Demand planning envisions achieving and maintaining an effectively lean supply equilibrium, one in which store inventories contain just as many products as demand dictates, but no more. While finding that perfect balance between sufficiency and surplus may prove to be complex, the right set of technology deployment can facilitate companies achieve the right equilibrium. “There is a big transformation in the demand planning space,” stated David Simchi-Levi, Professor – Engineering Systems, Massachusetts Institute of Technology (MIT) and Director, MIT Data Science Lab. “At a high level, companies are starting to use multiple sources of data to better understand and predict future demand.” This message emphasizes that effective demand planning warrants the use of demand forecasting techniques to accurately predict demand trends, which indirectly results in value-added benefits, such as heightened company efficiency and increased customer satisfaction. Our Cover Story this time presents the ‘A to Z’ of Effective Demand Planning for Successful Forecasting wherein subject-matter experts lay down a step-by-step approach for companies to achieve the perfect supply-demand matrix.
Crucial elements of an effective Demand Planning process
By combining these elements, businesses can not only predict and meet customer demands effectively but also build a demand planning process that is resilient, data-driven, and adaptable to the ever-changing business landscape.
Rayapati Srinath Reddy, Associate Director - Supply Chain Transformation, PepsiCo: Here's a distilled and refined perspective on the crucial elements of an effective demand planning process:
Data Mastery: Begin with accurate historical data. And Prioritize data quality and cleanliness.
Unified Collaboration: Foster seamless communication across departments. And create a unified approach to sharing insights and strategies.
Market Savvy: Analyze market dynamics and industry trends. And stay agile and responsive to market changes.
Segmentation Precision: Segment markets strategically. And tailor forecasting methods for each segment's unique characteristics.
Advanced Forecasting: Implement cutting-edge forecasting models. And continuously refine models based on performance feedback.
Lead Time Integration: Factor in lead times for procurement and production. And evaluate and mitigate potential supply chain disruptions.
Optimized Inventory Strategies: Integrate demand planning seamlessly with inventory management. And employ techniques like safety stock for efficient stock levels.
Resilient Scenario Planning: Develop scenarios for demand fluctuations and unexpected events. And maintain adaptable plans for different outcomes.
Tech-Driven Efficiency: Utilize advanced demand planning tools. And integrate technology for smooth cross-functional coordination.
Continuous Evolution Mindset: Regularly review and enhance the demand planning process. And learn and grow from past forecasting errors.
Performance Excellence Metrics: Establish and monitor key performance indicators. And metrics should include forecast accuracy and inventory optimization.
Himanshu Maloo, VP – Supply Chain Planning, Diageo: Demand Planning process in any industry is the backbone of supply chain. It has implications in every element of supply chain and if managed effectively can help company to create edge over competition. Crucial elements of Demand Planning process include adherence to planning drumbeat (Planning calendar); accurate use of demand history; using right tool and methods for statistical forecasting; bringing in market intelligence by following right set of demand drivers; aligning the demand plans with Marketing and Sales strategy; deploying demand sensing for even planning (e.g. big sale days, festival impact, etc.); building demand analytics for right decision making; and finally, aligning the numbers with all stakeholders.
Hanuman Swami, Global Planning & Fulfillment Manager, ABB: Synchronization between sales and marketing team, suppliers, stake holders are the vital ones for effective demand planning. Ignorance shouldn’t be there for external and internal trends. External trends usually influence businesses even more intensively than internal ones. Various external factors can impact the ability of a business or investment to achieve its strategic goals and objectives. These external factors might include competition, socio-cultural, legal, technological changes, economy, and political environment. Since a slight change by either increase or decrease in demand has a corresponding effect on revenues and profits, it is crucial for any business to improve forecasts and increase planning accuracy.
Yogesh Punde, Senior Industry Principal, Kinaxis: Supply chains are global with long lead times; however, customer demand remains volatile. Therefore, crucial elements of effective demand planning process include:
Forecasting to give desired level of visibility to suppliers. There are multiple demand signals from sales, marketing, distributors, and customers. These signals need to be reconciled to create bottom-up and top-down forecasts.
Advanced statistical forecasting techniques for configured item and option forecasting
As part of Sales & Operations Planning (S&OP), able to provide consensus Demand Plan
Aggregation and disaggregation of demand plan across different hierarchy levels
New product introduction incorporated with demand planning – ramp ups and ramp downs.
Guru Ananthanarayanan, Country Head – India, Blue Yonder: Blue Yonder highlights the following crucial components of a “multi-dimensional approach”:
Data integration: Integrating social media, weather, sales, and other external and internal data sources to provide comprehensive insights.
Machine Learning & AI: Using sophisticated algorithms to identify anomalies and forecast data accurately.
Scenario Planning: Modeling different scenarios to evaluate possibilities and hazards.
Collaboration & Consensus: Promoting buy-in and communication across departments (finance, marketing, sales, and so on).
Continuous Improvement: Monitoring and modifying the procedure on a regular basis in accordance with feedback and real-time data.
Rahul Vishwakarma, Co-Founder & CEO, Crest: The crucial elements of an effective Demand planning process are Appropriate product history; Internal trends; External trends; Events and promotions; Bottoms up Data Collection to enable Demand Sensing.
Loknath Rao, Managing Partner, The Management Technician: Companies must first need to understand the objective of demand planning. Is it for planning better distribution planning (deployment of stocks in regional warehouses) or long-term production planning or better shelf availability at stores? They shouldn’t rely too much on the past sales data and have forward-looking data too. E.g. Market Research, Industry reports. Demand Elasticity. Demand planners should be able to calibrate the demand forecast with human input too, e.g. short-term sales targets, marketing forecasts at product line or brand level. Companies much have a process in place to capture everyone’s view on forecast. Demand Planner is just a moderator not the only decision maker. Besides, there are a few relevant questions that must be addressed, such as:
Marketing Vs Operations – Where does the Demand Planning function sit? My view is it should be marketing. They generate demand. They are closer to customers.
Centralized Vs Decentralized – Are demand planners aligned to markets or line of products or countries? Decentralized demand planning organizations are more effective.
Choice of Software – You can forecast with excel too If you have access to the models. Do not be so obsessed with your forecasting software. They all have more or less the same set of time series and regression models
Leverage the modern algorithms to classify, segment and cluster products for leveraging the power of aggregation beyond ABC/XYZ/VED. Make it more dynamic.
Data Pipelines – Know the demand at point of sale. Leverage technology to record demand instantly. Do not make demand planning a ‘Monthly’ or weekly affair.
Understand that bad demand plans should not compromise the customer delivery service levels. You can always compensate for a bad demand plan with a more responsive supply plan(ners).
Know that whatever may be the accuracy of your forecast, you have a limited capacity and that too many SKUs, deplete capacity and utilization of capacity (e.g. machine output and human output) way sooner than you think.
Factors impacting Demand Forecasting
Effective demand forecasting integrates accurate data, market awareness, and adaptability to a dynamic landscape, treating these factors not as isolated components but as interconnected elements shaping a comprehensive forecasting strategy.
Rayapati Srinath Reddy: These are some of the most important factors that impact demand forecasting…
Accurate historical data is paramount: Data integrity is the bedrock; it determines the reliability of any forecast.
Market trends shape future demand: A keen awareness of market dynamics ensures forecasts align with consumer preferences.
Economic shifts influence consumer spending: Economic indicators act as critical signals for anticipating changes in demand.
Seasonal changes impact buying behaviors: Specialized models, attuned to each season, ensure accurate predictions during peak times.
A product's lifecycle stage affects demand: Strategic adjustments to forecasts based on lifecycle stages optimize planning.
Some industries exhibit cyclical patterns: Recognizing and adjusting for cyclical trends enhances long-term forecast accuracy.
Cultural shifts influence consumer behavior: Social awareness aids in adapting forecasts to evolving consumer values.
Historical Sales Data: A baseline is provided by trends and seasonality patterns.
Promotions & Marketing Campaigns: Plan for demand spikes driven by marketing campaigns.
Economic Indicators: Consumer confidence, unemployment, and inflation are all having an impact on purchasing power.
The competition: Adjustments to prices or the launch of new products might have an impact on demand.
External Events: Demand could be influenced by social media trends, weather, and natural disasters.
Rahul Vishwakarma: According to me, there are two important factors that need to be looked upon. These include internal and external factors. Internal Factors encompass sales and marketing strategies; pricing strategies; product life cycle; new product introductions; inventory levels; production capacity; and quality control. External factors include economic conditions; seasonality; competitor actions; technology and innovation; regulatory changes; social and cultural trends; natural disasters and external shocks; and global events. Businesses that take a holistic approach, considering a broad range of influencing elements, are better equipped to adapt their strategies and operations to meet changing demand conditions. Advanced analytics and machine learning help in processing large datasets and identifying patterns to improve the accuracy of demand forecasts in the face of these dynamic factors.
Challenges of Demand Forecasting and strategies to overcome them
What people want today might not be what they want tomorrow, especially when it comes to products and services that are highly volatile. Forecasting those changes is complex and can result in unreliable demand predictions.
Hanuman Swami: External factors include but are not limited to weather, competition and events, current macroeconomic conditions, social media buzz around products or services and many others. Forecasting those effects manually and including them in the forecasting process is a huge challenge.
Data is not perfect in most of the businesses, this is quite challenging in demand planning and forecasting. We often struggle with long lead time. All our efforts go wrong when we don’t get supply due to long lead time. Some companies need to create forecasts for many individual items, services, and product variants. This makes demand forecasting more difficult. Demand forecasting at the SKU level is a very time-consuming and complicated process, which is why it makes sense to rely on machine learning. The final reason why demand forecasting can be difficult is that it's often hard to get everyone on-board. Demand planning is not an exact science. There will always be some uncertainty associated with it. This can make it difficult to convince people to use forecasts as a decision-making tool.
We should always take expert opinion and avoid miscommunication between different departments within the supply chain. Also, I have seen that the sales team overpower the supply chain teams, which should be avoided. According to me, data clean-up is must before proceeding for any calculations. We should be considering input from experts, who works day in and day out with business/customers. There needs to be greater synchronization between supply chain departments, as well as sales and marketing.
Yogesh Punde: The biggest challenge is demand uncertainty due to volatility in market, knowing trends and seasonality, identifying outliers, and intermittent demand patterns.
Gathering demand input from all key stakeholders – including sales, marketing, finance, operations, and customers. Demand planning processes are slow, siloed, and inefficient. It takes weeks to make critical decisions when plans don’t align with reality. When changes do happen, communication between business functions often falters
Consideration of external digital signals like weather, holidays and economic indicators and its impact on demand changes
Improving short term and long term forecast accuracy.
Collaborating with various stakeholders and arriving at consensus demand plan.
With New product introduction, understanding the cannibalization of the forecast for a product or group of products.
Strategies to overcome demand planning challenges could be the following:
Use of advanced statistical Forecasting techniques to gauge Forecast value add.
100% forecast accuracy doesn’t exist, therefore, to manage the changes and volatility in demand, many companies are using ‘Concurrent Technique’ for planning to respond simultaneously and continuously when changes arise.
Segmentation: Categorize and prioritize customer demand based on product complexity, manufacturing process, customer service needs, strategic importance. Segmentation helps decide the best forecasting and demand planning strategies. E.g. High Value low volatility items will drive towards Make-to-stock policy. The higher volatility products lead towards make-to-order design and postponement strategies.
Having a single source of truth for all planning functions including demand planning provides a platform for strong collaboration amongst all stakeholders and the team can respond quickly to market changes.
Usage of AI/ML in demand sensing, particularly for refining short term forecast is on rise.
Guru Ananthanarayanan: Companies face difficulties because of:
Data Silos: Accurate forecasting is hindered by fragmented data across systems.
Market Volatility: Demand patterns are disrupted by unanticipated circumstances such as pandemics or economic disturbances.
Short Product Lifecycles: Agile forecasting changes are required for new products and trends.
To overcome such challenges, there are well-established strategies such as…
Centralized Data Platform: Integrate data sources for an in-depth analysis.
AI-driven Forecast Models: Identify emerging trends and adapt to changing market conditions.
Real-time Visibility & Collaboration: Exchange ideas and react quickly to arrive at informed choices.
Rahul Vishwakarma: There are a lot of challenges in demand forecasting based on the size and complexity of the supply chain across industries such as adopting forecasting too soon in the product life cycle; depending heavily on external market data; not leveraging live or near-live data to correct the plans, etc. However, solving them requires collaboration and Communication; scenario planning; risk management; technology enablement; improved flexibility and adaptability; feedback loops; and continuous improvement now also facilitated by technology.
Loknath Rao: There are many challenges associated with demand planning. These are:
Gathering Data – The best proxy for customer/market demand. In most firms, the distribution demand is the easiest proxy for demand.
Understanding Product Life Cycle Stage – New Vs Not so new Vs Regular SKUs – Planners are often blinded here. They over-average at times. Sometimes they are not even aware the new products demand plan should have been in place three months ago.
Understanding that not all demand is for anonymous customers. Some customer segments are more important than others. So, you need to Plan demand for your key accounts separately from the ‘rest’.
Not able to agree on a calendar of Demand-Supply review meetings, esp. if demand forecast directly needs to drive production planning early.
Not having a clear understanding of lead times results in wrong ideas like ‘Frozen Horizon’. If you can change the forecast, you must. In most companies, the planners on the supply side are not happy with demand plan changes but most often there isn’t really a reason to panic. Both are servicing customers. Not penalizing each other.
Tech play in Demand Planning
Technology plays a crucial role in demand planning by providing businesses with the tools and insights needed to anticipate customer needs, optimize inventory levels, and enhance overall operational efficiency.
Rayapati Srinath Reddy: By combining insights from AI with the wisdom of experienced demand planners, businesses can unlock a level of foresight, agility, and responsiveness that was once unimaginable.
Beyond Accuracy: Embracing the Uncertainty: Think of technology as a seer, not just a calculator. While accurate forecasts are vital, traditional methods often crumble under the weight of unpredictable markets and evolving customer behavior. This is where AI shines. Machine learning algorithms can uncover hidden patterns in diverse data, including social media sentiment, news trends, and weather forecasts, painting a richer picture of future demand. Imagine predicting a surge in sunscreen sales due to an unexpected heatwave - that's the power of technology transcending mere numbers.
From Prediction to Prescription: Demand planning isn't just about knowing what's coming; it's about being ready for it. Technology goes beyond forecasting by offering prescriptive recommendations. AI can suggest production adjustments, inventory shifts, and even dynamic pricing strategies based on anticipated demand fluctuations. This proactive approach allows businesses to out-maneuver uncertainty and capitalize on emerging opportunities.
The Agile Advantage: In today's fast-paced world, static plans are relics of the past. Technology injects agility into demand planning. Real-time dashboards provide transparent visibility into demand and inventory levels, enabling lightning-fast adjustments to the changing conditions. Imagine identifying a stockout threat hours before it happens and triggering automated replenishment - that's the agility technology brings to the table.
Collaboration Symphony: Demand planning isn't a solo act. It's a symphony of departments, all playing their instruments in harmony. Technology acts as the conductor, ensuring everyone operates from the same score. Cloud-based platforms create a single source of truth for data, fostering seamless collaboration between sales, marketing, operations, and even suppliers. This synchronized approach optimizes resource allocation and minimizes friction, turning silos into a unified orchestra.
Himanshu Maloo: In today’s context, technology plays a crucial role in demand planning. While use of the right tool to drive end to end process is important, use of AI and ML has been increasing in demand planning process. AI bases tools can help in doing multivariate analysis with inputs from various demand drivers like festival planning, promotion impact, seasonal variations, pricing, and competitive landscape, etc. There are several tools available in the market depending on the level of complexity to manage, scope of work, budget, level of automation etc. Several tools now a days have inbuilt forecasting models with AI/ML.
Yogesh Punde: In many industries, demand planning processes are manual, siloed, and inefficient. Digital supply chain has a crucial role in demand planning. Latest tools as the notion of a digital supply chain, becomes the norm, not the exception. Organizations need technology that provides better information, faster analytics, and automation to keep pace. Technology based on the concurrent technique of planning provides specific capabilities for effective demand planning plus the ability to connect data, process, and people.
Guru Ananthanarayanan: Technology is essential for:
Automating manual tasks enables planners to focus on strategic analysis.
Identifying hidden patterns: Machine learning uncovers information that human analysts are unable to see.
Maintaining stock levels to avoid shortages or overstocking is part of optimizing inventory.
Boosting forecast accuracy: Models driven by AI are continuously evolving and adapting.
Rahul Vishwakarma: One key aspect is the use of Enterprise Resource Planning (ERP) systems, which integrate various business processes and data into a unified platform. ERP systems enable real-time visibility into sales, inventory, and production data, allowing for more accurate demand forecasts. Order Management Systems (OMS) are essential for streamlining order processing, tracking, and fulfilment. OMS helps businesses manage customer orders efficiently, reducing lead times and ensuring timely deliveries. Warehouse Management Systems (WMS) play a pivotal role in demand planning by optimizing warehouse operations, improving inventory accuracy, and reducing order fulfilment errors.
Additionally, advanced analytics and machine learning technologies are increasingly employed for predictive modelling and forecasting. These technologies analyze historical data, market trends, and external factors to predict future demand more accurately. Collaborative Planning, Forecasting, and Replenishment (CPFR) tools facilitate communication and collaboration between trading partners, ensuring a synchronized approach to demand planning.
In our tech stack, we deploy robust ERP systems such as SAP or Oracle, OMS solutions like IBM Sterling Order Management, and WMS platforms such as Manhattan Associates. Advanced analytics tools like Tableau or IBM Cognos enhance data-driven decision-making, while machine learning algorithms are employed for predictive analytics. This integrated tech stack ensures a comprehensive and efficient approach to demand planning, allowing businesses to respond swiftly to changing market dynamics and customer demands.
Loknath Rao: Technology for demand forecasting hasn’t changed much. The basic time series and regression models were available even 20 years ago in most software. What has changed now is the data storage capacity and exploration of new data sources to refine your forecast. Calibrate your forecast. These days companies deploy apps to gather inputs from field sales. They do not need to ‘login’ to the ‘core’ application to ‘Enter’ their view of demand. Instead, they can simply typewrite it on the app that will then sync up with the demand planning books.
So, a combination of off the shelf software for demand planning + Apps for collaborative planning + Access to relevant Data on Big Data Servers or third-party data providers forms the basis of needed ‘technology’ for forecasting. Additionally, the implementation of Machine Learning based Algorithms like LSTM, Neural Networks, SVM helps BOTH with prediction of time series as well as tools to ‘Group’ your products better for forecasting better. E.g. A, M, W and Z ‘belong together’ in the sense that there is a strong association between sales, though they do not necessarily belong to the same brands or product lines.
Demand Planning for customer centric supply chains
Customer-centric supply chains are not just about reacting to demand; they are about proactively anticipating it and shaping it to your customers’ desires. By leveraging the power of demand planning, you can create a supply chain that delights customers, builds brand loyalty, and drives sustainable growth.
Rayapati Srinath Reddy: Demand planning plays a crucial role in creating customer-centric supply chains by putting the customer at the heart of every decision. Here's how:
Anticipating Customer Needs:
Accurate forecasting: Precise demand predictions ensure you have the right products and quantities available when customers need them. No more stockouts or overstocking, leading to happier customers and fewer lost sales.
Responding to trends: By analysing data and identifying emerging trends, you can adapt your product offerings and production plans to meet changing customer preferences. This proactive approach keeps you ahead of the curve and delivers what customers want before they even ask.
Enhancing Customer Experience:
Shorter lead times: Accurate demand planning allows for faster production and delivery, reducing lead times and getting products into customers' hands quicker. This translates to increased satisfaction and loyalty.
Improved product availability: By minimizing stockouts, you ensure customers can always find what they need, creating a seamless and frustration-free experience.
Personalized offerings: By understanding individual customer preferences and buying patterns, you can tailor your product offerings and promotions to their specific needs. This personalized touch builds deeper connections and fosters long-term loyalty.
Optimizing Supply Chain Efficiency:
Reduced costs: Accurate demand planning minimizes unnecessary inventory holding costs and waste, leading to increased profitability. This allows you to offer competitive prices and better value to your customers.
Improved resource allocation: By aligning production with actual demand, you can optimize resource allocation and reduce waste. This leads to a more sustainable and environmentally friendly supply chain.
Enhanced collaboration: Demand planning fosters collaboration between different departments, like sales, marketing, and operations, ensuring everyone is working towards the same customer-centric goals. This seamless alignment leads to greater responsiveness and agility.
Here are some additional ways demand planning can be used to create customer-centric supply chains:
Implementing customer feedback loops: Gather customer feedback on product offerings, delivery times, and overall experience to inform future demand planning decisions.
Utilizing customer segmentation: Tailor your demand planning strategies to different customer segments, catering to their unique needs and preferences.
Investing in technology: Utilize advanced data analytics and forecasting tools to gain deeper customer insights and improve demand planning accuracy.
By embracing these strategies and leveraging the power of demand planning, companies can transform their supply chains from a cost center into a customer-centric engine of growth and success.
Himanshu Maloo: By understanding demand patterns and ability to forecast accurately, demand planning can help in capturing the customer requirement more accurately and hence can help in meeting the demand.
Guru Ananthanarayanan: Precise demand forecasting allows businesses to:
Anticipate the needs of the customer: Make the right goods in the right quantities at the right times.
Minimize stockouts and overstocking: Boost customer satisfaction and minimize inventory costs.
React to trends and promotions: Fulfill spikes in customer demand driven by marketing campaigns.
Personalize offerings: Customize goods and services to meet the tastes of specific customers.
Rahul Vishwakarma: Demand planning plays a pivotal role in creating customer-centric supply chains by aligning production, inventory, and distribution with customer demand. Here are several ways in which demand planning contributes to the development of customer-centric supply chains, such as customer Satisfaction, responsive inventory management; order fulfilment efficiency; reduced lead times; product availability and variety; dynamic supply chain adjustments; personalized customer experiences; optimized product launches; cost optimization; enhanced communication and collaboration; and continuous improvement. Businesses that prioritize accurate demand forecasting and align their operations with customer demand are better positioned to build strong, lasting relationships with their customers.
Loknath Rao: Supply Chains ought to be customer centric. It always was but in the olden days when technology and communications were not mature, the firms depended on their distributors for the demand intelligence. Now with creative use of software, systems and data integration, you can build solutions to react to demand signals from both internal and external customers. At least in theory the use of the word Network in Supply Network intended. Because in a Network even your vendor can be your customer and your subcontractor can be your full-time vendor. With more mindful contracts between entities in a supply network, you can build customer centric supply chain solutions and processes. You need to talk to a consultant who is creative with solution ideas as the same software if implemented poorly can end up as a million dollar ‘typewriter’.
Demand Planning best practices
By implementing these best practices and continuously learning and adapting, companies can create a more accurate, efficient, and customer-centric demand planning system. This will lead to improved profitability, increased customer satisfaction, and a more sustainable supply chain.
Rayapati Srinath Reddy: Here are some key demand planning best practices companies can deploy:
Embrace Data-Driven Insights:
Go beyond gut feeling: Don't rely on intuition alone. Utilize data from sales, marketing, customer reviews, and external sources like weather trends to build accurate forecasts.
Invest in analytics tools: Leverage tools like Tableau or Power BI to visualize data and identify patterns, trends, and correlations.
Real-time monitoring: Implement real-time dashboards to track inventory levels, customer behaviour, and market changes, enabling quick adjustments to forecasts.
Example: FMCG conglomerates used machine learning to analyze social media data and predict surges in demand for specific beauty products during seasonal events. This allowed them to optimize production and inventory levels, avoiding stockouts and maximizing sales.
Foster Collaboration and Communication:
Break down silos: Ensure seamless communication and collaboration between sales, marketing, operations, and finance. Share forecasts, analyse trends together, and make informed decisions as a team.
Integrate with suppliers: Build strong partnerships with suppliers and share demand forecasts to ensure timely deliveries and avoid production disruptions.
Leverage technology: Utilize collaboration platforms like Slack or Microsoft Teams to facilitate communication and knowledge sharing across departments.
EXAMPLE: Automobile giant implemented a collaborative demand planning system that allowed all stakeholders across the supply chain to access real-time data and insights. This led to a ~15% reduction in inventory carrying costs and improved responsiveness to market changes.
Embrace Agility and Scenario Planning:
Prepare for the unexpected: Don't rely solely on a single forecast. Develop contingency plans for different scenarios, such as weather disruptions, competitor actions, or economic fluctuations.
Utilize scenario planning tools: Use tools that simulate different scenarios and their impact on demand and supply. This allows you to be prepared for any eventuality and adapt quickly.
Regularly review and update: Don't set your forecasts in stone. Regularly review and update them based on new data and market conditions.
EXAMPLE: Sports good giant used scenario planning to anticipate the potential impact of the 2022 Beijing Olympics on demand for athletic apparel. This allowed them to adjust production and distribution plans, resulting in a 20% increase in sales during the event.
Invest in Sustainability:
Optimize transportation routes: Use technology to optimize delivery routes and reduce carbon footprint.
Implement sustainable sourcing practices: Source materials from local suppliers and prioritize environmentally friendly packaging.
Reduce waste and overproduction: Improve inventory management and production planning to minimize waste and unnecessary resource consumption.
EXAMPLE: Streetwear giant implemented a closed-loop system for its fleece jackets, allowing customers to return worn-out jackets for recycling into new products. This reduced waste and improved brand loyalty among environmentally conscious consumers.
Continuously Learn and Adapt:
Stay up to date with industry trends: Regularly attend conferences, workshops, and webinars to learn about new technologies and best practices in demand planning.
Invest in employee training: Train your employees on new forecasting techniques, data analysis tools, and collaborative communication skills.
Measure and track results: Regularly monitor the performance of your demand planning processes and identify areas for improvement.
These are just a few examples, and the specific best practices will vary depending on the company's industry, size, and unique needs. However, by focusing on data-driven insights, collaboration, agility, sustainability, and continuous improvement, companies can build a demand planning system that helps them thrive in today's dynamic business environment.
Himanshu Maloo: Some of the best practices in demand planning include:
Deploying Integrated business planning – which is about extending demand forecasting to include Risk & Opportunity, New product introduction planning, P&L impact and demand-supply simulation.
Multi-horizon forecasting – from immediate demand to long-term demand forecasting
Even Planning - Assessing impact of various events on demand forecasting e.g. impact of New year, festivals, rain, weather change, financial budgets, e-commerce discount days, etc.
Point of consumption demand capturing – moving from primary sales data to actual consumption data to capture the demand in real time.
AI bases Impact assessment / forecasting based on demand of complementary / adjacent products.
AI bases Demand forecasting based on customer reviews, etc.
Guru Ananthanarayanan: Regularly evaluating and updating forecast models; integrating data from other sources; and holding collaborative planning sessions are some of best practices. For instance, Supervalu, a supermarket store, reduced missed sales by 15% by using Blue Yonder's technology to achieve 98% prediction accuracy. Mahindra, a key player in the auto industry, increased revenue by 10% by utilizing Blue Yonder solutions to optimize inventory levels.
Skillsets for Demand Planners
The specific skillset required for a demand planner can vary depending on the company size, industry, and job complexity. However, the core skills mentioned below are essential for any successful demand planner.
Rayapati Srinath Reddy: Demand planners need a diverse skillset to navigate the complex world of forecasting and supply chain optimization. Here are some key areas:
Data analysis: Familiarity with statistical tools, data visualization software, and the ability to interpret complex data sets is crucial.
Forecasting techniques: Understanding different forecasting models (ARIMA, exponential smoothing, etc.) and choosing the right one for specific situations is essential.
Inventory management: Knowledge of inventory control methods, safety stock calculations, and optimization techniques is important for efficient stock levels.
Software proficiency: Expertise in demand planning software, ERP systems, and other relevant tools is vital for day-to-day operations.
Problem-solving: Identifying demand patterns, anomalies, and potential disruptions, and then developing solutions to mitigate them.
Critical thinking: Evaluating data objectively, questioning assumptions, and making informed decisions based on evidence.
Attention to detail: Accuracy and precision are paramount in demand planning, as even small errors can have significant consequences.
Communication and Collaboration Skills:
Effective communication: Clearly communicating forecasts, insights, and recommendations to stakeholders across different departments.
Collaboration: Working effectively with sales, marketing, operations, and other teams to ensure alignment and achieve common goals.
Negotiation: Negotiating with suppliers and vendors to secure favorable terms and ensure timely deliveries.
Business acumen: Understanding the overall business strategy and how demand planning fits into the bigger picture.
Industry knowledge: Familiarity with the specific trends and challenges of your industry is highly beneficial.
Adaptability and flexibility: The ability to adjust to changing market conditions and new technologies is crucial in a dynamic world.
Here are some additional tips for developing your demand planning skillset:
Take online courses or workshops: Many online resources offer training in demand planning and related skills.
Get involved in professional organizations: Joining professional organizations can provide networking opportunities and access to valuable resources.
Read industry publications and blogs: Staying up-to-date on the latest trends and technologies in demand planning is essential.
Volunteer or intern: Gaining practical experience through volunteer work or internships is a great way to develop your skills.
By continuously honing your skills and staying ahead of the curve, you can become an asset in any organization and play a key role in optimizing your company's supply chain.
Himanshu Maloo: Three important skillsets each demand planner must have are Strong data analytics and technology adoption; Ability for demand sensing by identifying and understanding demand drivers; and Commercial acumen to understand business impact and align commercial stakeholders.
Hanuman Swami: A demand planner must be well versed with business and supply chain. Mare coding and ML expertise may not work for all the business. A demand planner must visit all the departments to see how those functions, this would help to be better in demand planning.
Yogesh Punde: Demand Planning for the future requires planners to communicate with the stakeholder groups to enrich the forecast (the most important stakeholder being the end consumer). They must be ready to respond to any eventuality. Demand uncertainty will require more flexibility and speed from the planners. They should have base case, best case, worst case, etc., scenarios ready to be put into action. Companies must allow segmentation strategy to help prioritize time and effort spent on different activities. While planners have been traditionally well versed with MS Excel, being future ready will require adopting new technology platforms.
Guru Ananthanarayanan: According to me, these skillsets hold immense importance for demand planners…
Analytical Skills: Data interpretation, trend identification, and statistical model expansion
Collaboration and communication: Working with cross-functional teams and sharing insights in an effective manner.
Critical thinking and problem-solving skills: Adapting to unforeseen obstacles and coming up with innovative solutions.
Technological Acumen: Knowing how to use AI tools and demand planning software.
Rahul Vishwakarma: Demand planners who possess a well-rounded combination of these skills are better equipped to navigate the complexities of demand forecasting, contribute to effective supply chain management, and add value to their organizations. Continuous learning and staying abreast of advancements in technology and industry trends are also important for staying competitive in this field. Some of the critical skillsets include:
Forecasting Software: Familiarity with demand forecasting tools and software applications, such as statistical modelling software, Enterprise Resource Planning (ERP) systems, and advanced analytics platforms.
Excel and Spreadsheets: Proficiency in spreadsheet software, particularly Microsoft Excel, for data manipulation, analysis, and visualization.
Industry Knowledge: Understanding of the Industry: Knowledge of the specific industry in which the organization operates, including market dynamics, seasonality, and factors influencing demand.
Understanding Customer Behavior: Ability to understand and anticipate customer behavior, preferences, and buying patterns to create more accurate demand forecasts.
Adaptability to New Technologies: Willingness to learn and adapt to new technologies, including advanced analytics, machine learning, and demand planning software.
Business Acumen: Understanding of Business Operations: Insight into broader business operations, including knowledge of supply chain processes, inventory management, and production planning.
Communication Skills: Effective Communication & Collaboration Analytical Skills, such as Statistical Analysis & Data Interpretation Problem-Solving Abilities, such as Critical Thinking; Adaptability; Precision and attention to detail are crucial for creating reliable demand forecasts and avoiding errors in data analysis; Time Management
Global Understanding: For organizations with a global presence, an understanding of different cultures and markets to account for regional variations in demand.
Loknath Rao: They must possess the ability to understand data and metrics. They should be able to view the data from different dimensions. Knowledge of basic ideas of descriptive and inferential statistics is important. The understanding that standard deviation can play in many ways – both good and bad. Apart from that, the planners must understand the markets and the larger Industry and competition in which the firm is operating. The ecosystem. E.g. if you are a demand planner for an alcohol manufacturing company, understand that your bottles and labels suppliers have only a fixed capacity and you cannot change your order quantity at a fag end. You need creative workarounds here, which involve buying additional capacity way in advance after comprehending the demand pattern.
Shaping the future of supply chain in the years to come
Demand planning isn’t a rigid set of spells; it’s a dynamic art form, perpetually evolving with technology and consumer preferences. Experiment, collaborate, and unleash your inner sorcerer. By wielding data as your wand and foresight as your shield, you can redefine the future of your supply chain, transforming it from a cumbersome entity into a streamlined, agile, and sustainable marvel.
Rayapati Srinath Reddy: Say farewell to antiquated spreadsheets and crystal ball gazing; demand planning is undergoing a metamorphosis, evolving into the apprentice of supply chain sorcery, using data and foresight to reshape the logistics landscape. Here's how it unfolds:
Transitioning from Murky Forecasts to Profound Precision: Bid farewell to peering through misty forecasting models. Artificial intelligence (AI) and machine learning now take on the role of oracles, intricately combining data, consumer insights, and even meteorological data to predict demand with remarkable precision. Envision inventories materializing precisely when needed, shelves brimming with sought-after products before customers click "purchase," and promotions perfectly timed to spark viral trends. This newfound clairvoyance morphs your supply chain from a sluggish entity into an agile cheetah, foreseeing and seizing opportunities.
Shifting from Segregated Strategies to Agile Alchemy: The era of silos is behind us. Demand planning has transformed into a collaborative crucible where sales, marketing, and operations collaborate, extracting potent insights from data cauldrons. Real-time dashboards act as crystal-clear mirrors reflecting inventory levels, customer sentiment, and competitors' manoeuvres. Picture it as a Jedi Council of supply chains, strategizing in harmony, predicting disruptions before they occur, and adapting like water flowing around obstacles. This united front ensures agility that paralyzes competitors, leaving them unable to respond to the ever-changing market dynamics.
Moving from Global Giants to Hyperlocal Heroes: The era of one-size-fits-all globalization is encountering a rebellion of hobbit proportions. Hyperlocal production is on the upswing, with agile micro-factories sprouting up near consumers. Picture fresh dosa batter being churned in Chennai for breakfast and khadi scarves woven in Jaipur for evening strolls. This localized enchantment reduces lead times, strengthens communities, and caters to diverse regional preferences. Envision a network of these micro-factories, each a unique strand in the intricate tapestry of your supply chain, buzzing with local flair and efficiency.
Transitioning from Cost-Cutting Wizards to Sustainability Magicians: Consumers are demanding eco-friendly products, and businesses are heeding the call. Demand planning is now infused with sustainability magic, optimizing routes, minimizing waste, and sourcing locally. Imagine solar-powered warehouses, electric delivery fleets, and packaging that vanishes like smoke and mirrors. This green alchemy not only diminishes your carbon footprint but also casts a potent spell on environmentally conscious customers, enhancing brand loyalty and sales.
Himanshu Maloo: The most important and critical factor to drive supply chain agility and reliability is to have the right demand planning process in place. With reducing lead times to fulfill customer orders, demand planning process should be flexible enough to capture very short-term demand factors which can have immediate implication of customer demand. With the evolution of technology, demand forecasting is moving towards the consumption point. This will help supply chains to be more responsive, reducing the bullwhip effect.
Yogesh Punde: Accurate demand planning (Forecast) results in
Reduced costs due to manufacturing, transportation, temporary storage, product scrap/ obsolescence, manpower.
Reduced inventory levels mean reduced carrying costs.
Improved customer service levels the right product at the right place at the right time (and in the right quantity).
Demand planning assisted by AI, automated machine learning, analytics and heuristics will help detect shifting data patterns, predict changes in demand and automatically update and optimize forecasts. Day by day, supply chains are becoming complex, and handles huge data therefore with automated data transformation, ML model selection and manage by exception workflows, planners will be able to save time and focus only on exceptions. ML generated results have explainable algorithms to know which data features affect demand predictions and will help in better understanding of business.
Rahul Vishwakarma: Demand planning is poised to be a driving force in reshaping supply chains for the future. The emphasis on the following will guide the evolution of supply chain strategies, making them more resilient, efficient, and adaptable to the dynamic nature of global markets. These aspect include agile and responsive supply chains; real-time decision-making; end-to-end visibility; collaborative supply chain ecosystems; customer-centric supply chains; predictive and prescriptive analytics; sustainability integration; demand-driven supply chains; AI-enhanced decision support; digital twins for supply chain optimization; adaptive supply chain networks; and establishing a continuous improvement culture.
Loknath Rao: By virtue of data availability, data storage costs, machine learning models availability, forecasting can be a real time activity. Systems can auto correct the forecast so that the supply Infra can respond faster. Advanced demand sensing models (e.g. ARIMA and Kalman Filters) are now able to predict extremely short-term demand (e.g. next 48 hours).
Demand Planning trends to watch out for…
Rayapati Srinath Reddy: The world of demand planning is constantly evolving, and the next few years are sure to bring even more exciting and innovative trends. Here are a few we can expect to see:
Hyper-personalization: Demand planning will move beyond simple segmentation to hyper-personalization, tailoring forecasts and inventory to individual customer needs and preferences. This will involve leveraging AI and machine learning to analyze vast amounts of data on customer behavior, purchase history, and even social media activity. Imagine a world where a clothing store can predict your next clothing purchase based on your online browsing habits and automatically stock the item in your local store!!
Real-time forecasting: Traditional forecasting methods based on historical data will be augmented by real-time data feeds from various sources. This includes things like weather patterns, social media trends, and even traffic data. By analyzing these real-time signals, businesses can make more accurate and agile demand predictions, adjusting inventory levels and production schedules on the fly.
The rise of the "demand chain": The traditional linear supply chain will be replaced by a more dynamic and interconnected "demand chain." This chain will blur the lines between planning, sourcing, production, and fulfilment, with all stages working together in real-time to respond to changing demand patterns. Think of it as a flexible ecosystem that can adapt to any situation, ensuring the right product is in the right place at the right time.
The integration of sustainability: Demand planning will become increasingly integrated with sustainability goals. Businesses will use forecasting tools to optimize resource usage, reduce waste, and minimize the environmental impact of their operations. This could involve things like predicting the demand for eco-friendly products, optimizing packaging materials, and even forecasting the availability of renewable resources.
The rise of the "citizen planner": With the increasing availability of user-friendly data visualization tools and AI-powered forecasting algorithms, even non-experts will be able to participate in demand planning. This democratization of data will lead to a more collaborative and informed planning process, drawing insights from all levels of the organization.
These are just a few of the exciting trends that are shaping the future of demand planning. As technology continues to evolve and businesses become more data-driven, we can expect even more innovative ways to predict and meet customer needs.
Hanuman Swami: I believe these are some of the upcoming trends in demand planning…
Sustainability & Ethical Sourcing: The main concerns would be ethical sourcing and focus on sustainability since it is going to be the next focus area primarily for all businesses. Supply chains are being compelled to adjust their strategies to accommodate this shift, ensuring that their offerings align with evolving ethical preferences and sustainable principles to conserve the planet's resources and ensure depletion at minimum. Since then, only we can stop the extinction of the planetary resources which are essential for keeping the balance of the planet's temperatures for sustainable life.
Software integration with Intelligence: Currently we manage inventory on spreadsheets, advanced algorithms will quickly analyze historical data, market trends, and customer behavior's to improve demand forecasting accuracy.
Demand Sensing: We use real-time data to sense future demand patterns. Demand sensing improves agility and responsiveness, reduces stock outs, optimizes inventory, and ultimately enhances customer satisfaction by aligning their operations with actual market demand.
Following Market trends: Following market trends enable businesses to align their production, inventory, and supply chain strategies with evolving customer preferences. This approach optimizes resource allocation, reduces excess inventory, and minimizes stockouts. By analyzing trends, businesses can anticipate shifts in demand, adjust their forecasts, and introduce new products more effectively. This approach enhances overall operational efficiency and customer satisfaction. Relying solely on following and reacting to historical trends is slow and places you at a disadvantage compared to your competitors. The key is to not only follow trends but to also anticipate changes in demand and adjust before your competition.
Loknath Rao: For me, these are going to be some of the most defining trends in demand planning…
Dynamic grouping, classification, and clustering algorithms to identify the right levels of aggregation to forecast.
Use of Short-term forecasting models.
Real Time Forecasting. Always on forecasting
Use of market intelligence to adjust forecasts and hence supply plans (e.g. why make it when I can’t make money by selling it)
Development of new forecasting models specific to a particular industry / class of products by using data lakes that store data relevant to that Industry.
Data Federation technologies that give you data on demand. Perhaps this will be a completely different set of people outside of the organization.