Tackling Latency in Sales Forecasts and Product Lifecycle changes


Tue, 22 Dec 2020

3 to 5 mins read

two employees in a coffee shop
A local coffee store on the North-side of Houston, Texas, with substantial footfall witnesses a rise in demand for ‘Irish Coffee’. It is time to figure out the inventory in stock and predict the consumption pattern over a period of time. Storage, investment, and supplier-relationship needed to buy in bulk are the next big things to be figured out.  

This is how usually small and growing businesses work with their sales forecasting and inventory determination, right? And while this is the legacy approach, a lot of businesses have been carrying on with the practice to date. 

Supply Chain latency and gaps 

When it comes to organizations entailed in the Supply Chain, a lot of them approach the concept in question with insufficient data in CRM, manual and error-prone spreadsheets, and mostly, lack of real-time data. If organizations committed to the cause of order fulfillment and customer delivery need to get back in the game, they need a shift in focus towards bringing down any such kind of latency and gaps. And in order to get it done, they  need to be proactive in sensing the changes in real time and using intelligent forecasts to drive decision-making. Accurate and real-time data is must for data-driven sales forecasts.

Why legacy organizations need to make the move?

Product Life- Cycle Stage

The unpredictable nature of the product life-cycle stage can typically lead to forecast errors as mature products tend to be more predictable than new or deteriorating products. Whether you’re rolling out a highly-requested feature to a product or introducing a new pricing model, keeping with these fluctuations can enable your sales team reduce sales cycle and win more Leads and business opportunities.

Lack of Forecast Validity

The forces of demand and supply change and its very important to stay on top of what is happening in the market. However, applying market intelligence to the wrong product and using inaccurate historical trends can cause big problems.

Limited visibility of supply and demand

While Supply chain organizations are constantly evolving, many organizations are struggling to keep up with the increasing complexity of supplier relationships due to a lack of insight into demand and supply across multiple channels. Most of these struggling organizations are still using legacy systems and manually downloading and cleansing demand information using spreadsheets which results in data latency and an inaccurate view of demand forecasts. On the supply side, many organizations have their outsourced their manufacturing activities and this makes the process of creating supply visibility difficult and almost impossible.


All Inventory Forecasts have inherent errors due to assumptions and hence are always inaccurate. Hence, they need to specify expected value, minimum and maximum value and percent of error.

If your organization finds itself in the space, there is need for it to anticipate customer demand across several channels and drive real-time decision making. This not only maximizes business outcome but also brings down the gap between when a product is ordered and the time in delivering it. The dynamic environment in which supply chain organizations operate today has made it necessary to detect supply and demand in real time, reduce cycle time, and provide accurate sales forecasts. 

Char comparison of traditional and machine learning forecasting solutions
Source: Altexsoft

Forecasting sales in a ‘Smart Operations’ environment 

The accuracy of your sales forecast plays a crucial role in the success of your organization because these forecasts allow leaders make smart decisions for goal setting. It also detects issues in order to avoid or mitigate them early rather than at the end of the month or quarter. 

For instance, if data shows that your sales team is trending 40% below quota, you can quickly figure out the problem and act. Accurate Sales forecasts enable manufacturing operations and businesses to forecast demand months in advance to ensure order fulfillment and adequate inventory available for supply. Forecasts are very important and crucial to your organization’s success but there are factors that can limit your forecasts and provide wild inaccurate results. 

Data sources for demand forecasting with machine learning
Data sources for demand forecasting with machine learning (Source: Institute of Business Forecasting & Planning)

The 'Smart Operations' course of action

Supply chains need to respond to changes in information and insight to drive better decision making and this requires a tight alignment between planning, collaboration and decision making. Organizations today are adopting the connected function of Smart Operations as part of their digital manufacturing pursuit to gain an edge over their competitors. Smart Operations synchronize all aspects of manufacturing and operations with the use of Connectivity, Intelligence, and Automation. This is how Smart Operations can help your organization improve sales forecasts:

Improve Data Quality with the use of integrated platforms and systems that eliminate data latencies and quality issues. Also, organizations can now use real-time information to adapt to changing business requirements and market conditions

The use of Smart Operations improves the visibility of demand across multiple channels by consolidating demand information to drive decision-making and adequate planning

Organizations can replace existing legacy systems with robust planning tools that support optimization and scenario modeling. 

Smart Operations can help increase the frequency at which S&OP occurs. When the S&OP process occurs frequently, business processes are better optimized and projected based on real-time supply chain conditions. 

Supply chains rely on information and insights and the integration of Smart operations can help these organizations detect changes in the market and also eliminate delivery lead time, data latency and quality issues. Emerging supply chain organizations will be able to sense demand across multiple channels and the use of advanced analytics will provide better flexibility, delivery, visibility, and efficient tools for real-time decision-making. 

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