Linda Mandyu, Client Success Manager, ATG

Argility to take trailblazing Google Research-based demand forecasting up the value chain, to CPG manufacturers

ajustus Oct 14, 2024 Consumer Packaged Goods, CPG, Demand Forecast, Predictive Analytics, Prescriptive Analytics, Value Chain

The Argility Technology Group, specialist retail predictive and prescriptive analytics organisation within global enterprise, Smollan, is moving up the value chain to offer its best-in-class demand forecasting service to consumer packaged goods (CPGs) manufacturers in South Africa.

Linda Mandyu, Client Success Manager at Argility, says Argility’s Predict Retail portfolio, which includes Predict Price, Predict Inventory and Predict Customer, will be expanded to meet demand from CPG manufacturers. The portfolio offers retailers analytics as a service, using the most advanced models and comprehensive retail data available in the local market. With the expansion of the portfolio, manufacturers will be better supported in forecasting demand and optimising planning, he says.

He explains that the Predict Retail portfolio harnesses AI, ML and best practice, with Google’s groundbreaking new Temporal Fusion Transformer (TFT) demand forecasting framework.

“TFT has outperformed all other advanced statistical models, reducing inaccuracies by up to 9%. As Google partners, we have access to this TFT framework and have tailored it for demand forecasting, specifically for retailers. Notably, Google’s specialist research results with TFT alone stand head and shoulders above others, but at Argility, we build on this framework and couple it with deep analytics expertise and decades of retail experience to offer best-in-class demand forecasting for the retail industry,” he says.

“Via our parent company Smollan, we also have access to DataOrbis’ expertise in analytics and reporting solutions for consumer brands, and the augmented analytics capabilities this adds to our offering. With these technologies, models and expertise, we have the unique ability to deliver highly accurate predictive analytics (what will happen?) and prescriptive analytics (how can we make it happen again?) for the retail sector value chain; this invariably deepens the partnership of brands and their channel partners.”

Mandyu says category managers and analysts in retail and CPG manufacturing companies can augment their existing toolkits as they harness these advanced tools and Argility’s expertise to significantly improve stockholding and distribution, and reduce costs, without having to allocate huge amounts of human capital internally.

“Argility’s offering is essentially demand forecasting as a service, to supercharge retail category managers’ forecasting capabilities,” Mandyu says.

He notes: “When a retailer or manufacturer has sales and demand forecasting at its most advanced level, they gain the price optimisation advantage. If you overlay this with deep retail sector experience and external data, you can start to pick up very nuanced insights into the impacts of pricing and demand. You can discover things like how much you can raise the price without losing volume, or the influence of factors like promotional prices, complements and substitutes. Argility has worked with retailers for decades, so we are bringing our institutional brains trust to bear, coupled with our class-leading knowledge of deep learning algorithms and price modelling capabilities to partner with brands to enrich their data-driven analysis,” concludes Mandyu.

Source: IT Web

ATG’s demand forecasting as a service – secret weapon for retail category managers’ toolkits

ajustus Oct 7, 2024 AI, Category Managers, Demand Forecast, Machine Learning, Predictive Analytics, Retail

Demand forecasting is a well-established and important field in retail, but accuracy and speed are becoming challenges in a fast-changing and ever evolving market. Category managers need advanced support to stay ahead in modern retail.

This is according to Linda Mandyu, Client Success Manager at the Argility Technology Group, specialist retail predictive and prescriptive analytics business within global enterprise, Smollan.

Mandyu says most category managers still attempt to manage demand forecasting on complex Excel workbooks, while some trailblazers are trying to push the envelope by integrating statistical analysis and BI tools. “It remains onerous and highly specialised, and requires very skilled statistical analytics, commercial and retail subject matter expert teams. These skills are hard to find, and when you do find them, they are generally experienced with industry and vertical-specific tools,” he says. “You might need a team of ‘unicorns’ to deliver what modern retail needs.”

As a specialist in the retail sector, Argility combines highly mathematical and statistical expertise with the most advanced predictive and prescriptive modelling, as well as leveraging modern cloud technologies, complete with a skills pool with over 40 years of retail experience.

“We’ve taken that deep experience, combined it with scientific, technical skills and built a product that is fit for the purpose of demand forecasting directly on the world-class Google Cloud platform, with the ability to analyse petabyte-scale data. Then we apply machine learning tools and AI to supercharge those capabilities,” he says.

Mandyu confirms that it is essentially “demand forecasting as a service” that allows retailers to, firstly, improve stock holding and distribution, and then to support price optimisation initiatives, thereby driving an increase sales and retention of your much-valued customers. “It’s like a secret weapon for category and brand managers alike, that arms them with highly valuable intelligence to inform precise decision-making.”

Mandyu explains that Argility’s Predict Retail services deep dive into enterprise data to uncover valuable insights. “For example, if they want to grow market share in a particular category, we look at what competitors have done, matched with their product-price elasticity boundaries, then give them high-level intelligence on where they are over or under priced. We also focus on recommended order levels so they are not over or understocked and can optimise their supply chains and better meet customer demand pre-emptively. This gives value out of the box much faster. It also frees up their skilled resources to do higher value human-specialised analysis that may fall out of the capability of artificial intelligence or machine learning efforts. They don’t have to do the number crunching, but rather pass it to a machine to do the heavy lifting of the incredibly large data sets stretching back over longer time series.”

Mandyu cites the example of an FMCG and perishable goods field service, which is expected to reduce millions of rands in returns through improved demand forecasting capabilities. “While the client closely manages stock holding and distribution and checks shelves several times a day, the process is flawed because it is based on human estimation. The business was incurring over R100 million in product returns each year. We have recommended improved order values to narrow the gap between ordering too much and ordering ‘just right’. The customer is now on track to achieve our conservative target, which is to save anywhere from 2% to 5% in returns, which in this case will amount to millions saved,” he says.

“Complex retail analytics business problems remain a source of endless excitement to the ATG team, a fact that may often seem intractable to our clients. But solving these problems present a perfect opportunity for converging the elusive ‘man and machine’ duo to achieve impactful success exponentially faster, and more accurately, from data-driven initiatives,” concludes Mandyu.

Source: IT Web | Intelligent CIO

Tanya Long, COO Argility -retail concepts and resources

Predictive analytics lights the way

ajustus Apr 19, 2024 Data-driven Decisions, Predictive Analytics, PredictRetail, Retail

Retailers and brands can realise the immense power of data-driven decision-making by using predictive analytics to glean faster insights.

By Tanya Long, CEO – Argility Technology Group

Previous articles published on ITWeb have explored some of the challenges and solutions for retailers’ consideration to limit the impact of load-shedding on operations. The Industry Insights examined how technology, augmented by partnering with trusted suppliers, can be used to help navigate this, but there is more that can be done.

There has been a radical change in production, supply chain and consumer behaviour which cannot be ignored. And here is where we lean on the technology innovations of this century: artificial intelligence (AI), machine learning, predictive analytics and cloud
solutions.

It is true that predictive analytics is not a crystal ball; however, the immense power it can give retailers and brands cannot be ignored.

To use a casino analogy as an example: Let’s say you play roulette. In years gone by, one would watch the tables, evaluate the trends of each croupier, and then decide at which table you would take your chance with your R100. Imagine if someone had whispered to you – ‘Table 1: 65% probability on red, 35% probability on black’. What decision would you have made? And if an insight like this was given every 15 minutes, would you have been inclined to include this in your next decision for action?

Predictive analytics is not a crystal ball; however, the immense power it can give retailers and brands cannot be ignored.

This is the advantage that predictive analytics can provide. In a nutshell, it is the power of data-driven decision-making.

With machine learning, fluctuating behaviour can continuously be used to understand supply and demand patterns with a speed that is incomprehensible when compared to five or 10 years ago. The traditional software approach was to create the system rules based on the known rules; however, with the increased landscape complexity, an intelligent approach is to use machine learning to identify the patterns and provide much deeper insight.

It’s often said the beauty of AI is that it never sleeps – it works 24/7 – just what is needed to help us in our current economy.

Retailers and brands now have the ability to glean faster insights from multiple inputs relating to stock availability, pricing, placement and customer demand.

Predictive analytics uses the principle of small, compounded gains. For example:

What positive impact would be experienced by having a 2% increase in stock-on-shelf availability?
What impact would be gained by having a 2% increase in cash flow due to lowering of aged stock surpluses?
What would a 1% increase in gross profit mean, if stock prices were able to be speedily and strategically managed by considering pricing appetite and competitor pricing across a basket of products?

Now, I realise all of this takes data, and prevalent discussions in the media has raised the question: how much data do I need? When do I start using analytics? The general consensus is if one hasn’t started yet, you are already backfooted.

The advent of cloud has given the ability to store and process petabytes of data, cost-effectively and without excessive hardware investment. It has also given us the ability to scale easily with increasing/changing needs and to leverage off always-on infrastructure provided by global players.

What problems can predictive analytics solve?

Let’s start with the concept of demand forecasting. In the old days, stock and sales forecasting were generally formulated based on moving averages. Today’s retailers would be ill-advised to continue in the same vein. Predictive analytics can add immense value in this area.

Another area that predictive analytics can play a huge role for retailers is focusing on customer patterns. Not only for personalisation by getting the right product in front of the right customer, at the right time and right price, but also to evaluate the change in shopping behaviours.

Using machine learning for clustering and segmentation, identifying purchase habits and preferred products would streamline a personalised recommendations strategy.

In a credit retail environment, the additional insights on areas such as propensity to buy and customer lifetime value has the potential of elevating prospecting efforts. Anticipating customer needs would definitely not only assist retailers on building customer loyalty, but also decrease the cost of spray-and-pray marketing efforts.

Labour management is another area where data and analytics can play an important role in improving operational efficiencies. One cost pressure which may be within a retailer’s control is looking at optimal workforce levels.

Gathering data on speed and accuracy of staff performance can allow retailers and brands to streamline their operations to efficiently meet fluctuating business demands.

These are by no means where the use-cases end. I suggest commencing in an area where there is a compelling business challenge and in which one can measure and refine the results in order to take appropriate action.

Business and project sponsor support is vital for success, although I believe many stakeholders would rather be proactive and stack the odds in their favour.

Source: IT Web