Managing seasonal profile management in the supply chain

seasonal profile adjustment in supply chain

Blog Written By | Progressive Grocer

Getting Seasonal Adjustment Right in Your Inventory Purchasing

From soups to nuts, seasonal profile adjustment is an important – but often overlooked – part of inventory forecasting. Are your organization’s forecasting models keeping up?

For inventory managers, predicting sales for products with seasonal, slow or otherwise intermittent demand is notoriously challenging. In a survey of wholesale suppliers, nearly half (45%) called seasonal profile management a pain point for their businesses. From the freezer case to OTC, seasonal items make up a significant portion of SJUs, which means accurate forecasting is critical. Yet many retailers are falling short, relying on outdated methods that don’t take seasonal dips and spikes into account.

As customer demand becomes increasingly complex in today’s flexible fulfillment environment, forward-thinking organizations are turning to new tools to help solve these challenges. By embracing a data-driven approach to inventory management, retailers can predict what they once considered unpredictable and manage their entire portfolio effectively.

Outdated Tools, Unreliable Results

While new supply chain tools can help dramatically improve forecasting, many retailers haven’t yet moved beyond manual forecasting methods – or even old-fashioned gut instinct. According to the same survey noted above, half of the suppliers haven’t implemented machine learning in their forecasting yet. By depending on outdated methods, retailers face challenges like:

  • Forecasts that don’t account for seasonal adjustment appropriately
  • Making seasonal adjustments that don’t improve forecast accuracy
  • Not being able to calculate whether a seasonal adjustment will improve accuracy

For retailers, inaccurately forecasting seasonal items means getting stuck with excess holiday baking supplies in January or Easter eggs in May. Stockpiling is a common tactic for organizations, the survey found, with more than 60% of wholesalers holding more than one month of inventory. Those high inventory levels don’t translate into higher service levels, however, with more than a quarter of organizations missing at least 4% of sales in 2019. Rather than ramping up inventory across the board, accurate forecasting requires optimizing levels by item – a practice that’s even more important for seasonal or other unpredictable products.

Going Beyond Guesswork with Machine Learning

To manage their inventory effectively, retailers first need to marry the optimal forecasting and replenishment strategy with each SKU, which requires a more advanced forecasting approach. Leveraging machine learning techniques can help retailers identify seasonal items more easily, generate more accurate forecasts and gain an edge on competitors still struggling with elementary demand models. To master seasonal profile management, here are four key areas to keep in mind.

  • Determining which items to adjust. Do you know every SKU that should have a seasonal profile? The right demand classification techniques can help you understand how sales are likely to fluctuate across a wide variety of demand behaviors. Using probabilistic forecasting and advanced analytics, you can create an accurate forecasting model for each item – even the most challenging ones.
  • Finding the right model. Many businesses rely on a classic exponential smoothing model to predict demand, which smooths sales activity throughout the year into a holistic forecast. But what happens if an item doesn’ have any sales for six months out of the year? Rather than smoothing periods of no sales into the forecast, which doesn’t produce the most accurate results, use advanced forecasting techniques for SKUs with intermittent demand. For example, aggregating sales to a higher level, such as category or location, can help provide a more precise forecast.
  • Measuring accuracy. How confident are you that your seasonal forecasts are actually correct? Machine learning-enabled tools can help businesses remove the guesswork from seasonal adjustment. Using pattern-matching algorithms, these tools help ensure that seasonal adjustments will actually improve accuracy before making the change.
  • Automating forecasts. The average retailer has thousands of SKUs, which makes it all but impossible to manually create forecasts for each seasonal product. Beyond improving accuracy, inventory forecasting tools can deliver big efficiency improvements by producing product-level forecasts, with no need for user intervention.

As technology continues to evolve, emerging techniques like unsupervised machine learning can help businesses improve accuracy further. Using near human-level intelligence, these tools can identify trends like seasonal pattern clustering, and adjust inventory levels accordingly.

For retailers just beginning to explore advanced forecasting tools, a supply chain partner can help organizations identify and implement the tools that offer the greatest value. By taking a data-driven approach to seasonal profile management, businesses can right-size inventory to meet dynamic, complex demand patterns all year long.

Contact allonline365 today to explore ways to digitally transform your business and manage your supply chain effectively. Call us on  +27 (21) 205 3650 or email

Using Technology to build an emotional brand connection


Apparel and fashion companies need an IT framework that is flexible and futureproof as they work to nurture an emotional brand connection with consumers around the world

The end goal for fashion brands ins a timeless one: Connecting with consumers on an emotional level. But achieving it is full of modern-day challenges, from omnichannel commerce to global distribution to the mobile consumerization of technology.

When a shopper is emotionally attached to a brand, he or she often will go to great lengths to secure the desired style. Having it is connected to all sorts of feelings of fulfillment and satisfaction. Yet even the most devoted followers have their limits.

Consumers expect instant gratification more than ever. When apparel brands and retailers cannot fulfill the desired style immediately, shoppers are very quick to look elsewhere. “We as a society know what we want, and we want it now,” says Jerry Sheldon, vice president of technology, IHL Consulting Group. “We’re in an environment where there is a lot less brand loyalty and synergy. If you can’t fulfill it, I’m going to choose another retailer or a brick-and-mortar retailer. If you aren’t able to capture the sale right then and there, the chances are really high that you’re going to lose that sale.”

To create an emotional connection with today’s apparel shoppers, fashion companies must excel at multiple core competencies, including not only on-trend design and stellar customer service but also extremely sophisticated fulfillment. Doing so is difficult, requiring skill and strategy. There is no getting around that fact. However, the work to get there does not have to be impossibly tedious, expensive, error-prone or labor-intensive.

Building brand appeal: IT solutions’ supporting role

Technology plays a vital role in building bonds between consumers and fashion labels – connections tied to compelling products as well as flexible fulfillment. “Apparel retailers are very interested in releasing new products to market on a far more frequent basis, continually moving that product and minimizing discounts,” says Thomas O’Connor, principal research analyst, global retail supply chain, Gartner Inc.

Brands who sell to multiple retailers increasingly view their fulfillment capabilities as a competitive differentiator, he says. Their capacity to get products to end consumers, regardless of the channel, usually correlates with fewer markdowns and optimized margins for them and their retail partners. “When you’re operating via multiple channels, you need to be thinking about how to provide optimal service to the consumer in a cost-effective manner,” he says. “That’s a core component, and it’s something technology can definitely support.”

The latest generation of end-to-end fashion IT solutions can help brands to build strong connections with consumers, alleviating the need for a maze of bolt-on solutions. These solutions touch processes from concept to consumer. Their consumer-facing functionality enables retailers to create a multifaceted front-end marketing and shopping experience. This customer experience encompasses traditional touchpoints, such as stores, as well as e-commerce websites, apps, and social media.

For example, the latest fashion ERP technology includes e-commerce and social features so that shoppers can easily add products to their wish lists on Facebook, Pinterest, or other social networks. They also help retailers connect with their own social media followers, engaging with them based on their personal preferences and fashion flair.

“As consumers have evolved in how they want to shop, with myriad devices in their hands, it’s a necessity for retailers to support these multiple channels, whether it’s mobile, tablet, computer, catalog, or social media,” Sheldon says. “Retailers realize they have to go where the opportunity is.”

With the mobile consumerization of IT, that opportunity often lives on smartphones. Mobile consumerization if IT refers to consumers’ heightened comfort level in using mobile devices to manage many different facets of life, including shopping. In April 2016, The Wall Street Journal reported on surging use of shopping apps in an article entitled “Shoppers Flock to Apps, Shaking Up Retail.”

“The so-called application of shopping promises to radically transform the retail industry by creating new shopping habits, reshaping sales tactics, and carving out winners and losers,” said the article. “Instead of placing one big order from a computer, people are increasingly making smaller purchases in short bursts throughout the day on their phones, a phenomenon retailers call ‘snacking’,” according to the article.

To make sure they can fulfill these snacking orders as well as in-store and other demand, retailers require their technology solutions to support a reliable back-end delivery experience. Coming through on delivery promises is enabled in large part by real-time inventory visibility and distributed order management. “With a distributed order management tool, they can take the different orders as they’re coming in and take their real-time inventory visibility and automatically decide where the item should be sourced from and determine the most time-and-cost-efficient way to fulfill the order,” says O’Connor.

Integrated Design and PLM matters

Omnichannel fulfillment is critical, but first a fashion retailer or brand must have a marketable product portfolio. A strong emotional bond between retailers and their target audience is nourished by a steady diet of fresh fashions. The most appealing brands know complexity is a reality. They need IT solutions that are specifically designed to support overlapping collections of multiple brands and product lines.

It’s increasingly the norm for companies to release new collections every six weeks vs. three or four times annually. It’s also more common for brands to offer a broad and deep product assortment. “With the advent of fast fashion, we’ve seen a lot of apparel retail moving from the traditional model of two or four launch seasons per year to the extreme where they are launching new seasons on almost a monthly basis,” says O’Connor. “That is an interesting challenge for a lot of retailers.”

Technology can help busy fashion professionals keep it all straight. For example, within some end-to-end ERP solutions, there are PLM capabilities that allow companies to tailor many different product matrices to suit different target markets. Within the same solution, the firm uses for sales and distribution, product developers can manipulate style dimensions related to jeans. They can manage different sizes, lengths, washes, and fits such as skinny, boot, cut, or relaxed. With a click or two, they can easily shift their attention to intimate apparel collections, where the dimensions are entirely different, including bra cup size, etc.

The most flexible and robust ERP platforms include many automated functions for planning and producing multiple lines much faster than was possible with legacy solutions and spreadsheets. For instance, there are ‘wizards’ and one-click steps for handling processes such as:

  • Viewing the status of overlapping collections and seasons
  • Arranging product rollouts by channel
  • Prioritizing sales orders for fulfillment by channel if there are product shortages or vendor shipment delays
  • Creating new products quickly by choosing from hundreds of default values
  • Automatically updating washing and care instructions when fabrications change
  • Recalculating a style’s grading across all sizes if a change is made to the pattern (through integration with Adobe Illustrator)
  • Applying a ratio curve of sizes to order, i.e., automatically calculating how many of each size should be included based on programmed parameters or actual sales history

IT enables global and omnichannel expansion

Being an omnichannel, global brand is within reach for any innovative brand, thanks to technology advances. It’s not a matter of if you can go multichannel and global but how you want to prioritize your expansion. Cloud-based computing has been a huge factor in helping companies of all sizes to grow their businesses internationally and across all channels. One reason is that with the cloud, powerful applications no longer have to be managed on-site by the apparel business’ IT department. Cloud hosting frees companies from a lot of maintenance time and expense. “It’s democratizing technology for businesses,” says O’Connor. “It’s driving down a lot of the costs associated with technology. It puts technology within reach.”

To help apparel companies compete globally, IT solutions must keep the business humming along in a multicurrency, multi-language, multichannel world. Global expansion can be simpler when associates work on a common IT solution with a familiar user interface. This was a big benefit London-based fashion retailers Charles Tyrwhitt saw in ERP technology from K3 Software Solutions. K3’s ax|is a fashion solution that is completely embedded within the Microsoft Dynamics AX ERP platform. “Our users really enjoy working with Dynamics. They find it very easy to get on with, using that traditional Microsoft look and feel,” says Simon Kerry, CIO for the retailer, which has 16 stores in the UK, one in Paris and five in the US. Technologies have evolved to the point that they “support the ability to be international relatively easily,” says Sheldon. And as for omnichannel, “software today allows for more of a single view of the customer across all the channels,” he says.

How to ‘futureproof’ your business

As fashion companies consider their IT investment strategy, they want solutions that are able to support today’s needs and provide a path to tackling tomorrow’s brave new retail world. Some call this ‘futureproofing’. Whether apparel brands are ready or not to dive into the Internet of Things (IoT), their IT solutions should be IoT-ready – able to process different types of heavy data streams. For instance, leading ERP providers have laid the groundwork to support 3-D body scanning (from in-store scanners or at-home game consoles), wearables, hologram applications, virtual dressing rooms, and other innovations. They understand that fashion businesses need a helpful analysis of IoT data to get real returns.

At a very pragmatic level, futureproofing for the next 12 months or 12 decades is likely to involve attaining real-time inventory visibility and omnichannel fulfillment capabilities. That’s one reason Gartner is devoting a lot of research to ‘algorithmic business’, in which machine learning continually adapts to changing data inputs, automatically proposing optimal decision-making paths. IT solutions set up to support real-time inventory management and distributed order management rely on om these algorithms. This type of technology will play a major role in futureproofing fashion businesses.

To future-proof their businesses, one of the most important things fashion executives can do is create a well-defined technology roadmap, says Sheldon, ensuring their solution partners’ strategies and capabilities align with where they want to go. “You need to look at who the market leaders are in the space,” he says. “They tend to have the broadest feature-function set. And typically a broader feature-function is one of the best ways to future-proof the technology decisions that you’ve made.” 

Resource Credit | K3 Technologies 

Using AI to Determine Price and Boost Revenue in Grocery


Grocers need to surf the waves of ever-changing customer expectations and do whatever it takes to not to join the ranks of the fallen retail empires. Everyone is citing customer experience as priority No.1 for retailers. However, another major lever for retailers to pull to stay alive and even thrive is the price of an item.

Big names highly recognize the importance of price, along with frictionless customer experience, a wide range of offered products and well-planned marketing activities.

Retail giants invest in price optimization heavily, which allows for creating the right price perception and persuade customers.

Advanced retailers recognize that traditional pricing approaches are broken. They are determined to embrace AI for pricing. But how exactly can technology make it easier for smaller operators to stand up and fight, and win?

AI to help retailers compete

Retail giants have been harnessing advanced analytical software like AI to set the right prices for years. Such technology used to be extremely expensive and available to the select few.

Today, more companies, including smaller operators, are getting the chance to grow as such software is becoming simultaneously more sophisticated and accessible to a wider number of players.

According to a series of market tests held by retail price optimization company Competera, elasticity-based machine learning algorithms can help retailers set competitive prices and raise revenue by 5 percent and beyond.

Vladimir Kuchkanov, pricing solutions architect at the company, comments on how artificial intelligence can be advantageous for smaller supermarkets competing with the likes of Walmart.

“The retail king has better chances from the beginning. Thanks to the huge amount of products it buys from suppliers, the company can negotiate better purchase prices,” he says. ‘To compete with it, smaller food chains need to identify a pool of products which have to be priced lower than at Walmart – even if they are selling at a loss. This would keep attracting customers. ”

However, it is just the start. “On the other hand, businesses need to indicate a group of items that can be priced higher without risking to scare off shoppers. This way retailers can compensate for low or negative profit margins they inevitably have in their fight with Walmart. But this begs two major questions: How to identify such items and how to price them. Managers can not do that as there are too many parameters to take into account. That’s why retailers bring AI into play,” adds Vladimir.

Another challenge the technology helps retailers to tackle is the management of private label products, which are essential for creating a unique and recognizable brand and winning the hearts of customers.

Retailers can choose one of two options when it comes to adopting AI in pricing. They can either invest heavily in developing an internal system or find a technological partner offering a turn-key solution evolving along with the needs of the retailer. 

Technology’s efficiency in recommending optimal prices

AI-powered algorithms enhance people with enormous computational power, making them very rapid and precise in their decisions. Such algorithms process massive and unyielding – for humans – amounts of data regarding hundreds of pricing and non-pricing parameters to suggest the optimal prices for the whole product portfolio.

The data AI needs to analyze include competitive prices, customer behavior, the retailer’s past performance, and current business goals, as well as weather and cross-price elasticity. Algorithms browse through the infinite number of pricing scenarios which equals the number of atoms in the universe to come up with the most beneficial one in real-time.

When it comes to calculating optimal prices for private label items, the technology identifies latent clusters of similar products and assigns such items to the most affinitive clusters.

What are the benefits for people? AI-enhanced managers switch to data-driven pricing, get ahold of unmanageable promotional campaigns, set the right prices for private label products which differentiate them from competitors, and finally have time to turn to high-level decision-making and improve customer experience.

As Microsoft’s chief technology officer, enterprise, Norm Judah explains: “AI is about augmenting human ingenuity. Whether you’re a seller, marketer, a lawyer or something else, AI will change the way you make decisions. It can help you navigate vast amounts of data and give you advice and recommendations about how to proceed. What you do with that advice is up to you. ”

The technology available to the big retailers is slowly becoming more accessible to companies. So, why not try out its proven potential to raise revenue and grow? If you are interested in transforming your business with AI technologies and business intelligence solutions contact allonline365 to get some advice on the best software systems that are affordable and the right fit for your business. Contact us on or  +27 (21) 205 3650.

Resource Credit | Progressive Grocer

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