To adjust demand in supply chain models.
How Much Pent up Shopping Demand?
by George Anderson in Retailwire with further expert comment at the link.
When retail stores across the country began closing to customers back in March, the hope was that Americans would come back and make up for lost time and sales when states began to let merchants reopen. Based on at least some initial reports, many merchants are pleasantly surprised to find out that there was more pent up demand for goods than they expected.
American Eagle Outfitters, Kohl’s and Macy’s are three chains that have reported that, despite fewer hours, limited shopper capacity and concerns about the spread of the novel coronavirus, people are showing up to shop.
American Eagle reported last week that the 556 stores it has reopened are generating about 95 percent of the sales the retailer reported last year. At the same time, digital sales which picked up significantly while stores were closed, have remained strong. COO Michael Rampell told analysts on the retailer’s first quarter earnings call that digital sales for the company’s namesake chain were up about 50 percent on a quarter-to-date basis and that Aerie’s online business was up more than 100 percent. .... "
Showing posts with label demand planning. Show all posts
Showing posts with label demand planning. Show all posts
Wednesday, June 10, 2020
Monday, March 09, 2020
Competing Pricing Algorithms
In HBSWK, interesting article on competing pricing algorithms, below the intro, reading:
Warring Algorithms Could be Driving Up Prices
Companies increasingly use software to conduct rapid price changes. Alexander MacKay explains why firms might benefit but consumers should be worried.
The widespread use of pricing algorithms is reshaping the nature of competition in online markets and potentially driving up the prices of retail goods, according to recent research.
These automated, price-adjusting software programs may also be catching the eye of government regulators and antitrust authorities, who fear they could ultimately harm consumers by raising prices above typical competitive levels.
It doesn’t seem too long ago when a price change was a major strategic decision for companies, requiring extensive data analysis, management consensus, coordination with advertising schedules, and other factors in an era when computers were helpful but not critical to pricing strategy. The result: A price change was more an annual or semiannual event. But these days, when companies can analyze consumer data and use technology to raise or lower prices in the blink of an eye, changes can be made not just once a year but multiple times daily.
“WHAT WE SHOW, THEORETICALLY, IS THAT (ALGORITHMIC COMPETITION) LEADS TO HIGHER PROFITS FOR BOTH FIRMS.”
Enter the rise of pricing algorithms, where software monitors prices posted by competitors and makes adjustments using parameters developed by the company’s marketers and strategists.
“They want to react to changing demand and supply conditions,” says study author Alexander J. MacKay, an assistant professor of business administration at Harvard Business School who studies competition, including pricing, demand, and market structure. .... "
Warring Algorithms Could be Driving Up Prices
Companies increasingly use software to conduct rapid price changes. Alexander MacKay explains why firms might benefit but consumers should be worried.
The widespread use of pricing algorithms is reshaping the nature of competition in online markets and potentially driving up the prices of retail goods, according to recent research.
These automated, price-adjusting software programs may also be catching the eye of government regulators and antitrust authorities, who fear they could ultimately harm consumers by raising prices above typical competitive levels.
It doesn’t seem too long ago when a price change was a major strategic decision for companies, requiring extensive data analysis, management consensus, coordination with advertising schedules, and other factors in an era when computers were helpful but not critical to pricing strategy. The result: A price change was more an annual or semiannual event. But these days, when companies can analyze consumer data and use technology to raise or lower prices in the blink of an eye, changes can be made not just once a year but multiple times daily.
“WHAT WE SHOW, THEORETICALLY, IS THAT (ALGORITHMIC COMPETITION) LEADS TO HIGHER PROFITS FOR BOTH FIRMS.”
Enter the rise of pricing algorithms, where software monitors prices posted by competitors and makes adjustments using parameters developed by the company’s marketers and strategists.
“They want to react to changing demand and supply conditions,” says study author Alexander J. MacKay, an assistant professor of business administration at Harvard Business School who studies competition, including pricing, demand, and market structure. .... "
Thursday, February 20, 2020
Coronavirus Influences P&G China Business
My former employer reports. Would seem now that the prediction of epidemics and their influence needs to be added to supply chain demand.
Some in-store demand has shifted to the Internet, but delivery capability is limited, P&G says.
P&G warns coronavirus is disrupting China business in FoxBusiness
The country is P&G's second-largest market
Procter & Gamble warned the coronavirus outbreak is curbing in-store sales and limiting the ability of its digital operations to meet demand.
Shares of the Cincinnati-based consumer goods maker were little changed following the update.
“China is our second-largest market -- sales and profit. Store traffic is down considerably, with many stores closed or operating with reduced hours,” Jon Moeller, chief financial officer, said in a U.S. Securities and Exchange Commission filing on Thursday. “Some of the demand has shifted online, but supply of delivery operators and labor is limited.” ... '
Some in-store demand has shifted to the Internet, but delivery capability is limited, P&G says.
P&G warns coronavirus is disrupting China business in FoxBusiness
The country is P&G's second-largest market
Procter & Gamble warned the coronavirus outbreak is curbing in-store sales and limiting the ability of its digital operations to meet demand.
Shares of the Cincinnati-based consumer goods maker were little changed following the update.
“China is our second-largest market -- sales and profit. Store traffic is down considerably, with many stores closed or operating with reduced hours,” Jon Moeller, chief financial officer, said in a U.S. Securities and Exchange Commission filing on Thursday. “Some of the demand has shifted online, but supply of delivery operators and labor is limited.” ... '
Tuesday, September 24, 2019
Healing a Supply Chain with Machine Learning
Some instructive thoughts about direction. Finding patterns in anything can be a first step to its improvement.
How machine learning can heal a supply chain By Polly Mitchell-Guthrie
" ..... Machine learning opportunities in supply chain are abundant – improving forecast accuracy, inspection of physical assets, improved modeling for new product introductions, predictive asset maintenance, and great visibility across the collaborative supply chain network are a few. In fact, Deloitte has proclaimed that the days of “cognitive planning” are upon us, where computing advances, the maturation of machine learning, and the data available in connecting systems enable this step. They have christened it “synchronized planning,” a world in which data can constantly flow throughout the supply chain and allow organizations to far more accurately match production of supply to demand than ever before.
At my own company, Kinaxis, we use a related term, concurrent planning, to illustrate the importance of being able to plan, monitor and respond to changes across the supply chain in a single, harmonious environment. Based on the foundation of data in our in-memory database, we launched our own machine learning journey.
Our focus is to increase the efficiency of the supply chain for our customers, and when analysis of data from a major customer revealed that 53 percent of their lead times were wrong as designed, we started there. .... "
How machine learning can heal a supply chain By Polly Mitchell-Guthrie
" ..... Machine learning opportunities in supply chain are abundant – improving forecast accuracy, inspection of physical assets, improved modeling for new product introductions, predictive asset maintenance, and great visibility across the collaborative supply chain network are a few. In fact, Deloitte has proclaimed that the days of “cognitive planning” are upon us, where computing advances, the maturation of machine learning, and the data available in connecting systems enable this step. They have christened it “synchronized planning,” a world in which data can constantly flow throughout the supply chain and allow organizations to far more accurately match production of supply to demand than ever before.
At my own company, Kinaxis, we use a related term, concurrent planning, to illustrate the importance of being able to plan, monitor and respond to changes across the supply chain in a single, harmonious environment. Based on the foundation of data in our in-memory database, we launched our own machine learning journey.
Our focus is to increase the efficiency of the supply chain for our customers, and when analysis of data from a major customer revealed that 53 percent of their lead times were wrong as designed, we started there. .... "
Monday, September 16, 2019
Autonomous Planning in Food Retail
Advanced Planning using machine learning techniques are suggested for procurement. We always had advanced planning, its just how well you could integrate it with predictions of demand, and in particular unusual elements of forecasts. New analytics have emerged, but how well will they deliver?
The invisible hand: On the path to autonomous planning in food retail from McKinsey
It’s not news to food retailers: sometimes your stocks are too high, sometimes they’re too low. Advanced planning now gives them entirely new options for solving the expensive problem—and cuts costs in the process. ....
Procurement planners in food retail today are not to be envied. They have to please customers who have never made more exacting demands on availability, freshness, and range. And they ignore such expectations at their peril: the competition is relentless, driving all market participants to seek out improvements incessantly. Those who stick to their legacy processes can only make comparable progress at the cost of mounting stocks, increasing write-offs, and an increasingly complex supply chain.
Internally, planners are often struggling with outdated IT systems that are isolated from each other, unreliable sources of information, and in some cases, largely manual and poorly coordinated processes. Forecasts are commensurately inaccurate and personnel expenses high. Externally, on the other hand, decision makers are faced with an increasingly unfathomable offering from digital service providers that—although they can process huge volumes of data with their solutions—cannot give retailers any advantages of relevance as long as they leave their operating models unchanged.
The future will likely be very different. A look at online retail already reveals the shape of things to come: leading companies are developing highly integrated planning systems that already use the most advanced analytics and machine-learning solutions available today. These high-tech methods, also referred to as “advanced planning,” will, in the future, take control of steering in food retail as well. And they set exacting requirements on companies: they entail tapping the entire wealth of transaction data along with external parameters as sources. Retailers need a completely different process landscape, new capabilities, more computing power, and advanced algorithms. ... "
The invisible hand: On the path to autonomous planning in food retail from McKinsey
It’s not news to food retailers: sometimes your stocks are too high, sometimes they’re too low. Advanced planning now gives them entirely new options for solving the expensive problem—and cuts costs in the process. ....
Procurement planners in food retail today are not to be envied. They have to please customers who have never made more exacting demands on availability, freshness, and range. And they ignore such expectations at their peril: the competition is relentless, driving all market participants to seek out improvements incessantly. Those who stick to their legacy processes can only make comparable progress at the cost of mounting stocks, increasing write-offs, and an increasingly complex supply chain.
Internally, planners are often struggling with outdated IT systems that are isolated from each other, unreliable sources of information, and in some cases, largely manual and poorly coordinated processes. Forecasts are commensurately inaccurate and personnel expenses high. Externally, on the other hand, decision makers are faced with an increasingly unfathomable offering from digital service providers that—although they can process huge volumes of data with their solutions—cannot give retailers any advantages of relevance as long as they leave their operating models unchanged.
The future will likely be very different. A look at online retail already reveals the shape of things to come: leading companies are developing highly integrated planning systems that already use the most advanced analytics and machine-learning solutions available today. These high-tech methods, also referred to as “advanced planning,” will, in the future, take control of steering in food retail as well. And they set exacting requirements on companies: they entail tapping the entire wealth of transaction data along with external parameters as sources. Retailers need a completely different process landscape, new capabilities, more computing power, and advanced algorithms. ... "
Sunday, May 12, 2019
Price and Demand
A simple yet useful example of revenue - price elasticity. With a full Python code example We used to run these models in SAS in big CPG for every product we sold, advertised or proposed. Despite all the fancy things being done these days, this is still a useful to do. Also a good simple Python example.
Optimizing price, maximizing revenue
Posted by Mab Alam in DSC
Problem statement
Price and quantity sold are the two determinants of business revenue/profit. At higher price the revenue is expected to be high. But this is not the case all the time. We know from our everyday experience, as price of something goes up, people have less tendency to buy it.
The reverse is also true, that is, as price is down, sales goes up (think what happens in a block buster sales event in a nearby shopping mall). But sales going up doesn’t always mean that the revenue will also go up, because of the trade-off created by price drop.
Setting a right price of products/services is one of the most important decisions a business can make. Under-pricing and over-pricing both can hurt a company’s bottom line. So where is the sweet spot, the right price, that maximizes revenue and profit?
With a simple example let’s examine how to optimization price to maximize revenue/profit. ... "
Optimizing price, maximizing revenue
Posted by Mab Alam in DSC
Problem statement
Price and quantity sold are the two determinants of business revenue/profit. At higher price the revenue is expected to be high. But this is not the case all the time. We know from our everyday experience, as price of something goes up, people have less tendency to buy it.
The reverse is also true, that is, as price is down, sales goes up (think what happens in a block buster sales event in a nearby shopping mall). But sales going up doesn’t always mean that the revenue will also go up, because of the trade-off created by price drop.
Setting a right price of products/services is one of the most important decisions a business can make. Under-pricing and over-pricing both can hurt a company’s bottom line. So where is the sweet spot, the right price, that maximizes revenue and profit?
With a simple example let’s examine how to optimization price to maximize revenue/profit. ... "
Saturday, April 20, 2019
AI Impact on Demand Forecasting
Good short piece, makes good points about what is needed, and should be expected. I have taught forecasting in the enterprise, and its more about how the forecast is used than what it is. You would love to have it perfect, but it will not be. AI provides another useful component.
Is AI’s impact on demand forecasting more hype than reality? by Nikki Baird in Retailwire
Through a special arrangement, presented here for discussion is a summary of a current article from the blog of Nikki Baird, VP of retail innovation at Aptos. The article first appeared on Forbes.com.
The forecast error in retail is as high as 32 percent, according to some estimates. Will artificial intelligence (AI) technology do any better?
AI promises to change the way demand forecasting works in retail in six key ways, but those promises include a bit of hype: .... "
Is AI’s impact on demand forecasting more hype than reality? by Nikki Baird in Retailwire
Through a special arrangement, presented here for discussion is a summary of a current article from the blog of Nikki Baird, VP of retail innovation at Aptos. The article first appeared on Forbes.com.
The forecast error in retail is as high as 32 percent, according to some estimates. Will artificial intelligence (AI) technology do any better?
AI promises to change the way demand forecasting works in retail in six key ways, but those promises include a bit of hype: .... "
Tuesday, August 28, 2018
No Touch Planning: Human Error and Supply Chains
Remember that planning means predicting, forecasting and analyzing risk in context
The route to no-touch planning: Taking the human error out of supply-chain planning
Slow, manual supply-chain planning processes can be a thing of the past, with machines taking on repetitive tasks that aren’t a good use of human capacity.
The route to no-touch planning: Taking the human error out of supply-chain planning
By Ignacio Felix, Christoph Kuntze, Ildefonso Silva, and Eduardo Tobias Benoliel in McKinsey
Slow, manual supply-chain planning processes can be a thing of the past, with machines taking on repetitive tasks that aren’t a good use of human capacity.
Supply-chain planning keeps getting harder and more time-consuming, with the consumer goods sector as one of the most extreme examples. The causes are familiar: Online retailing’s endless shelf encourages consumers to be ever more demanding, yielding product portfolios that are ever more complex and lifecycles that are ever shorter. Retailers continue to increase their service and delivery requirements, with stiff financial penalties for non-compliance. On the flip side, more and more real-time data are becoming available, with automation technology rapidly getting cheaper, more capable, and easier to implement—raising the competitive bar for the entire sector.
Traditional planning processes and tools weren’t designed either to take advantage of technology’s advances or to address the demands it creates. By and large, planning still relies heavily on labor-intensive data aggregation and cleaning, manual analysis, and personal judgment. Worse, with more customer and consumer demand signals now available instantaneously, planners often feel compelled to keep tweaking their plans, despite the weaknesses of existing planning systems and processes. Well-intentioned adjustments end up creating more problems than they solve, introducing even more errors and subconscious bias that can increase costs and exacerbate service disruptions. .... "
Saturday, June 30, 2018
Flowcasting
Been a long time since we looked at JDA's Flowcasting system. We examined it even before its acquisition by JDA. Still don't know too many people who have been exposed to it. So when I happened on this piece in JDA's blog was worthwhile to look at it again, with the help of a well known supply chain game. Have reconnected to their blog.
The Power of Flowcasting: Live Supply Planning Collaboration & Analytics – Part 1
By Tom Drake-Supply Chain, Supply Chain Management-
It’s been a few months since I shared the learnings we’ve garnered from our Collaborative Flow Planning workshop – AKA – The Beer Game. Most supply chain professionals are familiar with this game and the supply chain planning pitfalls it exposes. Today, I’d like to focus on the second part of the game that was played, which was enabled by the Collaboration Workbench platform in JDA Flowcasting. We’re going to take a deeper look at how this platform was used to cut costs by two-thirds compared to those playing the game without the help of JDA Flowcasting .... "
The Power of Flowcasting: Live Supply Planning Collaboration & Analytics – Part 1
By Tom Drake-Supply Chain, Supply Chain Management-
It’s been a few months since I shared the learnings we’ve garnered from our Collaborative Flow Planning workshop – AKA – The Beer Game. Most supply chain professionals are familiar with this game and the supply chain planning pitfalls it exposes. Today, I’d like to focus on the second part of the game that was played, which was enabled by the Collaboration Workbench platform in JDA Flowcasting. We’re going to take a deeper look at how this platform was used to cut costs by two-thirds compared to those playing the game without the help of JDA Flowcasting .... "
Thursday, March 08, 2018
Forecasting Ride Demand
Forecasting any kind of demand by consumers is always of interest, so this likely has applications beyond just ride ride prediction.
Hail Technology: Deep Learning May Help Predict When People Need Rides
Penn State News By Matt Swayne
Pennsylvania State University (Penn State) researchers analyzing a large dataset of ride requests to Didi Chuxing, a Chinese car-hailing company, found computers may be better at forecasting demand for taxi and ride-sharing services. The team used two types of neural networks to extract patterns of taxi demand, and then to predict the demand patterns with significantly better accuracy than current technology, explains Penn State's Huaxiu Yao. When users need a ride they make a request via a computer application, and the researchers think tapping these requests, instead of relying on ride data only, reflects overall demand better. With the historical data, which includes the request's time and location, the computer could anticipate how demand will change over time, and the researchers were able to visualize how that demand evolved by plotting it on a map. "Basically, we used a very complicated neural net to simulate how people digest information, in this case, the image of the traffic patterns," notes Penn State professor Jessie Li. ... "
Hail Technology: Deep Learning May Help Predict When People Need Rides
Penn State News By Matt Swayne
Pennsylvania State University (Penn State) researchers analyzing a large dataset of ride requests to Didi Chuxing, a Chinese car-hailing company, found computers may be better at forecasting demand for taxi and ride-sharing services. The team used two types of neural networks to extract patterns of taxi demand, and then to predict the demand patterns with significantly better accuracy than current technology, explains Penn State's Huaxiu Yao. When users need a ride they make a request via a computer application, and the researchers think tapping these requests, instead of relying on ride data only, reflects overall demand better. With the historical data, which includes the request's time and location, the computer could anticipate how demand will change over time, and the researchers were able to visualize how that demand evolved by plotting it on a map. "Basically, we used a very complicated neural net to simulate how people digest information, in this case, the image of the traffic patterns," notes Penn State professor Jessie Li. ... "
Wednesday, September 21, 2016
On Predictability of Demand
Discussion in Retailwire:
" ... According to RSR’s benchmark report on omnichannel order profitability, unpredictable consumer demand is the top business challenge among retail respondents. After much consideration, I would offer that consumer demand isn’t really that unpredictable. Instead, retailers are just really bad at predicting it.
Part of the issue is one of “garbage in” — as in, “garbage in yields garbage out.” There seems to be a lot of garbage in the inputs retailers use to decide what to stock where. First, a lot of them start with last year’s plan, which means the bad assumptions made last year are automatically carried over into this year. ... "
" ... According to RSR’s benchmark report on omnichannel order profitability, unpredictable consumer demand is the top business challenge among retail respondents. After much consideration, I would offer that consumer demand isn’t really that unpredictable. Instead, retailers are just really bad at predicting it.
Part of the issue is one of “garbage in” — as in, “garbage in yields garbage out.” There seems to be a lot of garbage in the inputs retailers use to decide what to stock where. First, a lot of them start with last year’s plan, which means the bad assumptions made last year are automatically carried over into this year. ... "
Sunday, May 08, 2016
Smarter Bathrooms on the Supply Chain
A look at making consumer demand points made more intelligent. An intelligent endpoint in the supply chain. Here work by IBM and Kimberbly Clark. In Readwrite:
" ... Kimberly-Clark Professional’s new Intelligent Restroom app was built using IBM Bluemix development platform and through the use of the IBM Internet of Things Foundation service, facilities managers collect data and alerts from sensors integrated into restroom amenities, from soap dispensers to air fresheners, as well as non-amenities like entrance doors. All the data is managed and monitored through a central dashboard that can be viewed on desktops or mobile devices remotely. ... "
" ... Kimberly-Clark Professional’s new Intelligent Restroom app was built using IBM Bluemix development platform and through the use of the IBM Internet of Things Foundation service, facilities managers collect data and alerts from sensors integrated into restroom amenities, from soap dispensers to air fresheners, as well as non-amenities like entrance doors. All the data is managed and monitored through a central dashboard that can be viewed on desktops or mobile devices remotely. ... "
Tuesday, April 26, 2016
Omnichannel Complicates Demand Planning
Interesting conversation in Retailwire:
How is omnichannel complicating demand planning?
By Tom Ryan
Demand planning "has become an enigma" for supply chain professionals, according to the sixth annual "State of the Retail Supply Chain" from the Retail Industry Leaders Association (RILA) and Auburn University's Center for Supply Chain Innovation.
Broadly, the challenges include an ever-increasing number of SKUs, tremendous price pressures, demanding customers willing to switch loyalties in a moment, and a multitude of order fulfillment options. .... "
How is omnichannel complicating demand planning?
By Tom Ryan
Demand planning "has become an enigma" for supply chain professionals, according to the sixth annual "State of the Retail Supply Chain" from the Retail Industry Leaders Association (RILA) and Auburn University's Center for Supply Chain Innovation.
Broadly, the challenges include an ever-increasing number of SKUs, tremendous price pressures, demanding customers willing to switch loyalties in a moment, and a multitude of order fulfillment options. .... "
Tuesday, March 22, 2016
SAP Addressing the Supply Chain
We worked on early, optimization based uses of SAP data to improve the operation of the enterprise supply chain. Note the mention of constraint based systems, key for supply chains. Also the integration of real time operations. Will be interesting to see more details, have asked for that.
SAP releases latest addition to fast-growing cloud-based Supply Chain Planning Platform
WALLDORF, Germany, March 17, 2016 /PRNewswire/ -- SAP SE (NYSE: SAP) has released the SAP® Integrated Business Planning 6.1 application for response and supply, a new addition to its innovative cloud-based planning suite. The application allows companies to plan based on demand priorities and adapt quickly when faced with demand-upside opportunities or supply disruptions. Based on the powerful SAP HANA® platform, the new offering enables faster, more accurate decisions supported by what-if analysis, and easy analysis of constraints in the supply chain.
With this release, all of the major components of SAP Integrated Business Planning are now available. The cloud-based suite now supports the end-to-end planning processes of sales and operations planning, demand sensing and planning, supply and allocations planning, response planning and inventory optimization. These applications, along with embedded analytics, alerting and collaboration capabilities, enable SAP customers to achieve greater visibility, shorter planning cycles, and faster response to changes in demand and supply. ... "
SAP releases latest addition to fast-growing cloud-based Supply Chain Planning Platform
WALLDORF, Germany, March 17, 2016 /PRNewswire/ -- SAP SE (NYSE: SAP) has released the SAP® Integrated Business Planning 6.1 application for response and supply, a new addition to its innovative cloud-based planning suite. The application allows companies to plan based on demand priorities and adapt quickly when faced with demand-upside opportunities or supply disruptions. Based on the powerful SAP HANA® platform, the new offering enables faster, more accurate decisions supported by what-if analysis, and easy analysis of constraints in the supply chain.
With this release, all of the major components of SAP Integrated Business Planning are now available. The cloud-based suite now supports the end-to-end planning processes of sales and operations planning, demand sensing and planning, supply and allocations planning, response planning and inventory optimization. These applications, along with embedded analytics, alerting and collaboration capabilities, enable SAP customers to achieve greater visibility, shorter planning cycles, and faster response to changes in demand and supply. ... "
Monday, January 11, 2016
A Completely On-Demand Economy: Purple
In GigaOM:
" .... The “gig economy” is taking some knocks this month. But a new startup called Purple is showing us why on-demand labor is actually headed for a boom–no matter what employees or regulators say ....
The inconvenient, infeasible parts of “work” that can’t be done by a robot are simply farmed out to people, mechanical turk-style, putting workers today in a second machine age where humans and automated systems work together to complete all sorts of isolated tasks.
Purple, a gas delivery startup, is one sign of where things are going. While the government and labor ponder the future, Purple is already selling a service that allows you to “order” gasoline for your car and have it fueled wherever the vehicle is currently parked. .... "
And in BusinessInsider.
" .... The “gig economy” is taking some knocks this month. But a new startup called Purple is showing us why on-demand labor is actually headed for a boom–no matter what employees or regulators say ....
The inconvenient, infeasible parts of “work” that can’t be done by a robot are simply farmed out to people, mechanical turk-style, putting workers today in a second machine age where humans and automated systems work together to complete all sorts of isolated tasks.
Purple, a gas delivery startup, is one sign of where things are going. While the government and labor ponder the future, Purple is already selling a service that allows you to “order” gasoline for your car and have it fueled wherever the vehicle is currently parked. .... "
And in BusinessInsider.
Thursday, December 10, 2015
Kroger and Predictive Analytics
Kroger and predictive merchandising. Kroger's Improved Analytic Capabilities Powers Merchandising Decisions ....
" ... One key area where the in-house analytics team is being leveraged is in analyzing consumer behavior and predicting demand. As the healthy and organic craze moves from the buzz and niche phase to an established long-term trend, mega grocers like Kroger are quickly joining the likes of Whole Foods and Trader Joes and offering healthier food options to meet rising demand.
"We certainly do a lot of predictive analytics with data we see not just inside our stores but overall in the economy, trying to figure out where the consumer is going," Schlotman said. "It clearly does seem as though there's going to be persistence and shift to a more healthy lifestyle and maybe out of some of the traditional center-of-the-store categories into more fresh categories."
In typical Kroger style they are not just following the trend but setting the pace. The grocer not only sells natural and organic products but has its own store brand Simple Truth dedicated to providing healthy food options.... "
" ... One key area where the in-house analytics team is being leveraged is in analyzing consumer behavior and predicting demand. As the healthy and organic craze moves from the buzz and niche phase to an established long-term trend, mega grocers like Kroger are quickly joining the likes of Whole Foods and Trader Joes and offering healthier food options to meet rising demand.
"We certainly do a lot of predictive analytics with data we see not just inside our stores but overall in the economy, trying to figure out where the consumer is going," Schlotman said. "It clearly does seem as though there's going to be persistence and shift to a more healthy lifestyle and maybe out of some of the traditional center-of-the-store categories into more fresh categories."
In typical Kroger style they are not just following the trend but setting the pace. The grocer not only sells natural and organic products but has its own store brand Simple Truth dedicated to providing healthy food options.... "
Monday, November 30, 2015
Watson Trends looks at Holiday Toy Shopping
In Forbes. Looks fairly obvious, but it is the specific forecast number that make a difference to the elements of the supply chain involved.
Monday, June 29, 2015
Event Readiness Service for Demand
Interesting analytics effort. We are all, in effect, thinking about how to react to events in our enterprise context. Demand is a big example, but not the only one. Response to these events needs to be prioritized. Analytics can do this.
IBM Debuts Service to Help Brands Prepare for Spikes in Demand
Program Is Aimed at Retailers, Banks and Manufacturers
IBM Corp. on Tuesday introduced a new service called Event Readiness, which is designed to help brands prepare for unexpected -- as well as expected -- surges in online traffic due to
holidays, special sales and sudden spikes in demand
The product is being offered as part of the IBM Commerce suite of services, and includes software, consulting and analytics. It's aimed at marketing and IT executives in retail, banking, consumer products and manufacturing industries .... "
IBM Debuts Service to Help Brands Prepare for Spikes in Demand
Program Is Aimed at Retailers, Banks and Manufacturers
IBM Corp. on Tuesday introduced a new service called Event Readiness, which is designed to help brands prepare for unexpected -- as well as expected -- surges in online traffic due to
holidays, special sales and sudden spikes in demand
The product is being offered as part of the IBM Commerce suite of services, and includes software, consulting and analytics. It's aimed at marketing and IT executives in retail, banking, consumer products and manufacturing industries .... "
Wednesday, December 12, 2012
Demand Planning Secrets of P&G
A recent video and talk by former colleagues. Hardly secrets, which is what the CGT title boasts, but worth looking at for all practitioners of the science and necessarily art. " ... demand planning leaders from P&G candidly discussed how technology and process standardization is a key enabler for the rapid expansion of innovation ... "
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