Research shows that omnichannel consumers have higher expectations even as they engage their favorite brands in more complex ways. The Retail in 4 Dimensions study found that 63% think stock of well-known brands in-store is most important to their shopping experience and 42% are now shopping both online and in-store every week.
Retailers understand the modern consumer and their journey towards omnichannel proficiency continues according to the RIS News/Gartner 2018 Retail Technology Report. It states that 54% of retailers consider expanding omnichannel initiatives as a top technology-driven strategy over the next 18 months, with retail leaders nearly twice as likely to identify advanced analytics tools as top priorities.
Even results from KPMG's Top of Mind Survey found 60% of companies say most supply chains will be demand driven by 2020 and that 40% of retailers say increased sales is a top benefit of a demand-driven supply chains.
It's clear that forecasts are the foundation of every advanced analytical solution and accuracy is paramount.
Forecasting is complex. The basis for traditional methods is that history repeats itself, with the underlying assumption that historical demand is understood and future demand drivers are pre-determined. Retail, on the other hand, requires working on the basis of varying qualities of sales, inventory and promotional histories; fluid promotion strategies; new product introductions and fundamentally different behaviors in short and long lifecycle items. In our experience, getting the most accuracy and automation from your data requires retail-specific, best-fit retail science that pulls from machine learning, artificial intelligence and decision science disciplines. From multivariate statistical regression for modeling promotions to goodness-of-fit optimization for forecast model selection to unsupervised machine learning to pool similar performing item-locations for robust parameter estimation - Retail Science maximizes the value of retail data to drive better outcomes for retailers.
Consider your typical analyst that manages thousands of items across hundreds of locations, with an assortment that reflects a broad range of sales velocities, promotion emphases, competitive sensitivities and supply chain constraints. When hundreds of thousands of forecasts are ready for execution with best-fit science, your analyst can shift priorities to areas undergoing the most change, such as new assortments and sales strategies. Focusing your team where it counts with exception-driven processes that guide them to the action maximizes the impact of their insight through increased productivity and accuracy, ultimately driving higher margins. Regular interaction with forecasting processes should reflect high value-added activities, such as approving new item forecasts using attribute-based recommendations and reviewing significant forecast deviations from updated promotional strategies.
Science should reflect your reality, process should reflect your priorities and - as the pace of change in retail continues to accelerate - software must be as nimble as your strategy. Traditional Software-as-a-Service (SaaS) offerings, with the promise of keeping pace with the industry, falls short of your objective: Outrunning the Competition. Our extensible SaaS paradigm provides retailers with the stability of traditional SaaS along with the agility to extend core services – from defining key forecasting model decisions, such as promotional and pricing variables, to creating tailored, exception-driven processes oriented around key item and replenishment priorities to augmenting core analytics through our highly configurable platform and rich API of forecasting science procedures.
Oracle Retail Demand Forecasting Cloud Service (RDF CS) provides accurate forecasts that enable retailers to coordinate demand-driven outcomes that deliver connected customer interactions. With a single view of demand, RDF CS provides pervasive value across retail processes, including driving optimal strategies in planning, increasing inventory productivity in supply chains, decreasing operational costs and driving customer satisfaction from engagement to sale to fulfilment. The comprehensive solution is built upon 15+ years of forecasting experience across 160+ retailers worldwide to maximize the forecast accuracy for the entire product lifecycle; with tailored approaches for short and long lifecycle products; the ability to adapt to recent trends, seasonality, out-of-stocks and promotions; and reflect the unique demand drivers of each retailer. Our customers are benefiting from substantial operational value with Oracle Retail Demand Forecasting.
We evaluated our next generation forecasting solution against a retailer’s current forecasting solution where end-users were adjusting 50% of forecasts and found these outcomes:
Our commitment to developing purpose-built science for retail paired with our ability to rapidly bring innovation to life leveraging Oracle’s leading data science technologies is unmatched in the industry. As we look to the future, large-scale applications of artificial intelligence and increased maturity in cognitive computing will open new opportunities for retailers to predict and drive customer demand. Oracle is aggressively investing in this next generation technology through building a transformational startup within the company. In addition, Oracle Retail is working with researchers at MIT to drive predictive analytics down to the customer level and enable retailers to execute on targeted customer engagement strategies.
With Oracle Retail Demand Forecasting Cloud Service, you stay on the cutting edge of forecasting science and get the most for your team. Coupled with our 24/7 retail learning subscriptions, your team will build individual competencies that maximize the usage of your investments. A self-paced learning guide helps new employees gain proficiency and confidence, reducing ramp up time and accelerating utilization. Learn more about our comprehensive curriculum, delivered by Oracle Retail Supply Chain experts.
IT budgets are tight. Why prioritize demand forecasting over any other project? With partner implementation offerings starting from 8-12 weeks and total cost of ownership savings of up to 35-40% when compared to traditional on-premises delivery models, paired with the financial performance potential of a true demand-driven supply chain, high speed to value can be the cornerstone of your business case.