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How industrial companies can digitally supercharge their supply chains

Posted by: 
 | Jun 2019

In this fast-evolving world of globally integrated, dynamic and digital supply networks, businesses need to adapt to create agility and flexibility, through real time visibility of their supply chains.


Industrial companies today sit on a wealth of data across their supply chains – from external purchasing and flow side data, to internal flows across raw materials and work in progress. Yet one of the most frequent questions asked by our clients in this sector is: “How can  make better use of data to improve operational performance for my business?”

Supply chain capabilities in the digital age centre on better information management and analytics. Insights supported by advanced analytics, algorithmic planning and software platforms can move supply chain management from being reactive to proactive and predictive.

In this blog we explore how companies can leverage their data to move from information to true insights.


Data + Analytics = Opportunities

Getting complete visibility of supply chain data, and leveraging analytics to enable proactive fact-based decisions, can drive improvements across the supply chain. Becoming “data driven” leads to insights which enable industrial supply chain executives to move away from tactical and reactive, to strategic, proactive and disruptive decision-making to address their most pressing problems. Typical challenges we see among our clients include:

  • Gaining visibility into the P&L across divisions and at various reporting levels
  • Reducing lead time and syncing demand planning with production and logistics
  • Optimising production planning to improve margins
  • Identifying data-based opportunities to increase cashflow and profitability
  • Connecting planning, procurement, production and logistics through an integrated system


Enabling a digital supply chain strategy

We have developed a four-point guide to help industrial companies make more intelligent use of their supply chain data.

  1. Identify and prioritise business issues

The first step is to identify the business issues which need to be resolved using supply chain data and analytics. Deploying a new exciting technology solution without understanding the business needs is a classic way to ensure failure.

Business issues need to be prioritised by assessing them on a matrix of business impact and data readiness. Business impact is a hybrid of quantifiable and qualitative metrics such as production costs, profitability, risks, stakeholder engagement and external market movements. Data readiness needs to be evaluated on current systems, IT maturity, collaboration and ease of access.

  1. Don’t try to solve the data problem in one go

In the digital age, the real problem is not the lack of data but an abundance of it. Like any data-intensive domain, supply chain data typically sits in a myriad of locations and systems – from ERP, SRM and financial systems, and from both internal and third-party sources. Data gathered from multiple sources generally comes in a range of formats and structures, and at varying aggregation levels.

This data deluge can be overwhelming and is often a major roadblock to kick-starting any meaningful analytics programme.

The solution here is not to try and “boil the ocean” of data, but kick-off with a limited dataset relevant for a few of the top priority business problems. Clean, scrub, analyse and most importantly monetise this specific dataset before picking up the next lot. The end objective is to leverage both internal and external sources, through a piecemeal approach to creating a master dataset.

A solid master dataset can be used to move up the analytics value chain – implementing predictive and prescriptive techniques, and deploying more advanced analytics such as optimisation and simulation – leading to more in-depth insights with greater business value.

  1. Focus on insights and not visualisation

While visualisation of data is an important output, it is just a first step. Most companies get carried away with advanced visualisation tools – offline or online – but the key is to deliver insights and actions from the data.

These insights can be used to build scenarios to streamline the overall supply chain and optimise performance, consistently achieve margin improvements, reduce production costs and benchmark performance.

  1. Deploy a time-boxed and agile methodology

With all the complexity around data, IT infrastructure, policies and stakeholders, it is important to plan the execution using an agile framework and to time-box the delivery.

This approach enables supply chain executives to quickly test and assess business impact of decisions made from the insights, and provides regular touch points to review progress and value from the overall programme – key to ensuring tangible delivery in the short-term.

With additional inputs from Lucian Pasca, Manager, The Smart Cube.

A detailed look at how we helped a global industrial business address its supply chain challenges through data analytics can be found in this case study.

Learn more about how our Procurement Analytics solution could help you understand, map and connect disparate data sources to unearth opportunities to optimise, strengthen and enforce decisions across your supply chain. 

  • Alok Agarwal

    Alok is a research and analytics executive, and a business leader with 15+ years of experience across industries and functions. Based out of our London headquarters, Alok leads the Industrials, Manufacturing and Consulting practice in Europe. With his experience in owning client relationships and managing delivery, he is always keen to understand clients’ business challenges and design bespoke solutions to deliver the maximum value. He loves spending time with his two young kids, and recently started cycling and playing tennis.