Assignment Help Zone Assignment Help How to Achieve Excellence in Demand Planning?

How to Achieve Excellence in Demand Planning?

 

Estimating the future demand is among the most valuable activities in any business or organization. The plan of this demand impacts deeply throughout the business, starting from marketing and sales to distribution and manufacturing. A perfect yet intelligently made plan can help in balancing the inventory levels with the expenses and let the business proceed towards absolute cash flow and appreciate-able customer service. However, the facts remain where it was that the science and art of predicting the future demand is mostly misunderstood and results in lacking enough attention that it demands.

Best-in-Class firms improved forecast accuracy by 20 points when compared to other organizations, resulting in a 32 percent more accurate demand plan, according to a Group research “Demand Planning: Renewed Focus for Companies to Drive S&OP and Operational Improvements.” According to the same survey, Best-in-Class organisations are far more likely to invest in modern technology to aid in the development and management of demand strategies.

There is no doubt that technology plays a vital part in maturing the company’s supply chain. And, understanding the importance of technology and its usage can provide a great impact on the goodwill of your business. Since I have worked with many of today’s well-known organizations related to supply chain, I have concluded that there are 4 most important yet critical components that help in sustaining a company or business’s growing demand.

Without more chat, let’s proceed further:

Go Further Spreadsheets

Considering how essential is it to predict the profitableness and growth of a business, it is shocking that so many organisations are still using the old ERP systems and spreadsheets to get the forecast. According to a survey conducted by Logility and APICS of more than eight hundred and fifty organizations, 47 percent of participants said that they utilize a spreadsheet to manage the needs of future demand planning. Other 37 percent acknowledged that enterprise resource planning (ERP) system is their go-to resource. But, the common thing in these two groups is that they were not happy with the results.

While supply chain forecasts have long been done with little more than a “spreadsheet and a hunch,” Best-in-Class planning organisations strive for a multi-layered approach that uses a variety of statistical models in a non-biased manner to appreciate the several aspects that influence product demand in the marketplace through time

While looking at the procedure of planning the future demand, we can certainly understand how difficult is it to predict the demand growth of a single product. Even the most sophisticated spreadsheets are incapable of determining how a planner should adjust from the debut of a new product to the end of its life cycle.

From idea generation to concept development, business analysis, market testing, implementation, and finally commercialization, the process of developing and bringing a new product to market may be a long and arduous path. Quantitative forecasting algorithms based on historical data are only applicable to products with a demand history. More qualitative models, which incorporate subjective inputs like product features, market knowledge, and experience, may provide the best available direction for new goods.

If you are a finance student, you may know, as the life cycle of a product unfolds, it becomes critical to compare the real demand with the predicted future demand, students of finance have fear knowledge in this regard, although for their convenience they redeem online finance assignment help. Thanks to advanced technology, there are statistical algorithms that can be used to conveniently determine how much demand has deviated from the prediction. This prediction accuracy can be compared, and a different profile is employed when it better matches the actual demand signal.

For each and every product, even the ones that have short life cycle items, it is certain that no method can be a one-stop solution for future demand prediction. Different methods are needed to fulfil the demand requirements of products which they come across throughout their lifetime. Most of the time ERP systems and spreadsheets fail to execute many functions that are required such as select, model, and generate predictions. However, more than 80% of organisations still use these strategies to drive their company, creating a significant potential for Best-in-Class companies to gain a competitive advantage.

High-level Demand Forecasts Should Be Disaggregated to Tactical Levels

The disaggregation and aggregation of demand are essentials of creating the best possible prediction of future demands at all levels of roughness needed to combine strategic and tactical plans. According to Gartner, “The most challenging thing is to maintain the balance between bottom-up collaborative approaches and top-down statistical modelling.” Most planners are familiar with the demand aggregation hierarchy, which is a multi-layer approach in which the lower, more granular layers represent the demand for a greater number of sub-components at a specific location, while the upper levels total up demand by product family, group, area, and so on.

This hierarchy should, in practice, accommodate demand signals and input from a variety of sources, including customer forecasts, sales forecasts, management direction, and constraint-based forecasts, as well as external demand signals supplied via syndicated data and point-of-sale data.

Higher-level plans are broken down into detailed predictions related to product components such as style, color, size, sales channel, customer, region, and other factors using the hierarchy structure. It captures “how many of which kind” to be generated, stocked, and delivered for multilayer product architectures with time-phased dependent demand, such as accessories, components, consumables, and service parts.

While the hierarchy can be utilised “bottom-up” to aggregate more detailed levels up to an overall demand prediction, the estimating error of lower tiers is magnified, resulting in a greater value of error at the master forecast level.

The most accurate forecasts are typically obtained by disaggregating upper-level demand projections down to tactical levels, dividing the forecast inaccuracy inherent in the top tier into smaller mistakes at the lower levels of demand planning.

While CEOs may be more interested in an overall prediction by customer and less so in granular breakdowns, other stakeholders have different needs. The demand plan by geographical region is of relevance to distribution managers. The desire for a certain product style may be the focus of marketing teams. Component details are required for manufacturing: quantity of each size, taste, container, and so on.

Focus on High-Value Add Activities

The capacity of leading supply chain organisations to focus valuable planner resources on high-value-add activities like problem prevention, issue resolution, and optimization is one of the key differences between them and the others.

One example is using a management-by-exception approach to demand planning as it boosts planner productivity in the organisation.

A system checks for validity as actual sales data becomes available by comparing the existing demand curve to the actual demand signal. Important conditions that have diverged from established targets or expectations are highlighted via a centralised dashboard display and planner-specific real-time alerts (in-system, email, or mobile). Anomalies in sales and demand can arise at the level of SKUs, product groups, geographies, and so on. Planners will also be required to generate bespoke alerts as needed to meet the organization’s priorities and business goals.

ABC stratification is a powerful first step that is based on the common finding that for most manufacturers, 20% of SKUs drive 80% of sales, while the next 30% drives 15% to 19%, and the remaining 20% generates 5% or less. Setting up business rules that focus notifications on high-value products (the “A” items) allows more knowledge to be applied to the products that make the most difference. Departures from the forecast are flagged by management by exception, with tighter standards for A items than for B items. For the most business-critical items, system alerts to prompt the planner to take action at the first sign of a prospective variation. C item alerts can be addressed on a case-by-case basis.

Planners and other stakeholders can get a full picture of how effectively the forecasting effort is performing by creating and automatically monitoring a customized set of performance metrics. Forecast accuracy, inventory levels, service level, fill rate, and stockout percentage are all common KPIs.

Everyone stays on the same page about overall performance against unified customer service criteria by controlling one integrated set of KPIs across the organisation, from supply-side to demand-side, at every level of forecast aggregation.

Collaboration On Spotlight

Having access to what customers, partners, and internal stakeholders know can help the sales and operations planning (S&OP) team create a more accurate demand plan and provide trustworthy input. Collecting information as near to the demand signal as possible and obtaining feedback as soon as possible are the two most important factors in making effective S&OP decisions. According to Gartner research, acquiring customer demand insights has the widest disparity between importance (74 percent believe it is significant) and efficacy (44 percent think they are effective at it).

Collaborative Planning, Forecasting, and Replenishment® (CPFR®) and Vendor-Managed Inventory are two conventional strategies for enhancing forecast accuracy (VMI). There has been a drive in recent years to merge and improve the two principles into Collaborative VMI. In any case, the goal is for trading partners to share information and work together to feel demand as early as possible and respond quickly to changes in demand.

A collaborative VMI process is built on shared calendars and proactive meetings that allow trade partners to integrate and view point-of-sale data, promotional schedules, buyer and seller stocks, replenishment predictions, and new product introductions, among other things. When carried out in a trusting atmosphere, wholesalers do not respond irrationally to a retailer’s inventory level, and retailers are not caught off guard by, say, slide-in lead times. The planning function shifts its focus away from orders and toward actual consumer behaviour.

Formalized processes are required for success because they must accurately predict time-phased inventory and customer replenishment needs. Business rules trigger replenishment orders automatically, and criteria for controlling returns, setting inventory turn targets, and managing slow-moving products are developed. Relevant information should be viewed in the context of each trading partner’s business. A good collaboration facility enables planners and buyers to become proactive rather than reactive, focusing their attention on exception conditions before they become major concerns, by providing automated business warnings, event prioritisation, and a level of automatic conflict resolution.

S&OP brings marketing, sales, production, finance, sourcing, and other departments together on a regular basis to make fully informed decisions and develop a single collaborative strategy that best drives the organisation toward its business objectives. In truth, S&OP is the company’s most essential partnership activity. There is no other effort that has a greater impact on the company’s expectations, promises, costs, and profits than this one.

To link the operational plan with company financial goals, demand and supply scenarios must be weighed with cash flow targets, profit contribution, margin standards, and other criteria. Companies should be able to combine demand planning data for executive-level S&OP evaluations before delving down to a degree of depth that identifies concerns and opportunities related to smaller demand groups by sub-family, item, area, style, or other subsets.

A good demand planning platform feeds the S&OP process with the most accurate forecasting data available and enables a “one-number” plan that gives all departments a unified perspective of what’s to come. When the demand strategy is created on firm footing, supported by facts and proven methodologies rather than intuition, emotion, and even wishful thinking, internal negotiations between sales, marketing, and operations may progress with clarity and mutual understanding.

The demand plan can become the most effective weapon supporting sensible recommendations and better S&OP decisions every month by providing full visibility to inventory levels, point-of-sale data, promotional strategies, regional variances, and more.

Conclusion:

As we’ve seen, the ideal “one plan” prediction includes advanced data management technologies, disaggregating higher-level demand data, focusing on the key metrics, and working in a collaborative atmosphere. Excellence in demand planning is the foundation of any successful organisation, ensuring that all partners in your supply chain have faith in the figures and are working toward a single goal.

With time, technology is covering everything and providing ease to humanity in many different yet unique ways. For instance, students of today can redeem finance assignments help UK easily without burdening themselves.

We assume our contribution to the topic make it easier for company owner and organizations to move towards easier yet effective ways to predict future demand growth.

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