Demand Forecasting In Manufacturing – Part 1

What is Demand Forecasting?

Demand forecasting is one of the most necessary foundations for objective, convenient, and practical production. One of the most prominent challenges linked with demand forecasting for industry executives is comprehending the result of demand volatility on demand forecasting. 

Moreover, to address this challenge, organizations often include extra stock to guard against incorrect forecasts. Diverse studies have revealed that surplus inventory holding charges due to warehouse, obsolescence, spoilage, and taxes could improve costs by 20 percent to 30 percent. 

Tools and accuracy 

A more precise demand forecasting tool enables companies to lower products, enhance on-shelf availability, and underrate waste. With enhanced forecasting, companies can come to better conclusions concerning cash flow, risk control, capacity planning, and crew planning. One of the most significant levers known for business users to maintain costs down and enhance profitability is stock optimization via accurate demand forecasting. With the democratization of AI and Machine learning, even little and medium-sized companies can leverage powerful AI/ML technologies to create accurate forecasting models. AI-based forecasting keys can automate force administration tasks resulting in decreased functional costs and higher operating resiliency.

Significance of Data 

Manufacturing businesses are increasingly deploying IoT detectors on the factory floor. These detectors gather data and document, in real-time, the health of presentation machinery, track production statistics, monitor supply stations, and more. 

  1. The real-time nature of these sensors indicates that planners have, for the first time, the key to all the situations in the factory, in the storage, and in the logistics chain as it develops. It’s this rapid growth in visibility that is transforming the look of demand forecasting in the manufacturing industry. 
  2. The power to combine this operational data with outer information varying from shipping requirements, supply availability, partner or supplier problems, and so on is where the control of data comes into rays. 
  3. For demand forecasting, the precision of the data is important and with the correct application of current analytic tools business goals can be fulfilled. Any demand forecasting tool requires interpreting data from numerous channels to be capable to come up with a real and precise forecasting tool.

What are the Key sources of data to be analyzed?

Key references of data to be explored include: 

  • Real-time sensor data
  • Macroeconomic customer purchase trends
  • Sales funnel data
  • Production inventory data

For many communities, restricted data availability or the availability of valid data is still a challenge.

Demand forecasting movements in Manufacturing 

Cross-functional forecasting 

There is a sensation toward a holistic view of demand forecasting. The demand forecasting procedure at many organizations is still siloed with deals and processes utilizing their data, insights, and forecasts. However, many organizations are learning the advantages of an integrated approach directing to more precise forecasting and cost savings. 

360-degree demand sensing

With data addition, the capacity of the supply chain to adjust to demand changes is more difficult than ever. Thriving firms are adjusting by creating systems that can proactively sense order from numerous channels such as external macroeconomic factors like weather, customer sentiment, etc. 

Automation / BOTS 

With the beginning of IoT devices and BOTs, demand is transforming in real-time on the factory floor. Automatic bots can choose and meet demand through mechanization. More and more manufacturing businesses are counting on abilities to address real-time demand planning and forecasting to complete automation in satisfaction. 

Outgrowing excel with AI 

Most diminutive to medium-sized businesses are shifting away from disparate excel sheets between groups to utilizing cutting-edge analytics, AI, and Big Data solutions. As the price of computing and data warehouse has tremendously come down over the last years, even diminutive to medium-sized enterprise solutions can use AI-based forecasting

Why demand forecasting is important for business? 

Enhanced strategy for optimal pricing

Learning the demand for by-products at any provided time can support pricing it properly. 

Decrease extra inventory 

Surplus inventory improves the warehouse prices for the products. Also, the more extended stock is in storage, the more possible it is to fall in value. Demand forecasting can assist lower the cost of inventory investment and warehouse prices by having the right products at the correct time. 

Enhanced client satisfaction 

If proper inventory levels are not maintained customer orders will not be fulfilled in the time leading to customer satisfaction issues. Customers might look to competitors and businesses can lose customers permanently. 

Budget & production planning 

In manufacturing, it’s difficult to create a budget without market forecasting. Without forecasting, presentation planning is not feasible. When there is a possibility to finance a new product line or expand an existing one, objective demand planning is key. 

Bypass under-stocking and expedited shipping charges 

The flip flank of surplus stock is understocking. If the inventory is down and consumer request is high companies tend to expedite freight to meet consumer anticipations raising the costs to the industry. 

Demand forecasting methods

What are the demand forecasting methods?

Factors affecting demand forecasting have time horizons and types of forecasting. Companies must make sure they believe these elements before launching a clear explanation. 

Time horizon consideration

Forecast horizonTime SpanTime Span
Typical forecasted item
Unit of measure
Long-RangeYearsProduct Lines
Capital expenditure
New product planning
Facility expansion R&D
Dollars, Tons etc.
Medium-RangeMonthsProduct groups
Sales planning
Production planning
Dollars, Tons etc.
Short-RangeWeeksProduct quantities
Purchasing Production levels
Job assignments
Physical units

Forecasting method consideration 

Based on the availability of data kinds, firms must think about using appropriate forecasting methods. Ideally, both qualitative and quantitive strategies must be assessed for more dependable and powerful forecasting.

DescriptionQualitative methodQuantitative method
When to use?New products
Little data availability
Existing products

Availability of data
Required skillsIntution
Domain experience
Econometric analysis AI/ML Deep learning Statistical techniques
Data dependencyExternal market dataHistorical sales
Production data
Major techniquesDelphi
Market research
Sales force composite
Trend projection Exponential smoothing Econometric forecasting

Challenges for demand forecasting 

One of the greatest challenges encountered by business exec for real demand forecasting is demand volatility. In complement to volatility, as digital change goes mainstream in companies big and small, businesses are scuffling with numerous channels of demand.

Omni-channel growth 

In the manufacturing sector, derivative demand could arise from numerous channels like resellers, e-commerce sites, direct customers, sales, etc. This is generating fragmented market movements resulting in incorrect forecasting.

Demand instabilities

There are numerous facets affecting demand, ranging from weather fluctuations to placements by social media influencers, causing customers to often change their minds and driving demand changes. 

Obsolete models 

Several diminutive to medium-sized companies depend on fundamental forecasting devices that rely on preliminary information or forecasting models that are fixed and do not think for example promotions by prospects or new outcomes with no recorded data for forecasting.

Absence of macroeconomic idea 

The prevalence of enterprises lacks mechanisms to change forecasting depending on consumer or customer acquisition behavior, macroeconomic situations, and facets affecting demand in real time. 

Skill gap 

Developing more refined forecasting methods concerns understanding technologies like AI/ ML, developed statistics, and real-time data research.

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