Many businesses are so focused on building revenue, their profit suffers as a result. Organisations worth their salt know that selling at all costs doesn’t make good business sense. A more sophisticated way to measure and drive success is ROI (Return On Investment). What is the business cost of selling a line item (or SKU) and does it make commercial sense to invest in it in future? The faster the sale of a SKU and the higher the margin, the more profitable the item is to a business.
In order to truly optimise ROI, businesses need to measure product performance across their entire range, at an individual SKU level. By learning more about what’s working and why, organisations can start to optimise SKU productivity and increase ROI across the board. This is where Machine Learning combined with Big Data is invaluable. This power partnership can measure the impact of everything from the weather, to negative reviews to sports team performances on SKU performance. Able to handle massive amounts of information generated across multiple sales channels, smart forecasting tools can marry business statistics with data generated across the internet to learn more about SKU performance and empower planners to make more informed and insightful decisions about inventory levels.
This is where Machine Learning combined with Big Data is invaluable.
The most profitable (and sustainable) way to do business is to maximise efficiency and minimise waste. However, the reality is that demand and supply planners are often stuck in the middle of contradictory business functions - Finance trying to keep costs down, Sales pushing to spend more to shift product and Retailers managing space restrictions. If the game plan is less about balancing departmental needs and more about adhering to a high-level strategy, demand and supply planners are in a far less problematic position. And if the strategy moves from selling at volume, to ensuring the volume of held stock has been set to meet the demand of the market - ROI will inevitably go through the roof.
Automated systems can take a big part of the battle out of the demand and supply planners’ hands, creating an agnostic and unbiased consensus plan that works in the best interests of the business as a whole. Able to ingest vast amounts of data across hundreds of SKUs, smart systems can consolidate this information and turn it into actionable insights.
With this power at their fingertips, supply and demand planners can confidently advise other departments of SKU business value, according to hard facts and clear strategy.
The not-so-basics of Demand Planning At its simplest, effective Demand Planning means reducing the gap between held inventory and actual sales. It’s about meeting demand in the most efficient way possible to help retail organisations avoid stock-outs at one end of the scale, and wastage at the other. Yet this vital role is often drowned […]
Demand planning + supply planning = integrated business planning If demand planning is forecasting customer demand, while supply planning is managing supply according to these forecasts, you’d be forgiven for thinking that these functions went hand-in-hand. All too often though, demand planning and supply planning departments work to different agendas. One driven by ensuring sufficient […]
Top-down or bottom-up? How to approach forecasting in a data-driven world. The role of any good Supply Chain Manager is to ensure regular reporting of the variances between top-down executive targets, and the bottom-up demand of the market. Effectively, this is the budgeting process, and achieving a balance between demand and supply is the best […]
Forecast. To predict or estimate a future event or trend. Let’s start with a forecast we’re all very familiar with as an example - the weather. Once upon a time, forecasts were based on historical data captured around certain dates. Then, with the invention of telegraph networks, weather conditions could be observed and shared across […]
A formula for accurate demand planning at store level (Hint: it doesn’t start with “=”) Technology is the biggest enabler of people in the digital age. Yet arguably, it’s also the biggest threat to the modern day workforce. When it comes to using technology to enhance demand planning in business, harnessing digital tools in a […]
Inject the smarts into your new product planning Consumers now have more choice than ever before - of what to buy, and where to buy it. The explosion of e-commerce has driven product and competitor proliferation to all-new levels, so what worked before (i.e. analysing previous product performance) no longer serves as an accurate indicator […]
Demand forecasting is always wrong. Thankfully. If demand forecasting was a precise science, we would be out of business. Organisations would apply their tried and tested formulas, and would emerge from their endeavours armed with 100% accurate predictions to take into their next phase of demand planning. Their wash-ups would show an exact correlation between […]
Understanding demand patterns in the Data Age Demand pattern analysis is becoming increasingly valuable in business, as a way of predicting and preparing for future fluctuations in market demand. The problem is that the “best-practice” models that are still taught and employed today rely solely on historical patterns to make predictions. In reality, looking backwards […]
