Insights

How Technology and Machine Learning Are Disrupting Demand Planning

 

How Technology and Machine Learning Are Disrupting Demand Planning


Oct 23, 2018
Manufacturing and Distribution

Main_Demand Planning 
Managing the supply chain, inventory levels and capacity planning have been perennial challenges for manufacturers, and traditional practices have not always proven reliable. Perhaps that’s because demand planning, which encompasses these areas and more, is often considered a “big event” as part of a static yearly forecast. Because of the fast pace of today’s manufacturing climate, however, those forecasts can quickly become outdated.

Forward-thinking organizations see demand planning as an ongoing and fluid process that leverages data and analytics to update forecasts as things change. Insights and data from machine learning are disrupting the traditional demand planning process (in a good way) to increase accuracy, efficiencies and profits.

A Better Approach to Demand Planning

Demand planning is, in short, forecasting to meet the demands of your customers, and it touches multiple business processes, including production scheduling, purchasing, vendor pricing and inventory. There is independent demand for finished products and dependent demand for the materials needed to make those finished goods. Planning also needs to take historical performance, expected lead times, dates and available capacity into account.

In the past, demand planning was more of an art than a science. The problem with some arts is that insights can be subjective, and the outcomes can be difficult to interpret. A scientific approach, however, leaves little room for variances and is based on data, minimizing the potential for misinterpretation and errors.

Aligning multiple factors to produce an accurate forecast is nearly impossible through manual processes and multiple spreadsheets, which result in the “art” of demand planning being mostly a guessing game. Today, the consistent input of data into enterprise resource planning (ERP) technology provides the insights necessary to improve the demand planning process and its outcomes.

Automation produces real-time metrics and creates visibility into your entire operation — the supply chain, inventory, production, purchasing and more. This type of real-time reporting means you can not only start out with the best plan based on accurate data but also remain agile and respond as changes dictate.

When you have an accurate forecast, you can reap the benefits of reduced lead times, better machine utilization, better supplier pricing, improved customer service and smoother inventory levels that avoid inefficient peaks and valleys. Of course, this all leads to enhanced profitability and opportunities for your sales team to sell more.

Start With a Strong Foundation

The basis of the demand planning process is creating an accurate baseline forecast, which is based on statistical analysis of various factors, including historical data, trends, customer input, product promotions and product seasonality. Other factors include market changes, new product introductions and capacity constraints that might require outsourcing.

The difficulty with many of these factors is that they’re rarely the same from one month to the next. A modern ERP system can leverage machine learning to continually update and improve these insights by basically training itself to adjust for seasonality, promotions and other demand fluctuations, and then applying that knowledge to the forecast. In essence, machine learning teaches the software the “art” of demand planning by anticipating potential variances.

People Still Make the Difference

Unlike with many other automated technologies, some people don’t trust that this type of artificial intelligence (AI) is always reliable when it comes to demand planning. Many choose to manually override the outcomes, but studies have shown that these efforts may not yield the desired results. Demand planners need to understand which targeted intelligence is of greatest value to determine where manual adjustments to the forecast may need to be made — emphasizing the importance of having well-seasoned employees in the planning and setup process.

Any system is only as good as the information that’s put into it, and there’s a fair amount of testing that needs to happen along the way to ensure agility, efficiency and a seamless process. When planning your ERP implementation, be sure to leverage the tribal knowledge of your most experienced workers. This is especially important as many baby boomers leave the workforce, so make efforts to engage them in the set-up and implementation processes so that newer people are equipped and can keep things rolling.

Equally important is working with a well-seasoned and experienced ERP provider that is familiar with your industry and can guide you through each phase — from selection, implementation, testing, training, service and beyond. Reach out to the ERP specialists at Wipfli for a no-cost consultation today.

Author(s)

Suzanne Koss
Suzanne Koss, CPIM
Partner
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