Automating High-Volume / Low-Risk / Low-Cost Goods Purchases

Author: Greg L Towne (Diriger)

Organisational buying power is constantly being marginalised or eroded through a loss of productivity measures, supply chain interruptions, inflationary pressures, and difficulty in changing current practices in alignment with internal and external influences. As this has an impact across the whole enterprise, it’s past time for our strategies, tactics, and work practices to be re-examined and re-energised.


Let’s look at some of the problems we already know about:

  • Repair and maintenance costs are increasing by 10 – 15% annually, a significant impact on our strategies and planning

  • Underperforming assets account for approximately 30% share of operating expenses so we need to look at how we are not adding to this situation

  • Approximately $4 of profit is lost for each $1 spent (or overspent) on repairs

  • Operations and Maintenance activities are being delayed due to stock-outs and the delayed arrival of parts and commodities

  • Partly due to administrative overheads, the ‘Wrench time’ of our working staff in the field or on the shop floor has decreased to less than 4 hours daily on the direct execution of work

  • Furthermore, parts supply, availability of the right parts, and dead stock continues to marginalise the organisation’s buying power, viz. it has had an impact on working capital.

  • The above item is impacting the supply chain alignment to Operations and Maintenance (Fit, Form, Function) and the needed critical integration as a business support practice

  • Analytics show that 80% of goods purchasing transactions ONLY apply to 20% of the organisation’s spend on goods with each transaction (purchase order) costing ~$50 or more.

The High-Volume, Low-Risk, Low-Cost non-critical goods purchases are getting the same amount of attention and cost of transaction as critical spend items, placing a huge and unnecessary burden on all parts of the business.


The problems above have been accentuated by existing and predicted business expectations and goals, such as:

  • Doing more with less, eg. maximising reliability and availability so that impacts of current volatility on OEE can be readily assessed

  • Providing quick access to trusted asset information, usage metrics, and parts across all classes in real-time

  • Building workforce and systems collaboration to maximise skills utilisation through automation, AI and Machine Learning

  • Visualisation of AI-based insights that facilitate fast and accurate decision-making

  • Accurate remediation to close the loop, control costs and sweet your assets.

There is a HUGE need to drive risk and cost out of the equation.


Our approach is influenced by leveraging several megatrends impacting most asset-intensive operations.

  • The transition from human-based decisions to digital machine-assisted decisions

  • The gig economy where personnel do not stay in the same job for very long and their knowledge keeps walking out the door

  • The transition to sustainable and renewable resources means that traditional life cycle asset management has additional complexity and constraints that the supply chain must master

  • The rapid increase in new technologies and methodologies forces organisations to embrace change or risk obsolescence.

These megatrends drive towards increased use of digital technologies to support faster decisions and sustainability within increasingly complex environments and constraints.

Hence Supply Chain Management needs to re-focus and re-energise.


Our guiding concepts have been to introduce modern day methods and automation to tried and proven models and analytics. We have achieved this by using the models and tactics outlined below.


The model outlined below is indicative of our suggested re-focus and re-energising solution by using AI/ML Automation methods in a System-of-Systems environment so that the cost of these High-volume, Low-risk, and Low-cost items is reduced to ~50c instead of ~$50.

The interoperability capability provided by DirigerHUB makes the solution as seamless and scalable as possible, plus allows for flexible transition approaches aligned to the internal capability within the customer organisation.

Additional system-of-systems capabilities and data sources can be added as required; for example, Diriger’s solution does not stop at the automation of purchasing but optimises and extends through to the payment cycle with AI/ML based Invoice Processing Automation (See DirigerIPA documents and case studies).

Our solution also extends into the realm of Management of Change governance activities; in that the solution is linked to Asset Master Data Management processes to include the automated management of new assets and salient components coupled to the disposal of obsolete items from stock and the archiving of catalogue and attribute data that is no longer required.


More information at

Our DirigerHUB Enabling Platforms make use of the latest technologies, such as:

  • Artificial Intelligence and Machine Learning

  • Highly scalable and accessible cloud deployments

  • Micro Services and Containerisation

  • PWA (Progressive Web Applications) for mobile and offline capability

  • Our Integration, Business Rule, and Workflow platform technology

  • Design Thinking approach to Persona and Process-based low cognitive load UI/UX (Composable User Interface and Experience)

  • Interoperability aligned with the OIIE and ISO 18101

  • Multi-system synthesis in real-time with full audit and error recycle capabilities

  • Centralised security and User Profile controls (Delegations or Authority) that standardise across all systems-of-systems capability

  • Composable Customer-specific UI/UX configuration capability so that there are ‘No more Gaps’ in solution deployment and reduced Customer Technical Debt.