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Achieving Demand Planning Excellence

January 20, 2014 • Business Process, OPERATION, SPECIAL FEATURES, Strategic Spotlight, Supply Chain, Supply Chain Management

Demand planning excellence is the foundation for any successful business. Below, Karin Bursa argues that the demand plan is essential for ensuring all parties in your supply chain have confidence in the numbers and are working towards a common goal, and suggests that the art and science of forecasting demand is often misunderstood.

Estimating future demand is one of the most valuable activities in any organisation. The demand plan’s impact is felt throughout the business, from sales and marketing to manufacturing and distribution.  The right plan can balance inventory levels with costs and lead to higher customer service levels and positive cash flow. However, the art and science of forecasting demand is often misunderstood and lacks the attention it requires.

In the Aberdeen Group report, “Demand Planning: Renewed Focus for Companies to Drive S&OP and Operational Improvements”, Best-in-Class companies achieved a 20 point improvement in forecast accuracy when compared to other companies; translating into a 32% more accurate demand plan. The same report continues stating the same Best-in-Class companies are significantly more likely to invest in advanced technology to help them develop and manage their demand plans.

Technology is critical to advancing a company’s supply chain planning maturity. And, understanding how best to take advantage of the technology can transform your business. Working with many of today’s top supply chain organisations, I have realised there are four critical elements that support a company’s growing demand planning maturity.

 

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1. Move Beyond Spreadsheets

Considering how crucial the forecast is to the profitability of an organisation it is a wonder that so many companies still rely on spreadsheets and ERP systems to generate their forecast. According to a recent survey of more than 850 supply chain professionals conducted by APICS and Logility, 47% of respondents acknowledged they rely on spreadsheets to manage their demand planning needs. Another 37% rely on their enterprise resource planning (ERP) system. Neither of these groups were happy with their current planning process.

While supply chain forecasts have long been executed using not much more than a “spreadsheet and a hunch”, Best-in-Class planning organisations strive for a multi-layered approach that employs a variety of statistical models in an unbiased way to comprehend the many factors that influence demand for products in the marketplace over time.

When we look at the demand planning life-cycle (Figure 1) we can quickly see how complex the forecasting process can be for a single product. Even the most advanced spreadsheets are not able to help determine how a planner should adapt from new product introduction through to product end-of-life.

The process of developing and bringing a new product to market can be a grueling journey that spans from idea generation through concept development, business analysis, market test, implementation, and finally commercialisation. Quantitative forecasting algorithms, based on existing data, can only be used for products with an existing demand history in place. For new products, more qualitative models that include subjective inputs including product attributes, market knowledge and experience may provide the best available guidance.

As the product life cycle unfolds, comparing actual demand versus the forecast becomes critical. Statistical matching algorithms should be used to determine just how significantly actual demand has deviated from the prediction. This predictive accuracy can be compared, and when a different profile delivers a better match to the actual demand signal, it is implemented.

For all products, even short life cycle items, it is clear that no one statistical forecasting method is enough; several models are required to cover the wide range of demand situations products encounter throughout a lifetime. Spreadsheets as well as ERP systems are unable to automate many of the functions required to select, model and generate forecasts, much less automatically switch to the best fit model based on current market conditions. However, more than 80% of companies still rely on these techniques to drive their business—a large opportunity for Best-in-Class companies to move ahead.

 

2. Disaggregate High Level Demand Forecasts to Tactical Levels

Demand aggregation and disaggregation are key to creating the best possible forecast at all levels of granularity required to reconcile corporate (strategic) plans with operations (tactical) plans. As Gartner puts it, “the balance between bottom-up collaborative approaches versus top-down statistical modeling is challenging.” The demand aggregation hierarchy is a concept familiar to most planners: a multi-layer view in which the lower, more granular levels represent demand for a greater number of sub-components at a specific location, while the higher levels summarise demand by product family, group, region, etc. (See Figure 2).

 

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In practice, this hierarchy should support demand signals and input from multiple sources, including customer forecasts, sales forecasts, management direction, and constraint-based forecasts, as well as external demand signals generated by syndicated data and point-of-sale information.

The hierarchy structure breaks down higher level plans into detailed forecasts associated with product components such as style, color, size, sales channel, customer, region, and other elements. It captures “how many of which kind” need to be created, stocked, and distributed for multilevel product structures such as accessories, components, consumables, and service parts that have time-phased dependent demand.

While the hierarchy can be used “bottom-up” to aggregate the more detailed levels up to an overall demand forecast, this tends to magnify the estimating error of lower tiers into a larger coefficient of error at the master forecast level.

Generally, the most accurate forecasts are achieved by disaggregating higher-level demand forecasts down to tactical levels, thereby dividing the forecast error inherent in the higher tier into smaller inaccuracies at the lower levels of demand planning.

While executives may want to see an aggregate forecast by customer, and have less interest in detailed breakdowns, other stakeholders have other priorities. Distribution managers are interested in the demand plan by geographical region. Marketing teams may be focused on demand by product style. Manufacturing needs component details: quantities of each size, flavor, container, etc.

 

3. Focus on High Value Add Activities

One of the primary distinctions between leading supply chain organisations and all others is their ability to focus valuable planner resources on high-value-add activities like problem avoidance, issue resolution and optimisation.

One example of this is adopting a management-by-exception approach to demand planning as a way of maximising planner productivity in the organisation.

As actual sales data becomes available, a system monitors validity by comparing the existing demand curve to the actual demand signal. A centralised dashboard display and planner-specific real-time alerts (in-system, email or mobile) call attention to important conditions that have deviated from established targets or expectations. Sales and demand anomalies may occur at the level of SKU locations, product groups, geographies, etc. Planners will also need to create custom alerts as needed to support the priorities and business goals of the organisation.

ABC stratification, based on the universal finding that for most manufacturers ±20% of SKUs drive ±80% of sales while the next 30% drives 15% to19% and the balance generates 5% or less, is a powerful first step. Setting up business rules that focus alerts on the high-value products (the “A” items) brings more expertise to bear on products that contribute the most. Management by-exception flags deviations from the forecast using stricter thresholds for A items than for B items. System alerts spur the planner into action at the first indication of a possible variance for the most business-critical products. Alerts for C items can be handled on an as-needed basis.

Establishing and automatically monitoring a customised set of performance indicators provides planners and other stakeholders a comprehensive picture of how well the forecasting effort is working. Common KPIs include forecast accuracy, inventory levels, service level, fill rate, and stockout percentage.

By managing one integrated set of KPIs across the organisation, from supply-side to demand-side, at every level of forecast aggregation; everyone stays on the same page regarding overall performance against unified customer service metrics.

One of the primary distinctions between leading supply chain organisations and all others is their ability to focus valuable planner resources on high-value-add activities like problem avoidance, issue resolution and optimisation.


4. Spotlight on Collaboration

Getting visibility to what customers, partners and internal stakeholders know can further drive a more accurate demand plan and provide reliable input to the sales and operations planning (S&OP) team. There is no greater contribution to wise S&OP decision making than collecting information as close to the demand signal as possible, and receiving feedback as early as possible. Research from Gartner showed that gathering demand insights from customers presents the largest gap between importance (74% think it is important) and effectiveness (44% think they are effective at it).

Two traditional methods of improving forecast accuracy are Collaborative Planning, Forecasting and Replenishment® (CPFR®) and Vendor-Managed Inventory (VMI). In recent years, there has been a movement to combine and improve the two concepts into Collaborative VMI. In any form, the purpose is for trading partners to share information and cooperate to sense demand as early as possible and respond to changes in demand efficiently.

A Collaborative VMI process is based on shared calendars and proactive meetings that allow trading partners to integrate and view point-of-sale data, promotional schedules, buyer and seller inventories, replenishment forecasts, new product introductions, and more. When executed in an environment of trust, wholesalers do not blindly react to a retailer’s inventory level and retailers are not caught off-guard due to, for example, a slippage in lead times. The planning function moves beyond a focus on orders only and gets closer to actual consumer behaviour.

Success requires formalised processes to accurately anticipate time-phased inventory and customer replenishment needs. Business rules automatically
trigger replenishment orders, and parameters are established for managing returns, setting inventory turn goals, and managing slow moving goods. Each trading partner should view pertinent information in the context of its business. By providing automated business alerts, prioritisation of events, and a level of automatic conflict resolution, a good collaboration facility encourages planners and buyers to become proactive rather than reactive, focusing their attention on exception conditions before they turn into major issues.

S&OP brings together marketing, sales, manufacturing, finance, sourcing, and more on a regular basis to make fully informed decisions and create one collaborative plan that best drives the organisation toward its business goals. In fact, S&OP is probably the most vital collaborative activity in the organisation. No other program has a greater impact on the expectations, commitments, costs, and profits of the company. To synchronise the operational plan with corporate financial objectives, demand and supply scenarios must be in balance with cash flow objectives, profit contribution, margin criteria, etc. Companies should be able to aggregate demand planning data for the executive-level S&OP review and then drill down to a level of detail that may reveal challenges and opportunities related to smaller demand groupings by sub-family, item, geography, style, or other subsets.

An excellent demand planning program ensures the S&OP process is fed the most accurate possible forecasting insights, and supports a “one-number” plan giving all departments a unified view of what is to come. The internal negotiation between sales, marketing, and operations can proceed with clarity and mutual understanding when the demand plan is built on solid ground, supported by facts and proven techniques rather than intuition, emotion, and even wishful thinking.

With maximum visibility to inventory levels, point-of-sale data, promotional plans, regional variations, and more, the demand plan can become the most powerful weapon supporting wise recommendations and better S&OP decisions every month.

 

Conclusion

As we have seen, providing the best “one plan” forecast requires utilising advanced solutions to manage this data, disaggregating higher-level demand data, focusing on the metrics that matter, and working in a collaborative environment. Demand planning excellence is the foundation for any successful business ensuring all parties in your supply chain have confidence in the numbers and are working towards a common goal.

About the Author

Karin L. Bursa is a vice president at Logility, a provider of collaborative supply chain management solutions. Ms. Bursa has more than 25 years of experience in the development, support and marketing of software solutions to improve and automate enterprise-wide operations. You can follow her industry insights at www.logility.com/blog. For more information, please visit www.logility.com.

 

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3 Responses to Achieving Demand Planning Excellence

  1. Hello there! This article could not be written much better!
    Looking through this article reminds me of my previous roommate!

    He continually kept talking about this. I will forward this post to
    him. Fairly certain he will have a good read.
    Thanks for sharing!

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