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- Food Manufacturing on a Mission
- ABB Robotics
- Line Layout Strategies – Part 2: I-, U-, S-, and L-Lines
- Standardize Machine Integration to Accelerate Real-time Operations and Analytics Data Collection
- Warehouse Dictionary
- Poultry Feed Lines
- How Should You Organize Manufacturing?
- Manufacturing & Distribution
- Warehouse Management Software
Food Manufacturing on a MissionVIDEO ON THE TOPIC: How to make feeds with small feed mill plant?
Food retail is a tough and turbulent market. Grocery has never been easy, but the current business transformation is more dramatic than anything we have seen in decades. Looking at these trends and the challenges and opportunities they present, it is obvious that supply chain management will lie at the heart of the future successes and failures in grocery retail.
All food retailers need to make tough choices today about where to place their business bets. However, regardless of the strategies selected by the different players, if their grocery supply chains are not developed to match the chosen strategies, chances of success are slim. In addition, many of them will need to manage the complexity of operating multiple store formats and offering several fulfillment options in parallel. To achieve this, retailers need the right planning tools at their disposal.
Furthermore, they need to understand how to apply them:. In this best practice guide, we will highlight key approaches for increasing both responsiveness and efficiency in grocery supply chains. You will be hard pressed to find a single retailer employing all of these best practices.
Rather, we encourage you to prioritize the most feasible and impactful development areas from your own perspective. Demand forecasting is the engine running your supply chain. High quality forecasting requires making the most of all available data. The richer the data, the more accurate forecasts you can produce. It is impossible to talk about demand forecasting without discussing artificial intelligence AI.
In the context of AI, singularity means an AI of such intelligence and power that it starts to independently develop and improve to an extent that renders us inferior humans redundant in some apocalyptic future.
Claims of singularity and autonomy can safely be filed under nonsense. We are still far from some kind of autonomous AI even within the very narrow context of supply chain management. What we are seeing is great progress in specialized AI. Specialized AI means methods and algorithms optimized to perform a specific task. The original AlphaGo program that managed to beat the best human players in the game of Go had been specifically optimized for playing Go.
It was trained using a database of around 30 million moves. Yet, the data collected by playing is still processed by man-made optimization algorithms specifically designed for great performance when intelligent search through an enormous space of possibilities is needed.
It is also important to keep in mind that the data processing required comes at a cost. Specialized AI is growing increasingly common and is often used to run applications that at first sight do not look particularly intelligent.
Two factors are key to the recent developments in specialized AI: 1 The rapidly increasing availability of data and 2 the rapidly decreasing cost of processing data.
The current boom in AI was to a large extent fueled by inspiring advances in computer vision. Essentially, AI adds new, more sophisticated tools to your toolbox. These tools, such as machine learning algorithms, make it significantly easier to analyze very large amounts of data to identify new, sometimes surprising patterns or to detect patterns on a more granular level than ever before.
However, you also need to understand its limitations. Automating the bulk of demand forecasting is both desirable as well as quite feasible in food retail. Yet, the business environment is very dynamic due to changing consumer trends as well as the impact of external factors, such as the highly unusual weather in several parts of the world lately.
There is always a risk of forecasts being based on how things used to be instead of how they are now or will be in the future. For this reason, there will always be errors in the forecasts produced. For experts to be able to understand the errors, potentially correct them or at least predict when they will happen, transparency into how the demand forecast was formed is essential. They are also constantly reviewing forecasting performance and errors to support further improvements.
Different forecasting approaches have different strengths and weaknesses. Some forecasting methods may be highly accurate when given access to tons of data only to fail miserably when there is too little training data available.
Others may be computationally very effective and produce results that are roughly right but never stellar. Some forecasting methods are invaluable for short-term forecasting but do not add value when focus is on the longer term.
There is no such thing as one single best forecasting approach. In fact, it is often surprisingly difficult to even agree on one single best forecasting result. The best practice in demand forecasting is to use a combination of methods, ranging from traditional time-series forecasting to machine learning.
When combining several forecasting methods, we recommend using a layered approach see Figure 3. This means that different parts of the forecast, such as baseline sales and impact of weather, can be viewed separately. The layered approach creates transparency into how the final forecast has been derived, which in turn promotes understanding and confidence in the demand planners.
It also supports error correction and continuous development of the forecasting methods in use. Time-series forecasting is a solid and well-understood approach for estimating baseline sales.
By using a set of best practice statistical tests and time-series models, different kinds of sales patterns, such as trends, seasonality and weekday-related variation in demand, can be modeled accurately. This is far from true. The other retailers would have wanted to do day-level forecasting, but simply could not do it.
There are, thus, big differences in how well retailers have managed to implement time-series forecasting and, consequently, in the forecasting performance attained. The best forecasting systems automatically select the optimal forecasting models and parameters per store and SKU.
This is typically done based on an array of statistical tests which identify demand patterns, such as seasonality or trends. In retail, a typical challenge in demand forecasting is low sales volume at the day-SKU-store level. It is of central importance that the planning system is able to automatically move between day-SKU-store and more aggregate levels as needed to ensure that forecasts are based on sufficient data.
Below are some examples of combining day-SKU-store level forecasting with forecasting on more aggregate levels:. As time-series forecasting relies on finding patterns in historical sales data, additional routines are needed for dealing with new products. However, in sectors such as grocery retail, the number of new products per year can be massive. This means that manual identification and setting of reference products is infeasible or at least highly inefficient.
A much more efficient approach is to automatically assign reference products based on product attributes. Relevant attributes are, for example, product group, brand, pack size, color or price point. The same approach can, of course, be applied to finding suitable reference stores for new stores. Despite these changes being controlled by the retailer, their impact is in many cases not very accurately predicted.
But they wish they could. It is quite typical that a promotional uplift for one product results in reduced sales of another product.
If a supermarket carries two brands of lean organic ground beef — HappyCow and GreenBeef — it is reasonable to expect that promoting the HappyCow product will result in more people buying it, but also in some of the baseline demand from GreenBeef shifting to HappyCow. If the demand forecast for the GreenBeef product is not lowered, there is a high risk of stock-piling leading to waste.
For most center store products, such as canned food or cereal, cannibalization is not a big problem. If demand decreases temporarily, a replenishment order for the cannibalized product will simply be triggered later than usually. Manually adjusting the forecasts for all potentially cannibalized products is infeasible in food retail due to the large number of products and typically quite store-specific shopping patterns. Best-in class planning systems automatically identify cannibalization and adjust forecasts accordingly.
This can be achieved using regression analysis to identify relationships between the sales of different products. If an increase in sales is correlated with a decrease in the sales of another product, the products are considered to be cannibalizing each other. External factors such as the weather, local concerts and games, and competitor price changes can have a very significant impact on demand.
It is often intuitively easy to understand how, for example, weather impacts sales. High temperatures increase ice cream sales, rainfall increases the demand for umbrellas and so on.
However, when looking at the entire product range a retailer offers, it becomes more complicated. How can you effectively identify all products that react to the weather? How can you consider some weather effects being stronger in summer than in winter or stronger during the weekends than on workdays?
For a mid-sized retailer with stores and a range of 10, products, considering weather effects on a reasonably granular level would mean examining the strength of 2.
The use of weather data and forecasts is a great example of the power of machine learning. Machine learning algorithms can automatically detect relationships between local weather variables and sales of individual products in individual stores. In addition to mapping these relationships on a more granular and local level than any human would be able to do, these algorithms are able to detect less obvious relationships between weather and sales. In a manual process where demand planners or store personnel check weather forecasts and make decisions accordingly, focus necessarily has to be on securing supply when demand is expected to increase — for example by pushing additional ice cream into stores in expectation of a heat wave.
Usually, though, no one has time to adjust forecasts slightly downwards when rainy and cold summer weather reduces the appeal of barbecuing. As discussed in the introduction, we strongly recommend a layered forecast approach, which delivers transparency into the different components of the forecast. This is particularly important when using external forecasts such as weather forecasts, which include an element of uncertainty.
In this way, planners can decide on a case-by-case basis how much emphasis they want to put on the weather-adjusted demand forecasts in anticipation of, for example, a heatwave that might hit a region during the weekend. In a similar way, machine learning algorithms can be used to take advantage of other external data sources in addition to weather to independently look for relationships between external variables such as local football games and local sales of specific products.
In grocery retail, the following external data sources have been found particularly useful:. Even though traditional supermarkets have decades of experience dealing with fresh products, many still do not excel in this area. Their supply chains are reactive enough to support frequent deliveries, but their replenishment planning is not up to scratch. According to the North American grocers surveyed, the annual value of spoilage was on average around 70 million and up to several hundred million annually for the largest companies offering a wide range of fresh products.
This means that very granular control is needed to find the optimal balance between the risk of stock-outs and the risk of waste. Other fresh products face a similar challenge, just a bit less pronounced. Demand for a product in a specific store typically varies between different weekdays.
This means that the same safety stock does not fit all weekdays when dealing with short shelf life products. Roast beef, for example, tends to sell a lot more towards the weekend than after the weekend.
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Line Layout Strategies – Part 2: I-, U-, S-, and L-Lines
The layout of a line can make quite a difference in the performance of your line. The U-line is most famous, although in my view while good it may not be the right thing for all situations. There is also the I-line, the S-line, and the U-line. In my last post I described some general thoughts on line design and took a look at the big picture. In this post I want to look at and compare actual line layouts, in particularly the I, U, S, and L layout.
The manufacturing industry is highly automated, and the right technology is essential for safety and efficiency within manufacturing businesses. Many businesses in the industry credit their success to the use of state-of-the-art manufacturing apps, which keep employees accountable, efficient, and mobile. Ranging from universal CRM software to real-time equipment monitoring, manufacturing apps are incredibly fine-tuned for key tracking and record-keeping. Like a well-oiled machine, these apps make day-to-day operations flow seamlessly, wasting less time, and more importantly, promoting safety and organization. Manufacturing apps are a low-cost technology to integrate into your business—and pretty soon, you'll be boosting your ROIs. Our team at AppSheet has curated a list of our favorite manufacturing apps — ones that'll get you more productive and agile. Our list is sorted by usability, flexibility, easy management, and other factors we find important. AppSheet isn't an app in itself; it's an app development platform.
Standardize Machine Integration to Accelerate Real-time Operations and Analytics Data Collection
Warehouse Execution Systems WES   are computerized systems used in distribution operations Logistics and are functionally equivalent to a manufacturing execution system or MES. Distribution operations are a form of a manufacturing operation that receive, store and track inbound material and then select and combine assemble various materials to form a finished product, order, or shipment. WES software organizes , sequences and synchronizes work resources necessary to complete the assembly and shipment of finished product.
What is Warehouse Management Software? Capterra is free for users because vendors pay us when they receive web traffic and sales opportunities. Capterra directories list all vendors—not just those that pay us—so that you can make the best-informed purchase decision possible. NetSuite's inventory and warehouse management software allows you to consolidate your inventory systems into a single, integrated warehouse inventory control solution. With NetSuite's inventory control software, you can efficiently manage every stage of the product lifecycle, as well as your different lines of business. You'll be able to manage inventory levels and get stronger control of inventory operations. Learn more about NetSuite. Barcode and RFID scanning helps to locate items in their precise location anywhere in the warehouse. Bid goodbye to worries over handling multiple warehouses.
Food retail is a tough and turbulent market. Grocery has never been easy, but the current business transformation is more dramatic than anything we have seen in decades. Looking at these trends and the challenges and opportunities they present, it is obvious that supply chain management will lie at the heart of the future successes and failures in grocery retail. All food retailers need to make tough choices today about where to place their business bets. However, regardless of the strategies selected by the different players, if their grocery supply chains are not developed to match the chosen strategies, chances of success are slim. In addition, many of them will need to manage the complexity of operating multiple store formats and offering several fulfillment options in parallel. To achieve this, retailers need the right planning tools at their disposal. Furthermore, they need to understand how to apply them:. In this best practice guide, we will highlight key approaches for increasing both responsiveness and efficiency in grocery supply chains. You will be hard pressed to find a single retailer employing all of these best practices.
Poultry Feed Lines
Our contract food processing services go beyond delivering the highest quality products for our customers. We have a greater mission in mind: to leverage our success as a leader in the contract food manufacturing industry in order to foster whole life transformation for our employees, plant communities, and each individual we connect with on a daily basis. That means creating jobs, promoting a culture of respect and integrity, and fostering sustainable communities both locally and globally. And, of course, it means forging customer relationships built on trust, accountability, and communication. At PacMoore, we partner with you to make a difference. We manufacture your products with strict quality controls, go to great lengths to protect your information, and do not put your brands at risk by competing with you.
How Should You Organize Manufacturing?
Except for a few feed manufacturers who keep to the standards in poultry feed formulations, many feed companies in the. Free access to news on animal feed and animal nutrition.
Manufacturing & Distribution
Among the characteristics of a company that shape corporate and therefore manufacturing strategy are its dominant orientation market or product , pattern of diversification product, market, or process , attitude toward growth acceptance of low growth rate , and choice between competitive strategies high profit margins versus high output volumes. Once the basic attitudes or priorities are established, […].
Warehouse Management Software
A factory, manufacturing plant or a production plant is an industrial site, usually consisting of buildings and machinery, or more commonly a complex having several buildings, where workers manufacture goods or operate machines processing one product into another. Factories arose with the introduction of machinery during the Industrial Revolution when the capital and space requirements became too great for cottage industry or workshops. Early factories that contained small amounts of machinery, such as one or two spinning mules , and fewer than a dozen workers have been called "glorified workshops".
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