Demand Forecasting and
A step towards end-to-end optimized supply chain network
Demand Forecasting and Inventory Planning
Forecasting demand as a service is a cornerstone of the supply chain management which drives towards inventory optimization through production scheduling and aggregate planning with the help of data-driven predictive analytics along with the application of machine learning to lay best decisions in the age of hyper-competitive and increasingly volatile markets to derive amazing results.
Challenges of the 2020s Decade
Demand management as an element of Inventory management is an activity of data analysis to create reliable forecasts. The analysis of the historical sales data, transaction data, market demand, consumer preferences can be optimized with the application of IoT and Machine Learning which suggest a way to have an alignment between the Inventory levels while satisfying the true customer demands at the same time. There could be multiple challenges to have such an alignment between supply and demand amid such hyper-competitive markets:
- Excess/Obsolete stock availability generally occurs due to inconsistency in ordering processes and heavy reliance upon the suppliers. Efficient data-driven projection techniques are required to be implemented to avoid any hindrance in supply chain management.
- Occasional introduction of new products without historical data offers tough challenges in the way of mitigating the risk of overstocking. Big data analytics in play with the application of IoT and IIoT supports the usage of data referring to similar product lines and takes care of pricing and promotional strategies for the product to provide a useful way out towards efficient options to stock up.
- Demand planning becomes increasingly difficult when stock units are hard to monitor. Radical application of predictive analytics can quickly restrict the building up of unnecessary stock levels or can even prevent a situation where it could result in the outage of stock when the demand spikes.
Countering Challenges by leveraging data
Consumer preferences are driven by numerous factors and without the application of data analytics for inventory management, the chances of establishing a true consensus between supply and demand would be minimal and give rise to multiple issues. These issues create bottlenecks in the supply chain management process and eventually have an adverse impact on the liquidity position of the retailers due to the monetary pressure of capital being stuck.
Having too much stock in advance and a probability of being unable to sell means a high cost of holding inventory. Avoid hoarding stocks by having the right amount of the goods that the consumer wants. With the influence of predictive analytics, Inventory management is driven by Big data analytics and is sustained by applications such as the Internet of things (IoT), enables you to use digital methods to transfer data into efficient use to chalk out solutions with impressive demand planning.
Supply bottlenecks can also have an impact on the entire supply chain management. To mitigate any delays occurred, optimization of inventory planning with the use of supply chain analytics results towards Inventory optimization.
One of the major issues faced by retailers is highly increasing material waste. The use of Data Analytics enables the retailers to forecast the developments in the market, which then enables them to manage their resources efficiently helping to cut down environmental impacts while boosting profits.
Our studies suggest that there is a significant link between excess inventory and a lack of consistency in the demand forecasting process. The perfect balance between consumer demands and the inventory can be achieved with efficient Inventory planning through the application of data analytics.
Demand forecasting techniques are data-driven, which means that all variables influencing consumer preferences are obtained by applications such as the Industrial Internet of Things (IIoT) that helps retailers understand their process in an efficient manner. In a volatile market environment, the application of such forecasting techniques would lead up to a radical Inventory optimization.
- Devising and implementing efficient forecasting techniques leads to a marked reduction in chances of errors and nuances.
- Providing multi-step, flexible process solutions depending on the right mechanisms, information, and data-driven processes to boost the supply chain management made possible by optimizing inventory needs and product positioning.
- Gathering data to drive business analytics to provide an educated base for generating healthy decisions towards inventory optimization.
- Designing efficient budgeting processes. Purchasing and storage of products involve costs and with effective inventory planning, procurement of products in less expensive ways is possible. Big data analytics is applied to determine trends of product demand spikes and develop purchasing schedules to coincide with the trends.
- Sales forecasting and historical data analytics are always utilized to determine trends of consumer demands and to align inventory levels with consumer needs.
- Developing warehouse layout planning to channelize product flow to reach the customer and to estimate how efficiently the system can fill up the orders in addition to checking the return of unused and defective products to maintain an optimum level of inventory.
Strategic Sales Planning
- Data-driven projections of sales tactics, identifying and targeting the correct users and to determine the potential hurdles in the way.
- Determining the revenue goals based on the sales projections, formulating team strategies, and projecting resource requirements for achieving the targets.
- Prediction of the market trends to develop product positioning drive and promotional techniques.
Effectiveness of Our Offering for Your Business
Fasttrack approach consisting of Machine Learning, IoT enabled PA to radically improve the quality of business and attain a competitive edge in the market
Establishing a balance between profit yielding customer and supply chain by optimizing product marketing plan
Supply chain analytics based on historical data valuable customer data extracted digital machinery deployed for that purpose to make regular adjustments to the plan in response to changing market scenario
Purchase plans to align with the consumer demand spike trends and procuring relevant data for reference in case of positioning new products on the block. Procurement optimization and warehouse planning resulting in a better flow of the products to the customers.
Better liquidity position obtained by eliminating excess stock resulting in freeing up capital.
Facilitating the purchasing-schedule to avoid the shortage of necessary stock in the inventory
Incorporating dynamic pricing in tune with the price fluctuations in the economy