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Top challenges for companies in demand planning

The business landscape is becoming complicated. This makes demand forecasting and planning more difficult.

Here are the top challenges to Demand Planning:

        Changes in distribution channels: Online commerce, which creates a shift in demand, has also evolved significantly. During Covid-19, infection rates rose and dropped, disrupting physical retailers’ availability, consumers went online searching for substitutes. 71% of fashion executives expect growth of online shopping by 20% in 2021. While in retail, half of the consumers decided to stop physical stores activities as lockdowns are imposed. Moreover, new fulfillment methods further drive forecasting processes complicacy for grocery retailers. Meanwhile, the chemical industry is also getting more calls for changes and are increasing the adoption of low-touch, low-cost channels.

        Instant culture and personalization: To have more personalized products is becoming a new normal for customers in the CPG industries. And industry manufacturers are also adopting the trend with increasing mass customization. Fast-changing needs are impacting demand stability which beget demand planning shift. Simultaneously, increasing CPG customer expectation for shorter fulfilment times, as a result of the instant culture, adds another layer of planning complexity. Industry manufacturers need to be more proactive to protect themselves against disruptions and uncertainties.

        Compliance differences: Demand drivers like varying trade tariffs, changes in local regulations, commodity price fluctuations, and transport capacity constraints pose remarkable hindrances in planning demand. For example, the chemical and pharmaceutical industries face compliance differences between exporting and importing countries and regions for various categories. Mentioned trade tariffs and regulations can lead to inflated prices and other difficulties that affect consumers’ behavior and ultimately demand and the planning process.

        Sustainability, transparency, and corporate social responsibility: The age of information has unearthed and raised consumers’ awareness of the impacts their purchases have on the planet and society. Amazon found out that 73% of Z generationers and 68% of millennial consumers are more willing to pay more for sustainable or eco-friendly products. As this trend elaborates, brands are expected to get more transparent in their supply chains to their consumers and the world. Companies, especially those in the chemicals, pharmaceuticals, and manufacturing industries, are getting more clamor to reduce their plastic output, carbon footprint, and thus are increasingly investing in R&D to find clean options for materials. Take the fashion and apparel industry for example, consumers are voicing their concern for better treatment of workers, more conscious utilization of natural resources, and optimal processing of by-products and waste across the whole supply chain.

        Increasing internal and external competition: The increased competition (both internal and external) has forced companies to increase the extent of their product portfolio and promotion to win their market share. When there are too many alternatives to choose from, customer demand gains volatility, making them more unpredictable.

Internal Obstacles to overcome for companies to reach Advance Demand Planning systems

As external challenges arose, numerous organizations came to terms with the fact that relying on historical data to forecast the future is not a good plan. The current crisis has dwindled demand, product portfolios, consumer, and customer buying behavior, and more. Yet, most organizations have depended only on historical data, traditional statistical forecasting models, and manual corrections for their demand forecasting process. Hence organizations are experiencing poor forecasting performance, laden with high bias and the inability to respond to changes in the market in time, resulting in lost opportunities, service level pressure, and rising inventory levels.

The internal challenges that are raising the need for companies to upgrade their demand planning capabilities include:

          Attitude towards innovations and adoption of technologies: AI/ML-powered automation has become globally integrated, with an increasing amount of businesses embracing their changes. However, some companies are still clinging to the status quo due to reasons such as the uncertainty of skills required, impact on today’s ways of working, and the fear of black boxes.

          Many companies that are in their transition now face additional challenges: Lack of user adoption due to the black box and the users’ aversion to systemic changes. In addition, the flexibility of the new solutions, trackability of data, employability of insights, and usefulness of recommendations also lead to the difficulties to acquire user adoption. These can slow the change journeys’ progress.

          Neglected or insufficient data capabilities: Data is being generated internally and externally at breakneck velocity. Yet, not all enterprises are able to translate the big data into the desired actionable insights. Consequently, many companies are facing the challenges of having excessive data but without the ability to interpret them.

          Furthermore, tracking, collecting, and translating data from external sources is another challenge. Taking in external data may still come as strange to many contemporary systems. Meanwhile, customer behaviors are not well-described due to data unavailability or limited anticipation by internal sources only. Companies usually base their behavior analytics on intuition, and overlook external sources data that they cannot collect and interpret easily and in a reliable way.

          Besides, most companies still focus on historical data analysis for future outlook while skipping the rising importance of real-time information and visibility. Demand is also determined by real-time events like COVID-19. To manufacturers, the disruptions also raises the need to employ driver-based forecasting to improve OEM-provided forecast or forecast demand for new products without historical sales .

          This inability to collect and connect drivers of consumer demand results in the lack of contingency plans, limiting businesses’ ability to detect issues as they occur and develop rapid remedial actions.

As the challenges arise, there are now more and more shifts towards a next-generation integrated business planning (IBP) platform, which has built-in demand forecasting software to help companies overcome these challenges and capitalize on opportunities.


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