Disrupt demand – from MHD magazine

Tom Enright

Highly predictable future demand is the dream of most supply chain executives, all striving for an effective end-to-end supply chain. Companies have long struggled with average forecast errors of more than 27 per cent, according to a Gartner survey.
This challenge will only become tougher as supply chains become increasingly disrupted by new competitors, new business models and digitalisation. Demand will also shift to parts of the world where companies don’t have mature infrastructures.
You’ll need to improve your demand-sensing, shaping and forecasting capabilities to be successful. Move away from owning assets. Instead, move toward accessing and using them through implementing more collaborative supply chain network designs.

“You’ll need to improve your demand-sensing, shaping and forecasting capabilities to be successful.”

New opportunities to better predict demand
The sheer volume of data currently available is greater than most current demand technology can absorb and use for effective insight and decision making. This data needs to be used in a different way than it is today to improve demand planning and forecasting.
Improving demand accuracy is now intrinsically linked to the use of analytics to recreate the environment in which historical demand occurred. This means including more inputs than those of sales, inventory and variable prices in statistical forecasting today, which don’t sufficiently create a comprehensive set of attributes that influence historical demand.
Instead, elements such as weather, social commentary, demand transfer, competitor pricing, and shipping and returns policies need to become inputs to demand calculations. All of these influence how customers purchase, whether in B2B or B2C environments, across multiple industries. All of these elements should be considered when predicting future demand.
This new set of data inputs need advanced machine learning algorithms to learn from richer historical data to sense demand, predict and prescribe action. Unlike statistical forecasting, a machine learning approach uses a wider variety of data inputs, which can produce a more accurate demand plan.
Viewing technology as a source of competitive advantage is critical to understand the impact of disrupting demand for people, products and services, as well as appropriately reacting to it.

“Wealth and demand for products and services will increasingly shift to parts of the world where companies lack mature infrastructure in terms of sales, supply and recruitment.”

Participate in trading partner networks
To gain new insights from the proliferation of data and increase demand management performance, you’ll increasingly need to pool resources with other partner companies in your extended supply chain.
Each company will play a role in this network of suppliers and service providers, sharing people, information and technology. Rather than extracting value from its own asset, your company will gain value and advantage using data, people, technology and services belonging to others. Isolated companies will become weaker in influence in the overall supply chain.
The need to develop multi-enterprise collaborative supply chain infrastructures will define the future of supply chains across global industries. Extracting value from information, assets and people will no longer be based on ownership, but instead on accessibility and usage.
The sharing of supply chain assets will be accelerated by the emergence of digital platforms across manufacturing, warehousing and logistics. Ecosystems as a platform have been emerging for many years.
Cars have evolved to become platforms, for example, delivering a customer experience that draws on a cross-industry ecosystem of partners, from the car manufacturer to companies that specialise in communications, entertainment and navigation.
What’s new about ecosystems today is the infusion of digital connections, combined with the fact that they’re delivering digital products.
Forces such as globalisation, government pressure, network capacity constraints, freight margin reductions and increased outsourcing will drive companies to explore how to become more efficient in using their networks and resources across their ecosystem platforms.
Most companies will need to leverage an asset-light network that enables them to be flexible and timely in a cost-effective manner. Instead of looking internally and only optimising your own assets, connect with an ecosystem of third parties to share assets. This builds more responsive supply chains.
Individual customer orders will be fulfilled by whatever combination of partners meets the demand requirements at the time of execution.
Keep ahead of market forces
A global shift in population growth, wealth and workforce resources requires better demand-sensing and shaping capabilities.
Wealth and demand for products and services will increasingly shift to parts of the world where companies lack mature infrastructure in terms of sales, supply and recruitment. It’s likely we’ll see a large increase in purchasing power in less-developed countries in the coming decades.
These shifts in economic power will change demand and potentially how customers will buy. Will they want value products, or will demand for more choice and for premium products increase? Will they buy in urban stores, rural locations or will most purchases be done online?
Companies that fail to take action will find their existing markets declining in terms of spending power and as older consumers age.
Advanced analytics technologies – spanning predictive and prescriptive analytics – are playing an important role in helping companies to keep ahead of these market forces. The impact on supply chains is significant.
Predictive analytics are undoubtedly a powerful competency that enable companies to be proactive and take advantage of a future opportunity, or mitigate or avoid a future adverse event.
Prescriptive analytics on the other hand can improve decision making in functional areas like supply chain planning, sourcing, logistics and transportation. More importantly, prescriptive analytics can be deployed to improve the supply chain performance by recommending course of action that best manages trade-offs among conflicting functional goals.
Tom Enright is a VP analyst at Gartner, specialising in supply chain strategies and operations across the retail sector. His focus areas include distributed order management, in-store logistics and last-mile fulfilment. For more information visit

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