Big Data and Inventory Planning: Why Demand Forecasting Works

Market pressures can sometimes seem as if they fall out of the sky. If your business depends on having the right inventory at the right time, anticipating what you’ll need and when you’ll need it up until now has been more art than science.

But in today’s market, supply chains need certainty and assurance. The most successful businesses are the ones that can master inventory planning and anticipate where they will need certain stock.

Employing a team of planners and some outdated software built on 20-year-old technology that relies upon basic algorithms and historical averages just doesn’t cut it anymore. In a digital economy, trend changes, policy changes, monetary changes, even weather changes all have an impact on business and all happen faster than any team of people can analyze and process.

This is further complicated by the need to support multiple sales channels with your central inventory investment strategy. Throw in sourcing or scarcity issues and all these factors mean your inventory planning team must operate beyond their capacity.

Are businesses really doing everything they can to make sure the right products are at the right place at the right time? Are they balancing carrying and acquisition costs? Smart forecasting is no small chore for a team of buyers and planners using traditional on-premises software. Smart inventory planning is the edge today’s businesses need: Knowing exactly where to send just enough to meet true demand while minimizing costs to the business. Is your supply chain able to reach that Goldilocks zone?

The Desired Result, And the Challenges

What if planners were able to get goods to their prime destinations at the exact moment of demand? What if supply chains had a system that was not constrained by the vertical dimensions of SKUs, channels and locations, and could interpret and transform data into actionable information while dynamically adapting to the growth of the marketplace?

A medium-sized supply chain with five inventory planners sitting in a room for 24 hours a day, forecasting demand based on their calculations of past data, is not good enough for determining that Goldilocks zone. And neither is 10 buyers or even 50!

The first reason is complexity. There are simply too many variables to consider. Those planners have to figure out how to meet demand, balance risk, avoid stock outs, figure in supplier constraints and carrying costs and maybe even future stock conditions.  Multiply this by sales channels like stores, multiple modes of ecommerce like click and collect, ship from store or ship from central DC, considering dependent and independent demand. Then throw in a disruption or two.

Nobody can solve this complexity on their own and throwing bodies at it only serves to make the inventory planning process even more inefficient. That only leads to the second reason why the answer to the problem isn’t people: Cost.

No business has the capital to hire enough people to meet an exponentially complex and growing marketplace. It just can’t be done profitably without software to meet the scale the market demands.

Meeting the challenge of inventory planning and forecasting in a digital economy revolves around the ability to process and manage continuous data uptake. Inventory planning and demand forecasting is a big data problem. The solution today and in the future is all about processing speed and power and the ability to see patterns as they appear.

When markets and trends shift, a team of inventory planners would quickly fall behind without action-ready recommendations from their supply chain planning solution. So, what does optimal planning and forecasting need to work in the digital marketplace? Processors and machine learning, for starters. It’s big data to big decisions.

Getting to the Goldilocks Zone

Imagine if your inventory planner could calculate the stock levels needed for each and every inventory item, and those levels exactly met service and product goals. Imagine if your planner could build order forecasts that fulfilled forecast demands like a hand in glove. Never too much stock. Never too little. Always just right.

Imagine if your planners could adjust for special considerations, reconciling constraints, revising and re-optimizing after factoring in every sort of variable that a business could throw at it. That is a powerful edge. Inventory planning and forecasting does the heavy lifting and provides action-ready recommendations that help buyers and planners optimize their day.

Forecasting and optimization software keeps an eye on moving targets and updates accordingly. It also helps planning, forecasting and replenishment teams stay in the Goldilocks zone by automating the forecasting processes with the power of machine learning and the cloud. Very simply, planning technology helps supply chains process both historical and incoming data faster in real time.

An enterprise-ready forecasting automation platform increases performance and productivity by moving complexity to an engine and managing processes via one easy-to-use interface, down to individual SKUs.

The benefits of a precise, realistic day-by-day inventory plan even goes beyond cost effectively meeting demand. The resulting visibility enables better strategy and efficiency across your entire company. Integration of this “single version of truth” is essential to planning and executing effectively in today’s complex and fast-moving marketplace.

If survival is the game, then inventory planning is the edge that keeps your company in the zone.

Rod Daugherty is the Vice President of Product Strategy for Blue Ridge

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