{"id":18265,"date":"2024-04-11T13:44:17","date_gmt":"2024-04-11T11:44:17","guid":{"rendered":"https:\/\/eyeonplanning.com\/blog\/demand-forecasting-techniques-copy\/"},"modified":"2024-08-06T17:01:28","modified_gmt":"2024-08-06T15:01:28","slug":"supply-chain-forecasting","status":"publish","type":"post","link":"https:\/\/eyeonplanning.com\/blog\/supply-chain-forecasting\/","title":{"rendered":"Essential demand drivers to consider in supply chain forecasting"},"content":{"rendered":"

By Erik de Vos<\/a><\/em><\/p>\n

In <\/span>one of our <\/span>previous<\/span> blogs<\/span><\/a>, we mentioned <\/span>various types<\/span> of data integral to<\/span> supply chain<\/span> forecasting<\/span><\/a>: sales data, factual data, plan data, and market indicators. To shed more light on <\/span>the <\/span>smart-touch<\/span> planning and <\/span>forecasting<\/span> approach<\/span>, <\/span>let’s<\/span> dive deeper into some examples, introducing the concept of demand drivers. These are elements that drive change or explain <\/span>s<\/span>hifts <\/span>in your demand<\/span>. <\/span>Since they help to increase forecast accuracy and reduce bias, both play essential roles in supply chain <\/span>forecasting<\/span>.<\/span><\/span><\/p>\n

\"data<\/p>\n

1. Promotions<\/span><\/span>\u00a0<\/span><\/h3>\n

Take promotions, for instance \u2013 they’re catalysts for changes in demand. If you organize promotions, it’s reasonable to expect a shift in demand for your products.\u00a0<\/span>\u00a0<\/span><\/p>\n

Now, imagine knowing when you conducted past promotions and when you plan future ones. Armed with marker information during promotions, a machine could forecast potential demand changes and incorporate them into your forecast. By enriching promotion details \u2013 promo mechanisms, discount percentages, marketing investments, or indicators like second placements and folder advertisements \u2013 a machine can learn to link this information to sales data effects (peaks and dips) for a more accurate forecast.<\/span>\u00a0<\/span><\/p>\n

Consider the alternative \u2013 planning promotions without machine learning. It often involves copy-pasting from previous efforts, assuming a slight sales increase even with the same promotions, expecting a uniform uplift on all products, and assuming consistent phasing of promotional effects for all customers. Due to data complexity and the lower priority given to supply chain forecasting promotions compared to other tasks, these shortcuts become the only way to deliver a forecast.<\/span><\/p>\n

2. Order book data<\/h3>\n

Now, what about elements providing insights into <\/span>likely changes<\/span> in demand? Enter the order book, a typical example readily available in most ERP systems. Depending on your business, your order book horizon may differ. What if we could <\/span>leverage<\/span> this data to automate processes like allocation and short-term forecast tuning? By collecting data on when orders are received and their requested delivery dates, you can predict your order book’s future shape. Offset against your latest forecast, organized with supply and inventory considerations, you can <\/span>identify<\/span> service risks and overstock situations earlier, even asking the machine to distribute the remaining inventory based on <\/span>likely demand<\/span>.<\/span><\/p>\n

3. Market data<\/h3>\n

Moving to another example \u2013 the increasing focus on using market data. Your demand isn’t solely influenced by your actions but also by the broader market context. Incorporating information on how your market evolves and will evolve, can aid in driving part of your supply chain forecasting effort. <\/span>\u00a0<\/span><\/p>\n

Market growth indicators can help to tune the overall trend you take into your forecast. Information on past and future external events not under your control can be used to predict event-based disruptive demand e.g. during a World Cup. Information on how the renovation and housing market will evolve can help you to predict the longer-term demand for construction materials.<\/span><\/p>\n

\"marketing<\/p>\n

Granted, these examples focus more on trends and mid-term effects. Yet, in the dynamic landscape of continuous information and opinions, you’ll also encounter market effects impacting demand on much shorter notice. For instance, weather outlook information can predict short-term swings in demand, enabling you to steer logistical execution capacity effectively. Or think about social media product trends that can suddenly boost or destroy sales.\u00a0<\/span>\u00a0<\/span><\/p>\n

In relation to market data, we would expect to recognize 2 elements in a well-structured smart-touch planning and forecasting setup:<\/span>\u00a0<\/span><\/p>\n