In this customer driven market, key to success is how well a retail supply chain has modified and adapted itself as per peaks and troughs of a demand for a SKU. In the same year, demand for the same SKU can vary and effect the profit margin. For this reason, it is important for a business to keep a close eye on demand forecasting and map market requirement accordingly. Another important aspect in the present world is parallel growing or falling of demand through eCommerce which is markedly influential and has potency to decipher a new meaning for demand forecasting. For instance, a retailer might be aiming local customers but it won’t be a surprise if a request for an order comes from a different country for someone living locally. Location of order placement can be global but the destination of order could be local. This is the power of eCommerce retailing and it calls for attention!

What is Inventory Demand Forecasting?

Simply defined inventory demand forecasting predicts approximately how many units of a SKU would be needed in future or during peak season if external factors remain the same. It can optimize inventory, since forecasting is the outcome of study of the history of sales for a product, the ups & downs, trends, marketing & promotions and any upcoming events that can impact demand and sales. All of these play a significant role in predicting inventory levels for future. Forecasting provide data for comprehensive inventory management, prevents stockouts or over stocking and scales business profit as well as growth.

What are the Challenges of Inventory Demand Forecasting?

Inventory demand forecasting provide an insight into relationship between supply and demand of a product. With the help of available data about a product, a statistical report is prepared to facilitate information related with expected increase or decrease in the demand. Though an important aspect of business planning and marketing, it is not free from its own challenges.

• Quality & Quantity: With poor data quality and insufficient quantity of data, it would be tough to get the real dimensions of the demand. Reaching a concrete decision with obsolete, missing, and incomplete information; is not possible.

• Lack of Tools: Many a times, even though data is available there are no means to draw a conclusion for decision making. With outdated tools, businesses cannot beat competition. For accurate and fast predictions, modern analytical tools and updated technology is required.

• Problems with Data Collection: eCommerce is buzzing with activities, and there is continuous change of pattern. These need to be collected and analysed in real time and it is only possible with high-end data collection tools.

• Delayed Response: Demand forecasting is only useful only if it is done well in time. Delayed report is of no purpose and would not help in decision making.

• Data Sharing: Even when data has been collected, operations are not integrated and there is no sharing of data, these delays weaken the demand forecasting process.

What is the Impact of Inaccuracies on Inventory Management?

Demand forecasting follows customer purchasing pattern, behavior, response towards a product, sales graph and other external factors that can influence the demand curve of a product. Data needs to be verified with a systematic process, in the absence of which inventory management face serious business threatening inaccuracies.

1. Inventory Control: When data downplays market demand, there are chances of stock out. Similarly with over-escalation of demand forecast, overstocking would be done, which would bring heavy losses for retailers. It needs to be co-related and tallied properly for avoiding misleading information.

2. Logistics: Suppose demand forecasting predicts there will be a huge demand from a location A and not much from location B in the coming times. Obviously, going by this unverified information, retailers would move inventory from one location to other. This might turn out to be an unfruitful exercise if suddenly demand starts pouring-in from Location B. Also, proper logistics will not be deployed and there would be delay in order fulfillment.

3. Outdated Product: With accurate data, products can be improvised for higher consumption or just for the sake of novelty. This way products will not be considered outdated and old if proper data about consumer interests and on-going trends are available.

4. Comparison Between on-season and off-season Demands: During festive or peak seasons, demand increases and manufacturers should be aware of this fact. This helps in manufacturing and supplying approximate units of a SKU which can be sold and would not be required to stock up during off-season periods.

5. Warehouse Management: Accurate forecasting also helps in estimating exact requirement of warehouse. This is especially important for small businesses who might require bigger space to stock up their inventories.

AI and Machine Learning Algorithms: Enhance Demand Forecasting Accuracy & Optimize Inventory Levels

By leveraging AI and machine learning algorithms, demand forecasting can be verified and data obtained from different operations can be correlated. This collaboration of data is significant in altering supply chain management dynamics. Business can be taken to the next level and profit margins improve.

Artificial Intelligence reduces manual intervention and avoids chances of errors. The automated analytical process enables real-time analysis and give accurate demand forecast. The advanced technology is silo-free, can predict demand curve and how to improve supply chain for successful order fulfillment when demand rises in the peak seasons. Some of the significant impact which Artificial Intelligence and machine learning algorithms can make to optimize inventory levels are:

• Increase Visibility of Inventory: With the use of scan and bar codes, it is easy to locate inventory and then estimate the number of units of a SKU. Inventory control gets much organized and easy to handle if there is accurate information.

• Warehouse Management: When various operations software is integrated, synchronised data about available stocks can be obtained. With the help of analytical tool, market researchers can do a study on how the demand is moving from a particular location and what should be the expectation. This demand forecasting optimizes inventory levels and lowers over-stocking or stock-outs for future purposes.

• Improve Customer Satisfaction: Machine learning algorithms connect with customers; it informs them about the availability and expected date of delivery of an order. Also, it is extremely helpful in identifying their interests. If all these data can be collaborated and studied, manufacturers can develop or improvise products as per market demands.

• Discount Optimization: Artificial intelligence is an intelligent tool; it empowers retailers with enough information on how to use outdated products and how to apply discounts. This is better than holding back and incurring loss.

• Better Man-power Planning: With AI, it is easy to allocate manpower in more productive work as in research or get a mix of full-time or outsource work and bring down cost.

• Evaluate External & Internal Factors: Inventory and demand are the result of both internal and external factors. External factors can be weather condition, season, festivals, movies, political & economic factors and more. AI and machine learning algorithms make a study of all such features while producing an analytical study for inventory.

Market researchers are developing and improvising methodologies to study, understand and predict demand equilibrium for the benefit of a business with the help of artificial intelligence.