Supply Chain Management software of Hewlett-Packard Co. (HP)
Introduction
This is a critical review of the Geographic Analytics (GA), a supply chain management software of Hewlett-Packard Co. (HP) and its application in solving the supply chain issues confronting FastGood. This critical review is based on the Supply Chain Management Review Magazine article “Geographic Analytics: how HP visualizes the supply chain,” Acksteiner and Trautmann (2013) describing GA approach to render strategic supply chain choices quicker and more responsive by HP in 2010. This discusses the importance of GA and its relation to management decisions that include maximizing the network, improving network traffic and mitigating risk. They further underline the functionalities of GA such as adaptability and openness. Instead than focusing solely on data and complicated statistical formulae, the GA model was established by HP’s Strategic Planning and Modeling Team. It tends to take a framework which may seem easy to understand and express relevant information on a chart.
Geographic Analytics (GA) is, in basic terms, an analytical approach to project operations and data mining to accelerate supply chain optimization. Examples of these choices include selecting the site for a warehouse or scheduling an advertising campaigns for the areas. Data, information and positioning requirements are seen on maps to provide recommendations based on the findings
It was the major progression in the supply chain in HP’s history. It made it even easier by up to 50 per cent for network optimization projects. Additionally, the industry associations have also best sponsored initiatives guided by this strategy. Basically HP seeks to investigate whether the processing facilities within a given area can be reorganized. It begins by plotting the channel at all appropriate locations. It then contributes general details, including product volumes and square footage, for each site. It also extends a “smart” database system that enables sorting the relevant information to eliminate unwanted features.
Generally speaking, since much fewer data is needed, GA accelerates conventional data-driven strategies. The explanation for this is because very few bits of information, until shown on a chart, are adequate to provide any of the relevant details from the parties concerned. HP labels this “harvesting the intuition behind the issue,” a move that can improve data-driven research considerably. This strategy needs to make sure that the evaluation is guided from the beginning in the appropriate path.
Critical Review
The decision to skip detailed documentation in favor of the most specific details was central to the HP plan. The organization speeds up the decision-making process in the cycle while preventing “data stagnation” Geographic Analytics, according to the organization, has reduced decision-making time by and over 50 per cent relative to strictly data-driven analytical methods. Numbers do not really give the full picture, for all their considerable value. Distribution networks are affected by a number of variables that may be challenging to measure, including legislation, tax policy, and questions regarding networks (Kanda & Deshmukh, 2008). Those components are demonstrated on the chart with Geographic Analytics, offering the organizers a slightly higher compared-front perspective of changing situation. The solution is to integrate mathematics with human intelligence – the kind that understands from the outset that some decisions aren’t appropriate – to attain a quick and quite well-informed outcome.
Visual Experience
Visualization gives rise to indicators that mostly might not be obvious. For instance, a look at the map would show that the organization has several sites in one region. Connection to big roads, airports, seaports and highways will be seen. And the “traffic light” interface will be displayed to display which D.C.s have the maximum and minimum inventory levels. The program for mapping, called LAGOS, has been created from inside. It requires Google Earth as the foundation for identifying delivery locations and the functionality they cover. One can e-mail and handle a Google Earth application like a Word document, and the LAGOS software needs no I.T. Facility. Attached to the mapping system is HP’s official position directory of all positions in the production, distribution and service providers. The framework can be universally disseminated across the organization, needs minor repairs and is essential for monitoring and regulating the five-year system consolidation program of the business.
Dealing with Disruption
The strategy is extremely worthwhile for managing risks. When challenged with a natural catastrophe that has disadvantaged a huge proportion of its supply chain, HP cannot manage to be traumatized by data. Two or three times a year, the attempted to address significant disruptions and quick response is extremely important. Geographic analytics has managed to help to reduce the time of response from days to far less than an hour. At the very same period, emergency management staff were cut by 90 per cent, reducing 1,500 man-hours per emergency.
At a greater level, Geographic Analytics was a crucial feature in HP’s ultimate supply-chain implementation processes, resulting in savings of over $1bn and enabling upwards of 200 physical locations to be closed. Similar advantages were shown at the end of the production process, during which Geographic Analytics resulted in a huge decrease in inventory levels of replacement components (Souza, 2014).
Network Optimization
Among the most critical strategic tasks for supply chain management is streamlining the supply chain operations (Golgeci & Gligor, 2017). Over the years, cross-docks, storage facilities, and manufacturing plants have emerged as mushrooms in several global organizations. The outcome is sometimes a tangled mess that requires to be purified to accomplish a slender, top-notch distribution network. The conventional solution to this issue was to perform what is known as a “center of study of gravity.” A specialist, often an external expert, is called on for such an examination. Such individual gather data, load current places, streams, inventory levels, transportation costs and other data through a sophisticated software device that decides the best positions for the network quite automatically.
However the approach to center-of-gravity-analysis has its disadvantages. The effort to gather data and to clean it up is tedious and can be expensive. Besides that, the recommendations obtained by the software package often become hard to put into intervention. And if you then find that any of the tacit conditioning criteria do not adhere to your case, you are faced with the prospect of reconfiguring the whole examination. It is here that GA can speed up and improve the system
Suggestions
When using GA, people enjoy its convenience, integrity and capacity. When a plan is perceived in perspective, GA’s advantages are practically unquestionable. After all, getting the very first buy-in for the capital expenditure to set up the site directory and mapping techniques may prove challenging. There is a suggestion to use a prototype to conquer this barrier to show what is possible by GA. From such a prototype, facilitate for a location-database and a mapping system becomes so much simpler. Prototype can also help to properly analysis the GA as a viable solution for FastGood because of its time saving feature.
Conclusion
Steadiness, clarity and pace make GA a perfect tool for promoting decision-making in the strategic and operational supply chain (Zhu, Song, Hazen, Lee & Cegielski, 2018). Regionally visualized data is easy to comprehend, thus paved way extraction and decision-making of inferences. It limits the number of response choices by “harvesting the experience behind the problem” that is, by collecting the realistic knowledge of the interested parties, prior strong data-driven statistical methods join the situation. While GA is an exemplary platform for promoting regional optimization, it is intended to supplement – not substitute, conventional data-driven analytics. GA greatly shortens the spectrum and documentation criteria for supply chain issues when setting out the path for future data analysis. In summary, this is an important tool in the supply chain control of modern FastGood services.
References
Acksteiner, J. and Trautmann, C., 2013. Geographic analytics: how HP visualizes its supply chain. Supply Chain Management Review, 17(1).
Golgeci, I. and Gligor, D.M., 2017. The interplay between key marketing and supply chain management capabilities: the role of integrative mechanisms. Journal of Business & Industrial Marketing.
Kanda, A. and Deshmukh, S.G., 2008. Supply chain coordination: perspectives, empirical studies and research directions. International journal of production Economics, 115(2), pp.316-335.
Souza, G.C., 2014. Supply chain analytics. Business Horizons, 57(5), pp.595-605.
Zhu, S., Song, J., Hazen, B.T., Lee, K. and Cegielski, C., 2018. How supply chain analytics enables operational supply chain transparency. International Journal of Physical Distribution & Logistics Management.
