Supply chain redesign has become an area of critical focus as businesses try to ensure supply chains aren’t overly reliant on any one constituent part.
In Part One of our round-table discussion with Michael Ger, Managing Director of Manufacturing and Automotive, and Brent Biddulph, Global Managing Director, Retail and Consumer Goods at Cloudera they talked about the direct impact of COVID 19 on the retail and manufacturing sectors with Vijay Raja, Director of Industry & Solutions Marketing at Cloudera.
In Part Two they will look at how businesses in both sectors can move to stabilize their respective supply chains and use real-time streaming data, analytics, and machine learning to increase operational efficiency and better manage disruption. The 6 key takeaways from this blog are below:
|6 key takeaways
Hi Michael, Brent, Thank you for joining us again. To start off on part two of our discussion on the impact of COVID-19 on supply chains, can you tell us how businesses can improve scenario planning and why this is so important from a long-term planning perspective?
The pandemic has been a call to action for both the manufacturing and retail industries and that is the bottom line with COVID. Whatever digital transformation companies had underway pre-pandemic, they now have a better understanding of the business implications of ‘not being fully ready’ and the need to keep investing in data as an asset and competitive difference-maker.
Transformation needs to incorporate external signals beyond the four walls of the business, leveraging IoT and AI to automate and enable agile operations in terms of sourcing, production, logistics, and fulfillment to shift ‘on the fly’ to respond to disruption caused by geopolitical events, or pandemics.
Scenario planning and supply chain design
Scenario planning is critical and will certainly help decision-making. Businesses should be looking to simulate conditions and “stress test” their systems to see what they’re capable of handling. This requires a shift in approach from a more traditional, to a more proactive model where they’re evaluating and modeling different types of disruption in different geographies across a diverse range of scenarios.
For retailers, scenario planning for a global pandemic could begin with using historical natural disaster impacts as a starting point. Scenario planning would enable companies to react more intelligently based on historical internal and external data sources with applied data science and advanced analytics.
Scenario planning and data insights will help inform companies on when to scale up or scale back in the face of disruption and also allow them to communicate requirements ahead of time to manufacturers and producers. This will help to set focus with all channel partners, whilst simplifying and prioritizing internal replenishment and fulfillment tasks up and down the supply chain.
By having a Plan B in case of a crisis, that is supported by analytic modeling developed with input from impacted lines of business well in advance of the event – the teams can quickly gather to review data, tweak the model as needed, and discuss reactions to various scenarios relevant to the current crisis. Companies that don’t have those strategic plans (supported by analytics) in place are going to flail around for weeks which rapidly translates into lost revenues, skyrocketing costs, associated margin losses, and disappointed consumers.
Long term impact on retailers
Even if these types of scenarios are one-time events, the impact and disruption is lasting, the current pandemic’s effects will be felt at least until early next year – and in retail consumer shopping behavior has already been changed forever. For example – it is now estimated that retailers will need an additional one billion square feet of industrial real estate by 2025 to support the eCommerce boom COVID has accelerated. In that case, should retailers look to closing underperforming store locations and use them as ‘dark stores’ micro-fulfillment centers?
Some retailers already are, and they’re using scenario planning to help guide these decisions. So being able to react quickly through scenario planning will benefit everything from sourcing, production, logistics, all the way down to consumer fulfillment.
The inventory distortion gap
For retailers, inventory distortion is another key area that broader data sets can help improve. Retailers have struggled with having what the customer wants to buy in-stock when they actually want to buy it and from where – and recent research from IHL Group estimates the inefficiency cost at over $1.8trn worldwide for 2020. While the sector is improving, there is plenty of opportunity for retailers to better leverage scenario planning and supply chain redesign to improve inventory productivity and out-of-stocks, and COVID-19 has probably forced many to begin looking at overhauling their replenishment systems and business processes to drive meaningful improvements in this regard.
How can businesses better leverage data, real-time analytics, and machine learning to manage and stabilize supply chain disruptions?
In better dealing with disruption across all points of the supply chain, it all comes down to data. In today’s challenging COVID impacted business environment, securing uptime at manufacturing plants and distribution centers is non-negotiable and business’ survival depends on it.
The importance of real-time data
We’re now in a new world where diverse, real-time data from outside the four walls of a business is critical. Companies need to leverage more data and broader datasets, whether that is real-time data, whether that is external data or more specific to geolocations. Companies need to be more agile and proactive, able to react quickly to events and data that changes in such short time frames.
Companies are investing in supply chain systems, greater amounts of data, and more diverse data sources then allow these companies to enable more real-time actions and planning around things like demand forecasts and supply chain forecasts.
Automation and expertise
In order to reduce supply chain disruption, businesses need to remove bottlenecks where resources are poorly used, and to do this they need to leverage more automation in both manufacturing plants and distribution centers.
On the people side of things, there are two critical dimensions, there needs to be C-level commitment and downstream staffing and skills to realize the potential of broader digital transformation, and the value of data analytics for the business. Organizations require data scientists and engineers to turn data into meaningful analytics, stories, dashboards, and alerts. Data is the enabler but it requires people to understand how to refine it and investment from the business side to turn that data into meaningful capabilities.
The number one call to action starting with the data is to keep incorporating more external demand signals, whether that is geopolitical data, health data, local event data, social commentary, or weather data.
Greater visibility and forecast accuracy
Businesses need to better analyze and understand megatrends and be able to synthesize those into algorithms or business decisions so that they can plan around the potential implications.
A demand-driven supply chain needs sufficient visibility so that the sourcing and replenishment quantities are aligned with real-time demand and supply triggers. Demand-driven supply chain visibility is enabled by various streams of data and machine learning algorithms constantly in search of improving forecast accuracy at the item, day, and location level. With a greater number of datasets, types, and variables to be included in the analytic models, the potential for improved accuracy and better decision making is possible.
This provides greater context for understanding the implications of fluctuating consumer demands or changing circumstances. From a retail perspective, most businesses are still relying on internal transaction data, perhaps adding demographic data or even local market data but there is (now proven) need to broaden the net considerably.
For example, weather data can help retailers anticipate the impacts of natural disasters, a hurricane, or a tornado type event, on their businesses. They can even glean seasonal allergies by using pollen count data to better understand merchandising adjustments at a local level. They can better predict what categories, items will likely be most affected, and whether they need to stock up or whether they need to rapidly move specific products to a geographical area.
Some retailers are leveraging weather data but it’s rare to see the usage of other external sets such as local event calendars like high school football games, or a one-time marathon – that could really help retailers adjust inventories to help prevent out-of-stocks, and boost sales via simple in-store merchandising adjustments.
Broader data sets are key
And especially rare to see anyone using broader datasets such as geopolitical and health (e.g. CDC) data – this data would have obviously been helpful leading up to COVID-19.
Yet, is still another key dataset for retailers and consumer goods companies – as many goods they sell are produced outside of their own country, so there are implications around not being able to have those goods or understand potential implications of tariff impacts (for example) between countries. By including those bigger datasets, companies can continue to improve their replenishment planning, demand forecast accuracy, analytic modeling precision, and better address business issues (crisis or not) when they arise.
Finally, edge data (aka IoT) gives a real-time read on what is going on across the supply chain or physical location. Turns out, that according to McKinsey, both Manufacturing and Retail are two of the top four industries in terms of expected spend and business impact by 2025. IoT and streaming analytics is a huge opportunity for both industries because it provides immediate signals on operational and consumer changes at the ‘moment of truth’.
To find out more about how your business can better leverage big data, real-time analytics, and the cloud to manage supply chain disruption, visit our website or contact us to speak to a product expert about our intelligent Cloudera Data Platform.
The post Covid-19 Accelerates The Need for Retail, Manufacturing Supply Chains To Adapt – Part 2 appeared first on Cloudera Blog.