{"id":2420,"date":"2017-04-24T06:47:28","date_gmt":"2017-04-24T13:47:28","guid":{"rendered":""},"modified":"2023-07-07T13:18:12","modified_gmt":"2023-07-07T20:18:12","slug":"reducing-inventory-costs-risk-complex-supply-chains","status":"publish","type":"post","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/manufacturing-and-mobility\/2017\/04\/24\/reducing-inventory-costs-risk-complex-supply-chains\/","title":{"rendered":"Reducing Inventory Costs and Risk in Complex Supply Chains"},"content":{"rendered":"

Optimizing inventory levels to improve working capital without impacting service has long been a pressing goal for manufacturers. Tremendous strides have been made in the last two decades in change-over improvement, optimizing production flows and taking a holistic view of inventory levels across the supply chain.<\/p>\n

Unfortunately for manufacturers, existing inventory pressures driven by expanding product lines and rising raw material prices will soon be joined by trends including direct-to-consumer shipping and shrinking product life-cycles.<\/p>\n

So how can manufacturers manage inventory costs and risks as these trends emerge?<\/p>\n

The answer lies less in physical inventory level management and more in their ability to use information that will improve their forecasting decisions. As manufacturers continue to use traditional techniques to reduce inventory levels they must urgently re-think how their forecasting decisions are made.<\/p>\n

Current Challenges Combined with Future Trends<\/h2>\n

In a traditional Sales and Operations Planning (SOP) model, forecasted inventory levels drive activity throughout the supply chain. The delta between forecasted sales and actual sales has the greatest impact on excess and obsolete inventory at all stages of the supply chain (raw materials, WIP and finished goods). This estimation is made more difficult by the fact that most current models rely on lagging indicators of demand that are filtered through decision makers\u2019 biases at every step of the way.<\/p>\n

The difficult is further exacerbated by additional pressure to traditional models: direct-to-consumer shipping and shrinking product life cycles. Direct-to-consumer shipping will increase exponentially as traditional retailers try to counter Amazon\u2019s growing market share through larger product catalogues and improved delivery performance. This will allow retailers to shift the inventory risk to the manufacturer while still providing a wide range of products. Products with shrinking product life-cycles, where demand is closely tied to consumer trends and sentiments, will become more prevalent. Demand for these products will not respond to traditional demand management techniques such as discounts and promotions, increasing the risk of obsolete inventory.<\/p>\n

Forecasting with Big Data and Advanced Analytics<\/h2>\n

It\u2019s not all doom and gloom. There are ways manufacturers can prepare for the challenges that lie in store. The combination of large, fast-moving, and varied streams of big data and advanced analytics tools will provide the next generation of demand forecasting. Accuracy will improve, enabling manufacturers to react faster to demand changes, reduce inventory risks and improve overall responsiveness.<\/p>\n

We believe these big data and analytics tools should be deployed in four key areas to maximize impact. These areas are pinpointing future demands, condition and disruption monitoring, supply chain visibility and automatic inventory management.<\/p>\n

\"Digital<\/p>\n

Pinpointing Future Demand<\/h2>\n

Big Data and Advanced Analytics will enable manufacturers to obtain fast-moving data from suppliers and customers. This can be combined powerfully with contextual information such as weather, competitor behavior and real-time consumer sentiment to determine which factors could impact demand. Manufacturers can quickly adapt forecasts instead of waiting for the forecast to be adjusted via lagging indicators including actual sales figures or wider economic measures like the unemployment rate. Any changes to demand can be used to track and measure forecasting accuracy to help adjust future forecasting models based on real-world results.<\/p>\n

Supply Chain Visibility<\/h2>\n

Supply chain visibility is a proven concept that, until now, has only been achievable in a few ways. The best way involves the use common systems and common data models but can also be achieved by sharing information through an API or flat files.<\/p>\n

Advanced analytical techniques can be used to analyze data from a number of systems that speak different languages in the cloud without the need to normalize the data or limiting the data sources. This makes it possible to connect the supply chain through data rather than the systems themselves. These powerful connections can also find trends and correlations that have not been historically relevant but could disrupt the supply chain nonetheless. Since users have all the data rather than the data they believe they need, what-if scenario simulations become more valuable and automatically adapt to changing conditions.<\/p>\n

Condition and Disruption Monitoring<\/h2>\n

The introduction of sensors in products, packaging and material handling equipment via Internet of Things (IOT) models has significantly increased the amount of information manufacturers have about the movement and condition of their products. Traditional systems flag users to missed events or milestones as indications of potential disruptions in the supply chain, but could not give much beyond that. IOT connected sensors can now tell us exactly what is happening to products at any point in the supply chain, providing information that can be used to adjust forecasts, reroute shipments or re-position inventory.<\/p>\n

Automatic Inventory Management<\/h2>\n

As more information about demand, supply chain disruptions and changes to the products themselves becomes available there will be increased pressure for managers to make better inventory decisions. The challenge is to understand root cause and effect before taking action. The risk is that managers will take a wait-and-see approach based on traditional planning cycles. Big Data and Advanced Analytics can be used to automate inventory management decisions, recommending where managers should act and where they have hit a point of diminishing returns.<\/p>\n

As an example, production orders that have already been released to the manufacturing floor can be adjusted in real time based on information obtained from the supply chain. This minimizes the amount of obsolete inventory by making real-time adjustments outside of the normal decision-making cycle.<\/p>\n

The opportunity is here<\/h2>\n

These four concepts place significant value on a digital supply chain where information about the product, demand, suppliers and customers are used to make decisions that optimize the physical supply chain. By managing the digital supply chain using Big Data and Advanced Analytics we can minimize the traditional conflicts between inventory risks and customer responsiveness. This means manufacturers will be able to incorporate real-time demand data and other contextual information that can be used as leading indicators of demand instead of the traditional lagging indicators that are used today.<\/p>\n

With the customer cycle dramatically increasing in speed, manufacturers will need to be able to respond to changing conditions quickly and seamlessly. Data will need to be harnessed proactively to predict demand conditions in advance and on the fly. Digital transformation can provide manufacturers with the information they need to minimize risk and grow their business in the midst of major disruption to the way things have been done in the past.<\/p>\n

For more information about digital transformation services with Microsoft, learn more<\/strong> here.<\/strong> <\/u><\/a><\/p>\n\n\n\n
Learn More<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

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