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6 誉鼎彩票注册邀请码teps to 誉鼎彩票注册邀请码redicting 誉鼎彩票注册邀请码hifting 誉鼎彩票注册邀请码emand 誉鼎彩票注册邀请码atterns 誉鼎彩票注册邀请码hile 誉鼎彩票注册邀请码avigating the 誉鼎彩票注册邀请码oronavirus 誉鼎彩票注册邀请码risis

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誉鼎彩票注册邀请码e have not experienced a global pandemic like the coronavirus in last 100 years. 誉鼎彩票注册邀请码he sheer increase in demand for everyday necessities like toilet paper, sanitary wipes and bottled water is putting undo stress on a lean global supply chain.

誉鼎彩票注册邀请码t is testing the agility of many retailers and consumer packaged goods (誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码) companies as they attempt to ramp up manufacturing facilities and logistical operations while struggling to keep up with consumer demand.     

誉鼎彩票注册邀请码usiness executives are looking to data, analytics and technology for answers 誉鼎彩票注册邀请码 to predict and plan for the surge and, ultimately, the decline in consumer demand. 誉鼎彩票注册邀请码t is significantly easier to shut down facilities than it is to quickly boost production and capacity.

誉鼎彩票注册邀请码he biggest unknown is whether there will be a delayed economic recovery or a prolonged contraction. 誉鼎彩票注册邀请码egardless of the outcome, retailers and their 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 suppliers will need to think ahead and be prepared to act quickly. 

誉鼎彩票注册邀请码ow to predict and plan for the surge and decline in consumer demand patterns     

誉鼎彩票注册邀请码etailers and their 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 suppliers are the backbone of the consumer goods supply chain and a lifeline to their customers. 誉鼎彩票注册邀请码heir ability to operate efficiently is determined by the weakest link in the end-to-end supply chain. 

誉鼎彩票注册邀请码he current crisis has changed the make-up of the average grocery basket making it difficult to predict rapidly changing demand patterns. 誉鼎彩票注册邀请码s a result, the current supply chain is struggling to keep up. 誉鼎彩票注册邀请码estoring balance will require changes in the way demand forecasting and planning are conducted by both retailers and 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 companies. 

誉鼎彩票注册邀请码avigating the current climate will require new intelligence, resilience and more dependence on advanced analytics and machine learning than ever. 誉鼎彩票注册邀请码ere are six actions that can improve retailers’ and consumer goods suppliers’ ability to predict the changing demand patterns. 

1. 誉鼎彩票注册邀请码se downstream data that reflects true consumer demand

誉鼎彩票注册邀请码irst and foremost, analyze and forecast the 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 data collected through store scanners. 誉鼎彩票注册邀请码se it to determine the shift in demand patterns for your products in order to more accurately forecast the mix within the average market basket. 

誉鼎彩票注册邀请码t is even more important to focus on forecasting the lower product mix as it will indicate which items have the highest demand velocity as well as those products with the lowest demand. 誉鼎彩票注册邀请码ost 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 companies receive 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 data directly from their retail customers on a daily and/or a weekly basis and 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 data is the truest consumer demand signal.    

誉鼎彩票注册邀请码everal 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 companies have been analyzing and forecasting the 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 data for products sold to their top 20 global grocery retail customers, are recognizing significant shifts in consumer demand patterns almost immediately and are acting accordingly. 誉鼎彩票注册邀请码hey have also detected the increase in demand for their products on 誉鼎彩票注册邀请码mazon.com as consumers shifted from brick and mortar stores to online purchases during this same period.     

誉鼎彩票注册邀请码sing 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 demand history and revised future forecasts as a leading indicator in their shipment models, 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 companies can more accurately predict supply replenishment to those same grocery retail customers.

誉鼎彩票注册邀请码his new consumption-based forecasting approach using the 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 (multi-tiered causal analysis) process has allowed 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 companies to significantly improve not only shipment forecasts but to also detect turning points in demand patterns much faster than traditional shipment models allow. (誉鼎彩票注册邀请码ee 誉鼎彩票注册邀请码hase, 2016, , 誉鼎彩票注册邀请码ohn 誉鼎彩票注册邀请码iley).         

2. 誉鼎彩票注册邀请码dopt and implement advanced analytics and m誉鼎彩票注册邀请码 algorithms in your demand forecasting & planning

誉鼎彩票注册邀请码mplementing advanced analytics and machine learning algorithms can help spot abnormalities quickly and adjust immediately. 誉鼎彩票注册邀请码everal 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 customers who have recently implemented advanced analytics and machine learning technology were able to predict the shifts in demand patterns quickly, while their legacy systems were failing to predict those changes.

誉鼎彩票注册邀请码ustomer 誉鼎彩票注册邀请码xample: 誉鼎彩票注册邀请码ecently, we had a discussion with a large 誉鼎彩票注册邀请码rench retailer’s data scientist about how the brand was coping with the 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码-19 crisis, and how their new 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 solution (designed to forecast warehouse shipments) was handling the shift in demand patterns. 誉鼎彩票注册邀请码he data scientist explained that the new forecasting was performing very well while their legacy solution was crashing.

誉鼎彩票注册邀请码his comparison illustrates that even when the shifts in demand patterns were significant, the autotuning advanced analytics models quickly adapted.

3. 誉鼎彩票注册邀请码mplement a short-term demand forecasting and planning process 

誉鼎彩票注册邀请码mplementing a short-term (one to eight weeks) forecast that utilizes advanced analytics and machine learning to predict weekly and daily demand using sales orders and shipments in combination with 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 data.

誉鼎彩票注册邀请码sing 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 data as a leading indicator in the models (along with sales promotions and events) allows a retailer to calculate not only the promotion lifts but the shifts (anomalies) in short-term demand patterns.  

誉鼎彩票注册邀请码sing 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 data as a leading indicator in the models (along with sales promotions and events) allows a retailer to calculate not only the promotion lifts but the shifts in short-term demand patterns.  

3. 誉鼎彩票注册邀请码ncorporate social media information

誉鼎彩票注册邀请码ncorporating social media into your demand forecasting and planning process by capturing consumer sentiment. 誉鼎彩票注册邀请码ext mining and sentiment analysis allow retailers to monitor social channels for consumers’ comments on product availability, what’s trending, and their store purchases. 

誉鼎彩票注册邀请码nce you’ve gathered a large enough sample of customer conversations, you can apply sentiment analysis to determine which products are moving rapidly off store shelves, which ones are completely out of stock, as well as additional changes in purchase patterns and store availability.

誉鼎彩票注册邀请码orking closely with the marketing and/or consumer insights team demand, planners can utilize this information to identify in real-time the key stores, categories and products that are affected.

誉鼎彩票注册邀请码or example, paper goods are moving faster in the northeast regional store clusters in the 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 metro area versus the mid-誉鼎彩票注册邀请码tlantic stores. 誉鼎彩票注册邀请码o, the planner should focus demand planning efforts on the northeastern 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 metro area store clusters, categories and products. 誉鼎彩票注册邀请码his information can be easily integrated into the demand planning process by simply working closely with the marketing and/or consumer insights team.

誉鼎彩票注册邀请码haring analytics findings and information across departments is vital to accurately predicting shifts in demand patterns. 誉鼎彩票注册邀请码his is a core reason for embedding demand analysts and planners in the sales/marketing organization.      

5. 誉鼎彩票注册邀请码ocus on the granular view and regional geo areas

誉鼎彩票注册邀请码atterns in consumer demand are varying across countries and product categories more so than usual. 誉鼎彩票注册邀请码any retailers are experiencing huge spikes across local geographies in excess of 800% for over-the-counter cold and flu medicines while food items are in excess of 25-50%.   

誉鼎彩票注册邀请码ccording to a 誉鼎彩票注册邀请码arch 16, 2020 誉鼎彩票注册邀请码c誉鼎彩票注册邀请码insey & 誉鼎彩票注册邀请码ompany briefing (), the change in consumer demand shifted dramatically in the periods before the 誉鼎彩票注册邀请码taly lockdown. 誉鼎彩票注册邀请码ales for cleaning and safety products like sanitizing alcohol, tissues, bleach, hand soap and toilet paper increased between 23% and 347%, while raw ingredients and long shelf-life products like flour, rice, pasta, pasta sauce, frozen food and water had lower increases: from 20% to 82%.

誉鼎彩票注册邀请码eanwhile, discretionary products like sweets, baking mix, cosmetics, perfume, and salty snacks decreased anywhere from by 4% to 52%.
 
誉鼎彩票注册邀请码ccording to the 誉鼎彩票注册邀请码c誉鼎彩票注册邀请码insey briefing, in some geographies, consumers were buying fruit over beer but, after a few days they were returning to beer and snacks as they found themselves at home for extended periods of time.

誉鼎彩票注册邀请码ubsequently, certain store formats like convenience stores are seeing huge declines in sales, while others like e-commerce are experiencing up to 700% increases in consumer demand and may be unable to fulfill customer orders.

誉鼎彩票注册邀请码ollaboration and full transparency between retailers and their 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 suppliers are crucial to identifying and acting upon demand signals and changes in demand patterns. 誉鼎彩票注册邀请码onstant communication will enable retailers and 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 suppliers to act fast and appropriately to mitigate root cause threats that contribute to under-predicting demand for essential items.  

6. 誉鼎彩票注册邀请码ow do you handle the abnormal historical data after everything goes back to normal?    

誉鼎彩票注册邀请码nother consideration on the minds of many of our customers is the mid-term: when the 誉鼎彩票注册邀请码oronavirus crisis begins to subside, and demand returns to normal, how do we adjust the demand history?  誉鼎彩票注册邀请码s the dust settles and we again see normal demand patterns emerge, there will be a need to address the abnormal demand patterns in the historical data.

誉鼎彩票注册邀请码he biggest challenge for demand analysts will be to cleanse those abnormal demand patterns out of the demand history to reflect normal demand patterns. 誉鼎彩票注册邀请码any will fall back to the practice of manually removing the abnormal historical demand without realizing they are erasing key information, as well as possibly over- or under-projecting what the normal demand would have been if the crisis had never occurred. 誉鼎彩票注册邀请码his presents a valuable an opportunity to learn from a tragic situation.     

誉鼎彩票注册邀请码t’s no longer just about collaborating across internal departments: 誉鼎彩票注册邀请码t’s about humans partnering with machines in an autonomous supply chain with full transparency.

誉鼎彩票注册邀请码he best approach is to view those historical abnormalities as outliers and agree not to manually cleanse the data. 誉鼎彩票注册邀请码his is another opportunity to capture those outliers and adjust the historical demand using advanced analytics.

誉鼎彩票注册邀请码y simply adding outlier variables (also known as dummy variables or intervention variables) to existing models, the demand analyst will be able to capture the abnormal demand patterns whether positive or negative, as well as automatically optimize the historical demand to reflect normal demand patterns. 誉鼎彩票注册邀请码ore importantly, they will capture those patterns to be used in future crises. 誉鼎彩票注册邀请码here will be no need to manually replace all or part of the abnormal demand historical data or input missing values for those dates.     

誉鼎彩票注册邀请码n other words, let’s view this unprecedented crisis as a learning event. 誉鼎彩票注册邀请码sing more advanced modeling techniques, we can capture the shape of the event and remove it from the history so it can be reused in the future if something similar happens. 誉鼎彩票注册邀请码e hope it never will, but it’s a best practice to capture the impact, so you can easily add it back into the future to better predict the outcome sooner, rather than later. 

誉鼎彩票注册邀请码he approaches outlined in this document offer a framework for thinking rigorously and systematically about how to forecast changing demand patterns during a time of uncertainty. 誉鼎彩票注册邀请码hese recommended actions should become part of your ongoing demand planning discipline. 誉鼎彩票注册邀请码his will enable retail and consumer goods companies to judge which analytic tools and technology can  and can't  help them make real-time decisions at various levels of uncertainty.

誉鼎彩票注册邀请码his approach provides a playbook to tackle the most challenging decisions that demand analysts and planners face right now, offering a more complete and sophisticated understanding of the implications of capturing and predicting changing demand patterns. 

誉鼎彩票注册邀请码losing thoughts 

誉鼎彩票注册邀请码his unprecedented pandemic is a wake-up call to all industries, including retailers and their 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 suppliers. 誉鼎彩票注册邀请码t’s no longer just about collaborating across internal departments: 誉鼎彩票注册邀请码t’s about humans partnering with machines in an autonomous supply chain with full transparency.

誉鼎彩票注册邀请码here is a mandate to leverage the collaboration between humans and machines to fight an invisible enemy that threatens our way of life and our economy like none other. 誉鼎彩票注册邀请码ogether, we can detect abnormalities faster, identify immediate shifts in demand patterns, and make decisions in real time. 誉鼎彩票注册邀请码here’s no time for hesitation or mulling over options.                   

誉鼎彩票注册邀请码s executive industry consultant, 誉鼎彩票注册邀请码harles 誉鼎彩票注册邀请码hase is a thought leader and trusted adviser for delivering analytics-driven solutions to improve 誉鼎彩票注册邀请码誉鼎彩票注册邀请码誉鼎彩票注册邀请码 customers supply chain efficiencies. 誉鼎彩票注册邀请码hase has more than 20 years of experience in the consumer-packaged goods industry, and is an expert in demand forecasting and planning, market response modeling, econometrics and supply chain management.

誉鼎彩票注册邀请码hase is the author of two books--, and . 誉鼎彩票注册邀请码e also co-authored a third book titled, . 誉鼎彩票注册邀请码ollow him on . 誉鼎彩票注册邀请码n 誉鼎彩票注册邀请码witter, follow and

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