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 way that empowers people is the key to getting the balance right. So while there’s no substitute for the strategic human mind to help you plan your inventory levels, there are some tasks that in this day and age, should always be handled by technology.
Here’s a three-step pathway to a more efficient (and effective) way of doing things.
Step 1: Consider the limitations of a person with a spreadsheet
If you consider a large-scale retail business operating in various centres around the world, offering online purchasing and possibly third-party distribution channels - things get complex pretty quickly. With hundreds of SKUs, across multiple sites and various sales channels, maintaining a birdseye view of activity is hard. Add to that the challenges of applying data at a granular level, to forecast and plan accurately across an ever-more complex landscape, and you have a near impossible task. Expecting people to handle this kind of role armed with nothing more than Excel, is not only futile, but it deprives them of the chance to focus on something far more worthwhile - devising strategies to meet market demand and reduce commercial waste.
Step 2: Automate the admin
At the very least, automating store-level demand planning with an effective inventory management system is a must. When you remove the laborious admin associated with capturing and recording data from the equation, people are empowered to do what only people can do. Think laterally and strategically about business challenges and how to mitigate them.
Step 3: Bring in the big guns - AI, ML and Big Data
Once inventory management systems have been automated across a complex commercial landscape, the next step in accurate store level demand forecasting is to harness the power of cutting-edge technology. Artificial Intelligence and Machine Learning can help you make centralised decisions about your physical stores, on a case-by-case basis. Insights and predictions can be drawn from location-specific data sets and presented in an easily-digestible, actionable way. Being able to combine broadstroke business statistics (captured by an automated system), general market information and localised data makes it easier to make more accurate forecasts. Demand Strategists can then adjust stock requirements before items leave the warehouse, saving on both freight costs and unnecessary carbon emissions.
QU - a tool for the times
QU is an AI-powered SaaS demand forecasting tool that has been developed to measure, analyse and predict product performance in real time. Using the latest AI technology along with proprietary ML algorithms and innovative design, QU draws on millions of data points from hundreds of sources. It presents powerful insights in an easily-digestible and customisable dashboard that not only predicts buying behaviour, but adjusts forecasts of future sales right down to individual product level (SKU).
QU is a tool that is designed to empower people, giving them the insight and control they need to do better business.
So if your business is ready to start freeing up resource and firing up profitability…
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 […]
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 […]
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 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 […]
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 […]
Not all SKUs were created equal 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 […]
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 […]
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 […]
To provide the best experiences, we use technologies like cookies to store and/or access device information. Consenting to these technologies will allow us to process data such as browsing behavior or unique IDs on this site. Not consenting or withdrawing consent, may adversely affect certain features and functions.
The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network.
The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.
The technical storage or access that is used exclusively for statistical purposes.The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you.
The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes.