Factory operational strategies to secure revenue in a supply chain constrained environment
Assessing production plan robustness can only be done if all existing data across different systems (ERP, supplier portal, MES, WMS, etc.) is visible and workable. Only then can one have a bird’s eye view of the entire operation.
Similarly, production blockers can only be effectively identified and prevented once we are able to monitor, in real-time, the planning feasibility. This means instead of continuing today’s approach of systematic management, operations managers need to move towards exceptions-driven management. In other words, they need to use data insights to help them better prioritize.
Operations managers can adopt the following three operational strategies to improve their supply chain resilience, enhance their revenue streams, and protect their cash flow in a volatile supply chain environment.
Anticipate production blockers at granular level with better insight into data
Being several steps ahead of the game allows operations managers to have better foresight and gives them greater agility to respond to disruptions. Many, if not all, production blockers can be anticipated ahead of time with the help of advanced analytics. For example:
- Late internal production operations
- Lower yield than expected, therefore increasing the need for parts
- Supplier delays
- Unapproved purchase requests
- Lengthy quality inspections
- Quality issues at reception
- Delay shipments
- Expired supplier contracts
- Administrative blockers
It goes without saying that the sooner a production blocker is identified, the easier it is to deal with it. Take, for instance, the case of a missing part; AI can automatically suggest the "next enablement" date to reschedule a given work order and alternative work orders to carry out in order to avoid losing capacity. However, in the current set up, operations managers are all too often forced to dive into various siloed systems and processes involving different teams before being able to work towards a solution.
Receiving, and acting on, quick recommendations
Another essential strategy for operations managers is obtaining swift recommendations that enable supply chain & production control teams to make informed decisions. This strategy can be applied in the following areas:
1. Managing purchase orders
Advanced analytics can help operations managers in factories synchronize their supply chain orders with their real production needs — all in real-time. This means they are able to plan their purchase orders more effectively by analyzing data from various sources, such as sales forecasts, production schedules, and inventory levels, and prioritize parts based on real production signals (e.g. stock below target stock security level, over lead time) rather than "noise", thereby gaining visibility into what will block the production line and jeopardize sales.
a. preventing noise
This allows operations managers to avoid having to filter through all the noise (e.g. security stock alerts, over lead time, etc.), instead receiving actionable signals in real-time.
b. identifying pull-in opportunities
By holistically monitoring the supply chain and providing recommendations to synchronize all orders, advanced analytics enables operations managers to avoid production disruption as a result of missing parts. Take, for example, a scenario where you have acquired 999 out of 1000 needed parts, yet are unable to ship the product and generate revenue because of a single missing part. This scenario must be avoided at all costs, and advanced analytics can help prevent it. In doing so, operations managers can identify pull-in opportunities and create a prioritized supplier list to call, as well as more effectively arbitrage parts between different production lines. By instantly simulating (in real-time) new supplier delivery plans and their impact on production schedules, advanced analytics can also help managers prioritize conversations with suppliers based on parts that generate the biggest pay-off, and help suppliers make better tradeoffs by prioritizing certain parts over others. In other words, an intelligent use of data drastically accelerates the feedback loop between operations managers and suppliers.
c. identifying push-out opportunities
Analysing in detail parts coverage will also result in identifying parts for which the reception can be delayed to to avoid excess inventory and unnecessary cash spending.
d. arbitraging parts between different production lines
Advanced analytics is extremely effective at identifying opportunities to arbitrage parts from one production line to fill in gaps in another. What’s more, by analyzing maintenance records and production data, advanced analytics can even help predict which production lines are likely to experience shortages of certain parts — and even which parts are likely to fail — and recommend pre-emptive transfers to prevent production delays.
2. Suggesting stock transfers
Advanced analytics can also help predict demand patterns and optimize inventory levels to minimize stockouts and reduce excess inventory. More importantly, they can offer highly valuable signals to operations managers on when to transfer stock in the most efficient manner to minimize disruptions and maximize output.
3. Identifying blocked stock
By analyzing data from various sources, such as quality control reports and production data, advanced analytics can help operations managers identify blocked stocks. Algorithms trained on anomaly detection and clustering can identify issues with quality control, thus helping operations managers take proactive — rather than reactive — steps to improve the health of their inspection queues.
4. Reprioritizing inspection queues
Advanced analytics can process inspection data, quality control reports, and other relevant information to identify patterns and trends and help operations managers identify blockers in the inspection queues in advance and take mitigating action.
Improve your production plan
The third strategy that operations managers can apply is to define their operational goals and constraints before optimizing their production planning. As new constraints or bottlenecks appear due to daily volatility, teams should be empowered with the agility to react in an optimal way in coherence with the operational strategy.
Just as Waze or Google Maps automatically reroutes users when there is a traffic issue, advanced analytics can help operations managers monitor the projected on-time delivery of parts and take decisions based on a huge quantity of variables.
1. Optimize part allocations & better calculate tradeoffs
Imagine a situation where an order is being blocked by one missing component — and this occurs after another order was blocked by the same component, and so forth. The best course of action is to change the order, knowing that there will be a refill. This can only be done with the help of advanced analytics.
Similarly, when a part becomes scarce and is necessary to manufacture different products, data can help prioritize the product to manufacture in priority and assess the impact on other products. advanced analytics are also extremely effective at analyzing service records and customer data to identify opportunities to use used parts in service orders. Not only does this increase profit margins, but by looking at a wider sample of options, advanced analytics can help identify a greater number of available parts. This has the added benefit of increasing the life expectancy of equipment based on serviceable hours potential. For example, in the aviation industry, a part with 1000 hours of flight potential can only be allocated to equipment with less than 1000 hours of flight time to avoid reducing its lifespan. These recommendations are made algorithmically, thus eliminating the potential for human error.
2. Increase reliability of customer delivery dates
By breaking the wall between commercial & operational teams, advanced analytics can help synchronize customer demand with production plannings. That means teams can know in real time if demand is covered by the production planning and identify gaps at granular level, as well as anticipate delivery blockers in advance such as material coverage, late production, administrative blockers (such as export control certificates), quality control blockers, financial blockers, and even transport availability. Once these have been identified, operations managers can formulate mitigation scenarios for each blocker, for instance by rescheduling production, increasing quality control capacities, or expediting transport and delivery services. In a nutshell, AI allows them to statistically estimate production end dates and delivery dates while taking into account all blockers and their resolutions. By accelerating the feedback loop with the customer, this generates greater customer satisfaction and better quality service.
3. Automatically estimate shipment dates
Enlisting the help of sophisticated algorithms such as Pelico’s, which analyze material coverage and capacity feasibility to come up with highly precise shipment estimates, operations managers can be much more certain about when essential parts will be delivered.