SCM Data Architecture for Analytics – A Primer

Supply Chain & Logistics, Optimization and Analytics have been birds of the same feather flocking together since the beginning of time. However, while data had been generated at every link of the Supply Chain for decades, it is only recently that we have acquired the tools and the inclination to religiously record this data with a view to use it to generate insights and improve decision-making accuracy, speed and simplicity.

Supply Chain Management (SCM)
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Interesting “Bottlenecks” in Supply Chain Analytics 

  • Operations/Planning is done by different persons in the ecosystem at different levels of the Time and Product Hierarchy. Here, their Planning and Execution horizons and objectives may differ and consequently, data and reporting format requirements may be different as well.
  • Even if we agree upon and limit which data to record, data at different levels of the supply chain are usually different from each other due to a variety of reasons: for example, depending on the objectives of the level, the definition of a “good” can be different – they may be raw materials, or sub-assemblies, or semi-finished and finished goods etc. Here, codes used to indicate these goods would also differ as a good-on-hand before assembly/ production is entirely different from a good after.
  • Time and again we find that in SCM, data architecture is given step-brotherly treatment as compared to complex optimization techniques and algorithms and not treated as the stepping stone that it is. A demand signal cannot be traced back to the purchase execution beyond the pull-push boundary. Things are much more complicated at the push part of the system where individual objectives are more important than approximating to demand signal. Thus business proceeds as if each level is responding to its adjacent level only.

We believe that meaningful analytics necessarily involves collecting relevant data from various sources, bringing them to a single readable, well thought-out, and reduced format and then conducting analysis to derive information out of it. This information is then useful in making improved and well informed strategies and prompt decision making.

Supply chain analytics

Questions – Current Setup

The questions you should be asking yourself are these – with your current data setup,

  • Are you able to track your products life cycle?
  • Are you able to match Development Chain with Supply Chain?
  • Are you able to track the gap between plan and actual?
  • How do you track macroeconomic indicators and market sentiments?
  • Does long and short term planning complement each other?
  • What about long execution cycles and lead time?
  • How do you find out the root cause of the problem?
  • How do you leverage cost v/s earnings?

Our Take – Information

If you are unable to answer these questions then, how do we suggest you make sense of all this? First, understand the complexities of the data – take solace from the fact that the following data is mostly linked, with commonalities across levels:

  • Master Data: Customers, Products, Human Resource etc.
  • Execution Data: Sales, Goods transfer, Purchase, Production etc.
  • MIS: Inventory is the common denominator across all levels
  • Planning: Forecasting is common for planning at all levels

Assess the following:

  • Data Volume: Understand that because execution cycles at different levels are different, data volume generated at each level is different. POS (Point of Sale) data might be generated daily for a SKU every time a barcode is scanned at a till while raw material is purchased at quarterly or annual level. Know that this will influence planning horizons and data aggregation at each level, and structure your data accordingly.

Data Format: Check if you have a consistent data format for each level and, if yes, ascertain if these formats are consistent with formats of other levels. In most cases, the answer is a resounding NO as formats change from time to time and from level to level. Data architects can help you in constructing readable data and creating a data pipeline that is accurate, consistent and easy to maintain in addition to architecting sources of execution, planning, and master data to strengthen the MIS and planning process.

Supply chain analytics diagram1

Supply chain analytics diagram

  • Data Metrics: Design your metrics correctly i.e. serving the needs of decision makers to aid them make business decisions – metrics could be used for operational, tactical or strategic decisions and this will determine their complexity. Metrics of different complexity and levels could be required based on the mix of persons accessing the reports, and can be combined with external data like macroeconomic indicators to provide a more complete view.

Supply chain analytics diagram2

Potential Application – Insights & Impact

So what can we do with a solid data architecture backend for SCM analytics? A use case could be a simple application of Business Intelligence and Visualization used for prompt decision making.

  • Visualize inventory at multiple levels and notice clogs instantly to take a call to redirect inventory or reschedule production
  • Monitor your backlogs live to better manage execution of logistics or production
  • Visualize reverse logistics and reduce your rejection rates by better execution of production and logistics
  • Track planned V/S actual execution of business to balance any execution errors
  • Enhance your opportunities and cost savings in purchase through trend and spend analysis visualization
  • Manage your production plan in sync with demand plan and market signals
  • Evaluate performance of vendor and logistic partner service level
  • Evaluate your performance against targets, market and benchmarks

As complicated as it might seem, having a consolidated format for its supply chain data is of utmost benefit to any company. Such data will not only help in determining and managing pull and push systems but can be extended to other short and long term business problems like pricing, logistics execution, performance management and process monitoring. System wide analytics visualization is also a certain possibility. Matrix creation and dashboarding would streamline decision making. Simply put, an investment in managing data consistency and accuracy can open the shackles and mobilize the entire Supply Chain to effectively convert Information to Insights and consequently, Impact.