The Data Dilemma

While globalization may be good for business, it creates new requirements for IT systems that are designed primarily for domestic orders.  Often missing is easy access to global logistics information and critical supply chain data, such as capacity, sales forecasts, inventory levels, and product quality. Without this information, enterprises will not maximize productivity across the supply chain.

New Supply Chain Management (SCM) software products improve the enterprise's ability to evaluate global suppliers and share supply chain data with business partners. Enhanced supply chain information systems will allow the enterprise to:

• Optimize the supplier selection process on a global basis.
• Gain greater visibility into the entire supply chain, thus improving forecasting and reducing inventories.
• Be more adaptive to changes in demand.
• Improve compliance initiatives such as Sarbanes-Oxley as well as international tax laws.

Recommendations

1.Assess the enterprise's current data management capabilities. Start with an assessment of the enterprise's existing capacity to get and integrate supplier data domestically. Local data management is a critical first step in creating a broader strategy for data-sharing across a global supply chain.

2.Employ software tools to improve the supplier selection process. Some SCM tools allow potential suppliers to post bids online. These systems help enterprises automatically determine the best global supplier using information on tariffs, exchange rates, delivery times, profit margins, and geopolitical risk factors. This is a huge improvement over manually evaluating bids from multiple countries. Automating supplier selection will help ensure that the low-cost advantage of sourcing overseas is not eroded by logistical factors.

3.Select suppliers based on ability to meet data management mandates and key performance metrics, not solely on the basis of unit price. Often the selection of suppliers is left to the procurement department, without adequate attention to data-sharing capabilities. The total cost of the supplier might be lower for a supplier that has a strong data management strategy in place and a slightly higher unit cost than for a supplier with bargain basement prices. Consider factors such as service, delivery, flexibility, inventory carrying costs, the root causes of quality problems, and the opportunity cost of stock-outs.

4.Bring partners' systems up to speed. If existing supply chain partners do not have the capabilities to monitor critical data or generate reports, then there may be an opportunity for the enterprise to get involved. Large enterprises could consider either investing with partners to implement the necessary components or creating incentives for partners to pursue data collection on their own. Special attention should be given to suppliers in developing countries where implementation of SCM software may not be as widespread or sophisticated.

5.Different supply chains require different data to be tracked. Make sure that the data being collected is the right type of data for the enterprise. For example, a supply chain for fashion items has a short lifecycle, unpredictable demand, and a high customer focus. For enterprises involved in this business, timely demand information is important, as is a quick turnaround of information to suppliers and designers that may be a world apart. Metrics and feedback mechanisms that help improve response times and the speed at which new iterations can be launched are essential. Conversely, a supply chain for commodity products, such as PCs, must be focused on cost reduction. In this case, information about inventory levels, component pricing, exchange rates, and other cost drivers are of greater importance.

6.Move ownership of supply chain data away from individual enterprises. This is probably the most difficult aspect of executing a successful supply chain data management strategy. For example, it may be most effective to communicate demand information straight from retailers to manufacturers, thus eliminating forecasting errors and gaming. However, if partners in the middle of the supply chain (i.e. wholesalers) are used to being the brokers of information between retailers and manufacturers, this will require a change in mindset towards a more collaborative approach. Global divides will confound this process and require that extra effort be taken to bridge cultural and language barriers between information brokers.

7.Stress data cleanliness and consistent data definition. In a global environment, it is almost guaranteed that multiple platforms will be in use, thus confounding data-sharing initiatives. In order to improve data usability it may be necessary to gather data from various operations and translate it into a common language relevant to all supply chain partners.

8.Decide which relationships need to be managed. Managed relationships tend to occur in situations where the partner has less-developed management systems (e.g. some third-world suppliers) or in situations where a large enterprise has invested in its partners' data systems. In these instances, the enterprise has significant insight into the partner's operations and is able to monitor transaction-level details, manage the partner's performance, and track quality and cost issues to their root causes. Alternatively, autonomous relationships occur when the partner has in-house data management competencies and an established track record of managing component suppliers, monitoring quality, and delivering on-time. In this case, the partner receives performance targets from the enterprise and only reports on high-level metrics such as cost, delivery, and quality.

9. Look to third-party logistics providers. These companies may help reduce the complexity of trying to connect the SCM and ERP systems of global suppliers and partners. Instead, logistics providers can gather information from all enterprises and present a consolidated view up and down the supply chain.

Bottom Line

Globalization is creating new demands on enterprise information systems. Enterprises engaged in global supply chain networks must reevaluate data management strategies with an eye towards automated supplier selection and data collaboration up and down the supply chain.

The Bullwhip Effect

A common model used to illustrate the high cost of poor supply chain data is the Bullwhip Effect. Much like the wave movement down the length of a cracked whip the Bullwhip Effect causes exaggerations in consumer demand fluctuations as information moves up the supply chain from retailer to wholesaler to manufacturer.

The exaggeration and distortion of information leads to inaccurate forecasting, excess inventory and stock-outs, poor capacity planning, and overall higher costs at all points in the supply chain.