Leverage historical sales, market data, and predictive analytics to improve multi-dimensional supply chain planning and meet consumer demand.
- Omnichannel sales and delivery optimization is challenging, technologically and quantitatively.
- With new data comes new opportunities.
- Cloud forecasting and planning capabilities are reshaping supply chain.
The omnichannel challenge
As recently as the late 90s (the olden days), most retailers relied almost entirely on in-store sales data to track trends, forecast demand, and set preseason orders. Things were simple then. The availability of data was scant by today’s standards, but so were the choices consumers had in how they could browse and purchase goods.
Retail is now vastly more complex, and consumers wield tremendous power in shaping the commercial landscape. They browse in-store, purchase online, and vice versa. They expect products and services to be personalized and available on-demand.
In response, retail organizations scramble feverishly to track and coordinate activity across this expanding web of sales and delivery channels. They include at-home, in-store, and mobile; phone versus online; mail delivery versus in-store pick-up, and more (yes, these channels somewhat overlap).
From purchase through delivery, every combination of options in the fulfillment process must be tracked, measured, understood, and optimized. How else can retailers (and their value-chain partners) deliver a seamless experience, and at a profit? No doubt this is hard work, especially with evermore SKUs, channels, order fulfillment variations, shipping options, price pressures, and customer engagement preferences.
Whether in-house, through a technology partner, or both, omnichannel delivery is a quantitatively daunting labyrinth that requires sophisticated, omnichannel analytics and IT integration. Organizations with top-performing software platforms are at the forefront of the omnichannel demand & supply chain challenge.
New data, new opportunities
Fortunately, the rise of omnichannel sales and marketing is also generating much more data and better ways of processing that data to improve forecasts and optimize decisions such as:
- Where to position inventory
- Whether to ship from warehouse or store
- Which level of aggregation to forecast
- Which SKUs to cut
On the external front, smartphone data and shopping apps track consumer behavior at multiple touch points or draw on customers’ purchase histories to personalize online experiences. More organizations are beginning to use big data and predictive analytics to spot trends, target groups with specific attributes, and fine-tune product assortment and distribution.
Lastly, cloud computing and centralized platforms for integrated supply and demand planning helps organizations process data from multiple sources, optimize shipping points and inventory levels, and coordinate with supply chain partners to streamline commerce.
Omnichannel analytics in the cloud
Cloud-based forecasting and planning platforms have come a long way in recent years. The best systems apply advanced-analytic forecasting techniques to uncover trends, patterns, seasonality, and outliers in historical internal sales data. When this is done well, these techniques give demand and supply planners a reliable baseline forecast from which to start. With varying degrees of automation, these platforms drastically reduce the manual labor and error that is rife in spreadsheet-based forecast preparation.
Beyond advanced time-series forecasting, some platforms also include workflow features that make it easy for business users to adjust and override forecasts based on knowledge of new orders, new customers, new competitors, and more. This information complements historical data with real-time knowledge, which helps further increase forecast accuracy.
Best-of-breed forecasting and planning systems, such as the Vanguard IBP platform, can pull in external market data (economic, demographic, etc.) to refine predictions even further. Meanwhile, advanced modeling and simulation tools, which includes Monte Carlo simulation, can test multiple omnichannel delivery schemes (in seconds) under thousands of unique business scenarios.
Additionally, most retailers investing in enterprise-planning platforms want the ability to switch instantly between units and revenue (in any currency). This is key for supporting diverse user sets, such as Operations versus Sales or Finance, or U.S.-based versus Europe. Another key capability is to forecast easily at any level of aggregation: high-level for products with sparse or intermittent demand, or more granular for products with highly individual demand patterns. The aim is the same. to get the right items sourced and shipped to consumers, at the right time, in the right place.
Best-of-breed forecasting and planning for supply chain not only improves operating efficiency and profit, it helps deliver an outstanding customer experience in a multi-dimensional consumer environment. It also ensures that you’re getting the most out of your customer-transaction data.