I was taken aback at this recent article in SupplyChainBrain on Supply Chain Optimization in the New Analytics Economy which outlined five analytics-enabled objectives which did not include strategic sourcing decision optimization, which is the next logical step in the sequence. Consider the objectives:
- Supply Chain Visibility
Step one is to understand how much the supply chain is costing you.
- Demand Forecasting and Inventory Optimization
Step two is to segment the supply chain, forecast demand, and then optimize inventory for each segment.
- Network Optimization
Step three is to periodically perform TCO assessments on the different segments of the existing supply chain network to identify the optimal performance configuration.
- Predictive Asset Maintenance
Step four is to perform preventative maintenance to minimize downtime and maximize uptime.
- Spend Analytics
Step five is to understand how much is being spent on each procurement category and identify those with the most savings opportunities.
The next natural step is:
- Strategic Sourcing Decision Optimization
Once the categories with the biggest savings opportunities are identified, it’s time to optimally source them so the overall TCO is minimized and the utilization of the current networks, optimized in step three, is maximized.
How could you possibly stop at step five?
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While I’m not an expert in MPS, I am an expert in optimization, so, needless to say I was taken aback by a statement in this recent TEC bog post on Sorting Through the ERP, Lean MFG, APS, and MES Clutter that quoted experts as saying that ERP and APS systems force companies to make runners in EOQs. Now, while I am quite sure that your average ERP will apply EOQ to production scheduling, even though it’s often dead wrong to do so, I would think that a true APS would not be so foolish.
For those of you who aren’t manufacturing experts, here’s a brief guide to the terminology:
- APS: Advanced Planning and Scheduling – a system or methodology designed to plan plant floor operations to maximize throughput and resource utilization
- EOQ: Economic Order Quantity – the inventory level expected to minimize total inventory holding and ordering costs
- ERP: Enterprise Resource Planning – a system used to coordinate all planning and production processes
- Lean MFG: Lean Manufacturing – a production practice that attempts to eliminate all waste from the production process
- MES: Manufacturing Execution Systems – a set of systems used to control the manufacturing process on the shop floor
- MPS: Manufacturing Planning Systems – a set of systems used to plan the manufacturing process with the intent of creating a manageable schedule
- runner: a product that accounts for the majority of manufacturing workload; on average, 6% of products create 50% of the work
- WIP: Work in Process – refers to all (raw material) inventory that is currently in the production process
Given that so few products account for so much workload, you would think that these systems would recognize that
- it’s a must that each production run produce enough of a runner product to meet the total demand for the production period, but
- producing more runner product adds no relevant value unless enough product is produced to cover the next set of orders (as the line would need to be set up again anyway and it takes time to set up and tear down a production line) and
- EOQ, which is a measure designed for buyers, is not guaranteed to produce a number anywhere close to an appropriate value, even when order costs are replaced with production-line set-up costs.
As the article states, runners must be produced in optimal order quantities, as this is the only way to maximize the amount of time free to produce the remaining 94% of product. Other products can be scheduled based on a modified EOQ, as order quantities in any given period might not be sufficient to guarantee a profitable run otherwise, but runners and other high-volume runs must be treated differently. And if an “APS” system cannot differentiate between the two types of products, and optimize the run for each type appropriately, I’d argue it’s not an APS at all!
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The Sourcing Innovation Resource Site, always immediately accessible from the link under the “Free Resources” section of the sidebar, continues to add new content on a weekly, and often daily, basis — and it will continue to do so.
The following is a not-so-short selection of 14 webinars THIS WEEK that might interest you:
They are all readily searchable from the comprehensive Site-Search page.
As per a recent article in Strategy + Business on cleaning the crystal ball, which discussed the challenges associated with forecasting, we are reminded how the old game of estimating the number of jelly beans in a jar illustrates the innate wisdom of the crowd. In a class of 50 to 60 students, the average of the individual guesses will typically be better than all but one or two of the individual guesses. Furthermore, not only can you not identify the best guesser in advance, but that “expert” may not be the best individual for the next jar because the first result likely reflected a bit of random luck. Then there’s the fact that research by James Shanteau, professor of psychology at Kansas State, has shown that expert judgements often demonstrate logically inconsistent results. For example, medical pathologists presented with the same evidence twice would reach a different conclusion 50% of the time.
However, teams of forecasters often generate better results (and decisions) than individuals as long as the teams include a sufficient degree of diversity of information and perspectives. This is partially because a naive forecaster often frames the question a different way and thinks more deeply about the fundamental driver of the forecast than an expert who has developed an intuitive, but often overconfident, sense of what the future holds. But you can’t just throw a group of people together in a room and ask them to come up with a consensus because the most vocal or senior person might dominate the discussion and overly influence the consensus because most people put too much confidence in the most senior or highest-paid person.
So how do we harness the wisdom of the crowds and insure that no one voice dominates the forecast when the forecast is inherently risky and unpredictable? We look in the place that we are probably most familiar with — e-Sourcing. A blind RFX survey sent out to an interdisciplinary team of carefully chosen and randomly chosen individuals who, given a scenario description, past sales, and expected market trends (from third party analyst firms) are asked to provide their input to the short-, medium-, and long-term forecasts at the SKU and group level. Then, we simply average all of the responses, giving slightly higher, but individually equal, weighting to the carefully chosen respondents (who collectively complete an interdisciplinary team and do it as part of their jobs) and slightly lower, but individually equal, weighting to a section of random organizational individuals asked to weigh in with outside opinions. It won’t be perfect, but it will be substantially better than all but a few guesses — and since you won’t know what guesses will be good or bad in advance, it will substantially reduce your risk.
Thoughts? Comments? Criticisms?
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A recent article in Industry Week on sustainable manufacturing indicated that there are only a few bumps in the road before a smooth, green ride. In fact, immediately after noting that while, as a concept, sustainability in manufacturing is easy to define, it is far harder to practically interpret and adopt, the article immediately diverges into how to design an effective sustainability roadmap.
While the advice is good, I think it does a great disservice by completely skipping over a discussion of the bumps in the road, how big they are, and how long it’s going to take to get around them, especially in North America. In North America, we’re facing the following bumps, and they are all biggies:
- How do you market the benefits of sustainable manufacturing? Most people care about the end product, not the plant. And the last thing you want to do is be another greenwasher!
- Most people are not of a sustainable mindset. They’re of a profit mindset, and they still see sustainable as a cost and not a savings.
- Even those that understand that sustainable is not a cost but a benefit don’t want to spend the money it costs to upgrade production lines and factories to use more sustainable production methods. And this is the real kicker. Until this changes, it’s a long road ahead to sustainable manufacturing in North America.
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