Demand forecasting is indeed a crucial requirement for the efficiency enhancement of thesupply chain system. This is predominantly because of the afflatus fact that the supply chainprocesses the order only in accordance with the demand signal received.Needless to say, the accuracy of demand forecast increases the overall inventorymanagement, capacity planning, production scheduling and material requirement planning tosignificantly higher levels.
And what does a poor demand forecasting lead to? The retailer’s nightmare: A Bullwhipeffect. Bullwhip effect is basically the catastrophic result of the magnification of the variablefactors of demand in the supply chain. This will again lead to a highly inefficient supplychain system.In such cases, Artificial Neural Network (ANN) algorithms have been considered as theparadigm technique for demand forecasting. The prime element of an artificial neuralnetwork is the unique novel structure possessed by its information processing system. Thefunctionality of this structure depends upon a large number of highly interconnectedcomputing elements that work unitedly to solve complex or abstract problems.Neural networks do offer a number of advantages over the traditional methods. The notableones include less formal statistical training requirement, ability to detect all the possibleinteractions between the available predictor variables and the availability of numerousmultiple training algorithms.
So, how does the implementation of Neural networks aid in demand planning?The Neural network has certain key areas that take the demand planning curve to aconsiderably more ambitious level. Some of them are listed below:ORGANIC LEARNINGNeural networks have this immaculate ability to learn organically. This means that the outputproduced by an artificial neural network is never entirely limited by the inputs provided by anexpert system initially. ANNs have their own inbuilt ability to generalize the inputs. Thisability is useful for pattern recognition systems that can assist in producing a stable andaccurate demand planning in the long run.
NON-LINEAR DATA PROCESSINGNon-linear systems can effortlessly find shortcuts to reach certain computationally flawlessand expensive solutions. These systems can have the ability to infer connections between aset of data points, rather than waiting to explicitly link the records in a data source. So, thissystematic non-linear short-cut methodology is fed into ANN, thereby making it highlyvaluable for the commercial level of big-data analysis. And this is exactly the driving fuelthat’s required for demand planning.Neural Networks are undoubtedly intelligent and flexible data driven models equipped withhighly efficient properties for demand planning and forecasting. Compared to traditional oldschool statistical methods which are only useful for data with a certain trend pattern, theartificial neural network are far too powerful.
It can even easily accommodate data dependentupon certain special cases such as extreme crisis demand fluctuation or any kind of specialpromotion. ANN implementation in demand planning has already grabbed the attention ofretailers and they are thrilled by its ability to solve problems that seem impossible withtraditional methods.
