Supply Chain | Arkieva Supply Chain Blog - Page 13

Category: Supply Chain

How “Forecastable” is Your Data? It’s Complex Anyway You Look at It

These “key tools” balance a need for simple with a need to handle the complexity of SCM – following the IBM adage – complexity exists whether you ignore it or not, best not to ignore it.

Going Beyond Historical Data for Forecast Accuracy

Our focus in this blog series has been to establish forecast accuracy targets. In very general terms, the goal should be to add value to the business through the forecasting process. We have however focused on the forecast value add and using that to create a minimum acceptable forecast accuracy target in the previous blog. Now we will take that a step further and talk of ways of improving it.

The Ongoing Shipping Container Shortage and Possible Effects on Tomorrow’s Supply Chain

We have all seen it in the news. Covid-19 outbreaks, labor shortages at the port as well as in trucking, and port delays coupled with high demand from consumers are causing major supply chain issues. Shipping containers are in short supply, or perhaps a better way to say it is that they are waiting to be unloaded or loaded resulting in a shortage. In this blog, let us try to enlist some possible future impacts of this situation. Let us look at it from the perspectives of the different players in the supply chain.

What is Happening to Our Supply Chains?

Everyone is talking about supply chains these days. Ever worsening weather, a global pandemic, and a labor shortage have generated a perfect storm that has pushed global supply chains to their breaking point. I propose that the problem has been building for some time and this perfect storm may just be the reset we need.

3 Aggregation Mistakes When Reporting Forecast Accuracy

It is important to measure and improve the forecast accuracy at the right level of aggregation. If you measure at too high a level, your accuracy picture will look better than what it needs to be as the data at high (aggregated) levels is more forecastable. By contrast, at too low a level...

Sustainability in Supply Chain Planning: Waste or Revenue?

When many companies produce products, they also produce by-products. Often, they have no use for these by-products and so they are discarded or sold off as scrap. But companies that embrace sustainability do not accept this fate. Some companies have found a way to turn their waste streams into revenue streams...

How Forecastable is Your Data?

Having access to an accurate forecast is very beneficial for businesses. If used correctly, it can provide better margins, increase market shares, and many other positive results. At a more tactical level, it can help reduce the costs associated with meeting the customer demand and make the supply chain more efficient.

Sustainability in Supply Chain Planning: In Practice

Integrated Supply Chain Planning is the best way to be sure that you are running your business in a way that is best for your whole business. When you address your energy needs independent of your supply chain planning you can drive up costs unnecessarily and create rework. Industrial gas companies know this better than most because their production process is an extremely energy-intensive one. Keeping energy use to its minimum is a pinnacle of smart sustainable business practices.

Effect of Lockdowns on Essential Goods Value Chain

These interesting times that we are currently living through have thrown open a lot of challenges. Organizations and decision-makers must be agile and innovative to overcome the challenges that COVID-19 has thrown at us. In India, we had 2 lockdown periods in the last 15 months. Being a supply chain student, I try to watch and analyze day-to-day situations with a lot of interest and I see one area that needs improvement when lockdowns are implemented.

Using Coefficient of Variation to Drive Safety Stock Related Decisions

In a previous blog post, we discussed how a high or low value of Coefficient of Variation (CV) impacts the first or second term of safety stock. Today we decided to put this to the test using real customer data - here we will discuss our findings.

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