republicans. Jackson, whose strength lay in the South and West, referred to his followers simply as Democrats (or as Jacksonian Democrats). Jackson defeated Adams in the 1828 presidential election. In 1832 in Baltimore, Maryland, at one of the country's first national political conventions (the first convention had been held the previous year by the Anti-Masonic Movement), the Democrats nominated Jackson for president, drafted a party platform, and established a rule that required party presidential and vice presidential nominees to receive the votes of at least two-thirds of the national convention delegates. This rule, which was not repealed until 1936, effectively ceded veto power in the selection process to minority factions, and it often required conventions to hold dozens of ballots to determine a presidential nominee. (The party's presidential candidate in 1924, John W. Davis, needed more than 100 ballots to secure the nomination.) Jackson easily won reelection in 1836, but his various opponents—who derisively referred to him as "King Andrew"—joined with former National Republicans to form the Whig Party, named for the English political faction that had opposed absolute monarchy in the 17th century (see Whig and Tory).


text:
Gear Review
Horrid Fit
- Gender: Female
These shoes have the potential to be amazing but the construction is horrid. The top part of the shoe where your foot would be inserted in sewn closed. You would need the skinniest foot without bones, to slip it inside this shoe. I was so heartbroken.
View More Details on The North Face Abby IV Luxe Boot - Women's


text:
Praiz will be ending the year on a high note with the release of his third consecutive video, this one for the hit single 'Rich and Famous'.The video simply tells a tale of a young upcoming star who thinks he's ready for fame and fortune only to meet disappointment everywhere he turns. He then has his shot at fame and eventually succeeds
The video was directed by Josh Clarke.
Expect Praiz who is signed to X3M Music release his debut album in 2013.
Check this out!!
Omg. What a voice!
mil gracias por todos los aepotrs en verdad estoy muy contento con esta web sobre todo por jumbofiles es el mejor de los servidores aka y me encantaria el regreso de mediafire y les pido DE FAVOR QUE UTILICEN EL SERVER LLAMADO (GENERICO) IGUAL DE BUENO QUE JUMBO Y MF
9ce video
Praiz I go crazy when I first heard the song .U will be rich and famous bro,Keep ur gud work up,u will mk it
Praiz I go crazy when I first heard the song .U will be rich and famous bro,Keep ur gud work up
wow! Nice one bro.. diz Video z reality mehn, cuz people only recognise d Rich nd Famous Ones..(())Kudos
oh. there were so many many feelings in this piece. so many tihngs to ponder . 1st, i know how she felt when her friend started growing and she hadn't. it's frustrating and u start to judge your body. Then comes the curiosity and in a certain way, the arousal.With this piece, the arousal blossomed into a preference, is she as lacking as she was when they were younger? does she still lag behind her friend? is that why she 's still painfully aroused by her friend (and maybe other women)? Then, the cancer that ate her friend's breast : was she feeling some mix of happy-sad that her friend was sick but losing the one thing she had always envied her?
The video is awesome
Greatwork praiz!!! I love u from my heart and all from kaduna loves u too.Thank u for nt disapointing ur root(kaduna).you shall be very rich and famous indeed. VIctor aka Vicvessel


text:
8 in 1 Bird Cage Protector
|8 in 1 understands the special needs of pet birds. The 8 in 1 Bird Cage Protector keeps cages free from lice and mites, and helps deodorize the cage at the same time. Application is simple; keep your bird's home clean, healthy and smelling great with just a few easy steps. Protects birds from lice & mites Deodorizes cage as it protects Simple application Directions For Use:It is a violation of Federal Law to use this product in a manner inconsistent with its labeling. Remove paper seal from container and hang Bird Protector on outside of cage with perforations facing cage. For best results, cover cage at night. Clean cage weekly with soap and hot water. Replace when slight odor of paradichlorobenzene cannot be detected in morning after cage is uncovered. Caution: Keep out of reach of children. Contents harmful if swallowed. Storage: Store in a cool dry area away from heat or open flame.Disposal: Do not reuse container. Rinse thoroughly before discarding in trash. Securely wrap original container in several layers of newspaper and discard in trash.Active Ingredients: Paradichlorobenzene.
Mite & Lice Bird Spray
|Ultra Care Mite & Lice Bird Spray is a safe, effective spray that uses an 8-in-1 treatment to kill lice and bird mites on contact, without harming the birds at all. For use on caged birds including parakeets and canaries. 8-in-1 treatment kills mites & bird lice For use on cage birds Completely safe and effective Ultra Care Bird Spray may be sprayed directly on the bird without causing harm. Read all directions before use.
Lixit Hand Feeding Syringe
|The Lixit Hand Feeding Syringe is used to feed baby animals that have not yet begun to eat on their own. On the other end of the age spectrum, it can also be used to feed sick or older small animals that have lost the ability to eat on their own. Hand feeding syringe for baby & older animals Feeds small animals that cannot eat on their own Oral use only


text:
The Sweetest Dream
- Style:
- Height: 20m
- Description:© (nmonteith)
A lot of jiggery pockery for one pitch! This section of rock can been seen while spending a lot of time belaying beginners up 'Sweet Dreams'. It was put up for reasons of scratching and itch rather then getting from the bottom to the top. Using double ropes rap in from 2 ring bolts at the top of the of the Sweet Dream 17 'Variant Finish' to the end of the traverse pitch belay anchor. Rap down again over to the right to a 3 U bolt semi hanging belay.
Climb right into the corner aiming for some fixed hangers (placed for some other climb going up the corner) As you climb in the corner you need to unclip some draws behind you so to reduce rope drag. From the fixed hangers follow U bolts out around the corner out the exposed orange face across to the right hand side out the head wall up to the ledge where there is a double U bolt belay. It doesn't go any where from here as the rock looks dubious. Back jump to clean or bring up seconder then rap off left to Sweet Dream's terror traverse. Then climb up 'Sweet Dreams'.
- Ethic: inherited from West Face (Main Area)
Sublime Point is an old climbing area, with many routes dating from the 1950s and 1960s. Aid and mixed climbing was the fashion then, and the easier routes can be pretty bold for a modern climber. In the last 10 years there has been a lot of well protected sport routes added. This has been seen as controversial by some climbers who consider the crag a 'mixed' climbign venue.
- Tags:
Route History:
- There is no known route history.
Located in Middle Cliffs approx:
Long/Lat: 150.337527,-33.735789
Some content has been provided under license from: © Australian Climbing Association Queensland (Creative Commons, Attribution, Share-Alike 2.5 AU)
Route Grade Citations
|Community registered grade
|24 *
|ACA Route Register
Route quality
Lead Styles
Comments keywords
Learn about creating circuits.


text:
Test 1
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License renewal—Continuing education—Penalties.
(1) Certification must be renewed on or before the expiration date shown on the certificate. The expiration date is the certificate holder's birth date. Effective July 1, 2011, each certified court reporter shall verify they have completed a minimum of five continuing education units annually at renewal in a manner defined by the director. Excess continuing education units from the previous reporting year shall not be carried over. Failure to renew the certificate by the expiration date will result in a penalty fee in an amount determined by the director. Certification may be reinstated for up to three years by payment of all renewal fees and a penalty fee for the period for which the certification had lapsed and documentation of five continuing education units completed in the past year.
(2) Continuing education units shall have direct relevance to the professional development of the certified court reporter. The program must be led by an instructor, be interactive, and involve assessment or evaluation. Approved programs include, but are not limited to, the following:
(a) Language skills:
(i) English or a foreign language;
(ii) American Sign Language;
(iii) Grammar;
(iv) Punctuation;
(v) Proofreading;
(vi) Spelling;
(vii) Vocabulary;
(viii) Linguistics, including regional dialects or colloquialisms;
(ix) Etymology;
(x) Word usage.
(b) Academics:
(i) Medical terminology and abbreviations related to any medical or medically related discipline (e.g., anatomy, psychiatry, psychology, dentistry, chiropractic, podiatry);
(ii) Pharmacology;
(iii) Surgical procedures and instruments, with emphasis on terminology and concepts encountered in litigation;
(iv) Pathology and forensic pathology, including DNA and other terminology encountered in litigation;
(v) Legal terminology and etymology;
(vi) Legal research techniques;
(vii) Presentations on various legal specialty areas (e.g., torts, family law, environmental law, admiralty, corporate law, patent law);
(viii) History of legal systems;
(ix) Technical subjects, with emphasis on terminology and concepts encountered during litigation (e.g., construction, accident reconstruction, insurance, statistics, product testing and liability, various engineering fields).
(c) Case law, federal and state statutes, and regulations:
(i) Federal and state rules of civil and criminal procedure and rules of evidence;
(ii) Codes of federal and/or state regulations;
(iii) Presentations on legal proceedings (depositions, trials, federal and state appellate procedure, administrative proceedings, bankruptcy proceedings, workers' compensation proceedings);
(iv) Any changes to (a), (b), and (c) of this subsection as they affect the certified court reporter.
(d) Technology and business practices:
(i) Computer skills;
(ii) Voice recognition technology;
(iii) Videotaping, video conferencing;
(iv) Reporting skills and practices (e.g., readbacks, marking exhibits, administering oaths);
(v) Transcript production, formats, indexing, document management;
(vi) Technological developments related to court reporting, real-time reporting, CART, or captioning;
(vii) Office practices, office management, marketing, accounting, personnel practices, public relations;
(viii) Financial management, retirement planning, estate planning;
(ix) Partnerships, corporations, taxation, insurance.
(e) Professionalism and ethics:
(i) Standards of court reporting practice applicable to individual states or governmental entities;
(ii) Professional comportment and demeanor as it relates to judges, attorneys, fellow reporters, witnesses, litigants and court and law office personnel.
(f) CPR/first-aid classes.
(g) In-house courses offered by court reporting firms.
(h) Vendor sponsored training, with the exception of sales presentations.
(i) Community based programs.
(j) Meetings that include educational or professional development presentations that otherwise meet Washington state criteria for award of continuing education units;
(k) Documented pro bono services on an hour-for-hour basis including, but not limited to:
(i) Presence at a court hearing or deposition;
(ii) Transcription;
(iii) Editing;
(iv) Proofreading.
(l) Documented teaching, research or writing for a planned, directly supervised continuing education experience that fulfills continuing education criteria where no payment is received. Continuing education units will be awarded only once for each separate item.
(3) Any course or activity previously approved by any nationally or state recognized association for court reporting professions shall be approved for continuing education units.
(4) Courses offered with a documented grade of C or better at an accredited college or university will be awarded continuing education units at the following rates:
(a) Semester course: 6 continuing education units.
(b) Trimester course: 5 continuing education units.
(c) Quarter course: 4 continuing education units.
(5) Activities that are not acceptable for continuing education units include, but are not limited to, the following:
(a) Attendance at professional or association business meetings or similar meetings convened for the purpose of election of officers, policymaking, or orientation;
(b) Leadership activities in national, state, or community associations and board or committee service;
(c) Attendance at entertainment, recreational, or cultural presentations;
(d) Recreation, aerobics, massage, or physical therapy courses or practice or teaching of same;
(e) Classes in the performing arts, studio arts, or crafts or teaching of same;
(f) Tours of museums or historical sites;
(g) Social events at meetings, conventions, and exhibits;
(h) Visiting vendor exhibits or attending vendor sales demonstrations;
(i) Jury duty;
(j) Any event for which the attendee receives payment for attendance;
(k) Any event which is part of the attendee's regular employment or is attended for the purpose of gaining employment;
(l) On-the-job training or other work experience, life experience, previous work experience.
(6) Individuals shall maintain documentation of continuing education units for at least three years and provide them to the department on request.
(7) An individual who fails to renew their certification by the expiration date forfeits all rights to represent themselves as a "shorthand reporter," "court reporter," "certified shorthand reporter," or "certified court reporter" until the certificate has been reinstated.
(8) An individual who has allowed the certification to expire for three years or more is required to file a new complete application and fee and must pass the state-approved examination. Upon passage of the exam a certificate will be issued.
[Statutory Authority: Chapter
, and 2010 c 49. WSR 11-01-119, § 308-14-100, filed 12/20/10, effective 7/1/11. Statutory Authority: Chapter
RCW and RCW
. WSR 90-10-009, § 308-14-100, filed 4/20/90, effective 5/21/90.]


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This job is no longer available
Further information
Our client is a provider of highway maintenance and asset care contract services to an impressive portfolio of public sector clients. With a view to service a significant forward order book, they are recruiting operationally focused personnel.
The Role
- You will act in a highly client facing role and you will lead by example ensuring that KPI's are achieved and budgets adhered to.
- You will focus on implementing a best in sector health and safety policy, ensuring that suppliers and sub-contractors adhere to the team ethos.
The Person
- Of obvious graduate calibre, you will have the ability to lead a geographic P&E of £40 million plus.
- A true team player, you will enjoy operating in a highly client facing role ensuring the adherence to quality and value.
In the first instance please forward relevant CVs by post or email including your current remuneration package quoting ProD/324/27


text:
Disclosed herein are systems and methods for demand forecasting that enable multiple-scenario comparisons and analyses by letting users create forecasts from multiple history streams (for example, shipments data, point-of-sale data, customer order data, return data, etc.) with various alternative forecast algorithm theories. The multiple model framework of the present invention enables users to compare statistical algorithms paired with various history streams (collectively referred to as "models") so as to run various simulations and evaluate which model will provide the best forecast for a particular product in a given market. Once the user has decided upon which model it will use, it can publish forecast information provided by that model for use by its organization (such as by a downstream supply planning program). Embodiments of the present invention provide a system and method whereby appropriate demand responses can be dynamically forecasted whenever given events occur, such as when a competitor lowers the price on a particular product (such as for a promotion), or when the user's company is launching new sales and marketing campaigns. Preferred embodiments of the present invention use an automatic tuning feature to assist users in determining optimal parameter settings for a given forecasting algorithm to produce the best possible forecasting model.
1. A method for forecasting future demand, said method comprising the steps of:
setting up a database and defining Demand Forecasting Units (DFUs), each of said DFUS pertaining particular product at a particular location;
preparing a forecast for each of the DFUS;
reviewing said prepared forecasts;
and for one or more of said DFUS, publishing said reviewed forecasts associated with said DFUS;
wherein said forecast preparing step further comprises: identifying active DFUS, and for each of said active DFUS: preparing a history stream;
associating a demand forecasting algorithm said history stream;
and using said history stream and said demand forecasting algorithm to generate an active DFU forecast;
wherein said step of using said history stream and said demand forecasting algorithm to generating an active DFU forecast further comprises: selecting history data related to said history stream, and performing a least-squares regression on said history data;
wherein said step selecting history data related to said history stream comprises identifying a maximum number of terms said history stream, and wherein the step of performing a least-squares regression comprises using only the maximum number of terms;
wherein said step of using said history stream and said demand forecasting algorithm to generating an active DFU forecast further comprises testing of said terms for significant amplitude.
2. The method of
This application claims priority from U.S. Provisional Patent Application Ser. No. 60/243,425, filed Oct. 27, 2000, the disclosure of which is hereby incorporated by reference in its entirety.
The present invention pertains to systems and methods for supply chain planning and demand forecasting. In particular, the present invention pertains to systems and methods for proactively predicting demand across multiple levels of the supply chain so as to avoid costly mismatches of demand and supply.
As is appreciated by those familiar with the art, mismatches of demand and supply are costly to sellers in a competitive market because such mismatches often result in missed sales opportunities, lost profits, excessive expediting costs, lost market share, and poor customer service. To maximize sales and marketing effectiveness, companies must accurately predict future customer demand and use this information to drive their business operations from manufacturing to operations to distribution. This need for accurate predictions of demand is especially important for those involved in e-business due to the ease with which buyers can find alternative sellers that can satisfy their demand.
In the manufacturing and distributing industries, supplying products in response to the current level of customer demand with a minimum of overstocking reduces stocking costs and distribution expenses and thus leads to a reduction of the sales unit price of products. Typically, this also leads to an enhancement of profit margins. It is therefore necessary for sellers to forecast product demand precisely such that they then can determine a sales plan, production plan, and distribution plan according to an accurate forecast of the demand trend of customers.
Conventional methods of forecasting demand by analyzing the trend of past sales results are performed with the goal of the forecaster being to apply the most accurate statistical analysis techniques and econometric modeling to provide the most accurate forecast possible. In these conventional methods, time series forecasting is performed which develops and uses various forecasting algorithms that attempt to describe the knowledge of the business and fluctuation trend of sales results as evidenced by past history in the form of a rule. Developing such forecasting algorithms (as well as computerized systems for utilizing such algorithms) is typically a labor-intensive task.
Unfortunately, however, it is common for product demand trends to change in a short lifecycle. When this is the case, the data used in providing a forecast rapidly becomes old and the precision of forecasting lowers. Therefore, in order to keep a high precision in forecasting, algorithms (and historic data points used to generate those algorithms) must be maintained on an ongoing basis as well as be able to adjust their forecasts relatively easily.
In addition to the difficulty in computing business demand in general is the complexity introduced by different periodic demand patterns in certain industries based upon day of the week, seasons of the year, or other recurring events. For example, in certain industries, such as the restaurant or retail industries, foot traffic, product preferences and sales volume vary according to day of the week. Similarly, predictable seasonal, annual, and/or periodic fluctuations in product demand are present to some extent in many industries. In addition, non-periodic events promotional programs, local events, holidays, and the like, all alter the demand levels faced by a business. An accurate forecast of demand must be able to accommodate these periodic and non-periodic effects on demand level. Unfortunately, the complexity of formulating a single algorithm for doing so accurately has traditionally relegated businesses to making educated guesses regarding what type of demand to expect based upon their experience and records regarding past business demand. As one might expect, as businesses become larger and more complex, making such predictions becomes more problematic and risky. Further, it is unpractical, if not impossible, for a human being to calculate and predict fluctuations in business demand on a more frequent basis (such as hourly or even daily) even though it may be desirable to do so.
Therefore, since one of the more important issues encountered in production planning stems from the uncertainties associated with future demand for products, a vast volume of literature and efforts within the industry has attempted to address this issue. Presently, however, production, materials and transportation planning based upon forecasted demand still presents a significant challenge. While there have been many studies in the demand planning theory area, the advances achieved thus far are either based upon oversimplified assumptions or alternatively are computationally infeasible for real world application. Thus, heretofore there has not been developed a flexible yet substantially automated manner by which sellers in a competitive market can satisfy their demand forecasting needs.
By way of example, U.S. patent application Ser. No. 6,049,742 to Milne et al. discloses a computer software tool for comparing projected supply planning decisions with expected demand profiles. The computer system disclosed by Milne provides routines that compare projected supply with actual experienced demand to help the user of the software tool to configure it to best meet their business's requirements. Unfortunately, like most known demand forecasting methodologies, Milne is inflexible in that it does not help users to develop and identify improved models by comparing multiple alternative models for various products within various markets.
Similarly, U.S. patent application Ser. No. 6,138,103 to Cheng et al. discloses a decision-making method for predicting uncertain demand. The Cheng system uses a matrix to represent potential demand scenarios and their relative probabilities of occurring, and these matrices are then used to calculate a production-planning schedule based upon the most probable outcome of the uncertain demand. Cheng, like Milne, fails to teach manners for developing alternative algorithms for forecasting future demand as well as fine-tuning such demand forecasting algorithms.
Additionally, U.S. Pat. No. 5,459,656 to Fields et al. discloses a system that measures and stores business demand data over a plurality of time intervals and projects the business demand for products in near-future time intervals using percentage-based demand curves. The Fields system allows the creation of a plurality of demand curves for the items to determine near-future demand by using defined functions and variables. Business demand projections for current and near-future time intervals can then be revised in response to variances in actual business demand data as it received. Fields, however, fails to produce forecasts that proactively take into account many factors that impact upon future demand including the variability of individual markets and the impact of product promotions.
Finally, U.S. Pat. No. 6,032,125 to Ando discloses a demand forecasting system that allows users to forecast demand based upon algorithms derived from data of various time periods including recent months, similar periods in previous years, and combinations thereof. The Ando patent, however, fails to take into account that data regarding past demand can come from various sources within a single organization or supply chain (such as sales data, returns, wholesale data, etc.). Further, Ando fails to teach systems or methods for deriving various demand forecasting models and revising those models over the passage of time.
Thus, there remains a need in the art for improved systems and methods that can proactively develop alternative models to predict demand across multiple levels of an organization's supply chain so as to avoid costly mismatches of demand and supply.
In light of the drawbacks inherent in the prior art, it is an object of the present invention to provide a system and accompanying methods for accurately forecasting future demand for many products and product types in many markets.
Also, it is an object of the present invention to provide a system and related methods that enables organizations to produce and compare alternative models of forecasted demand in order to constantly improve demand-forecasting capabilities.
Concurrently, it is an object of the present invention to provide such a system and method that operates across distributed computer networks whereby users can be located at various remote locations and the system can publish selected forecasts for use by an entire organization including downstream supply, manufacturing and transportation planning systems.
Similarly, it is an object of the present invention to provide a system and accompanying methods producing and identifying optimal demand forecasts that take into account independent causal factors, such as new competitive products and price promotions, that will impact upon future demand.
Further, it is an object of preferred embodiments of the present invention to a system and related method whereby users can easily account for current demand trends without having to produce an entirely new forecast.
Additionally, it is an object of preferred embodiments of the present invention to provide a system and related methods that allows users more easily to produce and compare forecasts for related products, products within related markets, and for products taking into account different forecasting models.
In response to the above-described and other needs, the present invention provides a solution for identifying and taking into account the many critical factors that drive demand by providing a multiple model framework that allows multiple alternative forecasting algorithms, including well known statistical algorithms such as Fourier and multiple linear regression ("MLR") algorithms and proprietary algorithms useful for modeling the causal factors relating to various products and/or industries, to be associated with various data streams of demand history to product advanced forecasting models. The present invention helps users to determine the forecast algorithm that best suits a given problem/industry/business from the available history streams of demand data by enabling the production of various forecasts for comparison with one another and, eventually, with incoming data relating to actual demand.
By employing multiple models for a single product or location, the present invention enables multiple-scenario comparisons and analysis by letting users create forecasts from multiple history streams (for example, shipments data, point-of-sale data, customer order data, return data, etc.) with various alternative forecast algorithm theories. As will be readily appreciated by one of ordinary skill in the art, there are various well-known and proprietary statistical algorithm alternatives that can be used in particular circumstances to predict future demand based upon historical data. The multiple model framework of the present invention enables users to compare statistical algorithms paired with various history streams (collectively referred to as "models") so as to run various simulations and evaluate which model will provide the best forecast for a particular product in a given market. Once the user has decided upon which model it will use, it can publish forecast information provided by that model for use by its organization (such as by a downstream supply planning program).
Preferred embodiments of the present invention use an automatic tuning feature to assist users in determining optimal parameter settings for a given forecasting algorithm to produce the best possible forecasting model. This feature significantly reduces the time and resources necessary for users to produce an acceptable demand forecast from history streams of demand data.
Embodiments of the present invention provide a system and method whereby appropriate demand responses can be dynamically forecasted whenever given events occur, such as when a competitor lowers the price on a particular product (such as for a promotion), or when the user's company is launching new sales and marketing campaigns. Utilizing the multiple model framework according to the present invention allows the selection, customization, and fine tuning of the appropriate forecasting algorithm to apply to a given demand problem by producing a plurality of forecasts, each forecast taking into account different demand causal factors and demand data history, and identifying and optimal forecast for adoption. According to such embodiments, users can accurately predict customers' buying plans and thereby forecast the demand for a product or product line by identifying and quantifying key variables that drive demand from the available history streams of demand data.
Embodiments of the present invention recognize that there are often several different areas for which forecast information is obtained. To this end, the multiple model framework of the present invention supports collaborative and comparative forecasting. Such collaborative and comparative forecasting can occur both internally within a single organization as well as externally across an extended supply chain community consisting of many business partners. In this manner, demand history data can be shared to create more accurate demand forecasts and adopted forecasts can be published across an entire organization or extended supply chain community. As will be readily appreciated by one of ordinary skill in the art, the demand forecasting capabilities of the present invention can be distributed across an entire enterprise or alternatively can be used regionally for specific geographic areas of an enterprise.
The present invention also provides intelligent event modeling capabilities and allows for management overrides and forecast adjustments given newly received data regarding changes in actual demand. As such, event data is incorporated into forecast models on an ongoing basis to help adjust forecast decisions on the fly and refine the forecasting process for future endeavors. By including both market planning and demand planning capabilities, the present invention links product mix, promotion, and price analyses with traditional demand forecasting.
Preferred embodiments of the present invention are computer networks and related methods that facilitate the forecasting of the demand of goods within a supply chain and associated planning functions. Preferably, in any one of the embodiments of the present invention a front-end user interface permits user to interact with one or more databases to define a plurality of decision making algorithms to customize those algorithms to leverage the expertise of the user regarding the organization. Additionally, the front-end user interface permits users to review and modify demand forecasts and adopt particular approved forecasts for publishing across the users' organization.
Embodiments of the present invention also utilize interactive graphical interfaces to help suppliers understand the impact of events on demand. Visibility of demand is thereby given across the entire trading network using collaborative forecasting capabilities that allow user to monitor demand throughout the organization. By helping to ensure that the entire organization is, or even group of collaborating organizations are, planning with the same forecast numbers, the present invention helps smooth demand variation across the trading network to facilitate more effective collaboration.
The present invention acts as an early-warning system, predicting future customer demand, alerting of potential supply problems, and finding patterns undetected by traditional solutions. It enables a company within a supply chain to understand customers' demand drivers, to more accurately predict customers' future needs, and to unify disparate planning processes through its multiple model framework and collaboration with other members of the supply chain.
Additional features and advantages of the invention are set forth in the description that follows, and in part are apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention are realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed.
The accompanying drawings, which are included to provide further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention. In the drawings with like reference numbers representing corresponding parts throughout:
Reference is now made in detail to the preferred embodiment of the present invention, examples of which are illustrated in the accompanying drawings.
Generally for a given seller organization that operates as a seller or supplier, demand forecasting must be performed on a recurring basis for each item in each market. Since the demand for an item varies according to the market and time frame in which it is selling, demand forecasts according to the present invention are identified according to its demand forecasting unit ("DFU"). As depicted in
The use of DFUs to categorize and organize forecasts according to preferred embodiments of the present invention allows users to correlate and associate related forecasts for a particular product (demand unit), for particular products within a given product category (demand group), for like products being sold to a particular type of customer (demand group), for a product or like products within a given market (location), or for a particular product within a particular market wherein different a forecast algorithm and history stream combination (model) is employed. As shown in
Referring now to
Similar to model variants, DFUs that differ only with respect to locations are referred to herein as "location variants." As will be readily appreciated by one skilled in the art, demand at different locations can often be sufficiently parallel (such as for established products in separate and distant, yet similar, cities) such that the accuracy of a forecast for one DFU can be assessed by comparing the forecast with existing forecasts for its location variants.
Thus, due to the multiple model framework ("MMF") employed by the present invention, DFU model variants can have multiple types of history and multiple forecast algorithms combined to form several alternative models for a given product. In this manner, forecasts for DFUs which are model variants of one another can thereby be created for comparison and eventual adoption of the best model as described below.
Additionally, a judgement forecast (as defined and described below) for a first demand unit can be made taking into account the statistical forecast created for a second demand unit in the same demand group (i.e., such as in the case of similar products within the same product category). This aspect of the invention is especially useful in situations whereby a first product does not have a significant amount of demand history data and is similar enough that the user can expect the second product to have behave in a manner similar to how the first product is forecasted to behave.
The present invention recognizes that there are often several different areas from which forecast information is obtained. To this end, the multiple model framework ("MMF") the present invention can be employed to support collaborative and comparative forecasting. Such collaborative and comparative forecasting as described herein can occur both internally within a single organization as well as externally across an extended supply chain community consisting of many business partners. By employing MMF functionality, the present invention enables multiple-scenario comparisons and analysis by letting users create forecasts from multiple history streams (for example, shipments data, point-of-sale data, customer order data, return data, etc., originating from various sources) using various alternative forecast algorithm theories.
As will be readily appreciated by one of ordinary skill in the art, there are various well known and proprietary statistical algorithms that can be used alternatively in particular circumstances to predict future demand based upon historical data. The MMF of the present invention enables users to compare statistical algorithms paired with various history streams (collectively referred to as "models") so as to run various simulations and evaluate which model will provide the best forecast for a particular product in a given market. Once the user has decided upon which model it will use, it can publish forecast information provided by that model for use by its organization (such as, for example, by a downstream supply-planning program or shipping managers).
In preferred embodiments of the present invention, by employing the MMF organizations can import forecast values from various external groups or other parts of their organizations such as, for example, already completed sales or marketing estimates, to compare to with forecasts produced by statistical models is systems of the present invention. In this manner, statistical forecasts produced by a system or method of the present invention can be compared with external judgement forecasts to help users refine models and make forecast publication decisions accordingly.
To generate a particular forecast, the present invention provides organizations with the ability to calculate a variety of forecasts according to alternative models and then select the results of a particular forecast model that they feel is most accurate and would like to submit (or "publish") for subsequent supply planning. Algorithms as utilized in the MMF are composed of a series of mathematical calculations that are executed based upon history stream data in order to create a statistical forecast. Preferred embodiments of the present invention enable users to achieve a balance between strictly statistical forecasts and judgement forecasts by controlling the use of history data. Statistical forecasts assume that products generate stable demand. As such, statistical forecasts are typically more useful to reflect the characteristics of mature products that are produced and maintain long life cycles with many customers. These forecasts rely upon a base history (i.e., history not containing such anomalous or unusual data points) for predictable and repeatable demand.
On the other hand, judgement forecasts focus strongly on human estimates of unstable demand. Such forecasts are most useful with new products that are highly promoted, those products that have few customers, those that are fairly new, and/or those that have a short life cycle. Judgement forecasts are used for special events and non-repeatable occurrences and typically rely upon non-base history streams.
Referring to
When choosing the appropriate statistical algorithm to associate with a particular DFU history stream (i.e., combine them to create a model) according to the present invention, the appropriate algorithm should preferably be chosen based upon the characteristics of the underlying product. For example, Table 1 below illustrates how a particular product's life cycle or characteristics can have a strong influence upon its demand patterns.
According to preferred embodiments of the present invention, the demand planning system supports a combination of both well-known traditional algorithms and proprietary algorithms.
The task of creating and maintaining a statistical model to be associated with a particular DFU must take into account two basic elements in order to provide realistic statistical forecasts; it must take into account historical data to generate the statistical algorithm, and must project historical demand patterns into the future. Many statistical algorithms are based on Fourier series (due to the cyclical nature of the Fourier series) and contain customized sets of coefficients (determined from past historical data) that are intended to project demand for a given product into the future. Additionally, multiple linear regression ("MLR") is also used in statistical algorithms to calculate forecasts for products that have multiple external factors (causal factors) such as price, weather, or demographics which influence the selling patterns (i.e., demand) of products.
By way of example, it is known in the art of statistical demand forecasting to utilize Fourier series to model the demand cycle of stable products that have either constant demand or demand that changes at a predictable constant and/or periodic rate. Fourier series operate by fitting sine and cosine waves with increasing frequencies and phase angles into a time series. The first coefficient in a Fourier series when used for demand planning represents the overall level (or mean) of demand (a forecast 401 a taking into account mean only is depicted in
Additionally, statistical forecasting algorithms derived from MLR of demand causal factors are also used in industry. MLR extends the Fourier approach by integrating one or more causal factors (that is, external demand driving independent variables like price, weather, demographics, and other known economic indicators) into a trend or cyclical forecast to help predict the influence of such causal factors upon the selling patterns of particular products. MLR-based algorithms can be best applied when there are multiple known external factors that have a significant impact upon sales or demand. Often times, product price and advertising efforts are particularly strong causal factors. Thus, MLR utilizes level, trend, and seasonal effect predictors like the Fourier approach but also integrates independent variable causal factors and event data into the forecast. As evidenced by a comparison of forecasts 401 c and 401 d in
Once the maximum number of terms and the history data is selected, a least squares regression is performed on the history as is known in the art of statistical analysis at step 503. At step 503, the process 500 attempts to find the coefficients of the forecasting algorithm in question that produces a minimum squared error. In essence, step 503 of process 500 will attempt to fit the best model forecast to the data using the maximum number of terms allowed.
At this point, an initial forecasting model is produced. At step 504, process 500 performs a significant amplitude test to determine how many seasonal effect terms (an amount less than or equal to the maximum amount of terms set at step 502) should be included in the final model. Starting with the peak or valley (i.e., sine wave) with the smallest amplitude, step 504 progressively eliminates the smallest peak or valley coefficient until it finds the smallest amplitude peak or valley that still meets an amplitude significance test (such as exceeding a minimum percentage effect on base, i.e., level and trend, demand or by exceeding a threshold related to a value reflecting the amount of error in the model). As soon as the first peak or valley that has significant amplitude is found, the system stops checking and concludes that the remaining peak and valley coefficients represent the right amount of seasonal effects for the model. Thus, the value of the significant amplitude test factor can make it more or less likely for the process 500 to include many seasonal waves in the final forecasting model.
At step 505, a second least squares regression is performed on the history data selected at step 501. In this regression, however, the process 500 assumes the appropriate amount of seasonal effect terms is represented by the amount of peaks and valleys found to have significant amplitude at step 504. The least squares regression of step 505 thereby produces a final forecasting model.
As will be readily appreciated by one of ordinary skill in the art, a user can calculate a demand forecast employing an MLR algorithm by including only those variables that have a significant impact upon demand or alternatively by employing many causal factors. In either case, MLR-based algorithms automatically calculate the effect of each individual causal factor on sales (such as by calculating the T-statistics and associated P-values for each of the coefficients). In calculating these effects, the MLR model uses future causal factors data such as, for example, a proposed change in price.
Further, proprietary demand planning and forecasting algorithms are available that assume that business is not constant. Such proprietary algorithms are useful to model products with sporadic demand short life cycles, such as, for example, computer hardware. The present invention is adapted to utilize one or more of such proprietary models that have been or will be developed to forecast such sporadic product life cycles.
By way of example, according to the Fourier and MLR algorithms employed in embodiments of the present invention, the forecasting models derived therefrom can be generically described according to the equation
For the Fourier (i.e., seasonal) derived components of a forecast model, the S portion may be comprised of one or more seasonal effects (i.e, expected recurring peaks and valleys in demand). The statistical regression as described above, using the appropriately segmented history data streams, calculates a series of seasonal effect coefficients of automatically such that for time index 't' according to the equation:
For the MLR algorithm derived components of a forecast model, the CF portion may be comprised of one or more causal factor variables ("CVs") while related to, for example, price, weather and demographics. The statistical regression techniques as described above, using the appropriately segmented history data streams, calculates one or more causal factor variable coefficients automatically such that
Mathematically, to generate an MLR-based model, the present invention solves the following linear model for each causal factor identified by the user:
As will be readily appreciated by one of ordinary skill in the art, once causal factors for the demand of a particular DFU have been identified, the historical and anticipated future values (for example, a planned price discount for a particular demand unit) of those causal factors must be inputted by a user and stored in the system, such as in a database for use by the demand forecasting system in creating a forecast with a model that uses causal factors.
Once a MLR forecast has been generated and the forecast has been compared with actual demand the present invention enables users to fine-tune to coefficients of the MLR forecasting algorithms. For current history points that are anomalies (excessively high or low and will not be repeated in the future), demand planners can mask specific history points that are to be excluded from the model.
In preferred embodiments of the present invention, users can be prompted to create causal factor types when establishing history streams for DFUs that are going to use MLR algorithms for modeling. A causal factor type usually represents a category or grouping of causal factors in order to aggregate data points. For example, holidays or price changes can serve as causal factor types. Once causal factor types have been created, the user can be prompted to define individual causal factors that may be present within the history streams. For example, all holidays used as causal factors can share the same causal factor type of "holiday". However, two separate holidays, such as Christmas and Easter, will have individual history streams that are related to the historical demand changes that occur whenever the causal factor occurs.
For any causal factors that are included in a forecast algorithm calculation, the coefficient values and statistical values (such as T-statistics) will be stored and associated with the causal factor history. As is known in the art of statistical analysis, the T-statistic is used to measure the significance of an individual variable upon the result of a multivariable function. Generally speaking, a variable (causal factor) has a significant impact upon the value of the function (forecasted demand) if the T-statistic is equal to or greater than two, or equal to or less than negative two.
Referring to
Whenever products are added to or removed from the user's organization's offerings, the corresponding DFU information must be added, edited or removed from the DFU database. Thus, also at step 601, DFUs and their constituent parts are continuously updated, modified or deleted to reflect such product changes. Such changes to the DFUs are stored in the appropriate databases such that they then are reflected in subsequent runs of the demand planning process 600.
As shown by the figure, step 602 immediately follows step 601 and entails preparing a forecast for each DFU selected in step 601. Step 602 essentially comprises the performance of a set of procedures for generating a demand forecast; this set of procedures comprises a forecast generation cycle that will be disclosed in more detail by the discussion of
Referring now to
As will be readily appreciated by one of ordinary skill in the art, demand history data often includes data points (such as spikes or valleys in demand) that can cause problems if they are included within calculations to predict future demand. Examples of such problematic data include variation in demand caused by unusual market conditions, decreases in demand due to obsolete or superseded products, general data errors, and the inability of the user to distinguish between base and non-base data. In order to account for such problematic data in base or non-base history, at step 701, the user is provided the opportunity to adjust history to overcome such problems. Mechanisms by which a user can adjust history data include manually editing extremely high or low values, controlling the time period of the relative history used to ignore periods of sporadic and/or otherwise unreliable history, and masking specific periods of unreliable and/or sporadic history.
Once the relevant histories have been prepared and maintained at step 701, demand forecasting systems that utilize process 600 according to the present invention generate forecasts at step 702 (as described in detail by
In some preferred embodiments of the present invention, when models are created at step 702 the process 600 utilizes an automatic tuner when generating the model from the associated algorithm and demand history data. The automatic tuner is an iterative algorithm that, when activated by the user, identifies forecast model error helps to improve the quality of future demand forecasts. The automatic tuner performs an evaluation of the model prepared in step 702 (before passing to step 703) that attempts to identify an optimal model based upon two factors; model fit (the amount of error between the forecasted demand and the actual demand) and feasibility. The model's coefficients that have the best model fit but that still also satisfy the feasibility analysis is considered the optimal model according to the present invention. When determining error, the present invention can utilize any known and accepted statistical measurement of error including mean absolute deviation, mean squared error, smoothed mean squared error, root mean squared error, and root mean absolute deviation. In determining whether changes to a forecast algorithm coefficient proposed by the auto-tuner is feasible, present invention utilizes a fuzzy logic that evaluates and balances indicators of the following five attributes: smoothness of the dynamic mean, stability of the seasonal profile, distribution of positive and negative errors, forecast explosion, and continuity of the fitted forecast error during the most recent periods. By way of example, demand more often than not tends to be smooth rather than volatile. Thus, the auto-tuner attempts to prevent causal factors from making forecasted demand overly reactive. Additionally, by way of further example, more often than not demand does not experience extreme increases or decreases (explosions). In this manner, the fuzzy logic compares how changes in causal factors effects the five fuzzy logic factors as a whole. The automatic tuner thereby reviews the model generated by the statistical regressions and makes certain that the model produced is feasible on top of providing a good fit with the history data.
After an initial forecast has been produced at step 702, the demand forecasting process 600 of the present invention at step 703 enables users to leverage their knowledge of their organization in order to fine-tune their particular model. While fine-tuning, users can introduce and/or discontinue products, limit history to a particular time period, adjust individual parameters where coefficients manually, mask certain history points or periods, or manually edit base level or trend coefficients.
For example, if a user knows that a change in the demand pattern occurred at a certain point in history (i.e., following a large and successful advertising campaign), he or she might desire to develop a model based only upon demand history subsequent to that change. Additionally, the users may decide to fine-tune parameters of a statistically model manually by, for example, instructing the system to weigh the most recent history data with more impact in determining coefficients of the model than older history data. Further, after reviewing the initial forecast, the user can modify the model by revising the history (in a manner similar to that discussed above with respect to step 701) such as by masking irregular data periods. In this manner, a "fine-tuned" initial forecast is created and passed on to step 704.
At step 704, the system proceeds to adjust the statistical forecast produced by a particular model such that the forecast takes into account planned marketing strategies, competitive events, judgment-based forecast effects, or occasional one time occurrences. Such unusual occurrences come in the form of both external and internal events. An external event is a planned strategy designed by the user or its organization to stimulate demand. Additionally, external events can include the introduction of a new product by a competitor, or anticipated cannibalization of market by new products. An eternal event is a business condition such as a promotion, deal, or new product introduction. Additionally, such unusual occurrences can also include overrides that effect catastrophic situations (hurricanes, blizzards) and shifts in the market place.
For example, if the user knew that the organization desired to offer a promotion, such as a customer price rebate, for the active DFU and if the forecast model generated at step 702 had utilized relevant non-base history to account for the impact of customer rebates by introducing an appropriate causal factor, the user would be able to input various values for the causal factor (i.e., $5 rebate, $10 rebate, etc.) into the demand planning system. The system would then allow the user to view the impact that the various values of the causal factor has upon forecasted demand (such as by side by side graphical displays or otherwise). Once the user has decided upon a particular value for all the causal factors employed in the active DFU's model step 704 is complete.
After the forecast has been adjusted to account for such unusual occurrences at step 704, the DFU forecast is finalized at step 705. This finalized forecast is stored in the forecast database and is thereby made available to the user for comparison with finalized forecasts for other DFUs at step 603 in
After the forecast is finalized and stored in the database at step 705, the demand forecasting process comes to a close for the active DFU. At step 706, the demand forecasting system of the present invention stores historical forecast data, stores forecast performance data, and initializes the database to store updates of demand histories as new data streams are obtained such that this new data can be used in future runs of process 600 or optionally later in the current demand forecasting process 600 at step 605 as will be described below.
By storing the forecast data at the end of each demand forecasting cycle for a particular DFU, the demand forecasting system of the present invention builds a history of forecast and model data that it can then use, together with actual demand, to track performances of individual models over a time period. This forecast performance data can be used to provide feedback to the forecasting process and help to improve its accuracy. Additionally, to maintain and evaluate demand forecasts that are current, the system continuously updates relevant DFU histories with actual demand data.
As shown by
Referring again to
When the forecasts for the selected DFUs are published at step 604, these demand forecasts are in essence made "official" in that they then are associated with (i.e., as opposed to an alternative model variant DFU's forecast that was found to be less desirable at step 603) the appropriate demand unit, demand group and location combination across the entire organization. In this manner, published forecasts can be used by other parts of the organization including separate departments (such as sales and marketing) or by downstream electronic planning systems (such as manufacturing planning, supply chain planning, and transportation network planning systems).
According to more preferred embodiments of the present invention, adjustments can be made to a published demand forecast at step 605 (depicted in broken lines to emphasize the optional nature of the step) in order to account for the relative popularity of a product promotion program (rebate, drop in price, etc.) once the program is begun by using actual demand (typically, sales) data as it becomes available. In such embodiments, the invention uses available actual demand data to calculate a forecast adjustment factor "FAF" which is used to modify the current published forecast according to the equation:
It will be understood by one of ordinary skill in the art that demand forecasting process 600 will require different amounts and types of interaction and oversight by users depending upon the amount of "new" information being processed. In instances wherein demand history stream data has already been segregated into base and non-base history (during previous runs of demand forecasting process 600) and no new history is available, step 701 could merely entail the demand forecasting system identifying the appropriate previously-segregated history stream in the appropriate database. Similarly, if the user over time has developed a particularly accurate and trusted model for forecasting demand for a particular DFU, then review of a finalized forecast at step 603 above would most likely serve as a mere formality before the forecast is published at step 604. Conversely, in instances wherein a new product or DFU has been defined, relatively more interaction by the user will be necessary. However, in this manner, as records are built up in the various databases regarding previous runs of demand forecasting process 600, the work of the user in making decisions (identifying and segregating histories, etc.) typically is lessened.
Referring now to
Forecast generation system 801 provides the processing power, support electronics and logic necessary to create models for DFUs from appropriate history streams, statistical forecasting algorithms and user inputs according to the processes described above with respect to
GUI and Web platform system 803 acts a front-end to the forecasting system 800 and allows users to provide inputs as necessary to the system as well as view and compare forecasts. As is known in the art of computer networking, a user can use a user interface device 805, such as a workstation or personal computer, to view and input data 805′ as required by the processes described above. Since preferred embodiments of the present invention are computer networks, a front-end user interface is necessary to permit users to interact with one or more databases in the database system 802 and to define a plurality of decision making algorithms. The front-end user interface permits users to review and modify demand forecasts and adopt particular approved forecasts for publishing across the users' organization. Preferably, the front-end user interface supports graphical interaction and access over the distributed networks such as the Internet. As such, the present invention helps organizations understand the impact of events on demand. Further, the use of a distributed network can provide visibility of demand forecasts across an entire collaborative organization.
Database system 802 comprises electronic data storage devices to store various information used by the forecast generation system 801 to generate demand forecasts according to the present invention. The database system may comprise a plurality of relational databases (such as a history database, DFU database, and forecast database) or a single unified database table. Information can be obtained from the databases by the GUI and Web platform system 803 or the forecast generation system 801 as necessary or can be obtained therefrom.
Optionally, demand forecasting system 800 can be electronically interconnected to external computing systems to provide additional flexibility and utility (such external computing systems are shown in broken lines in
One of ordinary skill in the art will appreciate that the demand forecasting algorithms may be modified to take into account specific needs and/or problems encountered in particular industries or situations. Thus, the illustrative algorithms should not be construed to limit the present invention as is claimed.
Although the present invention is preferably implemented in software, this is not a limitation of the present invention as those of ordinary skill in the art can appreciate that the present invention can be implemented in hardware or in various combinations of hardware and software, without departing from the scope of the invention. Modifications and substitutions by those of ordinary skill in the art are considered to be within the scope of the present invention, which is not to be limited except by the claims that follow.
The foregoing description of the preferred embodiments of the present invention has been presented for the purposes of illustration and description. It will be apparent to those of ordinary skill in the art that various modifications and variations can be made to the disclosed embodiments and concepts of the present invention without departing from the spirit or scope of the invention as claimed.
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Weekly Market Update - Week of September 20th, 2010
By Robert Sofia
Platinum Advisor Strategies
In his famous "irrational exuberance" speech on December 5, 1996, then Federal Reserve chairman Alan Greenspan posed a rhetorical question: "How do we know when irrational exuberance has unduly escalated asset values, which then become subject to unexpected and prolonged contractions?" With those words, he underscored the influence that emotions can have on financial assets.
Thinking along these lines, it is interesting to note that if there is such a thing as "irrational exuberance", the opposite must also exist. Let's call this converse emotion "irrational negativity". Is there such a thing? The ABC News Consumer Comfort Index is a rolling average based on telephone interviews with 1,000 randomly selected adults over the previous four-week period. Last week, 89% rated the economy negatively, while only 54% rated their own finances negatively. Translation: People think we are worse off than we actually are (irrational negativity).
For the year, the S&P 500 is up 0.94%, the Dow is up 1.72%, and the Nasdaq is up 2.05%. Company earnings are strong, and stocks have moved steadily higher in September thanks to a series of encouraging signals on the economy. Despite this fact, gold set another record last week, and Treasury prices edged higher in a sign that investors remain cautious.
Will people eventually come around? There are reasons to believe they will. One good sign is that Americans are spending more. The U.S. Census Bureau announced Tuesday that consumer spending rose 0.4% from July to August, and 3.6% from 12 months ago, with retail sales up 0.5% and 3.7% respectively. This was the 10th month in a row of year-over-year growth in consumer spending estimates from the Census Bureau, following 14 months of year-over-year declines. All told, Americans spent $363.7 billion at retailers in August – the most since April. Since consumer spending accounts for more than two-thirds of the U.S. economy, this provides yet another silver lining in what many still view as confusing times.