The fundamentals of demand planning
有效的需求计划可以提高收入预测的准确性，将库存水平与需求的峰值和谷槽保持一致，并提高特定渠道或产品的盈利能力。
什么是需求计划？
需求计划供应链管理过程的预报了吗ing demand so that products can be reliably delivered and customers are always satisfied. Effective demand planning can improve the accuracy of revenue forecasts, align inventory levels with peaks and troughs in demand, and enhance profitability for a particular channel or product.
Demand planners keep an eye on internal and external factors that could impact demand, such as labor force issues, natural disasters, weather patterns, and news events or other influences. Gathering information from all possible sources is the best way to generate an accurate forecast and ensure integration with the supply forecast to efficiently meet customer demand.
需求计划的重要性
市场可以转移一毛钱，需求计划需要以不断变化的市场速度移动。如果无法通过敏捷性调整需求计划，公司可能会以库存和不满意的客户或充满未使用的库存，不愉快的财务经理以及数百万美元的浪费资本的仓库。
在一个理想的world, demand planners must stay ahead of the market instead of merely reacting to it, and make decisions based on near real-time market data, rather than solely on historical data. That’s not always possible, but with the advent of cloud-based planning platforms, it’s closer to reality than ever before.
Elements of the demand planning processes
Let’s take a look at a few of the processes involved in demand planning.
Trade promotion management
Trade promotions是营销策略（通常在零售公司中），专注于通过折扣，赠品，店内促销和其他类似技术来产生店内需求。贸易促进管理旨在通过高度协调的促销活动来帮助品牌脱颖而出，并与零售商建立更牢固的联系。
贸易促进计划者试图在详细和汇总级别进行协作计划，以便他们可以在不长期延迟的情况下调整产品，活动和促销，使可选的贸易促进支出计划保持一致，该计划纳入了所有时间段，产品和地理位置上分销商和客户的最终信号。
Top-down and bottom-up trade promotion management and analysis includes profit and loss data, creating insights around promotion spending. It also tracks and identifies which promotions don’t optimize margins due to ineffective trade promotion spending and poor brand growth from trade promotions, and creates maximum ROI using a broad range of data.
产品组合管理
产品组合管理is the process of managing every facet of the product lifecycle, from new product introduction to end-of-life planning. The goal of product portfolio management is to maintain a high-level view of the entire portfolio and reveal where product lines are interconnected and interdependent.
产品组合管理包括计划将新产品纳入现有产品组合中，了解引入这些新产品将如何影响其他产品（蚕丝化）以及依恋率的分析（一种产品的销售方式如何影响另一种产品的销售）。参与产品组合管理的规划人员大量参与了方案计划，以确保他们了解每个产品线对其他产品线的影响，以优化产品组合，最大程度地利用产品线的利润率，并增加全球市场份额。
当推出新产品时，重要的是要知道新产品将如何影响全球计划策略，引入新产品的成本以及新产品将产生的收入和利润。使用智能产品组合管理技术，可行性模型连接到构想过程，基于方案的盈利能力模型，并加速了将产品从想法到商业化的过程加速。
当此过程是协作的时，实时的颗粒预测模型可以确定各个地理位置各个市场的不同市场细分如何以什么价格购买该新产品。在协作系统中，新产品介绍将销售和供应链链接起来，从而导致关键连接到sales and operations planning (S&OP)processes, production planning, and allocation planning.
Statistical forecasting
Statistical forecasting需求计划利用历史数据来使用各种高级统计算法生成供应链预测。在需求计划中，必须进行数据支持的预测，以避免库存或投入量，并确保客户满意。
统计预测如何使需求计划更有效，有多个方面。需求计划者可以通过审查每个模型的准确性和偏差措施来分析许多算法，并确定哪种预测最准确。然后，他们可以从每个产品和产品系列的最佳型号中进行选择。
而且，当预测仪表板是方程式的一部分时，可以更轻松地自定义预测算法假设并使用诸如平均绝对百分比误差之类的技术来衡量准确性。通过统计预测，需求计划者可以根据用户定义的参数（包括标准偏差或四分之一间范围）快速识别离群值和排除。
Seasonality has a major impact on demand planning. Retailers have many factors to sort through to ensure that they’re prepared for various seasonal events. Will they be ready for the holiday shopping rush? What if weather patterns shift and all those winter coats they’ve stocked aren’t purchased? With statistical forecasting in demand planning, these questions are easy to answer because multiple statistical simulations can be run, including models to forecast the impact of intermittent demand, multi-linear regression forecast quantity, price, attach rates, and discounts.
The skills that demand planners will need in the future
需求计划is undergoing large-scale radical change with an emphasis on digital transformation. Artificial intelligence and machine learning are already beginning to make an impact on how demand planners operate.
Algorithmic “touchless” supply chains that weave in the power of big data, blockchain, robotics, and 3D printing may soon be the rule rather than the exception. Demand planning of the future will be always on, dynamic, and non-linear. The power to react quickly and make value-based decisions is essential to staying ahead of the market.
To lead the way into a transformative future, demand planners need to combine technical and business knowledge with collaboration and communication skills. The ability to influence department leaders that partner with demand planning is key, as well as the skills to interact intelligently with leaders across the organization because supply chain initiatives often reach across business units. Strong business acumen is a must-have—you’ll be more effective working with your counterparts in finance, sales, and marketing if you can speak their language.
The effective demand planning leader of tomorrow is tech-savvy and comfortable working alongside the world of machines. Some have said that artificial intelligence (AI) won’t replace managers, but managers who work with AI will replace managers who don’t. This highlights the transformation taking place in supply chain management: Humanity is essential but so is technology. It’s not a paradox—it’s the new normal. The new demand planning leader is digitally dexterous and also skilled with people.
The already many-faceted role of a demand planning leader is changing. To thrive in this new world, demand planning professionals must grow their capacities in collaboration, communication, and leadership, and pair those skills with in-depth technical knowledge if they want to become and stay a powerful force for the future of demand planning.
Digital demand management: The next generation of demand planning
需求计划的未来是供应链脑的调用“数字需求管理”(DDM). It’s centered around implementing demand-driven structures, frameworks, and digital enterprise architectures. Multiple groups connect to facilitate a seamless exchange of information, ideas, and solutions that are synchronized with the omnichannel buying habits of consumers.
即使在我们个人购买习惯中，在过去几年中，竞争性需求格局也发生了根本性的变化。有迅速新兴的数字市场，新的竞争对手和更快的市场变化，可能威胁到任何无法适应的企业的灭绝。DDM使复杂的数据可理解，可操作，预测性和规定性，因为它提供了实时同步知识库，可改善客户的关注。
The development of DDM starts by challenging traditional linear thinking across supply chains. The old linear thinking accepted the inevitability of forecast cycles that are weeks or months old, poor visibility into SKU locations, and the inability to address ongoing variability that disturbs traditional network and inventory optimization systems. Linear decision-making adds unnecessary time and causes potentially false demand signal amplification.
必不可少的变化是用DDM替换传统的功能指标，从而在整个供应链中促进协作执行。最终的需求管理组织使一家企业能够在水平和垂直方面进行动态计划，从而实现真正的数字协作。
DDM requires a new planning model that enables causal and external factor analysis, along with a process-control approach to smart digital network management. The beauty of DDM is that it works within a range of pre-defined acceptable variability. Using real-time DDM forecasts—based on standard deviations along with the range of consumer behaviors that are likely to occur—participants can agree to ranges of performance, commitment horizons (periods of risk), and exception conditions. The result is a beneficial digital collaboration that enables next-generation demand planning.
致谢
Supply Chain Brain white paper (in partnership with Anaplan):“Digital demand management: The new normal.”