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Forecasting: it’s what’s hot in Supply Chain Analytics

Better Data, Better Forecasts

In the world of commerce, every business ecosystem has a type of supply chain that is critical to corporate operations. These supply chains rely on a network of plants and facilities to add value to and transform raw materials into a final product. Any disruption or sub-optimization in the supply chain can and will significantly impact a company’s profitability.

The ability to effectively forecast demand is essential for supply chain management decisions. In fact, demand forecasts are used throughout the supply chain including supply chain design, purchasing, operations, inventory, and sales and marketing. In large part due to computer processing power, new advances in forecasting and the abundance of new data sources have helped to increase forecast reliability. Value-add forecasting is one way companies are now realizing incremental improvements in their forecast quality and reliability.

Descriptive, Predictive, and Prescriptive Analytics Explained

The two-minute guide to understanding and selecting the right analytics

With the flood of data available to businesses regarding their supply chain these days, companies are turning to analytics solutions to extract meaning from the huge volumes of data to help improve decision making

Companies that are attempting to optimize their S&OP efforts need capabilities to analyze historical data, forecast what might happen in the future. The promise of doing it right and becoming a data driven organization is great. Huge ROI’s can be enjoyed as evidenced by companies that have optimized their supply chain, lowered operating costs, increased revenues, or improved their customer service and product mix.

Looking at all the analytic options can be a daunting task. However, luckily these analytic options can be categorized at a high level into three distinct types. No one type of analytic is better than another, and in fact, they co-exist with, and complement each other. In order for a business have a holistic view of the market and how a company competes efficiently within that market requires a robust analytic environment which includes:

Is Big Data the Silver Bullet for Supply Chain Analytics?

Leveraging “Big Data” is  coming up more and more as a “must have” in conversations I’m having with clients and prospects, not only in the food and beverage industry, but in other industries that have supply chain applications as an integral part of their operations.  The commonality between the companies I talk to is that they are all trying to seek out that additional growth, revenue and profit, and the lure of using Big Data to do so is very appealing.   Some of the pressure translates into more and more frequent requests by logistics and supply chain teams to use “Big Data” to optimize inventory allocation decisions and to develop a comprehensive view of what exactly is selling and at what velocity it is moving through the supply chain.  But is Big Data the super solution for besting competitors or driving value from in-house data that all hope it is?

The answer is it depends.

Intelligence Input = Sales Output

As true today as it was 50 years ago?

clock_raymajorTime capsules can be interesting. They can also be humbling. More on that in a moment.

When we hear time capsule, most of us think of a dented iron box filled with photos, knick-knacks and documents buried under the cornerstone of the courthouse in a Midwestern hamlet.  

Those doing the burying often specify the amount of time the capsule is to remain in the ground. In some cases, the timespan must remain undefined. For example, four well-known time capsules are "buried" in space. The two Pioneer Plaques and the two Voyager Golden Records were attached to spacecraft for the benefit of music-loving space-travelers in the distant future. Will they prefer Chuck Berry to Mozart? Inquiring minds want to know.

A fifth space-bound time capsule, the KEO satellite, to be launched circa 2016, will carry individual messages from Earth's inhabitants addressed to earthlings around the year 52,000, when it is due to return. Setting aside navigation and language issues, I suspect DVD player parts will be hard to come by then, even on Craigslist. Good luck with that.

Five Tips to consider when designing Supply Chain Key Performance Indicators

http://higherlogicdownload.s3.amazonaws.com/AXUG/UploadedImages/eb3c6bc2-b78d-4d20-9098-4a5c4f90dbeb/kpis.pngYou can’t predict anything with 100% certainty, and your predictive power wanes the farther out you gaze. The study of KPIs over time is all about finding patterns and signals, then applying intelligence in order to make better decisions and gain wisdom.

In a previous post I focused on the pitfalls associated with supply chain KPI and metrics development.  In this post, I’ll cover how businesses can improve their supply chain measurement processes by avoiding the common pitfalls by keeping in mind a few simple hints.

1) Don’t start by asking for a list of everyone’s metrics

You don’t need to create a collection of all things measurable. With massive amounts of new (and sometimes big) data being added to corporate databases every day, the permutations will grow and the questions that can be asked simply become more confusing. Start fresh and start at the top. Focus on KPIs that shed light on progress toward achieving strategic business goals.

Implementing Sales and Operations Planning, Why Oh Why is it so difficult?

Sometimes the hardest part about implementing Sales and Operations Planning (or any project) is just getting started

Sisyphus-e1298413740742A few weeks ago I was thinking about conversations I’ve been having with clients in the early stages of implementing an S&OP strategy.  They were relying on a phased approach, they knew that they needed an integrated set of business processes to go with their newly purchased technology.  They understood that the focus needed to be on information, not just the volumes of data they had at hand.  They knew that in order to implement a successful S&OP they needed clean, current, and accurate data.  As with many organizations, time and effort was being wasted gathering data that had minimal importance to the overall project.  But in this case, senior leadership was able to articulate the business problem they were trying to solve, and were able to help define, with some difficulty mind you, the minimum data necessary for the project.

It all sounds wonderful on paper, and we were destine for success!   But, like other businesses their attempts to implement S&OP were frustrated by internal tensions between departments.  What followed was this seemingly innocent statement on my part:  “Not everyone will be a convert immediately, so we watch for resistance and address it as part of our strategy.  Push, but not too hard, or we will get resistance.”

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