When you’re talking about demand planning, a few things come to mind. You want to know the demand because you want to be able to plan your buying, manufacturing, logistics, and sales.

Demand planning is the key driver of the supply chain. With proper knowledge of demand, manufacturing has a sound basis on which to develop production and inventory plans. Through proper demand planning, logistics will have the right information and resources to develop distribution plans for products across a range of storage facilities, sites, and customers. Simply stated, demand forecasting is the key driving force behind the supply chain, with the demand planner being the driver of the forecasting process. The problem is: you can’t plan for demand unless you have a forecasting tool and a lot of experience to base your projected demand on.

The different types of demand explained

You have 2 main types of demand. Dependent demand and independent demand. Independent demand is demand for a finished product. Something a consumer (or customer in B2B) could buy for example. A lamp, a chair a laptop, or a loaf of bread. In B2B these may be trucks, steel fences, or wind turbines. Dependent demand is demand for parts of the finished product. Component parts. Sub assemblies. Ingredients. The create the loaf of bread you need water, flour, and yeast.

If in a given period we expect to sell 100 loaves of bread (the independent demand) we can calculate based on that demand the amount of dependent demand. Bread is relatively easy to make. So it doesn’t have a lot of inter-dependencies between the ingredients. But if you are a car manufacturer you have a lot of interdependencies in the supply chain. For 1 car you need thousands of parts. If not all the parts for the car’s dashboard are delivered. The dashboard can’t be manufactured. If the dashboard can’t be finished, the car also doesn’t get finished.

This system of manufacturing requires a system called material requirements planning (or MRP). This system considers the number of parts needed to build the finished product. It also takes into account the time to produce the parts, shipment time. Even the time it takes to make sub-assemblies (the dashboard) so it comes just in time to be installed in the car.

The bullwhip effect

The hard part about planning is that you don’t know what you don’t know. So you have to base your numbers on 2 things: the demand there is for your products and the experience you have with your product’s demand. There’s a common effect in demand planning called the bullwhip effect.

If in a given period demand goes up by 5% a retailer might think that demand will go up more. So he orders 7% more product. The wholesaler thinks the same thing. He orders 10% more product. By the time the orders reaches you, the manufacturer, the original demand change could have quadrupled.

The impact of this bullwhip effect can be very harmful to both your business and other businesses in the supply chain. Think of the costs of excess stocks, losing market share to the competition, and disgruntled customers leading to a loss in customer loyalty. Ways to minimize this bullwhip effect include, yet are not limited to:

  • Recognize and grasp the bullwhip effect within a company and across partners in the supply chain
  • Enhance the raw material planning process
  • Optimize the inventory planning process Improve teamwork, collaboration, and information sharing across the supply chain
  • Enhancing predictability by longer-term agreements on order volume or price

Analytics & demand planning

The term analytics is used in a number of different ways. It applies to several areas of prediction: eg time series analysis, data/text mining, and demand planning. Correlation plays a pivotal role in these areas of prediction. Correlation is defined as the degree of association between two variables or two data sets. That degree of association may be causal or not. The quantity of data is now so vastly abundant compared to just 10 or 20 years ago that the tools we use to gain information from it must recognize any changes in the situation. Individual data points are such a small part of the big picture that less time should be spent getting concerned about whether they will bias the final result. In some cases, even incorrect values and actual mistakes in individual observations may be relatively unimportant in determining the final outcomes. The use of analytics and vast amounts of data is not a Must-Have for demand planning across all industries or companies. It does however provide a great new instrument for demand planners that want to move up the maturity ladder in their discipline.