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 1. Trinetix
 2. Insights
 3. Demand Forecasting in Supply Chain: A Comprehensive Guide
    


DEMAND FORECASTING IN SUPPLY CHAIN: A COMPREHENSIVE GUIDE

AI/ML
2.26.24
Dmytro Ivanov
MACHINE LEARNING ENGINEER
Daria Iaskova
COMMUNICATIONS MANAGER

Over the past years, control over logistics operations and warehouses has become
imperative for both A-list global retailers as well as smaller local suppliers
and manufacturers. 

Multiple disruptions, fluctuating demand, and price volatility—in line becoming
the reason for business inefficiencies. These challenges have made logistics
professionals seek more resilient and future-forward solutions, one of which is
data-driven demand forecasting in supply chain management.

In this guide, we’ll redefine the demand planning concept considering the
changing global dynamics and explore the most efficient demand forecasting
methods in supply chain that exist so far—all with the aim to provide businesses
with a hands-on understanding of this practice and useful tips to get started.

Table of contents
 * What is demand forecasting?
 * Demand forecasting in supply chain: the backbone of successful logistics
 * Demand forecasting methods in supply chain
   * Qualitative forecasting
   * Time series analysis
   * Causal inferences
   * Machine learning and predictive analytics
   * Collaborative planning, forecasting, and replenishment (CPFR)
   * Delphi method
   * Simulation models
   * Consumer surveys and market research
 * Challenges of demand and supply forecasting
 * How technology revolutionizes demand forecasting in supply chain management
 * How to get started with demand forecasting in the supply chain?
   * Making the most of strategic demand planning with Trinetix

Table of contents
 * What is demand forecasting?
 * Demand forecasting in supply chain: the backbone of successful logistics
 * Demand forecasting methods in supply chain
   * Qualitative forecasting
   * Time series analysis
   * Causal inferences
   * Machine learning and predictive analytics
   * Collaborative planning, forecasting, and replenishment (CPFR)
   * Delphi method
   * Simulation models
   * Consumer surveys and market research
 * Challenges of demand and supply forecasting
 * How technology revolutionizes demand forecasting in supply chain management
 * How to get started with demand forecasting in the supply chain?
   * Making the most of strategic demand planning with Trinetix


WHAT IS DEMAND FORECASTING?

In a nutshell, demand forecasting is a business practice that utilizes
statistical models, historical data, market trends, and other factors to
estimate the demand for a particular product or service over a specific period.

The aim of demand forecasting is ensuring a business can deliver the right
products and in the right quantities to satisfy customer demand and fulfill
their promises. Just like that, the concept of demand forecasting goes
hand-in-hand with logistics and supply chains.

EXPLORE OUR TECHNOLOGY OFFERINGS FOR GLOBAL LOGISTICS, SUPPLY CHAIN, AND
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DEMAND FORECASTING IN SUPPLY CHAIN: THE BACKBONE OF SUCCESSFUL LOGISTICS

Efficient supply chain management is the lifeblood of businesses, regardless of
their industry or size. Whether in manufacturing, retail, healthcare,
technology, or service-oriented sectors, businesses rely on a well-orchestrated
supply chain to ensure the timely and cost-effective delivery of products and
services to customers. And today, more than ever before, supply chains depend on
strategic planning.

> The changing market and global realities have made demand planning a synonym
> of efficient supply chain management. Predicting demand helps companies build
> short- and long-term business strategies, optimize their inventory costs,
> foster customer loyalty, and withstand market competition.
> Dmytro Ivanov, ML Engineer at Trinetix

Let’s overview how demand forecasting redefines supply chain management,
becoming its integral backbone.





 * Strategic planning

9 out of 10 organizations achieved measurable value from data and analytics
investments in 2023. NewVantage Partners

Demand forecasting provides valuable insights into future demand trends,
enabling businesses to make informed strategic decisions. This information helps
in aligning production, procurement, and distribution strategies with
anticipated market needs.

 * Inventory management

70% of consumers are likely to go to another brand once they face a stockout
situation while shopping online. ERC Europe

By predicting future demand, businesses can optimize inventory levels, reducing
the risk of overstock or stockouts. This ensures that products are available
when needed, minimizing holding costs and improving overall operational
efficiency.

 * Effective communication with suppliers

45% of supply chain professionals either have no visibility into their upstream
supply chain or can see only as far as their first-tier suppliers. McKinsey

Demand forecasting builds visibility into potential supply chain issues and
allows organizations to operate numbers and facts while they are communicating
with suppliers. This allows businesses to build stronger relationships and
ensures that suppliers are prepared to meet the upcoming demand, fostering a
more resilient and responsive supply chain.

 * Adaptability and resilience

In 2023, supply chain resilience and agility were the priorities of 55% of
medium-sized businesses with 2,500 to 5,000 employees. Fictiv 

Anticipating changes in demand allows businesses to proactively adapt their
supply chain processes. This adaptability enhances the resilience of the supply
chain, ensuring it can quickly respond to market fluctuations, disruptions, and
unforeseen challenges.

 * Cost efficiency

Nearly two-thirds of small and mid-sized businesses reported supply chain delays
resulted in 15% or more losses in revenue during 2022. Anvyl

Accurate demand forecasts enable businesses to allocate resources efficiently.
By aligning production and distribution with expected demand, organizations can
reduce unnecessary costs associated with excess inventory, storage, and
expedited shipments as well as maximize their revenues.


WHO BENEFITS FROM SUPPLY CHAIN DEMAND FORECASTING?

> Typically, companies dive into demand planning because a particular department
> or business unit urgently needs it. Yet, the impact goes beyond—soon enough,
> you see the results making waves globally, affecting and benefiting more than
> anticipated.
> Dmytro Ivanov, ML Engineer at Trinetix

Picture a common scenario: an online electronics store faces peak demand during
the Black Friday season. To satisfy this demand and prevent stockouts, the
company would normally analyze customers’ behavior for the past years as well as
check the most recent sales data to detect ongoing trends and try to predict the
type and amount of the most desired products. 

In this case, the need to implement demand planning may come directly from the
sales team. At the same time, there are far more parties who will benefit from
demand and supply forecasting. Let’s review the same scenario to elaborate on
these parties.

SALES AND MARKETING

Tailor promotional strategies considering the expected customer geography,
gender, interests, and income level. This way, they can highlight popular
products, and align marketing efforts with anticipated customer preferences,
maximizing sales opportunities.

INVENTORY MANAGEMENT AND LOGISTICS

Plan and optimize stock levels and warehouse operations. With accurate
forecasts, they can ensure that sufficient inventory is available, preventing
stockouts and streamlining logistics to handle increased order volumes
efficiently.

OPERATIONS

Plan for product availability, strategically place inventory in distribution
centers, and fulfill customer orders preserving high operational efficiency and
customer satisfaction.

FINANCE AND BUDGETING

Align budgets with expected sales, reduce financial risks associated with either
excessive or insufficient inventory, and efficiently allocate resources during
the peak season, ensuring that the financial planning is closely tied to
anticipated demand fluctuations.

TECHNOLOGY AND IT

Prepare the e-commerce platform to handle increased website traffic and
transactions, ensuring a seamless online shopping experience, and preventing
website crashes or slowdowns that could negatively impact customer satisfaction
and sales.

CUSTOMER SUPPORT

Anticipate potential issues related to product availability or shipment delays,
provide accurate information to customers, manage expectations, and address
inquiries more efficiently during the peak demand period.

In this scenario, demand planning emerges as a strategic objective that, on one
hand, ensures uninterrupted operations during a holiday season, and on the other
hand, secures the company’s competitive advantage in the long run, earning them
a good reputation and customer trust.

For global logistics companies, transportation providers, and manufacturers,
forecasting supply chain management goes beyond guaranteeing operational
efficiency and market leadership, helping them secure business continuity, and
making sure goods reach their destinations despite port congestion, heavy road
traffic, or severe weather conditions.


Learn to overcome cost and demand fluctuations with our guide on smarter freight
management
Download PDF


DEMAND FORECASTING METHODS IN SUPPLY CHAIN

Approaching supply chain management forecasting usually requires executives to
have a basic understanding of the key methods that help organizations implement
its mechanisms in practice.

In essence, demand forecasting methods in supply chain encompass various
techniques for predicting future demand based on historical data. These
techniques involve the systematic processing and analysis of different types of
data, considering factors such as time series patterns, influencing variables,
and market dynamics. Let’s briefly overview the most common forecasting methods
that exist so far.




QUALITATIVE FORECASTING

Qualitative forecasting method relies on subjective judgment, expert opinions,
and market research to predict future demand. This approach is suitable when
historical data is limited, and there are significant uncertainties or when
entering new markets.


TIME SERIES ANALYSIS

Time series analysis uses historical data to identify patterns and trends over
time. Methods such as moving averages and exponential smoothing are employed.
This technique is effective when demand patterns exhibit seasonality or specific
trends that can be identified through historical data.

Get our expert’s extended view on time series analysis and forecasting
Read article


CAUSAL INFERENCES

Causal inferences establish a cause-and-effect relationship between demand and
various influencing factors, such as advertising, promotions, or economic
indicators. This method is used when there is a clear understanding of the
factors influencing demand, and when businesses want to assess the impact of
specific variables on sales.


MACHINE LEARNING AND PREDICTIVE ANALYTICS

Advanced algorithms and machine learning models analyze large datasets,
identifying complex patterns and making predictions based on historical and
real-time data. This method is effective when dealing with large datasets and
complex demand patterns, offering accuracy and adaptability to changing market
conditions.


COLLABORATIVE PLANNING, FORECASTING, AND REPLENISHMENT (CPFR)

CPFR involves collaboration between supply chain partners, sharing information
and jointly developing forecasts to enhance accuracy. This technique is usually
used in industries with complex and interconnected supply chains, where
collaboration among different entities can lead to more accurate predictions.


DELPHI METHOD

The Delphi method involves soliciting opinions from a panel of experts through a
series of rounds, with feedback and revisions in each round. This approach is
applied in scenarios where the collective wisdom of a diverse group of experts
is essential for making informed predictions, decisions, or planning for the
future. 


SIMULATION MODELS

This technique deals with the creation of virtual scenarios to assess the impact
of various factors on demand. For instance, Monte Carlo simulations consider
multiple variables and potential outcomes. Such models are valuable for testing
different scenarios and understanding the potential variability in demand under
various conditions.


CONSUMER SURVEYS AND MARKET RESEARCH

This method presupposes directly engaging with customers through surveys and
market research to gather information about their preferences and expectations.
It is particularly useful in industries with rapidly changing consumer
preferences or where customer feedback plays a significant role in shaping
demand.


CHALLENGES OF DEMAND AND SUPPLY FORECASTING

Regardless of chosen methods and the aims companies strive to achieve with
demand forecasting, building predictive models is a tedious endeavor that
encompasses several industry-specific and process-related challenges. 

GLOBAL SUPPLY CHAIN COMPLEXITY

Managing demand across global supply chains with multiple suppliers, varied
regulations, and diverse market conditions can increase the complexity of
forecasting and require a more nuanced approach.

UNCERTAIN MARKET CONDITIONS

Rapid changes in market conditions, influenced by factors such as economic
shifts, geopolitical events, or unexpected disruptions, can be a major roadblock
to accurate planning and predictions.

SEASONALITY AND TRENDS 

Identifying and accurately predicting seasonal patterns and trends, especially
in industries with distinct peak seasons, requires sophisticated forecasting
models to avoid underestimating or overestimating demand.

EVOLVING CONSUMER BEHAVIOR 

Changes in consumer preferences, shopping channels, or buying habits can
significantly impact demand forecasting accuracy. Adapting to these changes
requires continuous monitoring and adjustment of forecasting models.

DATA QUALITY AND AVAILABILITY 

Inaccurate or incomplete historical data can compromise the effectiveness of
forecasting models. Ensuring data accuracy and addressing data gaps can be
challenging, especially in industries with rapidly changing product portfolios.

COLLABORATION ACROSS DEPARTMENTS

Ensuring effective communication and collaboration among different departments,
such as sales, marketing, and operations, is crucial. Siloed information can
lead to misalignment and inaccurate demand forecasts.

Aiming to address these challenges, businesses turn to implementing and
integrating advanced forecasting technologies, such as machine learning or AI.

> Today, more and more businesses evaluate the potential of machine learning and
> AI applied to supply chain demand forecasting. Despite holding significant
> implementation roadblocks, these technologies in fact shape the way supply
> chain management will look in the next five years. That’s why investing in
> their adoption is worth every penny.
> Dmytro Ivanov, ML Engineer at Trinetix


HOW TECHNOLOGY REVOLUTIONIZES DEMAND FORECASTING IN SUPPLY CHAIN MANAGEMENT

As of 2023, 73% of supply chain leaders still use spreadsheets for planning and
forecasting. At the same time, 90% of them have put technology upgrades on their
agenda and are now taking steps towards innovation adoption. 

Among the technologies these organizations plan to adopt, machine learning and
demand forecasting are the objectives of 4 out of 5 supply chain executives.



Here at Trinetix, we systematically evaluate the capabilities of emerging
technology applied to various business fields, including supply chain
management. Now, isolated from the excitement the term “artificial intelligence”
creates in the global corporate landscape, let’s explore the changes AI/ML can
bring to supply chain demand forecasting.

TRADITIONAL DEMAND FORECASTING

AI-POWERED DEMAND FORECASTING

TYPES OF DATA USED FOR PREDICTIONS

 * Data from previous years (historical data)

 * Historical data
 * External data sets considering multiple factors (macroeconomic, promotions,
   etc.)

TECHNIQUES TO DEPLOY

 * Classical statistical methods
 * Recurrent neural nets
 * Forecast frameworks

 * Gradient boosting (an ML technique used to build predictive models that can
   effectively capture complex patterns and relationships within the data)

USAGE SCENARIOS

 * Stable market performance of the product
 * Calculating market rates for a brand in general

 * Unstable market conditions

Both traditional and AI-powered forecasting have significant power to fuel
demand and supply planning. The major difference between them however is the
scale of agility and accuracy applied to different scenarios and market
conditions.

The increased accuracy and reliability of technology-driven demand forecasting
is explained by the fact that AI algorithms are continuously learning and
adapting to the new environment. In addition, they promote cross-department
contribution and help to consider a significantly broader spectrum of factors
when building predictions. 

WHY IS IT SO IMPORTANT IN 2024?

As of now, 6 out of 10 global organizations recognize geopolitical instability
is having a detrimental impact on their supply chains. Constantly rising fuel
prices, multiple regulatory changes, growing data volumes, and heightened
customer expectations—the objective reality makes accurate supply chain planning
a real must-have. Along with ensuring stable operational performance, it leads
organizations to improved cost efficiency, helps to build resilience to changes,
and as a result, enables continuous market leadership that is so desired in
today’s competitive business landscape. 


HOW TO GET STARTED WITH DEMAND FORECASTING IN THE SUPPLY CHAIN?

No matter if you go for traditional supply chain demand forecasting or choose to
reinforce your strategic efforts with AI and machine learning, achieving the
desired results would require developing a clear implementation roadmap. Let’s
visualize how this one could look.



Leveraging demand planning is an iterative process that requires continuous
result monitoring and improvement. While the above plan is just a rough
representation of how the process usually looks in practice, it becomes clear
that even a mature business will find it challenging to go through it on their
own.

THE FUTURE IS DEMAND-DRIVEN

PREDICT IT WITH TRINETIX

Book a free discovery call


MAKING THE MOST OF STRATEGIC DEMAND PLANNING WITH TRINETIX

At Trinetix, we recognize the transformative impact of data-driven demand
planning on supply chains and keep researching the capabilities of emerging tech
to provide businesses with future-forward solutions to their strategic
challenges.

Acting as a dedicated technology partner, we focus on developing highly
personalized and agile forecasting models that guarantee long-term value and
adapt to the evolving market landscape. If you aim to maximize your logistics
efforts, ramp up business productivity, and optimize operational costs with
accurate supply chain demand forecasting, let’s chat about the ways to get
started.

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