According to the Volatility Index, the past two years have had the most economic volatility since the 1980s. Increasing material costs, global supply chain issues and fluctuating access to resources have all made markets notably less predictable.
If you’re a business owner or manager, having ineffective demand forecasts can be extremely consequential. If your demand forecasts are too low, you’ll end up missing out on opportunities to increase sales and revenue. If your demand forecasts are too high, you may end up carrying inventory or locking up capital.
The negative effects of inaccurate demand forecasts can compound across a manufacturer’s supply chain from raw materials orders, into WIP (work-in-progress), finished goods inventory ability to deliver on time. The bullwhip effect is a supply chain phenomenon describing how small fluctuations in demand at the retail level can cause progressively larger fluctuations in demand at the wholesale, distributor, manufacturer and raw material supplier levels. The effect is named after the physics involved in cracking a whip. The current economic, supply chain and geopolitical volatility are all potential sources of changes in demand, which will be felt more by manufacturers than others in the value chain.
Using Data to Enhance Demand Forecasts
Your company’s demand forecast—data output that projects what your future sales volumes might be that triggers the start of your supply chain—will only be as useful as the inputs from which it was derived. The old mantra “garbage in, garbage out” undoubtedly applies to the forecasting process. A good forecast will require many reliable inputs, including accurate sales numbers, revenue numbers, current and optimized pricing, and more.
Suppose your business manufacturers electronic components. How will a 1% change in an essential material, like copper, affect the demand for components? What about 10% change in the price of copper? And how might an attempt to pass costs onto your customers by increasing price affect demand and future revenue?
Price is too often lowered and (ab)used by sales teams to increase demand [volume]. But in today’s economic uncertainty, price can also serve as a tool and be equally as important to help to throttle demand and slow it when a manufacturer is unable to keep up. Selling less may be better than selling and not fulfilling orders on time in the eyes of customers whose supply chains are equally as dependent on their suppliers.
Ideally, your demand forecast is dynamic, rather than static, to manage risk and enable greater resiliency. If your systems and operations are equipped to adjust, demand forecasts should be driven by data and updated frequently as both enterprise and broader market conditions change. Fortunately, the proliferation of artificial intelligence (AI), machine learning and various types of automation have helped make it easier to create forecasts that are responsive and adapt to changing conditions and continuously improve over time.
Better Forecasting Leads to Better Decision-Making
Gathering data alone will not be enough. Ultimately, the purpose of obtaining deeper insights and conducting analytics is to help you identify the types of actions that will be most beneficial to your business.
There are many different variables that can change the demand for a given product or service. PROPHET identifies the parameters that have the greatest impact on both price and demand. If your enterprise can predict changes in demand sooner and your supply chain is agile enough to adapt, greater efficiency and less waste results.
Regardless of what action you end up taking, it is obvious that having access to up-to-date, granular and easily accessible data can help you make the decisions that are objectively best for your business. Sales and pricing can become a manufacturer’s most valuable tools to manage supply chain risk when powered by data, data science and technology.
