Industry Coverage icon

How CIOs Can Get Manufacturing Demand Planning Right

Straight line to results: Optimize demand planning to effectively meet rapidly evolving customer demand.

Unlock a Free Sample

Demand planning has been difficult to master because:

  • Market conditions have been extremely volatile over the last three years.
  • Planning is not an exact science and has to be continuously improved.
  • There is limited usage of best-fit planning tools.

Our Advice

Critical Insight

Optimized demand planning requires well-integrated demand planning software that can enable:

  • Scenario analysis and simulation
  • Clear visibility into plans and related metrics
  • Scalability for application of IBP, Demand Excellence and CPFR concepts

Impact and Result

Improving demand planning has an impact on both the top-line and bottom-line.

How CIOs Can Get Manufacturing Demand Planning Right Research & Tools

1. How CIOs Can Get Manufacturing Demand Planning Right Storyboard – Research that outlines advanced demand planning concepts, explains software's contribution, and maps the journey from basic to optimized end state.

The purpose of this deck is to explain the components and maturity model for demand planning so CIOs know the right technology strategy for this functional area.

2. Referential Self-Assessment of Technology Maturity – A template to help understand the current state of demand planning in your organization with a defined set of steps to reach the next maturity level.

A self-assessment deliverable to analyze current state of demand planning and understand attributes of other maturity levels.

Unlock a Free Sample

How CIOs Can Get Manufacturing Demand Planning Right

Straight line to results: Optimize demand planning to effectively meet rapidly evolving customer demand.

Analyst Perspective

Shreyas Shukla

Even before COVID-19 hit, the manufacturing industry had been contending with demand volatility. However, the pandemic highlighted the impact that external factors had on consumer demand: global supply chains stalled, a drastic reduction in the number of SKUs supplied by businesses, prolonged shortages of popular products, and a dramatic shift to online retail.

Conducting accurate demand forecasting and planning has never been an easy task. Effective demand planning has far-reaching consequences to your top-line and bottom-line by ensuring enough goods are produced and sold through the right channels, production planning is conducted in an optimum manner, the right level of inventory is maintained, and the total cost of supply chain ownership is low. There are several challenges businesses must overcome to get to the ideal operating state with demand planning. Legacy forecasting tools and Excel often cannot handle the amount of data required for an accurate plan, nor can they capture all available opportunities.

At the other end of the spectrum, traditional ERP systems do not provide planners with the flexibility they need to simulate options, test forecasting models, and adjudicate the accuracy of their demand planning logic. The answer to optimization challenges is a two-pronged approach; use a built-for-purpose demand planning system, and take a holistic view of your planning philosophy and performance management framework. In this research, we introduce the modern supply chain planning landscape, take a deep dive into demand management and its challenges, give you an overview of the ideal technology end state for demand planning, and introduce fit-for-purpose software. We then discuss some case studies spanning durable, non-durable, food and beverage, and chemical manufacturing industries to give you an idea of how other companies have approached the optimization of demand planning.

Shreyas Shukla

Principal Research Director, Manufacturing Industry

Info-Tech Research Group

Executive Summary

The most critical aspect of optimized demand planning is choosing a fit-for-purpose demand planning system which will provide a business with the capability and flexibility it needs to perform accurate demand planning.

Your Challenge

Dealing with demand volatility has been an ongoing challenge made more severe by the Covid-19 pandemic. While most businesses have realized the value that accurate demand planning brings, they continue to struggle with transitioning demand planning from being administrative to strategic.

    Poor demand planning can result in:
  • Challenges in capitalizing on sales opportunities
  • Overstocking on finished product leading to margins being squeezed.
  • Continuous fire-fighting to maintain supplier relationships.
  • Costs related to expediting goods.

Common Obstacles

Businesses will have to take a holistic view of their demand planning ecosystem to reach an ideal, optimized end state. Several obstacles pose a threat to improving demand planning maturity:

  • Volatile customer behavior and the shift to eCommerce.
  • Using the right simulation models to forecast demand.
  • Managing a complex data landscape that can provide critical insights into customer demand.
  • Planning for multiple channels and the complexity associated with it. Using basic, manually managed Excel-based tools or inflexible systems instead of fit-for-purpose solutions.

Info-Tech's Approach

Businesses have realized that predicting demand accurately is essential. While demand planning is not an exact science, choosing the right approach and technology will improve the demand planning process and accuracy.

Info-Tech will provide:

  • An overview of a framework to support demand planning optimization efforts with cross-industry examples of good demand planning.
  • A snapshot of fit-for-purpose demand planning software and its features.
  • A guide to gauge your Demand Planning maturity.

How CIOs Can Get Demand Planning Right

Straight line to results: Optimize demand planning to effectively meet rapidly evolving customer demand.


COVID-19 made accurate demand planning extremely challenging

COVID-19 injected new complexities into the formal forecasting and planning processes. Businesses found that traditional forecasting and planning methods couldn’t keep up with constant market volatility. Demand cratered, then recovered, and reached unprecedented levels before entering the still-evolving soft downturn of early 2023.

New demand patterns emerged in 2020, forcing businesses to rethink their demand strategy. how covid changed demand patterns and products impacted % of sales over 3 weeks in March 2020 versus 2019

Source: McKinsey, 2020

Businesses had to respond rapidly to a new kind of consumer

Disruptions during the pandemic, ongoing trade wars, geopolitical events, and rising inflation have resulted in a new global environment. Businesses have had to make a valiant effort to continue their manufacturing operations to stock shelves with product, while dealing with overtaxed and elongated supply chains.

how covid changed demand patterns and products impacted % of sales over 3 weeks in March 2020 versus 2019

Source: The New Consumer, 2023

The start of 2023 had some challenges in confidence

Businesses seem to be struggling with a large stockpile of inventory Companies may be forced to pause, delay or cancel orders, impacting supplier cash flow in favor of their own cashflow; this will negatively impact suppliers. There are indicators of a looming downturn led by a collapse in demand across the transport and logistics sector worldwide. While the economic environment has changed significantly since the pandemic began, 2023 is beginning to feel like a relapse of early 2020.

Quarter on quarter % growth of global transaction volumes

continuing trend of improvement on global transaction volumes in 2022

Source: Tradeshift, 2023

Global order volumes have been in freefall since Q4 2021. While trends show order volumes in contraction territory, there has been marginal improvement all through 2022.

Year-over-year change in US consumer spending

US consumer spending, avg. transaction size and transaction volume trending downward.

Source: Earnest Analytics. 4-week trailing average, 2022

Transaction volumes in the US seem to doing worse quarter-on-quarter. The US Dept. of Commerce suggests that consumer spending is trending downward and US supply chains are sitting on high stockpiles of inventory.

US Consumer Sentiment Index

US consumer sentiment was at an all time low in June 2022.

Source: University of Michigan Surveys of Consumers, 2022

Consumer sentiment was at an all-time low in June 2022, but started showing marginal signs of improvement.

Change in US Consumer Price Index

Inflation at 9.1% in June 2022, compared to 2010s average of 2%.

Source: US Bureau of Labor Statistics, 2022

Inflation in the US continues to remain at 40-year highs, despite some minor relief toward the end of 2022.

Transportation and manufacturing sectors are seeing a slowdown

Logistics managers are concerned for the future. The November 2022 Logistics Manager’s Index is at 53.6, the second lowest reading in its history. The lowest reading of 51.3 was from April 2020. This is due to low inventory levels, lower than expected manufacturing activity, expensive warehousing and a post-holiday spending lull with an expected downturn in 2023.

Transportation and logistics sector is firmly in slowdown territory. Overall warehouse capacity and transportation prices contracted considerably in 2022.

Manufacturing saw a slight improvement in Q4 2022 due to consistent easing of supply bottlenecks. The manufacturing sector, however, continues to remain in contraction territory due to waning demand after the holiday season, and careful spending by consumers due to fears of a looming recession.

Retail has been performing near expectation for the majority of 2022. The holiday season has been lucrative with month-on-month sales up by over 7% from November to December.

Technology Spending outperformed all other sectors in Q4, 2022 despite the hammering that tech stocks have received due to economic headwinds. Businesses continue to invest in digital transformation initiatives and worldwide IT spending is expected to grow by 5.1% in 2023.

Global quarter on quarter % growth of global transaction volumes by sector

Growth of global transactions by quarter throughout 2022

“CEOs and boards tell me they are cautious, particularly in the near term. They're rethinking business opportunities and would like to see more stability before committing to longer-term plans. Many firms have started preparing for tougher times, focusing on factors within their control.”

- David Solomon, CEO, Goldman Sachs; quoted in Business Insider India, January 21, 2023.

Source: Tradeshift, 2023

Planning is the primary focus going forward.

Supply chain planning is the top priority for organizations in 2023 (Supply Chain Management Priorities and Challenges survey, APQC, 2023). Demand planning and forecasting is the top focus area within supply chain planning.

Supply chain planning is top focus area in 2023

Continued disruptions and demand volatility mean that demand planning and forecasting will be a key competitive differentiator. From the start of the COVID-19 pandemic till now, most businesses feel that investing in better planning capabilities will ensure they meet and / or exceed their goals.

Top 3 areas of focus for planning in 2023.

Source: APQC, 2023

Get demand planning right by addressing the following dimensions. Planning Concepts, forecasting methods, performance management, software and maturity.

Section 1

Introduction to demand planning and advanced planning concepts

Section 1

  • Evolution of supply chain planning
  • Overview of demand management
  • Introduction to advanced planning concepts

section 2

  • Differences between forecasting and planning
  • Overview of modeling techniques / methods

section 3

  • Introduction to essential and optional KPIs
  • KPI definitions

section 4

  • Overview of demand planning software and capabilities
  • Demand planning software integration

section 5

  • How demand planning differs by industry
  • Deep dive into case studies

Optimize demand planning to effectively meet rapidly evolving customer demand

Evolution of the supply chain planning landscape

The evolution of supply chain planning has been characterized by increasing integration of separate tasks. In the 90s, all planning elements of the supply chain became integrated under a single management perspective. In the 2000s, evolving IT and communication technologies allowed for a complete integration under the supply chain management function. Since the 2010s, the growing level of digitalization and automation has been a dominant element in the evolution of planning.

evolution of supply chain planning from 1960s fragmentation model to 2010's digitalization method.

Source: SCM Dojo Blog, 2023

The modern supply chain planning landscape

Supply chain management deals with the complete flow of goods (and services) starting with the raw material and ending with the finished product in the hands of the customer. R.K. Oliver and M. Webber coined the term “supply chain management” in 1982.

Supply chain business planning is a critical building block of supply chain management. It cuts across long-term, mid-term and short-term time dimensions to incorporate preparation and execution steps for finished goods. Supply chain business planning covers several business domains and produces many outcomes:

Short Term, mid term and long term supply chain management steps.

Long-term business plans are created based on strategic planning decisions. These can cover 2- to 10-year periods. Long-term business plans help to establish objectives, policies, and the operating footprint of the supply chain.

Mid-term business plans are created based on tactical and some operational planning decisions. These typically cover 1 to 1.5-year periods but can range anywhere from 6 months to 2 years. They are updated on a rolling basis and deploy resources to match supply with demand.

Short-term business plans are created based on operational and execution planning decisions. These typically cover daily, weekly, monthly, and sometimes quarterly planning periods and are also updated on a rolling basis. Short-term plans are used to schedule, monitor, control, and adjust production activities as well as build, store, and transport finished product.

Source: European Journal of Operational Research, 2005

Implementing the 3 levels of supply chain planning

Effective implementation of the 3 levels of supply chain business planning is essential for optimum use of available resources and a profitable business. Each level has a distinct set of characteristics, activities, outcomes, priorities and challenges. These must be balanced to ensure goals and objectives are met.

Long-term, mid-term and short-term levels of supply chain planning. Each level has a set of characteristics, activities, priorities and challenges.

Source: Quality Gurus, 2022

Bringing it all together from planning to execution

A typical supply chain plan involves complex relationships between components of the business planning layers. For successful execution on the shop floor, different stakeholders need to constantly communicate with each other. This relationship needs to run like a well-oiled machine.

Business planning layers: Inputs, product strategy and program development and the complex relationships between each of the components. Components need to constantly communicate with one another.

Introduction to demand management

Demand management consists of two components: demand forecasting and demand planning. Demand management requires responding to or influencing the pattern or consistency of demand in line with organizational objectives. This involves forecasting unconstrained demand and subsequently layering it with operational details and constraints that a financial, market-related, supplier-related, operational, self-inflicted, and customer specific.

A brain showing two halves - Demand forecast on top of demand planning.

  • Demand forecasting is the process of predicting future demand through analysis of historical facts and near real-time data. This is a simple prediction of future events to help calculate what product, and how much of it, customers will demand in the future.
  • Demand planning is the “the management process within an organization which enables that organization to tailor its capacity … to meet variations in demand.” (The Chartered Institute of Procurement and Supply (CIPS)). Demand planning draws from and operationalizes the demand forecast by managing future demand to reach operational and financial goals of the organization.
“There is only one true forecast – the unconstrained demand forecast, or as close as possible to unconstrained, given the inherent constraints, whether self-inflicted or customer specific. There is no such thing as a shipment forecast, financial forecast, or sales forecast. They are all plans created from the unconstrained consumer demand forecast.”

- Charles W. Chase, SAS Institute

Demand management involves complex relationships between strategy and operations

Strategic demand management activities require implementation of specific operational processes through which daily activities will be executed and measured.

Strategic sub-processes, process interfaces and operational sub-processes and their relationships with each other.

Source: ExploreSCM, originally adapted from “The Supply Chain Management Process,” The International Journal of Logistics Management, Vol. 12, No. 2 (2001).

Demand planning flow of activities

The key activities for demand planning flow - data collection, demand forecasting, demand planning, allocation, gap analysis and order fulfillment.

Chosen demand planning software should be able support these advanced concepts

Demand Planning software should support: Integrated business planning, Demand excellence and Collaborative planning, fulfillment and replenishment.

Use integrated business planning to synchronize key decisions

IBP is the intersection of demand planning, inventory management, supply planning, material requirements planning and production planning.

Integrated business planning takes a holistic view of procurement, production, distribution and sales. IBP integrates strategic planning with shop-floor execution and enables executive level participation for impactful decision-making IBP is achieved by accurately anticipating the demand for finished goods and figuring out the financial, material, and logistical considerations of trying to meet this demand in line with strategic and executive objectives. IBP is at the intersection of several planning activities:

  • Integrated business planning bridges the gap between strategic S&OP and execution by translating outcomes into financial, material and resource requirements.
  • Sales and operations planning enables executive decision-making to achieve a profitable financial outcome.
  • Demand planning projects future demand for a product and adjusts output to meet that demand.
  • Inventory management maintains the right amount of inventory to meet demand by balancing investment constraints and logistics costs with goals and objectives of the company.
  • Supply planning deals with availability of raw material, its distribution, manufacturing, production, and procurement in line with planned demand.
  • Material requirements planning specifies what is needed, how much of it, and when it is needed, assuming no production constraints.
  • Production planning is the process of creating a guide explaining the steps involved in the production of a product or service.

Source: QAD DynaSys, 2023

Use demand excellence to improve the quality of your outcomes

Demand excellence refers to a demand planning ecosystem that delivers quality plans through a holistic view of the people, processes, technology, data, and measurements involved. Demand excellence is supported by demand statistics, demand planning, and demand consensus as the core process areas.

Venn Diagram of demand planning ecosystem. Demand Statistics, demand planning and demand consensus

  • Demand statistics involves generating the best quality statistical forecast by using the right combination of models / algorithms, and measuring the statistical performance of applied portfolio of models.
  • Demand planning involves generating the best quality demand plan using the right mix of inputs, planner judgement, production parameters, and other activities in the ecosystem.
  • Demand consensus involves generating a one-number plan through consensus-based decision making across participating departments. Consensus aims for transparency of outcomes throughout the organization.

grey circle with the number 1 printed in white.

A one-number forecast creates alignment and ensures that the whole company works to the same set of assumptions (The Institute of Business Forecasting & Planning). In some cases, the one number could be a common range that serves as the objective for the organization.

Source: Spinnaker

Use CPFR to collaborate with your suppliers and distributors toward a common objective

Collaborative planning, forecasting and replenishment (CPFR) enables companies to work together with their trading partners to improve forecasts, reduce inventory and production costs, and increase sales and profit (The Institute of Business Forecasting & Planning). CPFR was a concept jointly developed by Warner – Lambert and Walmart in 1995 to ensure the right amount of Listerine was available on Walmart’s store shelves.

Collaborative planning, forecasting and replenishment leads to plan accuracy, customer satisfaction and agility and promotions.

Benefits of CPFR

Improved forecast accuracy Optimized inventory levels Close relationships between partners
Reduced supply chain uncertainty Realization of cost reductions Prediction and mitigation of risks
Faster release of working capital for partners Improved material flow Efficient production

“From the time we started the collaboration process at Meijer until the time I left, we had reduced our total logistics cost by more than 50 percent.”

- Ed Nieuwenhuis, Supply Chain Manager, Meijer Stores: Supply Chain Digest, 2008.

Improve revenue through technology led demand sensing and shaping capabilities

Demand sensing is used to predict near-term demand based on the historical demand.

Demand sensing is used to predict near-term demand using short-term or recent data. While demand planning is effective in the short to mid-term and forecasting for the mid to long-term, demand sensing is used to fill the gap for near-term demand prediction. This is especially effective in volatile markets where demand sensing can be employed to accurately predict what will happen in the immediate future using short-term trends.

Organizations will employ demand shaping, such as promotions, to match the demand with the supply.

Demand shaping uses incentives, substitutions and dynamic cost adjustments to influence near-term demand. Organizations will typically employ demand shaping to match demand with the planned or available supply of certain products. Most promotions are common examples of demand shaping efforts. Demand shaping has immediate impacts on top line and inventory levels.

Source: AI Multiple via LinkedIn

Optimized demand planning has been challenging to achieve

Demand planning has a variety of pain points and irritations across the people, process, technology, data and measurement dimensions. This is usually due to the overall strategy pursued by businesses toward solving these challenges, often by means of more system implementation. Some of the most common challenges are:

Dinosaur fossilized head iconPre-Pandemic Mask iconPandemic bandaid iconPost-Pandemic
  • Demand planning is treated as an admin task and not strategic to objectives.
  • Forecasts are siloed, budget-limited or aggressively management-directed. Assumptions and techniques are not challenged.
  • Choosing right forecasting models is a challenge.
  • Phase ins / phase outs are not part of regular workflow.
  • Planning teams are conservative and won’t experiment with more advanced models.
  • Supply chains became elongated and demand planning assumptions had to be updated to account for this phenomenon.
  • Availability of stock was a challenge and companies became aggressive procuring / scaling up production.
  • Performance measures were abandoned in favor of aggressive procurement strategies.
  • There was a rush toward system implementation, and options chosen were not necessarily best-fit.
  • Spending is trending downward, and customers have become price-sensitive.
  • Businesses today have too much dead stock and scaled up production during the pandemic too rapidly. This is eating into margins.
  • Finding experienced and skilled planning talent is challenging.
  • Bias and accuracy continue to be critical areas of focus.
  • Widespread use of Excel sheets or inflexible stand-in systems (like ERP) have been a roadblock to planning optimization.

Source: Contributor interviews, 2023

Section 2

Contrasting forecasting with planning and introduction to modeling methods.

Section 1

  • Evolution of supply chain planning
  • Overview of demand management
  • Introduction to advanced planning concepts

section 2

  • Differences between forecasting and planning
  • Overview of modeling techniques / methods

section 3

  • Introduction to essential and optional KPIs
  • KPI definitions

section 4

  • Overview of demand planning software and capabilities
  • Demand planning software integration

section 5

  • How demand planning differs by industry
  • Deep dive into case studies

Optimize demand planning to effectively meet rapidly evolving customer demand

“Demand forecasting” and “demand planning” are often used interchangeably

It is important to understand the differences between the two, and the techniques and methods employed for forecasting and planning.

    Objectives of demand forecasting

  1. Understand organizational objectives.
  2. Continuously collect and analyze new information.
  3. Estimate future events and provide a broad framework for planning based on assumptions.
  4. Identify cause and effect of performance.
  5. Communicate with stakeholders and obtain feedback continuously.
  6. Focal point of forecasting is facts and sources of objective, quantitative information.
  7. “Forecasting is a systematic attempt to probing the future through inference from facts that are already known”

    - Louis Allen, Author, The Management Profession: Management Study Guide.

    Objectives of demand planning

  1. Define strategies to achieve organizational objectives within parameters defined by forecasting.
  2. Define clear operational objectives and key results expected.
  3. Identify, analyze, and communicate challenges or obstacles to achieve organizational objectives.
  4. Develop performance tracking processes in line with defined key results per area.
  5. Execute all steps as per plan.
  6. Focal points are facts and expected results.
  7. “Planning is deciding in advance what to do, how to do and who is to do it. Planning bridges the gap between where we are, to where we want to go.”

    - Harold Koontz and Cyril O'Donnell, Authors, Principles of Management

Source: Management Study Guide, 2022

Demand planning software should enable a spectrum of methods

The portfolio of models applicable to a specific organization could be any combination of pure qualitative methods and pure quantitative methods. Depending on the organization, planning teams and overall objectives, there are cases where machine learning is required and others where planners’ judgement is key.

Qualitative and Qualitative methods that require machine planning versus those where planer's judgement is key.

Source: Institute of Business Forecasting & Planning, 2017

Section 3

Measuring performance using KPIs

Section 1

  • Evolution of supply chain planning
  • Overview of demand management
  • Introduction to advanced planning concepts

section 2

  • Differences between forecasting and planning
  • Overview of modeling techniques / methods

section 3

  • Introduction to essential and optional KPIs
  • KPI definitions

section 4

  • Overview of demand planning software and capabilities
  • Demand planning software integration

section 5

  • How demand planning differs by industry
  • Deep dive into case studies

Optimize demand planning to effectively meet rapidly evolving customer demand

Demand planning KPIs provide critical intelligence

Successful demand planning requires continuous performance monitoring through specific key performance indicators. These KPIs will provide intelligence on critical processes and outcomes. KPIs help gauge results of the planning effort, provide insights about customers and demand, review ordering metrics, locate errors, and understand how sales predictions are resulting in performance on the ground. This information is critical to the bottom line.

Demand planning KPI's provide intelligence on critical processes and outcomes. They gauge results of the planning effort, provide insights about customers and demand, review ordering metrics, locate errors, and understand how sales predictions are resulting in performance on the ground.

Source: Oracle NetSuite, 2021

Demand planning software should report on essential KPIs at a minimum

Forecast error Measures forecasted sales v. actual sales numbers Forecast accuracy Indicates how accurate predictions for demand and sales are Monthly product category forecast error Identifies changes in sales and marketing strategy Bias Identifies if forecast errors are tending in one direction or another Tracking signals Identifies the presence of persistent bias
Mean absolute deviation Indicates forecast accuracy by averaging magnitudes of forecast errors Mean absolute percentage error Statistical technique to measure forecast accuracy Symmetrical mean absolute percentage error For scenarios where the product or market are new Weighted mean absolute percentage error Measures errors by weighing it using actual sales volumes Mean square error Determines performance by averaging the squares of the forecast errors
Root mean squared error Identifies the severity of forecasting errors Actual sales conversions vs. sales assumptions Provides insights into the effectiveness of marketing Order fill rate Used for inventory and helps identify customer satisfaction levels Perfect order rate Indicates how many orders were shipped as expected by customer Weekly item location forecast error Identifies mistakes by measuring forecast accuracy
Early warning for demand variation Monitors MAPE and/or forecast accuracy KPIs Pareto analysis Follows the behavior of the top 20% of customers which impacts 80% of sales Prebooking orders Identifies if number of pre-orders are in line with forecasted expectations Phase-out products Helps time the phase out of existing products Marketing intelligence Determines actual demand for products

Source: Oracle NetSuite, 2021

Section 4

Demand planning software, key features, and integration requirements

Section 1

  • Evolution of supply chain planning
  • Overview of demand management
  • Introduction to advanced planning concepts

section 2

  • Differences between forecasting and planning
  • Overview of modeling techniques / methods

section 3

  • Introduction to essential and optional KPIs
  • KPI definitions

section 4

  • Overview of demand planning software and capabilities
  • Demand planning software integration

section 5

  • How demand planning differs by industry
  • Deep dive into case studies

Optimize demand planning to effectively meet rapidly evolving customer demand

This is what an optimized demand planning technology ecosystem looks like

    Current state

  • Forecasts are imposed top-down and not discussed / challenged.
  • There is little / no knowledge of forecasting models.
  • Product lifecycle is not incorporated into the planning process.
  • Little / no analysis or understanding of what changed between forecast and actuals.
  • Little / no formal planning technology roadmap and business priorities are not considered.
  • Tools are Excel-based and not integrated with upstream / downstream systems.
  • Planning data management is manual, with many versions of critical data.
  • Little / no formal planning performance monitoring or analysis. Reporting is manual and ad-hoc.

    Optimized technology end state

  • Formal technology roadmap exists, with business requirements and priorities incorporated.
  • Clear visibility into plans and performance metrics across management hierarchies.
  • Ability to apply several models, simulate results, and reduce expected error.
  • Product lifecycle activities integrated into the planning process with clear tracking of outcomes and owners.
  • Well integrated SAAS / PAAS products employed with user training and user manuals.
  • Master data is centrally managed, access is controlled, and single source of truth clearly defined.

Pick a planning solution that enables improved outcomes

Each maturity level builds on the previous by enablement of new capabilities through advanced solutions and embedded artificial intelligence. This results in improved planner productivity, optimized process, and organizational visibility.

No. of forecasts / forecast accuracy versus Value.

Source: Supply Chain Digest, 2020

Demand planning software and overview of core features and capabilities

Core features of Demand planning software.

Source: ScienceSoft, 2023

Demand planning software will need to integrate with other critical applications

Applications that handle: CRM, PMS, OMS, PS, PLM, IMS, ERP will need to be integrated into Demand planning software.

Comparison of prominent options in the demand planning software space

Source: SoftwareReviews, 2023

Implementing demand planning software is a complex exercise

It is important for CIOs to track the return on investment (ROI) for an undertaking of this size and complexity. While successful planning is highly planner-dependent, implementing planning software and ensuring its integration with other critical applications is not an easy task.

More than 50% of organizations accomplish ROI within 1 yea. More than 80% of organizations accomplish ROI within 3 years.

Section 5

Differences in demand planning by industry and deep dive into case studies

Section 1

  • Evolution of supply chain planning
  • Overview of demand management
  • Introduction to advanced planning concepts

section 2

  • Differences between forecasting and planning
  • Overview of modeling techniques / methods

section 3

  • Introduction to essential and optional KPIs
  • KPI definitions

section 4

  • Overview of demand planning software and capabilities
  • Demand planning software integration

section 5

  • How demand planning differs by industry
  • Deep dive into case studies

Optimize demand planning to effectively meet rapidly evolving customer demand

Industries use different supply chain models for competitive differentiation

There are six distinct supply chain models. Though the components of the supply chain do not vary greatly between industries, the way they work and the outcomes they achieve vary.

Description of various supply chain models, their focus and examples. Types of Supply Chain models and their use in different industries.

Source: Institute for Defense & Business, 2021

Demand planning complexity varies across industries

This analysis is modeled on the profile and product portfolio of the Fortune 1000 companies. However, the patterns start blurring once small and medium enterprises and companies headquartered in other countries are added to the analysis.

Demand planning complexity originates from the following attributes.

A high complexity pattern has:

  1. High demand variability
  2. High product variety
  3. High product lifecycle
  4. High degree of seasonality
  5. High number of product alternatives

Patterns representative of some of the largest players in each of these industries are illustrated on this page.

Analysis of 1000 companies across various industries showing demand planning complexity.

Case Study

How Danone fulfills its growth objectives with optimized end-to-end supply chain planning capabilities.

Danone Logo

INDUSTRY: Food and Beverage

SOURCE: o9 Solutions, Inc.


Danone is a French multinational food and beverage corporation headquartered in Paris. They were struggling to respond quickly to market opportunities because of their reliance on offline, siloed planning processes. Feasibility of their plans and impact on P&L was not understood until monthly IBP meetings were conducted.

Downstream teams couldn’t view material or capacity constraints till demand-supply reconciliation was completed. This resulted in a high number of order expedites, low-fill rates and high inventory levels.


Danone implemented o9’s Supply Chain Control Tower, IBP with extended simulation capabilities, collaborative Demand Planning and Sensing software. o9 enabled end-to-end and cross-functional visibility that allowed Danone to clearly understand the P&L impact of their plan. Real-time scenario planning provided Danone with options to best respond to risks and opportunities, and production scheduling optimization ensured that key commodities were used in the most profitable way.


  • Danone added €13-16m to its annual top line due to improved service levels and more profitable decisions.
  • Danone saved €106m in inventory costs.
  • Plan reconciliation, feedback, and updates are now in real-time and can be done in seconds instead of waiting for common meetings which could take days.

Case Study

How Lush Cosmetics capitalized on a growing, competitive market with enhanced planning and reporting.

Lush: Fresh handmade cosmetics Logo

INDUSTRY: Non-durable Goods

SOURCE: Arkieva


Lush Cosmetics is a British multinational retailer of handmade, vegetarian cosmetics. Lush’s North American subsidiary is headquartered in Vancouver, Canada and manages over 250 stores. They struggled to manage their strong year-over-year growth in the region, as all their planning was done on Excel sheets. These were error-prone and extremely difficult to maintain because of the large amount of data they had to handle. Additionally, the integrity of data in these Excel sheets was questionable because of the number of versions that existed.


Lush evaluated over 15 vendors and chose the fully integrated Arkieva One Plan S&OP Solution.

Lush replaced their manual Excel sheets with Arkieva’s software for improved demand planning, inventory optimization, executive reporting, and financial analysis. This gave them improved visibility into their inventory levels, advanced analytics capabilities, and integration with other applications, thus maintaining integrity of data and the ability to run scenarios on how demand affects revenue


  • Lush was able to capitalize on the rapid growth of the North American cosmetics market, estimated to be worth $100+ Billion in 2022 using Arkieva. Revenue grew by 11% YoY.
  • Management reporting became easy and multiple views and perspectives could be generated instantly; it used to take a couple of days.
  • In addition to advanced planning capabilities to handle Lush’s growing business volume, Arkieva also enabled training to ensure easier user adoption.

Case Study

How ZF Friedrichshafen is driving the future of the automotive industry with enhanced demand planning.

ZF Friedrichshafen Logo

INDUSTRY: Durable Goods



ZF Friedrichshafen AG is a manufacturer of car parts and mobility systems headquartered in Germany. ZF’s Aftermarket division manages a comprehensive portfolio of aftermarket parts for their customers globally. Their portfolio of demand planning solutions was outdated and did not provide them the flexibility and visibility they needed to handle such a large number of SKUs. Additionally, market and business intelligence was conducted in a silo and not incorporated into the process of understanding demand.


ZF Aftermarket chose to replace their legacy planning software with SAP Integrated Business Planning and SAP’s Supply Chain Control Tower. Together, these solutions provided ZF enhanced S&OP, forecasting and demand planning capabilities, and a better view of their supply chain.

SAP IBP connected with other landscapes to retrieve data critical for planning, driving insights to marketing, and maintaining flexible control over planning for location, product, channel, and customer.


  • SAP IBP enabled global demand planning for ZF by unifying data into one environment from multiple, disparate systems and databases.
  • Provided ZF the ability to run nine times more planning scenarios and simulations to reduce forecast errors.
  • Forecasts now run in under 15 minutes instead of the hours it previously took.

Case Study

How MilliporeSigma discovered the solution for high inventory levels and inaccurate forecasts through replenishment planning .

MillaporeSigma's logo

INDUSTRY: Chemical Mfg.

SOURCE: Logility


MilliporeSigma is an American manufacturer of biochemicals and reagents for life science research and discovery, headquartered in Burlington, Massachusetts. MilliporeSigma sells 100,000+ chemicals globally, deals with 300,000+ SKUs, and has 80,000+ customers in 20 global locations. MilliporeSigma often needs to forecast for 7+ million items with complex fulfillment requirements, sometimes at very small volumes. It has a team of 100 demand and replenishment planners struggling with forecast accuracy and identification of the right amount of inventory.


MilliporeSigma chose Logility as an upgrade over its previous planning systems. Logility deployed their demand planning and inventory optimization modules to help MilliporeSigma create forecasts that were more accurate and at several different levels of granularity. Planners could run simulations to improve service level performance for the company while staying within budget. Logility allowed MilliporeSigma to optimize inventory through easy incorporation of the company’s use of the ABC method for inventory management.


  • MilliporeSigma improved the accuracy of its forecast for their A,B,C and bulk items.
  • Logility allowed MilliporeSigma to apply time-phased inventory policies to improve customer service levels.
  • MilliporeSigma was able to reduce inventory levels through alternate sourcing management and excess stock and capacity use enabled by Logility.

Case Study

How iNova improved planner productivity by replacing their Excel spreadsheets.

inova pharmaceuticals logo

INDUSTRY: Pharmaceuticals

SOURCE: Logility


iNova Pharmaceuticals is an Australian manufacturer of OTC and prescription medication in 20 countries across Asia, Australia, New Zealand, and South Africa. iNova planners depended on Excel spreadsheets and statistical forecasting for demand and supply planning, which varied by country. Data handling was done entirely manually and there was no single source of truth across the company. Planners struggled to manage complexities such as promotional pricing, and ranging and grading of products using Excel; this was an impediment to iNova’s expansion plans.


iNova replaced their Excel planning tools with Logility’s Demand & Supply planning and Data Management software. This helped streamline iNova’s fragmented planning ecosystem and provided some much-needed automation capabilities to planners. For example, statistical forecasting operated autonomously and at greater speed, thus freeing up planners’ time to focus on high volume and class A SKUs. Management of data was seamless, and Logility was able to pull from multiple systems to establish a single data environment to enable advanced analytics.


  • Planning and reporting became consistent across borders. Planners could now model safety stock levels.
  • Automation of PO-creation reduced time to create a PO by 40%.
  • Logility provided planners with the ability to perform user-defined, multi-level forecasts that could be automatically updated when changes were made at any level.
  • Automation of statistical forecasting reduced forecast errors by 50%.

Next steps

  1. Demand Planning Technology Maturity Model

    • Review the current state of your technology maturity using the attached framework.
    • Plan and prioritize initiatives in accordance with your current maturity, with the aim of reaching an optimized technology end state.
    • Download the model.
  2. Review related research

    Wholesale demand planning report: An industry core technology
  3. Speak with an industry analyst

    • Book an analyst call and discuss your current challenges, transformation questions and latest trends
    • Contact Us

Research Contributors & Experts

Arjit Saran

Demand Planning Lead



Professor, Supply Chain Management

American University


Becdach, Camilo, et al. “Rapid Demand Forecasting during COVID-19 | McKinsey.” McKinsey & Company, 21 Apr. 2020. Accessed 13 Feb. 2023.

“Demand Planning in a COVID-19 Disrupted World.” CT Global Solutions, 2020. Accessed 13 Feb. 2023.

“Ensuring demand planning stability in response to COVID-19.” SAS Institute, 2020. Accessed 14 Feb. 2023.

Curoe, Matt. “5 Post-COVID Demand Planning Tips.” Redwood Logistics, 23 Sept. 2020. Accessed 14 Feb. 2023.

“Demand Planning in the COVID-19 Pandemic.” The Smart Cube, 17 Sept. 2020. Accessed 14 Feb. 2023.

“November 2022 Logistics Managers’ Index.” The -lmi, 2023. Accessed 15 Feb. 2023.

“Mastercard SpendingPulse: U.S. Retail Sales Grew 7.6% This Holiday Season.” Mastercard, 26 Dec. 2022. Accessed 15 Feb. 2023.

“2023 Supply Chain Top Focus Areas | APQC.” APQC, 21 Feb. 2023. Accessed 15 Feb. 2023.

“Digital Supply Chain Planning Processes.” QAD DynaSys, 2023. Accessed 16 Feb. 2023.

Stadtler, Hartmut. “Supply Chain Management and Advanced Planning–Basics, Overview and Challenges.” European Journal of Operational Research, vol. 163, no. 3, June 2005, pp. 575–88. Accessed 16 Feb. 2023.

Samanich, Keith Oliver, Anne Chung, and Nick. “Beyond Utopia: The Realist’s Guide to Internet-Enabled Supply Chain Management.” Strategy+business, 1 Apr. 2001. Accessed 16 Feb. 2023.

“The 3 Levels Of Supply Chain Management And How They Effect Your Business.” Quality Gurus, 17 Apr. 2022. Accessed 17 Feb. 2023.

“How COVID-19 will permanently change consumer behavior.” Accenture, Apr. 2020. Accessed 17 Feb. 2023.

“Difference between Planning and Forecasting.” Quick Guide 4 You, 30 May 2022. Accessed 17 Feb. 2023.

“Planning Function of Management.” Management Study Guide. Accessed 20 Feb. 2023. Web.

“Planning Function of Management.” Management Study Guide. Accessed 20 Feb. 2023. Web.

“Top 20 Demand Planning KPIs & Metrics.” Oracle NetSuite, 3 Nov. 2021. Accessed 20 Feb. 2023.

“Demand Management.” ExploreSCM. Accessed 20 Feb. 2023.

Keeley L. Croxton, Sebastian J. Garcia-Dastugue, Douglas M. Lambert, and Dale S. Rogers, “The Supply Chain Management Process,” The International Journal of Logistics Management, Vol. 12, No. 2 (2001), p.19. Accessed 20 Feb. 2023.

“Demand Excellence - A Holistic Model for Optimizing Demand Planning.” Spinnaker, n.d. Accessed 21 Feb. 2023. Web.

“CPFR – Business Process Guide.” Voluntary interindustry Commerce Solutions, 6 May 2004. Accessed 21 Feb. 2023.

O’Byrne, Rob. “An Introduction to CPFR in the Supply Chain.” Supply Chain Secrets, 12 Apr. 2016. Accessed 21 Feb. 2023.

“Introduction: Collaborative Planning, Forecasting and Replenishment (CPFR): A Tutorial | SCM | Supply Chain Resource Cooperative (SCRC) | North Carolina State University.” Supply Chain Resource Cooperative, 21 Mar. 2011. Accessed 22 Feb. 2023.

Laporte-Roy, Cedrick. “Collaborative Planning, Forecasting & Replenishment (CPFR).” Demand Planning, 12 Dec. 2018. Accessed 22 Feb. 2023.

“The On-Going Battle over CPFR.” Supply Chain Digest, 5 Mar. 2008. Accessed 22 Feb. 2023.

Javaid, Shehmir. “Demand Sensing in 2023: A quick guide to get you started.” AI Multiple, 4 Jan. 2023. Accessed 23 Feb. 2023.

Kabongo, Glede. “Demand Shaping Strategies for a More Profitable Supply Chain.” Tools Group, 10 Feb. 2022. Accessed 23 Feb. 2023.

Iqbal, Jamsheed. “Best Practices for Demand Sensing, Demand Shaping, & Influencing Demand.” Causometrix, 12 Oct. 2021. Accessed 23 Feb. 2023.

“Best Demand Planning Software.” 2023. Accessed 22 Feb. 2023.

“Danone Growth Fueled By End-To-End Planning Case Study.” O9 Solutions, 2023. Accessed 24 Feb. 2023.

“Lush Teams Up with Arkieva to Improve Demand, Inventory, and Financial Planning.” Arkieva. Accessed 24 Feb. 2023.

“QC Manufacturing finds inventory optimization with Netstock.” Netstock. Accessed 24 Feb. 2023.

“SAP andZF Friedrichshafen Success Story | Customer Reviews and Testimonials.” SAP. Accessed 24 Feb. 2023.

“MilliporeSigma | Success with Logility.” Logility. Accessed 24 Feb. 2023.

“INova Pharmaceuticals | Success with Logility.” Logility. Accessed 23 Feb. 2023.

Canitz, Henry. “Demand Forecasting Maturity Curve.” Supply Chain Digest, 12 Mar. 2020. Accessed 27 Feb. 2023.

Duggan, Kelly. “Six Types of Supply Chain Models.” Institute for Defense & Business, 29 June 2021. Accessed 24 Feb. 2023.

Kalish, Ira, et al. “Globalization isn’t going anywhere.” Deloitte, 2023. Accessed 27 Feb. 2023.

“Consumer Trends 2023.” The New Consumer, Dec. 2022. Accessed 27 Feb. 2023.

“Key Consumer Behavior Thresholds Identified as the Coronavirus Outbreak Evolves.” NeilsenIQ, 20 Mar. 2020. Accessed 13 Feb. 2023.

“Surveys of Consumers.” University of Michigan, Feb. 2023. Accessed 13 Feb. 2023.

“2022 Holiday Shopping Off to Slow Start.” Earnest Analytics, 22 Nov. 2022. Accessed 15 Feb. 2023.

“Consumer Price Index News Release - 2022 M12 Results.” US Bureau of Labor Statistics, 12 Jan. 2023. Accessed 15 Feb. 2023.

Glover, George, and, Salfiti, Zinya. “Recession Risk and the Prospect of Fed Halting Rate Hikes Are Top of Mind for Executives at the Biggest US Banks. Here Are the 14 Best Quotes from Their Latest Earnings Calls.” Business Insider India, 21 Jan. 2023. Accessed 24 Feb. 2023.

“Evolution of Supply Chain Management and Logistics.” SCMDOJO, 25 Mar. 2022. Accessed 17 Feb. 2023.

Koontz, Harold, and O’Donnell, Cyril. “Principles of Management: An Analysis of Managerial Functions.” McGraw-Hill, 1968. Accessed 17 Feb. 2023.

“Demand Planning Software: All-Around Overview.” ScienceSoft, 2023. Accessed 21 Feb. 2023.

“What Is CPFR? | Institute Of Business Forecasting.” IBF.Org. Accessed 15 Feb. 2023.

“TheTradeshift Index of Global Trade Health.” Tradeshift Holdings, 2023. Accessed 15 Feb. 2023.

“Types Of Forecasting Models | Institute of Business Forecasting.” IBF.Org, 13 Dec. 2017. Accessed 20 Feb. 2023.

“Understanding the Different Types of Supply Chain Models.” Box Around The World, 6 Feb. 2019. Accessed 27 Feb. 2023.

“What Is Collaborative Planning, Forecasting and Replenishment (CPFR)?” Accelerated Analytics, 27 Oct. 2015. Accessed 22 Feb. 2023.

“What Is CPFR in Supply Chain? Meaning, Features, & Process.” 8th & Walton, 24 Feb. 2022. Accessed 22 Feb. 2023.

“What Is Demand Planning? A Brief Introduction.” Fuse Inventory, 17 Aug. 2020. Accessed 17 Feb. 2023.

Wilson, Eric and ACPF. “My Favorite Forecasting Model | Demand-Planning.Com.” Demand Planning, 24 June 2022. Accessed 20 Feb. 2023.

“How to do effective demand management?” Chartered Institute of Procurement & Supply, 2022. Accessed 17 Feb. 2023.

Chase, Charles W. “Next Generation Demand Management. People, Process, Analytics & Technology.” John Wiley & Sons, 2016, pp. 44. Accessed 20 Feb. 2020.

Straight line to results: Optimize demand planning to effectively meet rapidly evolving customer demand.

About Info-Tech

Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.

We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.

What Is a Blueprint?

A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.

Each blueprint can be accompanied by a Guided Implementation that provides you access to our world-class analysts to help you get through the project.

Talk to an Analyst

Our analyst calls are focused on helping our members use the research we produce, and our experts will guide you to successful project completion.

Book an Analyst Call on This Topic

You can start as early as tomorrow morning. Our analysts will explain the process during your first call.

Get Advice From a Subject Matter Expert

Each call will focus on explaining the material and helping you to plan your project, interpret and analyze the results of each project step, and set the direction for your next project step.

Unlock Sample Research


Shreyas Shukla


  • Arjit Saran, Demand Planning Lead, Signify
  • 1 anonymous company contributor
Visit our IT Cost Optimization Center
Over 100 analysts waiting to take your call right now: 1-519-432-3550 x2019