One of the key things for any organization is management of costs and ensuring that the costs incurred (direct or indirect) are aligned to the business needs. Production volume attainment, and ultimately profitability, of the plant is the primary focus for plant managers, but is continuously constrained by costs and efficiency issues that arise in the day to day workings of the plant.
Managers track various KPIs (including OEE) to ensure production target attainment for each shift and at the end of the month all labor and materials costs are combined with estimates of manufacturing overhead (depreciation, repairs/maintenance, utilities, etc). This is then used as a profitability scorecard for the plant manager and corporate leadership teams to allocate and align goals of production along with production targets and labor planning for the coming months. It also helps executive leadership to understand plant efficiency and some of the reasons for efficiency losses but still is a ‘rear-view’ way of approaching the business strategy. Knowing this process and how scheduling of material procurement, production shift schedules and the production plan it’s easy to see how various demand or labor fluctuations combined with asset reliability and quality yield issues can wreak havoc on any plan that is walking the line of profitability and production efficiency.
The efficiency losses and issues that arose in the previous week(s)/month(s) are not guaranteed to be the same in the coming week(s)/months(s), simply because the production plan for materials to produce will shift based on various forecasted demand signals. This will put different pressures on different production routes and material inventory and varying the supply chain avenues in which they flow. It will also lead to differences in the overall efficiency and in turn with shift the primary contributors of efficiency losses too. This is often why OEE is a singularly important metric but also one that can be frustrating for organizations. If you don’t catch the OEE identified losses as they are happening it can be difficult to get value out of the metric simply because often by the time the issues are identified they are no longer identified as the ‘primary’ areas of loss.
One way to improve this to be a more proactive approach to OEE, is to begin to apply a demand forecast and plan to the historical losses. When you apply the various demand forecast models, the historical losses broken down by each reason code, material, shift, and route that the material flows begin to show a clear picture to the business on the Cost of Doing Nothing. This Cost of Doing Nothing identifies the scope and scale of forecasted material losses and their typical reasons if no changes or investment is made to improve manufacturing. It also provides an executive level guiding light in identifying areas of biggest forward-looking opportunities for improvement while allowing customers to mature their investment strategy from reactive to proactive to get ahead of what's coming.
This will help to drive the organization to provide the appropriate amount of CAPEX or OPEX investments back into manufacturing, not only looking at historical issues but also applying the forward looking forecast of how likely these issues are going to be a factored into losses going forward too.
For more updates on what we are doing to support the next wave Industry 4.0 in manufacturing see our updates on Intelligent Factories.
Managers track various KPIs (including OEE) to ensure production target attainment for each shift and at the end of the month all labor and materials costs are combined with estimates of manufacturing overhead (depreciation, repairs/maintenance, utilities, etc). This is then used as a profitability scorecard for the plant manager and corporate leadership teams to allocate and align goals of production along with production targets and labor planning for the coming months. It also helps executive leadership to understand plant efficiency and some of the reasons for efficiency losses but still is a ‘rear-view’ way of approaching the business strategy. Knowing this process and how scheduling of material procurement, production shift schedules and the production plan it’s easy to see how various demand or labor fluctuations combined with asset reliability and quality yield issues can wreak havoc on any plan that is walking the line of profitability and production efficiency.
The efficiency losses and issues that arose in the previous week(s)/month(s) are not guaranteed to be the same in the coming week(s)/months(s), simply because the production plan for materials to produce will shift based on various forecasted demand signals. This will put different pressures on different production routes and material inventory and varying the supply chain avenues in which they flow. It will also lead to differences in the overall efficiency and in turn with shift the primary contributors of efficiency losses too. This is often why OEE is a singularly important metric but also one that can be frustrating for organizations. If you don’t catch the OEE identified losses as they are happening it can be difficult to get value out of the metric simply because often by the time the issues are identified they are no longer identified as the ‘primary’ areas of loss.
One way to improve this to be a more proactive approach to OEE, is to begin to apply a demand forecast and plan to the historical losses. When you apply the various demand forecast models, the historical losses broken down by each reason code, material, shift, and route that the material flows begin to show a clear picture to the business on the Cost of Doing Nothing. This Cost of Doing Nothing identifies the scope and scale of forecasted material losses and their typical reasons if no changes or investment is made to improve manufacturing. It also provides an executive level guiding light in identifying areas of biggest forward-looking opportunities for improvement while allowing customers to mature their investment strategy from reactive to proactive to get ahead of what's coming.
Intelligent Profitability Maturity Curve
This will help to drive the organization to provide the appropriate amount of CAPEX or OPEX investments back into manufacturing, not only looking at historical issues but also applying the forward looking forecast of how likely these issues are going to be a factored into losses going forward too.
For more updates on what we are doing to support the next wave Industry 4.0 in manufacturing see our updates on Intelligent Factories.
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